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Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
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Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/community/contribution_guide/document-contributor/index.html b/community/contribution_guide/document-contributor/index.html index eb88c6d6..6186143d 100644 --- a/community/contribution_guide/document-contributor/index.html +++ b/community/contribution_guide/document-contributor/index.html @@ -4,7 +4,7 @@ 文档贡献指南 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
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Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
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Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/development/environment-preparation/index.html b/docs/development/environment-preparation/index.html index 65bdf88f..720dfb4c 100644 --- a/docs/development/environment-preparation/index.html +++ b/docs/development/environment-preparation/index.html @@ -4,7 +4,7 @@ 开发环境准备 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/development/plugin/connector-develop/index.html b/docs/development/plugin/connector-develop/index.html index f7658953..e597c275 100644 --- a/docs/development/plugin/connector-develop/index.html +++ b/docs/development/plugin/connector-develop/index.html @@ -4,7 +4,7 @@ 数据源开发 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/development/plugin/engine-develop/index.html b/docs/development/plugin/engine-develop/index.html index fbf74b3b..1dfc909a 100644 --- a/docs/development/plugin/engine-develop/index.html +++ b/docs/development/plugin/engine-develop/index.html @@ -4,7 +4,7 @@ 执行引擎开发 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/development/plugin/metric-develop/index.html b/docs/development/plugin/metric-develop/index.html index 3d7f1a68..3b1e1a77 100644 --- a/docs/development/plugin/metric-develop/index.html +++ b/docs/development/plugin/metric-develop/index.html @@ -4,7 +4,7 @@ 检查规则开发 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/development/plugin/notification-develop/index.html b/docs/development/plugin/notification-develop/index.html index 55b15c6f..21b07ae9 100644 --- a/docs/development/plugin/notification-develop/index.html +++ b/docs/development/plugin/notification-develop/index.html @@ -4,7 +4,7 @@ 告警通道开发 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/development/plugin/registry-develop/index.html b/docs/development/plugin/registry-develop/index.html index 9af91a16..3bf7e639 100644 --- a/docs/development/plugin/registry-develop/index.html +++ b/docs/development/plugin/registry-develop/index.html @@ -4,7 +4,7 @@ 注册中心开发 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/development/source-module-explanation/index.html b/docs/development/source-module-explanation/index.html index 11d3c786..caf020dc 100644 --- a/docs/development/source-module-explanation/index.html +++ b/docs/development/source-module-explanation/index.html @@ -4,7 +4,7 @@ 源码模块解释 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/faq/index.html b/docs/faq/index.html index ef08a789..54a7e01c 100644 --- a/docs/faq/index.html +++ b/docs/faq/index.html @@ -4,7 +4,7 @@ 常见问题 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/catalog/catalog-intro/index.html b/docs/features/catalog/catalog-intro/index.html index 2361489b..953deb6c 100644 --- a/docs/features/catalog/catalog-intro/index.html +++ b/docs/features/catalog/catalog-intro/index.html @@ -4,7 +4,7 @@ 数据目录介绍 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/catalog/connector/connector-clickhouse/index.html b/docs/features/catalog/connector/connector-clickhouse/index.html index 06e86635..35f54c26 100644 --- a/docs/features/catalog/connector/connector-clickhouse/index.html +++ b/docs/features/catalog/connector/connector-clickhouse/index.html @@ -4,7 +4,7 @@ ClickHouse | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/catalog/connector/connector-databend/index.html b/docs/features/catalog/connector/connector-databend/index.html index 0548c865..764a6537 100644 --- a/docs/features/catalog/connector/connector-databend/index.html +++ b/docs/features/catalog/connector/connector-databend/index.html @@ -4,7 +4,7 @@ Databend | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/catalog/connector/connector-doris/index.html b/docs/features/catalog/connector/connector-doris/index.html index 86868233..f954281f 100644 --- a/docs/features/catalog/connector/connector-doris/index.html +++ b/docs/features/catalog/connector/connector-doris/index.html @@ -4,7 +4,7 @@ Doris | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/catalog/connector/connector-hive/index.html b/docs/features/catalog/connector/connector-hive/index.html index a80995fe..8a7a80a8 100644 --- a/docs/features/catalog/connector/connector-hive/index.html +++ b/docs/features/catalog/connector/connector-hive/index.html @@ -4,7 +4,7 @@ Hive | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/catalog/connector/connector-impala/index.html b/docs/features/catalog/connector/connector-impala/index.html index 977c76e0..65521e71 100644 --- a/docs/features/catalog/connector/connector-impala/index.html +++ b/docs/features/catalog/connector/connector-impala/index.html @@ -4,7 +4,7 @@ Impala | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/catalog/connector/connector-mysql/index.html b/docs/features/catalog/connector/connector-mysql/index.html index f549c2d5..2c9cdb41 100644 --- a/docs/features/catalog/connector/connector-mysql/index.html +++ b/docs/features/catalog/connector/connector-mysql/index.html @@ -4,7 +4,7 @@ MySQL | Datavines - + @@ -16,7 +16,7 @@

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Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/catalog/connector/connector-postgresql/index.html b/docs/features/catalog/connector/connector-postgresql/index.html index c2147486..b058c6bb 100644 --- a/docs/features/catalog/connector/connector-postgresql/index.html +++ b/docs/features/catalog/connector/connector-postgresql/index.html @@ -4,7 +4,7 @@ PostgreSQL | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/catalog/connector/connector-presto/index.html b/docs/features/catalog/connector/connector-presto/index.html index bbd274d2..c6283bad 100644 --- a/docs/features/catalog/connector/connector-presto/index.html +++ b/docs/features/catalog/connector/connector-presto/index.html @@ -4,7 +4,7 @@ Presto | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/catalog/connector/connector-starrocks/index.html b/docs/features/catalog/connector/connector-starrocks/index.html index eec42192..d1441135 100644 --- a/docs/features/catalog/connector/connector-starrocks/index.html +++ b/docs/features/catalog/connector/connector-starrocks/index.html @@ -4,7 +4,7 @@ StarRocks | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/catalog/connector/connector-trino/index.html b/docs/features/catalog/connector/connector-trino/index.html index 3ba929c3..bc4fc605 100644 --- a/docs/features/catalog/connector/connector-trino/index.html +++ b/docs/features/catalog/connector/connector-trino/index.html @@ -4,7 +4,7 @@ Trino | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/config/index.html b/docs/features/config/index.html index a07eaa0a..cc300ac3 100644 --- a/docs/features/config/index.html +++ b/docs/features/config/index.html @@ -4,7 +4,7 @@ 全局参数管理 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/engine/livy-engine/index.html b/docs/features/engine/livy-engine/index.html index 8dc7c6ec..ca774908 100644 --- a/docs/features/engine/livy-engine/index.html +++ b/docs/features/engine/livy-engine/index.html @@ -4,7 +4,7 @@ Livy 引擎 | Datavines - + @@ -17,7 +17,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/engine/local-engine/index.html b/docs/features/engine/local-engine/index.html index 4f451220..1b36bd7b 100644 --- a/docs/features/engine/local-engine/index.html +++ b/docs/features/engine/local-engine/index.html @@ -4,7 +4,7 @@ Local 引擎 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/engine/spark-engine/index.html b/docs/features/engine/spark-engine/index.html index 922cf266..29788832 100644 --- a/docs/features/engine/spark-engine/index.html +++ b/docs/features/engine/spark-engine/index.html @@ -4,7 +4,7 @@ Spark 引擎 | Datavines - + @@ -17,7 +17,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/error-data-storage/index.html b/docs/features/error-data-storage/index.html index 4d7116bd..a93880da 100644 --- a/docs/features/error-data-storage/index.html +++ b/docs/features/error-data-storage/index.html @@ -4,7 +4,7 @@ 错误数据存储 | Datavines - + @@ -20,7 +20,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/issues/index.html b/docs/features/issues/index.html index fb6a1a12..74e14f21 100644 --- a/docs/features/issues/index.html +++ b/docs/features/issues/index.html @@ -4,7 +4,7 @@ 质量问题管理 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/expected-value/expected-value-daily-avg/index.html b/docs/features/metric/expected-value/expected-value-daily-avg/index.html index 97054601..f80d50a9 100644 --- a/docs/features/metric/expected-value/expected-value-daily-avg/index.html +++ b/docs/features/metric/expected-value/expected-value-daily-avg/index.html @@ -4,7 +4,7 @@ 日均值 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/expected-value/expected-value-fix-value/index.html b/docs/features/metric/expected-value/expected-value-fix-value/index.html index c88938c1..95d1e951 100644 --- a/docs/features/metric/expected-value/expected-value-fix-value/index.html +++ b/docs/features/metric/expected-value/expected-value-fix-value/index.html @@ -4,7 +4,7 @@ 固定值 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/expected-value/expected-value-last30day-avg/index.html b/docs/features/metric/expected-value/expected-value-last30day-avg/index.html index 34423e60..79f5d07c 100644 --- a/docs/features/metric/expected-value/expected-value-last30day-avg/index.html +++ b/docs/features/metric/expected-value/expected-value-last30day-avg/index.html @@ -4,7 +4,7 @@ 最近30天均值 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/expected-value/expected-value-last7day-avg/index.html b/docs/features/metric/expected-value/expected-value-last7day-avg/index.html index e38407c6..9be0a6c3 100644 --- a/docs/features/metric/expected-value/expected-value-last7day-avg/index.html +++ b/docs/features/metric/expected-value/expected-value-last7day-avg/index.html @@ -4,7 +4,7 @@ 最近7天均值 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/expected-value/expected-value-monthly-avg/index.html b/docs/features/metric/expected-value/expected-value-monthly-avg/index.html index 0ff5a7c3..6246720a 100644 --- a/docs/features/metric/expected-value/expected-value-monthly-avg/index.html +++ b/docs/features/metric/expected-value/expected-value-monthly-avg/index.html @@ -4,7 +4,7 @@ 月均值 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/expected-value/expected-value-none/index.html b/docs/features/metric/expected-value/expected-value-none/index.html index ace5ffc7..67dc90ba 100644 --- a/docs/features/metric/expected-value/expected-value-none/index.html +++ b/docs/features/metric/expected-value/expected-value-none/index.html @@ -4,7 +4,7 @@ 无 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/expected-value/expected-value-table-rows/index.html b/docs/features/metric/expected-value/expected-value-table-rows/index.html index edf6907f..20927162 100644 --- a/docs/features/metric/expected-value/expected-value-table-rows/index.html +++ b/docs/features/metric/expected-value/expected-value-table-rows/index.html @@ -4,7 +4,7 @@ 表总行数 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/expected-value/expected-value-target-table-rows/index.html b/docs/features/metric/expected-value/expected-value-target-table-rows/index.html index 447d26ac..3e0016ad 100644 --- a/docs/features/metric/expected-value/expected-value-target-table-rows/index.html +++ b/docs/features/metric/expected-value/expected-value-target-table-rows/index.html @@ -4,7 +4,7 @@ 目标表总行数 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/expected-value/expected-value-weekly-avg/index.html b/docs/features/metric/expected-value/expected-value-weekly-avg/index.html index 0817e17d..04410f20 100644 --- a/docs/features/metric/expected-value/expected-value-weekly-avg/index.html +++ b/docs/features/metric/expected-value/expected-value-weekly-avg/index.html @@ -4,7 +4,7 @@ 周均值 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/multi-table-metric/multi-table-accuracy/index.html b/docs/features/metric/multi-table-metric/multi-table-accuracy/index.html index a16da09b..ec174e45 100644 --- a/docs/features/metric/multi-table-metric/multi-table-accuracy/index.html +++ b/docs/features/metric/multi-table-metric/multi-table-accuracy/index.html @@ -4,7 +4,7 @@ 跨表准确性 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/multi-table-metric/multi-table-value-comparison/index.html b/docs/features/metric/multi-table-metric/multi-table-value-comparison/index.html index 9c3998f2..69615b40 100644 --- a/docs/features/metric/multi-table-metric/multi-table-value-comparison/index.html +++ b/docs/features/metric/multi-table-metric/multi-table-value-comparison/index.html @@ -4,7 +4,7 @@ 两表值比对 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/result-validate-formula/formula-count/index.html b/docs/features/metric/result-validate-formula/formula-count/index.html index fb4ccb3a..6a49ac07 100644 --- a/docs/features/metric/result-validate-formula/formula-count/index.html +++ b/docs/features/metric/result-validate-formula/formula-count/index.html @@ -4,7 +4,7 @@ 实际值 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/result-validate-formula/formula-diff-actual-expected/index.html b/docs/features/metric/result-validate-formula/formula-diff-actual-expected/index.html index efb4cccd..9d6fab64 100644 --- a/docs/features/metric/result-validate-formula/formula-diff-actual-expected/index.html +++ b/docs/features/metric/result-validate-formula/formula-diff-actual-expected/index.html @@ -4,7 +4,7 @@ 实际值-期望值 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/result-validate-formula/formula-diff-percentage/index.html b/docs/features/metric/result-validate-formula/formula-diff-percentage/index.html index 48a837a7..07434866 100644 --- a/docs/features/metric/result-validate-formula/formula-diff-percentage/index.html +++ b/docs/features/metric/result-validate-formula/formula-diff-percentage/index.html @@ -4,7 +4,7 @@ |实际值-期望值|/期望值*100% | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/result-validate-formula/formula-diff/index.html b/docs/features/metric/result-validate-formula/formula-diff/index.html index 2c870e83..45f20db4 100644 --- a/docs/features/metric/result-validate-formula/formula-diff/index.html +++ b/docs/features/metric/result-validate-formula/formula-diff/index.html @@ -4,7 +4,7 @@ |实际值-期望值| | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/result-validate-formula/formula-intro/index.html b/docs/features/metric/result-validate-formula/formula-intro/index.html index 8c530c32..3c793a13 100644 --- a/docs/features/metric/result-validate-formula/formula-intro/index.html +++ b/docs/features/metric/result-validate-formula/formula-intro/index.html @@ -4,7 +4,7 @@ 公式介绍 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/result-validate-formula/formula-percentage/index.html b/docs/features/metric/result-validate-formula/formula-percentage/index.html index 11f768a6..028da117 100644 --- a/docs/features/metric/result-validate-formula/formula-percentage/index.html +++ b/docs/features/metric/result-validate-formula/formula-percentage/index.html @@ -4,7 +4,7 @@ 实际值/期望值*100% | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-avg-length/index.html b/docs/features/metric/single-table-metric/column-avg-length/index.html index fd23ae16..22afe70c 100644 --- a/docs/features/metric/single-table-metric/column-avg-length/index.html +++ b/docs/features/metric/single-table-metric/column-avg-length/index.html @@ -4,7 +4,7 @@ 平均长度检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-avg/index.html b/docs/features/metric/single-table-metric/column-avg/index.html index e1031245..448d1ef6 100644 --- a/docs/features/metric/single-table-metric/column-avg/index.html +++ b/docs/features/metric/single-table-metric/column-avg/index.html @@ -4,7 +4,7 @@ 平均值检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-blank/index.html b/docs/features/metric/single-table-metric/column-blank/index.html index d59cc48d..c6472b23 100644 --- a/docs/features/metric/single-table-metric/column-blank/index.html +++ b/docs/features/metric/single-table-metric/column-blank/index.html @@ -4,7 +4,7 @@ 无值检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-distinct/index.html b/docs/features/metric/single-table-metric/column-distinct/index.html index f1df253e..7a837da2 100644 --- a/docs/features/metric/single-table-metric/column-distinct/index.html +++ b/docs/features/metric/single-table-metric/column-distinct/index.html @@ -4,7 +4,7 @@ Distinct检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-duplicate/index.html b/docs/features/metric/single-table-metric/column-duplicate/index.html index 7b6d965c..398a0339 100644 --- a/docs/features/metric/single-table-metric/column-duplicate/index.html +++ b/docs/features/metric/single-table-metric/column-duplicate/index.html @@ -4,7 +4,7 @@ 重复值检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-freshness/index.html b/docs/features/metric/single-table-metric/column-freshness/index.html index 323c051b..41dc4f98 100644 --- a/docs/features/metric/single-table-metric/column-freshness/index.html +++ b/docs/features/metric/single-table-metric/column-freshness/index.html @@ -4,7 +4,7 @@ 及时性检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-in-enums/index.html b/docs/features/metric/single-table-metric/column-in-enums/index.html index 4f6318d6..93f05668 100644 --- a/docs/features/metric/single-table-metric/column-in-enums/index.html +++ b/docs/features/metric/single-table-metric/column-in-enums/index.html @@ -4,7 +4,7 @@ 枚举值检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-length/index.html b/docs/features/metric/single-table-metric/column-length/index.html index 8874381a..87bd67d5 100644 --- a/docs/features/metric/single-table-metric/column-length/index.html +++ b/docs/features/metric/single-table-metric/column-length/index.html @@ -4,7 +4,7 @@ 字段长度检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-match-not-regex/index.html b/docs/features/metric/single-table-metric/column-match-not-regex/index.html index ca6e50dd..0bbb7ede 100644 --- a/docs/features/metric/single-table-metric/column-match-not-regex/index.html +++ b/docs/features/metric/single-table-metric/column-match-not-regex/index.html @@ -4,7 +4,7 @@ 正则表达式[不匹配]检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-match-regex/index.html b/docs/features/metric/single-table-metric/column-match-regex/index.html index 347de0dc..a83187e8 100644 --- a/docs/features/metric/single-table-metric/column-match-regex/index.html +++ b/docs/features/metric/single-table-metric/column-match-regex/index.html @@ -4,7 +4,7 @@ 正则表达式[匹配]检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-max-length/index.html b/docs/features/metric/single-table-metric/column-max-length/index.html index 887d6b1e..dbab0763 100644 --- a/docs/features/metric/single-table-metric/column-max-length/index.html +++ b/docs/features/metric/single-table-metric/column-max-length/index.html @@ -4,7 +4,7 @@ 最大长度检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-max/index.html b/docs/features/metric/single-table-metric/column-max/index.html index 0083e55b..12795ca7 100644 --- a/docs/features/metric/single-table-metric/column-max/index.html +++ b/docs/features/metric/single-table-metric/column-max/index.html @@ -4,7 +4,7 @@ 最大值检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-min-length/index.html b/docs/features/metric/single-table-metric/column-min-length/index.html index 9323b4c7..7a53b413 100644 --- a/docs/features/metric/single-table-metric/column-min-length/index.html +++ b/docs/features/metric/single-table-metric/column-min-length/index.html @@ -4,7 +4,7 @@ 最小长度检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-min/index.html b/docs/features/metric/single-table-metric/column-min/index.html index 57ffba2b..03722168 100644 --- a/docs/features/metric/single-table-metric/column-min/index.html +++ b/docs/features/metric/single-table-metric/column-min/index.html @@ -4,7 +4,7 @@ 最小值检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-not-in-enums/index.html b/docs/features/metric/single-table-metric/column-not-in-enums/index.html index 84f5c059..38484f27 100644 --- a/docs/features/metric/single-table-metric/column-not-in-enums/index.html +++ b/docs/features/metric/single-table-metric/column-not-in-enums/index.html @@ -4,7 +4,7 @@ 枚举值[不在]检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-not-null/index.html b/docs/features/metric/single-table-metric/column-not-null/index.html index dc88e3c4..334411bb 100644 --- a/docs/features/metric/single-table-metric/column-not-null/index.html +++ b/docs/features/metric/single-table-metric/column-not-null/index.html @@ -4,7 +4,7 @@ 非空检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-null/index.html b/docs/features/metric/single-table-metric/column-null/index.html index ff658235..961a7668 100644 --- a/docs/features/metric/single-table-metric/column-null/index.html +++ b/docs/features/metric/single-table-metric/column-null/index.html @@ -4,7 +4,7 @@ 空值检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-std-dev/index.html b/docs/features/metric/single-table-metric/column-std-dev/index.html index 6c007d5c..02a48b94 100644 --- a/docs/features/metric/single-table-metric/column-std-dev/index.html +++ b/docs/features/metric/single-table-metric/column-std-dev/index.html @@ -4,7 +4,7 @@ 标准差检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-sum/index.html b/docs/features/metric/single-table-metric/column-sum/index.html index f8ad0889..e7633f2b 100644 --- a/docs/features/metric/single-table-metric/column-sum/index.html +++ b/docs/features/metric/single-table-metric/column-sum/index.html @@ -4,7 +4,7 @@ 总值检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-unique/index.html b/docs/features/metric/single-table-metric/column-unique/index.html index e90d7c5b..a3abd71c 100644 --- a/docs/features/metric/single-table-metric/column-unique/index.html +++ b/docs/features/metric/single-table-metric/column-unique/index.html @@ -4,7 +4,7 @@ 唯一性检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-value-between/index.html b/docs/features/metric/single-table-metric/column-value-between/index.html index a23d90c0..d0f88ef9 100644 --- a/docs/features/metric/single-table-metric/column-value-between/index.html +++ b/docs/features/metric/single-table-metric/column-value-between/index.html @@ -4,7 +4,7 @@ 值范围检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/column-variance/index.html b/docs/features/metric/single-table-metric/column-variance/index.html index b3881501..45caee0b 100644 --- a/docs/features/metric/single-table-metric/column-variance/index.html +++ b/docs/features/metric/single-table-metric/column-variance/index.html @@ -4,7 +4,7 @@ 方差检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/custom-aggregate-sql/index.html b/docs/features/metric/single-table-metric/custom-aggregate-sql/index.html index de30d86f..e367f7ed 100644 --- a/docs/features/metric/single-table-metric/custom-aggregate-sql/index.html +++ b/docs/features/metric/single-table-metric/custom-aggregate-sql/index.html @@ -4,7 +4,7 @@ 自定义聚合SQL | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/metric/single-table-metric/table-row-count/index.html b/docs/features/metric/single-table-metric/table-row-count/index.html index 2374d23e..6e7b9d20 100644 --- a/docs/features/metric/single-table-metric/table-row-count/index.html +++ b/docs/features/metric/single-table-metric/table-row-count/index.html @@ -4,7 +4,7 @@ 表行数检查 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/slas/slas-intro/index.html b/docs/features/slas/slas-intro/index.html index 91d73560..6531b08c 100644 --- a/docs/features/slas/slas-intro/index.html +++ b/docs/features/slas/slas-intro/index.html @@ -4,7 +4,7 @@ SLAs介绍 | Datavines - + @@ -19,7 +19,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/slas/slas-notification/notification-email/index.html b/docs/features/slas/slas-notification/notification-email/index.html index ce029b80..1edadead 100644 --- a/docs/features/slas/slas-notification/notification-email/index.html +++ b/docs/features/slas/slas-notification/notification-email/index.html @@ -4,7 +4,7 @@ 邮件通知 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/slas/slas-notification/notification-wecombot/index.html b/docs/features/slas/slas-notification/notification-wecombot/index.html index 36f3654a..17ad49eb 100644 --- a/docs/features/slas/slas-notification/notification-wecombot/index.html +++ b/docs/features/slas/slas-notification/notification-wecombot/index.html @@ -4,7 +4,7 @@ 企业微信机器人通知 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/tag/index.html b/docs/features/tag/index.html index 8846d836..a63466ee 100644 --- a/docs/features/tag/index.html +++ b/docs/features/tag/index.html @@ -4,7 +4,7 @@ 标签体系 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/features/time-variable/index.html b/docs/features/time-variable/index.html index 3caf20f5..1403f23c 100644 --- a/docs/features/time-variable/index.html +++ b/docs/features/time-variable/index.html @@ -4,7 +4,7 @@ 内置时间变量 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/get-started/deploy-from-source/index.html b/docs/get-started/deploy-from-source/index.html index 585ef9ad..5c4b3b6c 100644 --- a/docs/get-started/deploy-from-source/index.html +++ b/docs/get-started/deploy-from-source/index.html @@ -4,7 +4,7 @@ 用源码部署 | Datavines - + @@ -21,7 +21,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/get-started/docker/build-docker-image/index.html b/docs/get-started/docker/build-docker-image/index.html index c6e886cc..32ccd61b 100644 --- a/docs/get-started/docker/build-docker-image/index.html +++ b/docs/get-started/docker/build-docker-image/index.html @@ -4,7 +4,7 @@ 构建Docker镜像 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/get-started/docker/deploy-docker-image/index.html b/docs/get-started/docker/deploy-docker-image/index.html index ff9d89a2..774ef3ae 100644 --- a/docs/get-started/docker/deploy-docker-image/index.html +++ b/docs/get-started/docker/deploy-docker-image/index.html @@ -4,7 +4,7 @@ 运行Docker镜像 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/get-started/kubernetes-deploy/index.html b/docs/get-started/kubernetes-deploy/index.html index 93ad374d..872545b3 100644 --- a/docs/get-started/kubernetes-deploy/index.html +++ b/docs/get-started/kubernetes-deploy/index.html @@ -4,7 +4,7 @@ Kubernetes部署 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/integration/dolphin-scheduler/index.html b/docs/integration/dolphin-scheduler/index.html index bda8bb47..acb820b9 100644 --- a/docs/integration/dolphin-scheduler/index.html +++ b/docs/integration/dolphin-scheduler/index.html @@ -3,20 +3,20 @@ -DolphinScheduler | Datavines - +DolphinScheduler | Datavines +
-
Copyright © 2022-2024 Datavines Community.
- + \ No newline at end of file diff --git a/docs/roadmap/index.html b/docs/roadmap/index.html index a9b5a871..ec1ed04d 100644 --- a/docs/roadmap/index.html +++ b/docs/roadmap/index.html @@ -4,7 +4,7 @@ 未来规划 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/user-guide/local-mode/index.html b/docs/user-guide/local-mode/index.html index 137040fa..3163ee9b 100644 --- a/docs/user-guide/local-mode/index.html +++ b/docs/user-guide/local-mode/index.html @@ -4,7 +4,7 @@ 本地运行模式 | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

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- + \ No newline at end of file diff --git a/docs/user-guide/quick-start/index.html b/docs/user-guide/quick-start/index.html index dff0c812..61417a42 100644 --- a/docs/user-guide/quick-start/index.html +++ b/docs/user-guide/quick-start/index.html @@ -4,7 +4,7 @@ 快速上手 | Datavines - + @@ -26,7 +26,7 @@

Next-gen Data Observability platform

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主要特性

数据目录管理


自动获取数据源元数据，构造企业级数据目录；定时监听数据变更情况

数据质量监控


平台内置多个数据质量检查规则，开箱即用；提供告警消息管理帮助你更好把握质量情况

插件化设计


平台以插件化设计为核心，支持数据源、规则、执行引擎、告警组件、错误数据存储和注册中心等插件扩展

数据概览


让你能够更快更轻松地了解每个数据集的情况，做到心里有数

多种运行模式


不仅提供平台帮助你观测数据，也支持本地模式集成到调度系统的工作流中

容易部署&高可用


平台依赖少，容易部署；支持水平扩容，自动容错

- + \ No newline at end of file diff --git a/home/index.html b/home/index.html index 1e9f849b..b30e8987 100644 --- a/home/index.html +++ b/home/index.html @@ -4,13 +4,13 @@ Datavines - +
- + \ No newline at end of file diff --git a/home/intro/index.html b/home/intro/index.html index f04ba2d1..0a1e10f9 100644 --- a/home/intro/index.html +++ b/home/intro/index.html @@ -4,13 +4,13 @@ Datavines - +

Datavines

新一代数据可观测性平台，数据目录管理和数据质量监控平台

 GitHub Get started
- + \ No newline at end of file diff --git a/index.html b/index.html index e32bd692..ba4e3c15 100644 --- a/index.html +++ b/index.html @@ -4,7 +4,7 @@ Datavines | Datavines - + @@ -16,7 +16,7 @@

Next-gen Data Observability platform

Copyright © 2022-2024 Datavines Community.
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Code Conduct Code Conduct 下一页 贡献指南","s":"Code Conduct","u":"/datavines-website/community/contribution_guide/code-conduct","h":"","p":2},{"i":6,"t":"贡献指南 文档贡献指南 文档贡献指南 上一页 贡献指南 下一页 Pull Request 须知","s":"文档贡献指南","u":"/datavines-website/community/contribution_guide/document-contributor","h":"","p":5},{"i":8,"t":"贡献指南 Pull Request 须知 本页总览 Pull Request 须知 前言​ Pull Request 本质上是一种软件的合作方式，是将涉及不同功能的代码，纳入主干的一种流程。这个过程中，可以进行讨论、审核和修改代码。 在 Pull Request 中尽量不讨论代码的实现方案，代码及其逻辑的大体实现方案应该尽量在 Issue 或者邮件列表中被讨论确定，在 Pull Request 中我们尽量只关注代码的格式以及代码规范等信息，从而避免实现方式的意见不同而导致 waste time。 规范​ Pull Request 标题​ 标题格式：[Pull Request 类型-Issue 号][`模块名`] Pull Request 描述 其中Pull Request 类型和Issue 类型的对应关系如下： Issue 类型 Pull Request 类型 样例（假设 Issue 号为 1111） Feature Feature [Feature-1111][server] Implement xxx Bug Fix [Fix-1111][server] Fix xxx Improvement Improvement [Improvement-1111][alert] Improve the performance of xxx Test Test [Test-1111][api] Add the e2e test of xxx Sub-Task Sub-Task 对应的父类型 [Feature-1111][server] Implement xxx 其中 Issue 号是指当前 Pull Request 对应要解决的 Issue 号，模块名同 Issue 的模块名。 Pull Request 分支名​ 分支名格式：Pull Request 类型-Issue 号，举例：Feature-1111。 上一页 文档贡献指南 前言 规范 Pull Request 标题 Pull Request 分支名","s":"Pull Request 须知","u":"/datavines-website/community/contribution_guide/pull-request","h":"","p":7},{"i":10,"t":"贡献指南 贡献指南 本页总览 贡献指南 您可以报告 bug，提交一个新的功能增强建议或者直接对以上内容提交改进补丁。 提交 issue​ 在提交 issue 之前，请经过充分的搜索，确定该 issue 不是通过简单的检索即可以解决的问题。 查看 issue 列表，确定该 issue 不是一个重复的问题。 新建一个 issue 并选择您的 issue 类型。 使用一个清晰并有描述性的标题来定义 issue。 根据模板填写必要信息。 请对自己提交的 issue 保持关注，在讨论中进一步提供必要信息。 开发流程​ 1. 准备仓库 到 Datavines GitHub Repo fork 仓库到你的 GitHub 账号。 克隆到本地。 git clone https://github.com/(your_github_name)/datavines.git 添加 Datavines 远程仓库。 git remote add upstream https://github.com/datavane/datavines.gitgit remote -v 2. 选择 issue 请在选择您要修改的 issue。如果是您新发现的问题或想提供 issue 中没有的功能增强，请先新建一个 issue 并设置正确的标签。 在选中相关的 issue 之后，请回复以表明您当前正在这个 issue 上工作。并在回复的时候为自己设置一个 deadline，添加至回复内容中。 3. 创建分支 切换到 fork 的 dev 分支，拉取最新代码，创建本次的分支。 git checkout devgit fetch upstreamgit rebase upstream/devgit push origin dev # 可选操作git checkout -b issueNo 4. 编码 请您在开发过程中遵循 Datavines 的开发规范。并在准备提交 pull request 之前完成相应的检查。 将修改的代码 push 到 fork 库的分支上。 git add .git commit -m 'commit log'git rebase upstream/dev # 必要操作git push origin issueNo 5. 提交 PR 发送一个 pull request 到 Datavines 的 dev 分支。 接着会有其他开发者做 CodeReview，然后他会与您讨论一些细节（包括设计，实现，性能等）。当相应的开发者对本次修改满意后，会将提交合并到当前开发版本的分支中。 最后，恭喜您已经成为了 Datavines 的贡献者！ 6. 删除分支 在将 pull request 合并到 Datavines 的 dev 分支中之后，您就可以将远程的分支（origin/issueNo）及与远程分支（origin/issueNo）关联的本地分支（issueNo）删除。 git checkout devgit branch -d issueNogit remote prune origin # 如果你已经在 GitHub PR 页面删除了分支，否则的话可以执行下面的命令删除git push origin --delete issueNo 注意: 为了让您的 id 显示在 contributor 列表中，别忘了以下设置： git config --global user.name \"username\"git config --global user.email \"username@mail.com\" 感谢 本贡献指南参考Apache ShardingSphere的贡献指南，感谢他们做出的优秀贡献 上一页 Code Conduct 下一页 文档贡献指南 提交 issue 开发流程","s":"贡献指南","u":"/datavines-website/community/contribution_guide/contributor","h":"","p":9},{"i":13,"t":"在搭建 Datavines 开发环境之前请确保你已经安装一下软件 Git: 版本控制系统 JDK: 后端开发 Maven: Java包管理系统","s":"前置条件","u":"/datavines-website/docs/development/environment-preparation","h":"#前置条件","p":11},{"i":15,"t":"通过你 git 管理工具下载 git 代码，下面以 git-core 为例 mkdir datavinescd datavinesgit clone https://github.com/datavane/datavines.git","s":"克隆代码库","u":"/datavines-website/docs/development/environment-preparation","h":"#克隆代码库","p":11},{"i":17,"t":"如果使用MySQL数据库，请注意修改pom.xml，将 mysql-connector-java 依赖的 scope 改为 compile，使用 PostgreSQL 则不需要。 运行 mvn clean install -Prelease -Dmaven.test.skip=true","s":"编译源码","u":"/datavines-website/docs/development/environment-preparation","h":"#编译源码","p":11},{"i":19,"t":"Datavines 的元数据存储在关系型数据库中，目前支持的关系型数据库包括 MySQL 以及 PostgreSQL。下面以MySQL为例，启动数据库并创建新 database 作为 Datavines 元数据库，这里以数据库名 datavines 为例 创建完新数据库后，将 script/sql/datavines-mysql.sql 下的 sql 文件直接在 MySQL 中运行，完成数据库初始化","s":"数据库","u":"/datavines-website/docs/development/environment-preparation","h":"#数据库","p":11},{"i":21,"t":"下面步骤将引导如何启动 Datavines 后端服务","s":"启动后端","u":"/datavines-website/docs/development/environment-preparation","h":"#启动后端","p":11},{"i":23,"t":"打开项目：使用开发工具打开项目，这里以 Intellij IDEA 为例，打开后需要一段时间，让 Intellij IDEA 完成以依赖的下载 必要的修改 如果使用 MySQL 作为元数据库，需要先修改 datavines/pom.xml，将 mysql-connector-java 依赖的 scope 改为 compile，使用 PostgreSQL 则不需要 修改数据库配置，修改 datavines-server/src/main/resources/application.yaml 文件中的数据库配置 本样例以 MySQL 为例，其中数据库名为 datavines，账户名密码为 root/123456 spring: datasource: driver-class-name: com.mysql.cj.jdbc.Driver url: jdbc:mysql://127.0.0.1:3306/datavines?useUnicode=true&characterEncoding=UTF-8 username: root password: 123456","s":"必要的准备工作","u":"/datavines-website/docs/development/environment-preparation","h":"#必要的准备工作","p":11},{"i":25,"t":"启动 DatavinesServer 在VM Options中添加 -Dspring.profiles.active=mysql 中 mysql 表示指定的配置文件 -Dlogging.config=classpath:server-logback.xml 当你在Console里面看到 [INFO] 2022-04-10 12:29:05.447 io.datavines.server.DatavinesServer:[61] - Started DatavinesServer in 3.97 seconds (JVM running for 4.69) 日志的时候，证明服务已经启动成功了","s":"启动服务","u":"/datavines-website/docs/development/environment-preparation","h":"#启动服务","p":11},{"i":27,"t":"前置条件：本地安装好 node(v16.9.0) 和 npm(8.19.3) 依次执行以下命令，即可完成本地前端服务的启动以进行本地调试。 cd datavines\\uinpm installnpm fundnpm run dev:prod","s":"启动前端服务","u":"/datavines-website/docs/development/environment-preparation","h":"#启动前端服务","p":11},{"i":31,"t":"Metric 是 Datavines 中一个核心概念，一个 Metric 表示一个数据质量检查规则，比如空值检查和表行数检查都是一个规则。Metric 采用插件化设计，用户可以根据自己的需求来实现一个 Metric。下面我们来详细讲解一下如何自定义Metric。","s":"检查规则开发","u":"/datavines-website/docs/development/plugin/metric-develop","h":"","p":30},{"i":33,"t":"我们先了解下几个接口和抽象类，它们是实现自定义 Metric 的关键。 SqlMetric 接口​ SqlMetric接口中定义了规则的各种属性和操作的接口。 @SPIpublic interface SqlMetric { // 中文名 String getName(); // 英文名 String getZhName(); // 根据系统的语言进行名字返回 default String getNameByLanguage(boolean isEn) { return isEn ? getName() : getZhName(); } // 规则属于哪个维度，比如准确性、唯一性等等 MetricDimension getDimension(); // 规则的类型，包括单表检查、单表自定义检查 MetricType getType(); // 规则的级别，比如表级别、列级别 default MetricLevel getLevel() { return MetricLevel.NONE; } // 是否支持错误数据输出 boolean isInvalidateItemsCanOutput(); /** * 获取不符合规则的数据的SQL语句 * @return ExecuteSql */ ExecuteSql getInvalidateItems(String uniqueKey); /** * 计算实际值的SQL语句 * @return ExecuteSql */ ExecuteSql getActualValue(String uniqueKey); /** * 实际值的字段名 */ default String getActualName() { return \"actual_value\"; } // 实际值的类型，比如数字，百分比或者列表 default String getActualValueType() { return MetricActualValueType.COUNT.getDescription(); } // 对参数进行检查并输出检查结果 CheckResult validateConfig(Map config); //规则所需要的参数 Map getConfigMap(); //构造规则前需要做的检查 void prepare(Map config); default String getIssue() { return \"\"; } // 适合哪些字段类型 List suitableType(); // 是否支持多选，比如表行数检查支持多张表 default boolean supportMultiple() { return false; } // 对规则参数的重新构造，配合表行数多张表检查 default List> getMetricParameter(Map metricParameter) { return Collections.singletonList(metricParameter); }} BaseSingleTable 抽象类​ BaseSingleTable是实现了 SqlMetric 接口的抽象类，实现了表级别检查规则中所需要参数的添加、错误数据SQL语句构造和实际值计算SQL语句构造和对过滤条件的处理等。 这里定义了获取不符合规则的数据的基础SQL语句，判断类型的规则比如正则表达式检查和枚举值检查，只需要在基础SQL语句后面添加过滤条件即可。 protected StringBuilder invalidateItemsSql = new StringBuilder(\"select * from ${table}\"); 实际值计算SQL语句默认是计算不符合规则数据的行数 String actualValueSql = \"select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}\"; 计算平均值、汇总值等统计类型的规则需要重新实现getActualValue()中的ExecuteSql。 public abstract class BaseSingleTable implements SqlMetric { // 这里定义了获取不符合规则的数据的基础 SQL 语句，判断类的规则比如正则表达式和枚举值检查，只需要在基础SQL后面添加过滤条件即可。 protected StringBuilder invalidateItemsSql = new StringBuilder(\"select * from ${table}\"); protected List filters = new ArrayList<>(); protected HashMap configMap = new HashMap<>(); protected Set requiredOptions = new HashSet<>(); public BaseSingleTable() { configMap.put(\"table\",new ConfigItem(\"table\", \"表名\", \"table\")); configMap.put(\"filter\",new ConfigItem(\"filter\", \"过滤条件\", \"filter\")); requiredOptions.add(\"table\"); } @Override public ExecuteSql getInvalidateItems(String uniqueKey) { ExecuteSql executeSql = new ExecuteSql(); executeSql.setResultTable(\"invalidate_items_\" + uniqueKey); executeSql.setSql(invalidateItemsSql.toString()); executeSql.setErrorOutput(isInvalidateItemsCanOutput()); return executeSql; } @Override public ExecuteSql getActualValue(String uniqueKey) { ExecuteSql executeSql = new ExecuteSql(); executeSql.setResultTable(\"invalidate_count_\" + uniqueKey); String actualValueSql = \"select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}\"; executeSql.setSql(actualValueSql); executeSql.setErrorOutput(false); return executeSql; } @Override public CheckResult validateConfig(Map config) { return ConfigChecker.checkConfig(config, requiredOptions); } @Override public void prepare(Map config) { if (config.containsKey(\"filter\")) { filters.add(config.get(\"filter\")); } addFiltersIntoInvalidateItemsSql(); } private void addFiltersIntoInvalidateItemsSql() { if (filters.size() > 0) { invalidateItemsSql.append(\" where \").append(String.join(\" and \", filters)); } } @Override public MetricLevel getLevel() { return MetricLevel.TABLE; }} BaseSingleTableColumn 抽象类​ BaseSingleTableColumn是列级别的抽象实现类，主要是添加列级别规则的通用参数。 public abstract class BaseSingleTableColumn extends BaseSingleTable { public BaseSingleTableColumn() { super(); configMap.put(\"column\",new ConfigItem(\"column\", \"列名\", \"column\")); requiredOptions.add(\"column\"); } @Override public Map getConfigMap() { return configMap; } @Override public MetricLevel getLevel() { return MetricLevel.COLUMN; } @Override public boolean isInvalidateItemsCanOutput() { return false; }}","s":"第一步","u":"/datavines-website/docs/development/plugin/metric-develop","h":"#第一步","p":30},{"i":35,"t":"了解完上面的三个基础类以后，自定义一个Metric就变得格外简单了。 基础工作​ 在 datavines-metric-plugins 下创建一个新规则的 module 在 pom.xml 中添加 io.datavines datavines-metric-base ${project.version} 以 枚举值检查 规则为例来讲解​ 判断要实现的规则的级别，因为枚举值检查是列级别，所以继承 BaseSingleTableColumn 即可。 在构造函数中的configMap添加enum_list参数用于返回给前端进行展示，在requiredOptions添加enum_list用于参数的检查。 实现英文名、中文名、规则维度、规则类型这些基础的属性。 因为枚举值检查规则是为了找出在枚举值列表中的数据，所以只需要在fileters这个数组里面加入(${column} in ( ${enum_list} ))，prepare()方法会自动进行不符合规则的SQL语句构造。 实现suitableType()方法添加规则适用的字段类型。 public class ColumnInEnums extends BaseSingleTableColumn { public ColumnInEnums(){ super(); configMap.put(\"enum_list\",new ConfigItem(\"enum_list\", \"枚举值列表\", \"enum_list\")); requiredOptions.add(\"enum_list\"); } @Override public String getName() { return \"column_in_enums\"; } @Override public String getZhName() { return \"枚举值检查\"; } @Override public MetricDimension getDimension() { return MetricDimension.EFFECTIVENESS; } @Override public MetricType getType() { return MetricType.SINGLE_TABLE; } @Override public boolean isInvalidateItemsCanOutput() { return true; } @Override public void prepare(Map config) { if (config.containsKey(\"enum_list\") && config.containsKey(\"column\")) { filters.add(\" (${column} in ( ${enum_list} )) \"); } super.prepare(config); } @Override public List suitableType() { return Arrays.asList(DataVinesDataType.NUMERIC_TYPE, DataVinesDataType.STRING_TYPE, DataVinesDataType.DATE_TIME_TYPE); }}","s":"第二步","u":"/datavines-website/docs/development/plugin/metric-develop","h":"#第二步","p":30},{"i":37,"t":"非常重要的一步 在 resources 目录下创建META-INF/plugins目录。 在 plugins 目录下创建文件并且命名为io.datavines.metric.api.SqlMetric。 在文件中添加column_in_enums=io.datavines.metric.plugin.ColumnInEnums。 在datavines-metric-all模块下的pom.xml文件中添加新建的插件模块的依赖。 io.datavines datavines-metric-column-in-enums ${project.version}","s":"第三步","u":"/datavines-website/docs/development/plugin/metric-develop","h":"#第三步","p":30},{"i":39,"t":"打包成jar放到 datavines 目录下的libs目录下，重启服务即可。","s":"第四步","u":"/datavines-website/docs/development/plugin/metric-develop","h":"#第四步","p":30},{"i":41,"t":"Connector 是 Datavines 中一个核心概念，一个 Connector 表示一个连接器，用于连接数据源。Metric 采用插件化设计，用户可以根据自己的需求来实现一个 Connector。下面我们来详细讲解一下如何自定义Connector。","s":"数据源开发","u":"/datavines-website/docs/development/plugin/connector-develop","h":"","p":40},{"i":43,"t":"我们先了解下几个接口和抽象类，它们是实现自定义 Connector 的关键。 ConnectorFactory 接口​ ConnectorFactory接口中定义了 Connector 需要的Connector、Executor、Dialect、TypeConverter等接口。 @SPIpublic interface ConnectorFactory { String getCategory(); Connector getConnector(); ResponseConverter getResponseConverter(); Dialect getDialect(); ConnectorParameterConverter getConnectorParameterConverter(); Executor getExecutor(); TypeConverter getTypeConverter();} Connector 接口​ Connector 接口中主要定义了获取数据源中数据库、表和列、测试链接、配置参数和关键属性等接口。 public interface Connector { default ConnectorResponse getDatabases(GetDatabasesRequestParam param) throws SQLException { return null; } default ConnectorResponse getTables(GetTablesRequestParam param) throws SQLException { return null; } default ConnectorResponse getColumns(GetColumnsRequestParam param) throws SQLException { return null; } default ConnectorResponse getPartitions(ConnectorRequestParam param) { return null; } default String getConfigJson(boolean isEn) { return null; } default ConnectorResponse testConnect(TestConnectionRequestParam param) { return null; } List keyProperties();} Executor 接口​ Executor 接口中主要定义执行数据查询的接口。 public interface Connector { default ConnectorResponse queryForPage(ExecuteRequestParam param) throws Exception { return null; } default ConnectorResponse queryForList(ExecuteRequestParam param) throws Exception { return null; } default ConnectorResponse queryForOne(ExecuteRequestParam param) throws Exception { return null; } default ConnectorResponse deleteData(ExecuteRequestParam param) throws Exception { return null; }} Dialect 接口​ Dialect 接口中定义了数据源的各种方言、查询脚本和支持的特性。 public interface Dialect { String getDriver(); String getColumnPrefix(); String getColumnSuffix(); default Map getDialectKeyMap() { return new HashMap<>(); } List getExcludeDatabases(); default String invalidateItemCanOutput(){ return \"true\"; } default String invalidateItemCanOutputToSelf(){ return \"false\"; } default boolean supportToBeErrorDataStorage(){ return false; } default String getJDBCType(DataType dataType){ return dataType.toString(); } default DataType getDataType(String jdbcType) { return DataType.valueOf(jdbcType); } default String quoteIdentifier(String column) { return \"`\" + column + \"`\"; } default String getTableExistsQuery(String table) { return String.format(\"SELECT * FROM %s WHERE 1=0\", table); } default String getSchemaQuery(String table) { return String.format(\"SELECT * FROM %s WHERE 1=0\", table); } default String getCountQuery(String table) { return String.format(\"SELECT COUNT(1) FROM %s\", table); } default String getSelectQuery(String table) { return String.format(\"SELECT * FROM %s\", table); } default String getCreateTableAsSelectStatement(String srcTable, String targetDatabase, String targetTable) { return String.format(\"CREATE TABLE %s.%s AS SELECT * FROM %s\", quoteIdentifier(targetDatabase), quoteIdentifier(targetTable), quoteIdentifier(srcTable)); } default String getCreateTableStatement(String table, List fields, TypeConverter typeConverter) { if (CollectionUtils.isNotEmpty(fields)) { String columns = fields.stream().map(field -> { return quoteIdentifier(field.getName()) + \" \" + typeConverter.convertToOriginType(field.getDataType()); }).collect(Collectors.joining(\",\")); return String.format(\"CREATE TABLE IF NOT EXISTS %s (%s)\", table, columns); } return \"\"; } default String getInsertAsSelectStatement(String srcTable, String targetDatabase, String targetTable) { return String.format(\"INSERT INTO %s.%s SELECT * FROM %s\", quoteIdentifier(targetDatabase), quoteIdentifier(targetTable), quoteIdentifier(srcTable)); } String getErrorDataScript(Map configMap); String getValidateResultDataScript(Map configMap);} ConnectorParameterConverter 接口​ ConnectorParameterConverter 接口负责对输入的参数进行转换，转换成符合引擎执行的格式。 public interface ConnectorParameterConverter { Map converter(Map parameter); default String getConnectorUUID(Map parameter) { Map convertResult = converter(parameter); return DigestUtils.md5Hex( String.valueOf(convertResult.get(URL)) + convertResult.get(TABLE) + convertResult.get(USER) + convertResult.get(PASSWORD)); }} TypeConverter 接口​ TypeConverter 接口定义数据源中的字段类型转换方法，支持将原始类型与系统类型的互相转换。 public interface TypeConverter { DataType convert(String originType); String convertToOriginType(DataType dataType);} ConfigBuilder 接口​ ConfigBuilder 接口定义了连接器配置参数的构造，用于返回给给前端进行展示。 public interface ConfigBuilder { String build(boolean isEn);} datavines-connector-jdbc 模块​ datavines-connector-jdbc 模块是 JDBC 类型数据源的基础实现，实现了 JDBC 类型 的 Connector 的基本操作，如果要新增一个 JDBC 类型的 Connector，引入该模块再做一些修改即可。","s":"第一步","u":"/datavines-website/docs/development/plugin/connector-develop","h":"#第一步","p":40},{"i":45,"t":"了解完上面的接口和 datavines-connector-jdbc 模块以后，自定义一个JDBC类型 的Connector就变得格外简单了。 基础工作​ 在 datavines-connector-plugins 下创建一个新 Connector 的 module 在 pom.xml 中添加 io.datavines datavines-connector-jdbc ${project.version} 以 MySQL Connector 为例来讲解​ 新增一个JDBC类型的Connector 可以继承 datavines-connector-jdbc 模块中的各种实现类。 创建 MysqlConfigBuilder 继承 JdbcConfigBuilder，对 getPropertiesInput 进行重写，这里重写的目的是增加一些连接数据源的配置，如果不需要则无需重写。 public class MysqlConfigBuilder extends JdbcConfigBuilder { @Override protected InputParam getPropertiesInput(boolean isEn) { return getInputParam(\"properties\", isEn ? \"properties\" : \"参数\", isEn ? \"please enter properties,like key=value&key1=value1\" : \"请填入参数，格式为key=value&key1=value1\", 2, null, \"useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai\"); }} 创建 MysqlDataSourceInfo 继承 BaseJdbcDataSourceInfo，主要是实现数据源的连接URL、Driver等方法。 public class MysqlDataSourceInfo extends BaseJdbcDataSourceInfo { public MysqlDataSourceInfo(JdbcConnectionInfo jdbcConnectionInfo) { super(jdbcConnectionInfo); } @Override public String getAddress() { return \"jdbc:mysql://\"+getHost()+\":\"+getPort(); } @Override public String getDriverClass() { return \"com.mysql.cj.jdbc.Driver\"; } @Override public String getType() { return \"mysql\"; } @Override protected String getSeparator() { return \"?\"; } @Override protected String filterProperties(String other){ return other; }} 创建 MysqlConnector 继承 JdbcConnector， JdbcConnector里面已经默认实现了获取数据库列表、表列表、列列表、测试连接等接口，不同的数据源在通过 jdbc 连接时要传给 DatabaseMetaData 的参数可能都不太一样，需要根据不同的数据去实现。 public class MysqlConnector extends JdbcConnector { @Override public BaseJdbcDataSourceInfo getDatasourceInfo(JdbcConnectionInfo jdbcConnectionInfo) { return new MysqlDataSourceInfo(jdbcConnectionInfo); } @Override public ResultSet getMetadataColumns(DatabaseMetaData metaData, String catalog, String schema, String tableName, String columnName) throws SQLException { return metaData.getColumns(schema, null, tableName, columnName); } @Override public ResultSet getMetadataTables(DatabaseMetaData metaData, String catalog, String schema) throws SQLException { return metaData.getTables(schema, null, null, TABLE_TYPES); } @Override public ResultSet getMetadataDatabases(Connection connection) throws SQLException { DatabaseMetaData metaData = connection.getMetaData(); return metaData.getCatalogs(); } } 创建 MysqlConnectorParameterConverter 继承 JdbcConnectorParameterConverter， 该接口已经默认了参数的格式化转换，只需要对URL的构造做个性化的实现。 public class MysqlConnectorParameterConverter extends JdbcConnectorParameterConverter { @Override protected String getUrl(Map parameter) { String url = String.format(\"jdbc:mysql://%s:%s/%s\", parameter.get(HOST), parameter.get(PORT), parameter.get(DATABASE)); String properties = (String)parameter.get(PROPERTIES); if (StringUtils.isNotEmpty(properties)) { url += \"?\" + properties; } return url; }} 创建 MysqlDialect 继承 JdbcDialect， 针对不同的数据源的方言做一些个性化实现。 public class MysqlDialect extends JdbcDialect { @Override public Map getDialectKeyMap() { super.getDialectKeyMap(); dialectKeyMap.put(STRING_TYPE, \"char\"); return dialectKeyMap; } @Override public String getDriver() { return \"com.mysql.cj.jdbc.Driver\"; } @Override public String invalidateItemCanOutputToSelf() { return \"true\"; } @Override public boolean supportToBeErrorDataStorage() { return true; }} 创建 MysqlExecutor 继承 BaseJdbcExecutor， BaseJdbcExecutor中已经实现了各种数据查询的接口，如果数据源存在特殊的查询逻辑，可进行重写来实现自有的查询逻辑。 public class MysqlExecutor extends BaseJdbcExecutor { @Override public BaseJdbcDataSourceInfo getDatasourceInfo(JdbcConnectionInfo jdbcConnectionInfo) { return new MysqlDataSourceInfo(jdbcConnectionInfo); }} 最后创建 MysqlConnectorFactory 继承 AbstractJdbcConnectorFactory，将上面的实现类通过 Factory 接口进行实例构造。 public class MysqlConnectorFactory extends AbstractJdbcConnectorFactory { @Override public ConnectorParameterConverter getConnectorParameterConverter() { return new MysqlConnectorParameterConverter(); } @Override public Dialect getDialect() { return new MysqlDialect(); } @Override public Connector getConnector() { return new MysqlConnector(); } @Override public Executor getExecutor() { return new MysqlExecutor(); } @Override public ConfigBuilder getConfigBuilder() { return new MysqlConfigBuilder(); }}","s":"第二步","u":"/datavines-website/docs/development/plugin/connector-develop","h":"#第二步","p":40},{"i":47,"t":"非常重要的一步 在 resources 目录下创建META-INF目录，再创建plugins目录。 在 plugins 目录下创建文件并且命名为io.datavines.connector.api.ConnectorFactory。 在文件中添加mysql=io.datavines.connector.plugin.MysqlConnectorFactory。","s":"第三步","u":"/datavines-website/docs/development/plugin/connector-develop","h":"#第三步","p":40},{"i":49,"t":"打包成jar放到 datavines 目录下的libs目录下，重启服务即可。","s":"第四步","u":"/datavines-website/docs/development/plugin/connector-develop","h":"#第四步","p":40},{"i":56,"t":"ClickHouse 连接器用于与 ClickHouse 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-clickhouse","h":"#介绍","p":54},{"i":59,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-clickhouse","h":"#options","p":54},{"i":61,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-clickhouse","h":"#属性","p":54},{"i":63,"t":"本文针对 Datavines 中各个核心模块进行较为详细的介绍","s":"Datavines 代码架构解析","u":"/datavines-website/docs/development/source-module-explanation","h":"","p":62},{"i":65,"t":"datavines-server 是 datavines 中最核心的模块，核心功能包括对外提供 api 接口， 整个系统的数据管理， 数据源的元数据获取和管理， 数据质量规则的管理、调度和执行。它是无中心化设计，支持水平扩容提升性能，同时支持各种作业的容错处理。","s":"datavines-server","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-server","p":62},{"i":67,"t":"datavines-spi 是 datavines 中的 SPI 模块，主要功能是设计了SPI接口和PluginLoader，PluginLoader是负责扫描有@SPI的插件，把具体的插件加载到系统中。","s":"datavines-spi","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-spi","p":62},{"i":69,"t":"datavines-connector 是 datavines 中的数据源模块，datavines-connector-api 中定义了数据源的各种接口，比如 Connector 接口中定义了数据源各种元数据相关的接口，Executor 接口则是定义了数据源数据查询相关的接口，Dialect 接口里定义了数据源方言相关的接口。datavines-connector-plugins 中是各种数据源的具体实现，内置的数据源包括MySQL、Doris、Starrocks、Trino、Presto等10种数据源。","s":"datavines-connector","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-connector","p":62},{"i":71,"t":"datavines-metric 是 datavines 中的规则模块，datavines-metric-api 中定义了规则、期望值、结果计算公式的接口。SqlMetric接口主要是定义规则的类型、实际值计算逻辑等相关方法。ExpectedValue接口主要是定义了期望值的计算逻辑相关方法。ResultFormula接口主要是定义了检查公式的计算逻辑相关方法。","s":"datavines-metric","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-metric","p":62},{"i":73,"t":"datavines-engine 是 datavines 中的作业执行引擎模块。datavines-engine-api 定义了 engine 相关的接口，Execution 接口主要是定义了作业执行的相关方法，execute 方法主要执行 source 的读取，对数据进行 transform，最后把数据 sink 到目的地。datavines-engine-config 模块主要是定义了作业配置文件的构造逻辑和相关方法。datavines-engine-executor 模块是定义了作业执行器相关逻辑","s":"datavines-engine","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-engine","p":62},{"i":75,"t":"datavines-registry 是 datavines 中的注册中心。 datavines-registry-api 定义了 获取锁、释放锁、获取当前活跃节点、节点上下线通知等接口。datavines-registry-plugins 中内置了三个类型的注册中心，包括 MySQL、PostgreSQL 和 ZooKeeper。","s":"datavines-registry","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-registry","p":62},{"i":77,"t":"datavines-notification 是 datavines 中的通知通道管理。 datavines-notification-api 定义了通知通道的消息通知、配置参数等接口。datavines-notification-plugins 中内置了 email 类型的通知通道实现。","s":"datavines-notification","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-notification","p":62},{"i":79,"t":"datavines-runner 是 datavines 中负责脚本模式运行数据质量检查的模块，主要功能是读取配置文件、解析配置文件，执行数据质量检查，判断检查结果，运行告警处理。","s":"datavines-runner","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-runner","p":62},{"i":82,"t":"Hive 连接器用于与 Hive 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-hive","h":"#介绍","p":80},{"i":85,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP, 支持ZooKeeper的高可用连接，ZooKeeper地址通过 , 隔开 port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： hive.resultset.use.unique.column.names=false 若采用ZooKeeper高可用方式，需要输入以下配置： serviceDiscoveryMode=zooKeeper;zooKeeperNamespace={your_namespace}","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-hive","h":"#options","p":80},{"i":87,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 false invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-hive","h":"#属性","p":80},{"i":90,"t":"Doris 连接器用于与 Doris 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-doris","h":"#介绍","p":88},{"i":93,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-doris","h":"#options","p":88},{"i":95,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-doris","h":"#属性","p":88},{"i":98,"t":"Databend 连接器用于与 Databend 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-databend","h":"#介绍","p":96},{"i":101,"t":"name type required default value host string yes - port int yes - database string no - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-databend","h":"#options","p":96},{"i":103,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-databend","h":"#属性","p":96},{"i":106,"t":"MySQL 连接器用于与 MySQL 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-mysql","h":"#介绍","p":104},{"i":109,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-mysql","h":"#options","p":104},{"i":111,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 true","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-mysql","h":"#属性","p":104},{"i":114,"t":"PostgreSQL 连接器用于与 PostgreSQL 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-postgresql","h":"#介绍","p":112},{"i":117,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-postgresql","h":"#options","p":112},{"i":119,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-postgresql","h":"#属性","p":112},{"i":122,"t":"Presto 连接器用于与 Presto 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-presto","h":"#介绍","p":120},{"i":125,"t":"name type required default value host string yes - port int yes - catalog string yes - database string no - user string yes - password string no - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 catalog [string]​ catalog database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-presto","h":"#options","p":120},{"i":127,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-presto","h":"#属性","p":120},{"i":130,"t":"Impala 连接器用于与 Impala 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-impala","h":"#介绍","p":128},{"i":133,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-impala","h":"#options","p":128},{"i":135,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-impala","h":"#属性","p":128},{"i":138,"t":"Trino 连接器用于与 Trino 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-trino","h":"#介绍","p":136},{"i":141,"t":"name type required default value host string yes - port int yes - catalog string yes - database string no - user string yes - password string no - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 catalog [string]​ catalog database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，用&隔开","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-trino","h":"#options","p":136},{"i":143,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-trino","h":"#属性","p":136},{"i":146,"t":"StarRocks 连接器用于与 StarRocks 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-starrocks","h":"#介绍","p":144},{"i":149,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-starrocks","h":"#options","p":144},{"i":151,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-starrocks","h":"#属性","p":144},{"i":154,"t":"Livy 引擎是基于 Livy 开发的数据质量检查作业执行引擎，使得Datavines可以将 Spark 应用提交到 Livy 上去执行。","s":"原理解释","u":"/datavines-website/docs/features/engine/livy-engine","h":"#原理解释","p":152},{"i":156,"t":"在数据质量检查作业和数据比对作业中的引擎配置中选择 Livy 引擎，填好各种 Spark 相关的基础配置信息","s":"使用方法","u":"/datavines-website/docs/features/engine/livy-engine","h":"#使用方法","p":152},{"i":158,"t":"如果想要使用Livy引擎，需要完成Livy相关的配置 全局参数中配置livy开头的相关信息 将/lib/datavines-engine-spark-core-1.0.0-SNAPSHOT.jar上传到参数livy.task.jar.lib.path指定的位置 将作业任务相关jar (plugins目录下的jar) 上传到参数livy.task.jar.lib.path指定的位置，如果需要额外的jar，请自行上传 在作业配置jar具体为--jar xx.jar,zz.jar，若不指定--jar会默认按照livy.task.jars配置来执行,需要将相关文件上传","s":"注意事项","u":"/datavines-website/docs/features/engine/livy-engine","h":"#注意事项","p":152},{"i":161,"t":"Local 引擎是通过 Jdbc 的方式连接数据源，执行根据数据质量检查规则生成的 SQL 语句，得到相应的错误数据视图、实际值和期望值，最后将实际值和期望值写到 Datavines 系统的执行结果表以及将错误数据视图中的数据写到错误数据存储引擎。","s":"原理解释","u":"/datavines-website/docs/features/engine/local-engine","h":"#原理解释","p":159},{"i":163,"t":"Local 引擎的使用非常简单，创建数据质量检查作业时默认使用的是 Local 引擎，无需做任何额外的配置，保存好配置执行即可，需要关注一下注意事项。","s":"使用方法","u":"/datavines-website/docs/features/engine/local-engine","h":"#使用方法","p":159},{"i":165,"t":"用于创建数据源的用户需要有创建视图和删除视图的权限，如果你想要将错误数据写入到检查的数据源中某个库中，那么你的用户需要有创建表的权限。","s":"注意事项","u":"/datavines-website/docs/features/engine/local-engine","h":"#注意事项","p":159},{"i":168,"t":"全局参数管理是 Datavines 中用于管理系统运行过程使用到的各种参数，支持实时的增删改查，默认参数不允许删除。","s":"介绍","u":"/datavines-website/docs/features/config","h":"#介绍","p":166},{"i":170,"t":"点击右上角的创建参数按钮，在对话框中输入参数的参数名和参数值，点击保存即可。","s":"创建参数","u":"/datavines-website/docs/features/config","h":"#创建参数","p":166},{"i":173,"t":"错误数据存储引擎是用于存储数据质量检查中不符合规则的数据，便于用户可以查看有问题的数据来进行下一步的修复或者剔除操作，Datavines 目前支持写入至外部存储引擎和写入至检查数据源两种方式。 外部存储引擎目前只支持MySQL、写入至检查数据源支持MySQL、Doris、StarRocks。","s":"介绍","u":"/datavines-website/docs/features/error-data-storage","h":"#介绍","p":171},{"i":176,"t":"在错误数据管理页面创建存储引擎，填写上引擎的配置参数，点击测试连接，测试成功则保存存储引擎。 在作业配置页面进行配置，在错误数据存储配置里选择你所创建的引擎 写入外部存储引擎需要进行数据的读取和写入，为了保证系统的稳定，目前只支持前10000条错误数据的存储。","s":"写入外部存储引擎","u":"/datavines-website/docs/features/error-data-storage","h":"#写入外部存储引擎","p":171},{"i":178,"t":"无需进行额外的配置 在作业配置页面进行配置，在错误数据存储配置里, 开启 是否输出至源数据源 配置，并且输入用于存储错误数据的数据库名。","s":"写入检查数据源","u":"/datavines-website/docs/features/error-data-storage","h":"#写入检查数据源","p":171},{"i":180,"t":"点击数据质量检查作业的 查看执行记录，在执行记录中点击 错误数据 即可查看错误数据。","s":"错误数据的查看","u":"/datavines-website/docs/features/error-data-storage","h":"#错误数据的查看","p":171},{"i":183,"t":"日均值 期望值类型表示在数据质量检查过程中使用的期望值是根据今日运行该规则得到的实际值所计算得出的当日的均值。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-daily-avg","h":"#概念解释","p":181},{"i":185,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 日均值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-daily-avg","h":"#使用方法","p":181},{"i":187,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 unique_code：会根据每个规则的独有的配置信息生成一个唯一编码 Local 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >=date_format(${data_time},'%Y-%m-%d') and data_time < date_add(date_format(${data_time},'%Y-%m-%d'), interval 1 DAY) and unique_code = ${unique_code} Spark&Livy 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >=date_format(${data_time}, 'yyyy-MM-dd') and data_time < date_add(date_format(${data_time}, 'yyyy-MM-dd'),1) and unique_code = ${unique_code}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-daily-avg","h":"#计算方式","p":181},{"i":191,"t":"Spark 引擎是基于 Spark 开发的数据质量检查作业执行引擎，SparkDataVinesBootstrap 是应用的执行入。Datavines 会根据用户所配置各种信息构造成配置文件并传入到 SparkDataVinesBootstrap中，SparkDataVinesBootstrap 会解析配置文件并选择不同的 source、transform 和 sink 插件来执行连接数据源、质量检查语句和将执行结果写入到相应的存储引擎中的操作。","s":"原理解释","u":"/datavines-website/docs/features/engine/spark-engine","h":"#原理解释","p":189},{"i":193,"t":"在数据质量检查作业和数据比对作业中的引擎配置中选择 Spark 引擎，填好各种 Spark 相关的基础配置信息","s":"使用方法","u":"/datavines-website/docs/features/engine/spark-engine","h":"#使用方法","p":189},{"i":195,"t":"如果想要使用 Spark 引擎，那么需要把 Datavines 部署在能执行 spark-submit 的服务器上 需要配置可以执行 spark-submit 的服务器用户 和 环境配置 服务器用户指的是 root、hadoop、ods等等这些服务器创建的用户 环境配置指的是 在服务器上 export 的各种配置，比如下面的SPARK_HOME2 export SPARK_HOME2=/usr/hdp/2.6.3.0-235/spark-2.4.8-bin-hadoop2.7 需要在全局配置那里进行yarn配置的修改 standalone 模式 yarn.mode=standaloneyarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%s #第一个%s需要被替换成yarn的ip地址yarn.resource.manager.http.address.port=8088 ha 模式 yarn.mode=hayarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%syarn.resource.manager.http.address.port=8088yarn.resource.manager.ha.ids=192.168.0.1,192.168.0.2","s":"注意事项","u":"/datavines-website/docs/features/engine/spark-engine","h":"#注意事项","p":189},{"i":198,"t":"固定值 期望值类型表示在数据质量检查过程中使用的是用户输入的期望值，内置使用到的参数是expected_value。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-fix-value","h":"#概念解释","p":196},{"i":200,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 固定值，在固定值输入框中输入期望值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-fix-value","h":"#使用方法","p":196},{"i":203,"t":"月均值 期望值类型表示在数据质量检查过程中使用的期望值是根据本月运行该规则得到的实际值所计算得出的当月的均值。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-monthly-avg","h":"#概念解释","p":201},{"i":205,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 月均值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-monthly-avg","h":"#使用方法","p":201},{"i":207,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 unique_code：会根据每个规则的独有的配置信息生成一个唯一编码 Local 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_format(curdate(), '%Y-%m-01') and data_time < date_add(date_format(${data_time},'%Y-%m-%d'),interval 1 DAY)\" + and unique_code = ${unique_code} Spark&Livy 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_format(${data_time}, 'yyyy-MM-01') and data_time < date_add(date_format(${data_time}, 'yyyy-MM-dd'),1) and unique_code = ${unique_code}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-monthly-avg","h":"#计算方式","p":201},{"i":210,"t":"最近7天均值 期望值类型表示在数据质量检查过程中使用的期望值是根据最近7天运行该规则得到的实际值所计算得出的最近7天的均值。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last7day-avg","h":"#概念解释","p":208},{"i":212,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 最近7天均值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last7day-avg","h":"#使用方法","p":208},{"i":214,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 unique_code：会根据每个规则的独有的配置信息生成一个唯一编码 Local 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_sub(date_format(${data_time},'%Y-%m-%d'),interval 7 DAY) and data_time < date_add(date_format(${data_time},'%Y-%m-%d'),interval 1 DAY) and unique_code = ${unique_code} Spark&Livy 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_add(date_format(${data_time}, 'yyyy-MM-dd'),-7) and data_time < date_add(date_format(${data_time}, 'yyyy-MM-dd'),1) and unique_code = ${unique_code}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last7day-avg","h":"#计算方式","p":208},{"i":217,"t":"最近30天均值 期望值类型表示在数据质量检查过程中使用的期望值是根据最近30天运行该规则得到的实际值所计算得出的最近30天的均值。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last30day-avg","h":"#概念解释","p":215},{"i":219,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 最近30天均值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last30day-avg","h":"#使用方法","p":215},{"i":221,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 unique_code：会根据每个规则的独有的配置信息生成一个唯一编码 Local 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_sub(date_format(${data_time},'%Y-%m-%d'),interval 30 DAY) and data_time < date_add(date_format(${data_time},'%Y-%m-%d'),interval 1 DAY) and unique_code = ${unique_code} Spark&Livy 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_add(date_format(${data_time}, 'yyyy-MM-dd'),-30) and data_time < date_add(date_format(${data_time}, 'yyyy-MM-dd'),1) and unique_code = ${unique_code}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last30day-avg","h":"#计算方式","p":215},{"i":224,"t":"表总行数 期望值类型表示在数据质量检查过程中使用的期望值是计算检查的表的总行数。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-table-rows","h":"#概念解释","p":222},{"i":226,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 表总行数。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-table-rows","h":"#使用方法","p":222},{"i":228,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 Local&Spark&Livy 引擎 select count(1) as expected_value_${uniqueKey} from ${table}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-table-rows","h":"#计算方式","p":222},{"i":231,"t":"目标表总行数 期望值类型表示在数据质量检查过程中使用的期望值是计算检查的目标表的总行数。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-target-table-rows","h":"#概念解释","p":229},{"i":233,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 目标表总行数。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-target-table-rows","h":"#使用方法","p":229},{"i":235,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 Local&Spark&Livy 引擎 select count(1) as expected_value_${uniqueKey} from ${target_table}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-target-table-rows","h":"#计算方式","p":229},{"i":238,"t":"周均值 期望值类型表示在数据质量检查过程中使用的期望值是根据本周运行该规则得到的实际值所计算得出的当周的均值。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-weekly-avg","h":"#概念解释","p":236},{"i":240,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 周均值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-weekly-avg","h":"#使用方法","p":236},{"i":242,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 unique_code：会根据每个规则的独有的配置信息生成一个唯一编码 Local 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_sub(${data_time},interval weekday(${data_time}) + 0 day) and data_time < date_add(date_format(${data_time},'%Y-%m-%d'),interval 1 DAY) and unique_code = ${unique_code} Spark&Livy 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_sub(date_format(${data_time},'yyyy-MM-dd'), (7- datediff(next_day(date_format(${data_time}, 'yyyy-MM-dd'),'Sunday'),date_format(${data_time}, 'yyyy-MM-dd')))-1) and data_time < date_add(date_format(${data_time}, 'yyyy-MM-dd'),1) and unique_code = ${unique_code}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-weekly-avg","h":"#计算方式","p":236},{"i":245,"t":"无 期望值类型表示在数据质量检查过程不需要使用到期望值，默认设置null","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-none","h":"#概念解释","p":243},{"i":247,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 无","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-none","h":"#使用方法","p":243},{"i":250,"t":"点击创建规则作业，选择数据比对作业 进入作业页面选择 两表值比对 规则 进行源表和目标表的配置信息，编写各自的检查SQL语句","s":"使用方法","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#使用方法","p":248},{"i":253,"t":"name type required default value database string yes - table string yes - actual_execute_sql string yes - database2 string yes - table2 string yes - expected_execute_sql string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 actual_execute_sql [string]​ 实际值执行SQL，注意 as 后面的别名一定要是 actual_value，否则统计会出错。 database2 [string]​ 目标表数据库名 table2 [string]​ 目标表数据库中的表名 expected_execute_sql [string]​ 期望值执行SQL，注意 as 后面的别名一定要是 expected_value，否则统计会出错。","s":"Options","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#options","p":248},{"i":255,"t":"{ \"metricType\": \"multi_table_value_comparison\", \"metricParameter\": { \"database\": \"cbs\", \"table\": \"cbs_ratio\", \"actual_execute_sql\": \"select count(1) as actual_value from ${table}\", \"database2\": \"cbs\", \"table2\": \"cbs_ratio\", \"expected_execute_sql\": \"select count(1) as expected_value from ${table2}\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#配置文件例子","p":248},{"i":258,"t":"比较某张表(有时间分区列)今天的数据量和昨天的数据量，如果今天的数据量小于昨天的数据量就需要告警。","s":"场景","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#场景","p":248},{"i":260,"t":"使用两表值比对规则配合内置时间参数。 使用两表值比对的规则，通过编写 SQL 语句来统计今天和昨天的数据量，然后比较两个值，如果今天的值大于昨天的值则为真，否则为假。","s":"思路","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#思路","p":248},{"i":262,"t":"选择数据比对作业中的两表值比对规则 选择对应的数据库和表，并编写计算实际值 SQL 语句。 语句里面用到了$[today]时间变量，系统会自动替换成今天的日期，格式为yyyy-MM-dd，也可以自己配置格式 $[today(yyyyMMdd)]。 注意 as 后面的别名一定要是 actual_value，否则统计会出错。 表名可以用${table}，系统会自动替换，也可以直接写正确的表名。 select count(1) as actual_value from ${table} where data_date='$[today]' 选择对应的数据库和表，并编写计算期望值 SQL 语句 语句里面用到了$[yesterday]时间变量，系统会自动替换成昨天的日期，格式为yyyy-MM-dd，也可以自己配置格式 $[yesterday(yyyyMMdd)]。 注意 as 后面的别名一定要是 expected_value，否则统计会出错。 表名可以用${table2}，系统会自动替换，也可以直接写正确的表名。 select count(1) as expected_value from ${table2} where data_date='$[yesterday]' 配置结果判断公式 结果公式选择：实际值-期望值 比较符选择：>= 阈值：0 如果公式实际值-期望值 >= 0的结果为真, 那么就证明今天的表行数大于昨天的表行数，否则就证明今天的表行数小于昨天的表行数，结果是异常的，需要告警。","s":"步骤","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#步骤","p":248},{"i":265,"t":"实际值 比对公式表示在进行结果判断时所使用的是实际值和期望值的差的绝对值。","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-count","h":"#概念解释","p":263},{"i":267,"t":"点击创建规则作业，选择数据质量作业 在比对公式中选择 实际值","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-count","h":"#使用方法","p":263},{"i":269,"t":"@Overridepublic Double getResult(Double actualValue, Double expectedValue) { return actualValue;}","s":"计算公式","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-count","h":"#计算公式","p":263},{"i":272,"t":"跨表准确性是用于检查源表与目标表指定字段的数据差异，举个例子 表：test1 C1 C2 a 1 b 2 表：test2 C21 C22 a 1 b 3 如果对比c1和c21中的数据，则表test1和test2完全一致。 如果对比c2和c22则表test1和表test2中的数据则存在不一致了。","s":"概念解释","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#概念解释","p":270},{"i":274,"t":"通过 【源表 LEFT JOIN 目标表 on 源表.列1 = 目标表.列21 where 源表.列1 不等于 NULL 且 目标表.列21 等于 NULL】 SQL 语句来筛选出源表中存在的数据而目标表中不存在的数据.","s":"实现原理","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#实现原理","p":270},{"i":276,"t":"点击创建规则作业，选择数据比对作业 进入作业页面选择 跨表准确性 规则 进行源表和目标表的配置信息，选择要检查的字段","s":"使用方法","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#使用方法","p":270},{"i":279,"t":"name type required default value database string yes - table string yes - database2 string yes - table2 string yes - mappingColumns string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 database2 [string]​ 目标表数据库名 table2 [string]​ 目标表数据库中的表名 mappingColumns [string]​ 要进行检查的字段，column 为 源表的字段，column2 为目标表的字段，格式如下： [{\\\"column\\\":\\\"job_execution_id\\\",\\\"operator\\\":\\\"=\\\",\\\"column2\\\":\\\"job_execution_id\\\"}]","s":"Options","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#options","p":270},{"i":281,"t":"{ \"metricType\": \"multi_table_value_comparison\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_actual_values\", \"database2\": \"datavines\", \"table2\": \"dv_actual_values1\", \"mappingColumns\": \"[{\\\"column\\\":\\\"job_execution_id\\\",\\\"operator\\\":\\\"=\\\",\\\"column2\\\":\\\"job_execution_id\\\"}\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#配置文件例子","p":270},{"i":283,"t":"如果你进行检查的源表和目标表都在同一个数据库实例中，那么可以使用 Local 引擎来执行，如果源表和目标表在不同的数据库实例中，那么只能使用 Spark 引擎。","s":"注意事项","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#注意事项","p":270},{"i":286,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#场景","p":270},{"i":288,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#思路","p":270},{"i":290,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#步骤","p":270},{"i":293,"t":"实际值-期望值 比对公式表示在进行结果判断时所使用的是实际值和期望值的差值。","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-actual-expected","h":"#概念解释","p":291},{"i":295,"t":"点击创建规则作业，选择数据质量作业 在比对公式中选择 实际值-期望值","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-actual-expected","h":"#使用方法","p":291},{"i":297,"t":"@Overridepublic Double getResult(Double actualValue, Double expectedValue) { BigDecimal av = new BigDecimal(Double.toString(actualValue)); BigDecimal bv = new BigDecimal(Double.toString(expectedValue)); return av.subtract(bv).doubleValue();}","s":"计算公式","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-actual-expected","h":"#计算公式","p":291},{"i":300,"t":"|实际值-期望值|/期望值*100% 比对公式表示在进行结果判断时所使用的是实际值和期望值的差值的绝对值除以期望值的百分比。","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-percentage","h":"#概念解释","p":298},{"i":302,"t":"点击创建规则作业，选择数据质量作业 在比对公式中选择 |实际值-期望值|/期望值*100%","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-percentage","h":"#使用方法","p":298},{"i":304,"t":"@Overridepublic Double getResult(Double actualValue, Double expectedValue) { double result = 0; if (expectedValue > 0) { result = Math.abs(expectedValue - actualValue) / expectedValue * 100; } return result;}","s":"计算公式","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-percentage","h":"#计算公式","p":298},{"i":307,"t":"|实际值-期望值| 比对公式表示在进行结果判断时所使用的是实际值和期望值的差的绝对值。","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff","h":"#概念解释","p":305},{"i":309,"t":"点击创建规则作业，选择数据质量作业 在比对公式中选择 |实际值-期望值|","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff","h":"#使用方法","p":305},{"i":311,"t":"@Overridepublic Double getResult(Double actualValue, Double expectedValue) { return Math.abs(actualValue - expectedValue);}","s":"计算公式","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff","h":"#计算公式","p":305},{"i":314,"t":"比对公式 指的是在进行结果判断时所使用的计算公式，一般配合比较符（comparator）和阈值（threshold）一起使用，完整的判断公式为 [比对公式][比较符] [阈值]","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-intro","h":"#概念解释","p":312},{"i":316,"t":"检查结果的判断公式为：[比对公式][比较符] [阈值]，如果结果为真，就是成功，否则为失败 举个例子：[实际值-期望值] [>] [0]， 实际值为10，期望值为9，代入公式以后就是 [10-9][>][0]，这个结果为真，那么检查结果就是成功。","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-intro","h":"#使用方法","p":312},{"i":319,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 平均值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#使用方法","p":317},{"i":322,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#options","p":317},{"i":324,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#配置文件例子","p":317},{"i":326,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select avg(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#检查过程中自动生成的-sql-语句","p":317},{"i":329,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#场景","p":317},{"i":331,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#思路","p":317},{"i":333,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#步骤","p":317},{"i":337,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 平均长度检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#使用方法","p":335},{"i":340,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#options","p":335},{"i":342,"t":"{ \"metricType\": \"column_avg_length\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#配置文件例子","p":335},{"i":344,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select avg(length(${column})) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#检查过程中自动生成的-sql-语句","p":335},{"i":347,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#场景","p":335},{"i":349,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#思路","p":335},{"i":351,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#步骤","p":335},{"i":354,"t":"实际值/期望值*100% 比对公式表示在进行结果判断时所使用的是实际值除以期望值的百分比。","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-percentage","h":"#概念解释","p":352},{"i":356,"t":"点击创建规则作业，选择数据质量作业 在比对公式中选择 实际值/期望值*100%","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-percentage","h":"#使用方法","p":352},{"i":358,"t":"@Overridepublic Double getResult(Double actualValue, Double expectedValue) { double result = 0; if (expectedValue > 0) { result = actualValue / expectedValue * 100; } return result;}","s":"计算公式","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-percentage","h":"#计算公式","p":352},{"i":361,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 Distinct检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#使用方法","p":359},{"i":364,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#options","p":359},{"i":366,"t":"{ \"metricType\": \"column_distinct\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#配置文件例子","p":359},{"i":368,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL， 输出不重复的行数 select count(distinct(${column})) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#检查过程中自动生成的-sql-语句","p":359},{"i":371,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#场景","p":359},{"i":373,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#思路","p":359},{"i":375,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#步骤","p":359},{"i":378,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 重复值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#使用方法","p":376},{"i":381,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#options","p":376},{"i":383,"t":"{ \"metricType\": \"column_duplicate\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#配置文件例子","p":376},{"i":385,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select ${column} from ${table} where ${filter} group by ${column} having count(${column}) > 1 计算实际值的 SQL，输出的实际值是 重复值>1 的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#检查过程中自动生成的-sql-语句","p":376},{"i":388,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#场景","p":376},{"i":390,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#思路","p":376},{"i":392,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#步骤","p":376},{"i":395,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 及时性检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#使用方法","p":393},{"i":398,"t":"name type required default value database string yes - table string yes - column string yes - begin_time string yes - deadline_time string yes - datetime_format string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 begin_time [string]​ 开始时间 deadline_time [string]​ 结束时间 datetime_format [string]​ 日期格式","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#options","p":393},{"i":400,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"update_time\", \"begin_time\":\"2023-10-01 08:00:00\", \"deadline_time\":\"2023-10-01 09:00:00\", \"datetime_format\":\"yyyy-MM-dd HH:mm:ss\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#配置文件例子","p":393},{"i":402,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select ${column} from ${table} where (DATE_FORMAT(${column}, '${datetime_format}') <= DATE_FORMAT('${deadline_time}', '${datetime_format}') ) AND (DATE_FORMAT(${column}, '${datetime_format}') >= DATE_FORMAT('${begin_time}', '${datetime_format}')) and ${filter} 计算实际值的 SQL，输出的实际值是 列值在时间范围内 的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#检查过程中自动生成的-sql-语句","p":393},{"i":405,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#场景","p":393},{"i":407,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#思路","p":393},{"i":409,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#步骤","p":393},{"i":412,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 字段长度检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#使用方法","p":410},{"i":415,"t":"name type required default value database string yes - table string yes - column string yes - comparator string yes - length string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 comparator [string]​ 比较符，>,>=.<,<=,=,!= length [string]​ 用于比较的长度","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#options","p":410},{"i":417,"t":"{ \"metricType\": \"column_in_enums\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\", \"comparator\":\">\", \"length\":\"5\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#配置文件例子","p":410},{"i":419,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (length(${column}) ${comparator} ${length})) and ${filter} 计算实际值的 SQL，输出的实际值是列的长度符合判断规则的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#检查过程中自动生成的-sql-语句","p":410},{"i":422,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#场景","p":410},{"i":424,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#思路","p":410},{"i":426,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#步骤","p":410},{"i":429,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 枚举值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#使用方法","p":427},{"i":432,"t":"name type required default value database string yes - table string yes - column string yes - enum_list string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 enum_list [string]​ 枚举值列表，用,隔开","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#options","p":427},{"i":434,"t":"{ \"metricType\": \"column_in_enums\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" \"enum_list\":\"'database','table'\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#配置文件例子","p":427},{"i":436,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (${column} in (${enum_list})) and ${filter} 计算实际值的 SQL，输出的实际值是列的值在枚举值列表中的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#检查过程中自动生成的-sql-语句","p":427},{"i":439,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#场景","p":427},{"i":441,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#思路","p":427},{"i":443,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#步骤","p":427},{"i":446,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 正则表达式[匹配]检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#使用方法","p":444},{"i":449,"t":"name type required default value database string yes - table string yes - column string yes - regexp string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 regexp [string]​ 正则表达式","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#options","p":444},{"i":451,"t":"{ \"metricType\": \"column_in_enums\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" \"regexp\":\"\\d\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#配置文件例子","p":444},{"i":453,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (${column} regexp '${regexp}') and ${filter} 计算实际值的 SQL，输出的实际值是列的值匹配上正则表达式的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#检查过程中自动生成的-sql-语句","p":444},{"i":456,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#场景","p":444},{"i":458,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#思路","p":444},{"i":460,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#步骤","p":444},{"i":463,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 最大值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#使用方法","p":461},{"i":466,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#options","p":461},{"i":468,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#配置文件例子","p":461},{"i":470,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select max(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#检查过程中自动生成的-sql-语句","p":461},{"i":473,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#场景","p":461},{"i":475,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#思路","p":461},{"i":477,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#步骤","p":461},{"i":480,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 最小值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#使用方法","p":478},{"i":483,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#options","p":478},{"i":485,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#配置文件例子","p":478},{"i":487,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select min(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#检查过程中自动生成的-sql-语句","p":478},{"i":490,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#场景","p":478},{"i":492,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#思路","p":478},{"i":494,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#步骤","p":478},{"i":497,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 正则表达式[不匹配]检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#使用方法","p":495},{"i":500,"t":"name type required default value database string yes - table string yes - column string yes - regexp string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 regexp [string]​ 正则表达式","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#options","p":495},{"i":502,"t":"{ \"metricType\": \"column_in_enums\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" \"regexp\":\"\\d\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#配置文件例子","p":495},{"i":504,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (${column} not regexp '${regexp}') and ${filter} 计算实际值的 SQL，输出的实际值是列的值没有匹配上正则表达式的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#检查过程中自动生成的-sql-语句","p":495},{"i":507,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#场景","p":495},{"i":509,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#思路","p":495},{"i":511,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#步骤","p":495},{"i":514,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 枚举值[不在]检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#使用方法","p":512},{"i":517,"t":"name type required default value database string yes - table string yes - column string yes - enum_list string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 enum_list [string]​ 枚举值列表，用,隔开","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#options","p":512},{"i":519,"t":"{ \"metricType\": \"column_in_enums\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" \"enum_list\":\"'database','table'\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#配置文件例子","p":512},{"i":521,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (${column} not in ( ${enum_list} ) or ${column} is null) and ${filter} 计算实际值的 SQL，输出的实际值是列的值不在枚举值列表中的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#检查过程中自动生成的-sql-语句","p":512},{"i":524,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#场景","p":512},{"i":526,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#思路","p":512},{"i":528,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#步骤","p":512},{"i":531,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 非空检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#使用方法","p":529},{"i":534,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#options","p":529},{"i":536,"t":"{ \"metricType\": \"column_not_null\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#配置文件例子","p":529},{"i":538,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where ${column} is not null and ${filter} 计算实际值的 SQL select count(1) as actual_value_\" + uniqueKey + \" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#检查过程中自动生成的-sql-语句","p":529},{"i":541,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#场景","p":529},{"i":543,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#思路","p":529},{"i":545,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#步骤","p":529},{"i":548,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 最大长度检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#使用方法","p":546},{"i":551,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#options","p":546},{"i":553,"t":"{ \"metricType\": \"column_avg_length\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#配置文件例子","p":546},{"i":555,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select max(length(${column})) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#检查过程中自动生成的-sql-语句","p":546},{"i":558,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#场景","p":546},{"i":560,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#思路","p":546},{"i":562,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#步骤","p":546},{"i":565,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 总值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#使用方法","p":563},{"i":568,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#options","p":563},{"i":570,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#配置文件例子","p":563},{"i":572,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select sum(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#检查过程中自动生成的-sql-语句","p":563},{"i":575,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#场景","p":563},{"i":577,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#思路","p":563},{"i":579,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#步骤","p":563},{"i":582,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 唯一性检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#使用方法","p":580},{"i":585,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#options","p":580},{"i":587,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#配置文件例子","p":580},{"i":589,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select ${column} from ${table} where ${filter} group by ${column} having count(${column}) = 1 计算实际值的 SQL，输出的实际值是 列值的重复数=1 的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#检查过程中自动生成的-sql-语句","p":580},{"i":592,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#场景","p":580},{"i":594,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#思路","p":580},{"i":596,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#步骤","p":580},{"i":599,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 空值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#使用方法","p":597},{"i":602,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#options","p":597},{"i":604,"t":"{ \"metricType\": \"column_null\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#配置文件例子","p":597},{"i":606,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where ${column} is null and ${filter} 计算实际值的 SQL select count(1) as actual_value_\" + uniqueKey + \" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#检查过程中自动生成的-sql-语句","p":597},{"i":609,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#场景","p":597},{"i":611,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#思路","p":597},{"i":613,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#步骤","p":597},{"i":616,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 最小长度检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#使用方法","p":614},{"i":619,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#options","p":614},{"i":621,"t":"{ \"metricType\": \"column_avg_length\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#配置文件例子","p":614},{"i":623,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select min(length(${column})) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#检查过程中自动生成的-sql-语句","p":614},{"i":626,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#场景","p":614},{"i":628,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#思路","p":614},{"i":630,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#步骤","p":614},{"i":633,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 方差检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#使用方法","p":631},{"i":636,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#options","p":631},{"i":638,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#配置文件例子","p":631},{"i":640,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select variance(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#检查过程中自动生成的-sql-语句","p":631},{"i":643,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#场景","p":631},{"i":645,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#思路","p":631},{"i":647,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#步骤","p":631},{"i":650,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 标准差检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#使用方法","p":648},{"i":653,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#options","p":648},{"i":655,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#配置文件例子","p":648},{"i":657,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select stddev(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#检查过程中自动生成的-sql-语句","p":648},{"i":660,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#场景","p":648},{"i":662,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#思路","p":648},{"i":664,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#步骤","p":648},{"i":667,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 无值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#使用方法","p":665},{"i":670,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#options","p":665},{"i":672,"t":"{ \"metricType\": \"column_blank\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#配置文件例子","p":665},{"i":674,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (${column} is null or ${column} = '') and ${filter} 计算实际值的 SQL select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#检查过程中自动生成的-sql-语句","p":665},{"i":677,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#场景","p":665},{"i":679,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#思路","p":665},{"i":681,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#步骤","p":665},{"i":684,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 自定义聚合SQL 规则 选择要检查的数据源信息，编写自定义聚合 SQL 语句","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#使用方法","p":682},{"i":687,"t":"name type required default value database string yes - table string yes - actual_aggregate_sql string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 actual_aggregate_sql [string]​ 自定义聚合SQL，注意 as 后面的别名一定要是 actual_value，否则统计会出错。","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#options","p":682},{"i":689,"t":"{ \"metricType\": \"custom_aggregate_sql\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_actual_values\", \"actual_aggregate_sql\": \"select count(1) as actual_value from ${table}\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#配置文件例子","p":682},{"i":692,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#场景","p":682},{"i":694,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#思路","p":682},{"i":696,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#步骤","p":682},{"i":699,"t":"支持用邮件通知","s":"介绍","u":"/datavines-website/docs/features/slas/slas-notification/notification-email","h":"#介绍","p":697},{"i":702,"t":"参数 描述 mail.smtp.host SMTP 服务器地址 mail.smtp.port SMTP 服务器端口 mail.sender 邮件发送者 mail.smtp.auth 是否需要认证 mail.user 验证用户名 mail.passwd 验证密码 mail.smtp.starttls.enable 如果为true，则在发出任何登录命令之前，允许使用STARTTLS命令（如果服务器支持）将连接切换到 TLS-protected 的连接。 如果服务器不支持STARTTLS，则在不使用TLS的情况下继续连接。 mail.smtp.ssl.enable 如果设置为true，则默认情况下使用SSL进行连接并使用SSL端口。对于“smtp”协议，默认值为false，对于“smtps”协议，则默认值为true。 mail.smtp.ssl.trust 如果已设置，并且尚未指定套接字工厂，则启用 MailSSLSocketFactory。如果设置为“*”，则所有主机都是受信任的。如果设置为以空格分隔的主机列表，则这些主机是受信任的。否则，信任取决于服务器提供的证书。","s":"通知发件人配置","u":"/datavines-website/docs/features/slas/slas-notification/notification-email","h":"#通知发件人配置","p":697},{"i":704,"t":"SLA 是系统的核心功能，也是通知用户的通道。用户可以给每个规则配置多个SLA来通知不同的用户群，一个SLA也可以给多个规则检查作业使用，目前系统只支持邮件。","s":"SLAs介绍","u":"/datavines-website/docs/features/slas/slas-intro","h":"","p":703},{"i":706,"t":"在sla中创建sla， 在通知里面创建一个通道 进去sla后，在sla中创建个通知关联上这个通道","s":"配置 SLA","u":"/datavines-website/docs/features/slas/slas-intro","h":"#配置-sla","p":703},{"i":708,"t":"创建数据质量检查作业时可以在配置页面选择SLA，当检查结果为失败时会通过该SLA里面所配置的告警通道进行告警","s":"SLA 使用","u":"/datavines-website/docs/features/slas/slas-intro","h":"#sla-使用","p":703},{"i":711,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 值范围检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#使用方法","p":709},{"i":714,"t":"name type required default value database string yes - table string yes - column string yes - min number yes - max number yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 min [number]​ 最小值 max [number]​ 最大值","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#options","p":709},{"i":716,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\", \"min\":\"0\", \"max\":\"10\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#配置文件例子","p":709},{"i":718,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select ${column} from ${table} where ${column} >= ${min} and ${column} <= ${max} and ${filter} 计算实际值的 SQL，输出的实际值是 列值的重复数=1 的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#检查过程中自动生成的-sql-语句","p":709},{"i":721,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#场景","p":709},{"i":723,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#思路","p":709},{"i":725,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#步骤","p":709},{"i":728,"t":"支持用企业微信机器人通知","s":"介绍","u":"/datavines-website/docs/features/slas/slas-notification/notification-wecombot","h":"#介绍","p":726},{"i":730,"t":"参数 描述 webhook 企业微信机器人 webhook 地址. 如 https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=12345678-xxxx-xxxx-xxxx-123456789999","s":"属性配置","u":"/datavines-website/docs/features/slas/slas-notification/notification-wecombot","h":"#属性配置","p":726},{"i":734,"t":"时间变量是Datavines系统内置的变量，可用于检查作业中的过滤条件和自定义SQL中，","s":"介绍","u":"/datavines-website/docs/features/time-variable","h":"#介绍","p":732},{"i":736,"t":"在过滤条件或者自定义SQL中可以使用 $[表达式] 的形式来使用，假设今天是 2023-10-01，目前支持的表达式如下： $[today]: 获取今天的日期，格式为yyyy-MM-dd，例如2023-10-01 $[yesterday]: 获取昨天的日期，格式为yyyy-MM-dd，例如2023-09-30 $[tomorrow]: 获取明天的日期，格式为yyyy-MM-dd，例如2023-10-02 $[yyyy-MM-dd HH:mm:ss]: 获取当前格式的日期或者时间 $[(时间格式)(-/+)n]: 获取当前格式的日期或者时间加减n天 $[yyyy-MM-dd-2]: 获取当前格式的日期的前两天，就是2023-09-29 $[add_months(时间格式, (-/+)n)]: 获取当前格式的日期或者时间加减n个月 $[add_months(yyyy-MM-dd, 2)]: 获取当前格式的日期的后两个月，就是2023-12-01 $[month_begin(时间格式, (-/+)n)]: 获取当前格式的日期所在月的第一天加减n天 $[month_begin(yyyy-MM-dd, +2)]: 获取当前格式的日期所在月的第一天的后两天，就是2023-10-03 $[month_end(时间格式, (-/+)n)]: 获取当前格式的日期所在月的最后一天加减n天 $[add_months(yyyy-MM-dd, -2)]: 获取当前格式的日期所在月的最后一天的前两天，就是2023-10-29 $[week_begin(时间格式, (-/+)n)]: 获取当前格式的日期所在周的第一天加减n天 $[week_begin(yyyy-MM-dd, -2)]: 获取当前格式的日期所在周的第一天的前两天，就是2023-09-23 $[week_end(时间格式, (-/+)n)]: 获取当前格式的日期所在周的最后一天加减n天 $[add_months(yyyy-MM-dd, -2)]: 获取当前格式的日期所在周的最后一天的前两天，就是2023-09-29","s":"使用方法","u":"/datavines-website/docs/features/time-variable","h":"#使用方法","p":732},{"i":739,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 表行数检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#使用方法","p":737},{"i":742,"t":"name type required default value database string yes - table string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#options","p":737},{"i":744,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#配置文件例子","p":737},{"i":746,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select count(1) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#检查过程中自动生成的-sql-语句","p":737},{"i":749,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#场景","p":737},{"i":751,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#思路","p":737},{"i":753,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#步骤","p":737},{"i":756,"t":"安装k8s 构建datavines镜像 创建或下载datavines on k8s的配置文件 https://raw.githubusercontent.com/datavane/datavines/dev/deploy/k8s/datavines.yaml","s":"环境准备","u":"/datavines-website/docs/get-started/kubernetes-deploy","h":"#环境准备","p":754},{"i":758,"t":"指定命名空间执行如下命令。 kubectl apply -f datavines.yaml -n ","s":"部署","u":"/datavines-website/docs/get-started/kubernetes-deploy","h":"#部署","p":754},{"i":760,"t":"获取命名空间下的pod，查看datavines是否正常运行。 kubectl get pod -n ","s":"验证","u":"/datavines-website/docs/get-started/kubernetes-deploy","h":"#验证","p":754},{"i":764,"t":"该文档主要介绍了如何通过 Dockerfile 来制作 DataVines 的运行镜像，以便于在容器化编排工具或者快速测试过程中可迅速拉取一个 Datavines Image 来完成集群的创建。","s":"构建Docker Image","u":"/datavines-website/docs/get-started/docker/build-docker-image","h":"#构建docker-image","p":762},{"i":766,"t":"Dockerfile编写需注意以下几点： 基础父镜像选用经过 Docker-Hub 认证的 OpenJDK 官方镜像，版本用 JDK 1.8 版本。 应用所需二进制包需自行打包,参考 用源码部署 章节。 应用在Docker内启动时应使用 start_container 参数 构建平台应与运行平台保持一致。","s":"准备 Dockerfile","u":"/datavines-website/docs/get-started/docker/build-docker-image","h":"#准备-dockerfile","p":762},{"i":768,"t":"编写 Dockerfile 文件，可参考如下Dockerfile,可通过wget进行下载。 复制打包好的二进制包到 Dockerfile 所在目录。 校验 Dockerfile 文件中二进制包名一致性。 通过如下命令进行打包。 docker build -t datavines:dev .","s":"构建步骤","u":"/datavines-website/docs/get-started/docker/build-docker-image","h":"#构建步骤","p":762},{"i":770,"t":"登录 DockerHub 账号 docker login -u 登录成功会提示 Success 相关提示。 更改镜像标签 docker tag datavines:dev //datavines:dev 推送镜像 docker push //datavines:dev","s":"推送镜像至 DockerHub 或私有仓库","u":"/datavines-website/docs/get-started/docker/build-docker-image","h":"#推送镜像至-dockerhub-或私有仓库","p":762},{"i":773,"t":"在安装Datavines之前请确保你的服务器上已经安装下面软件 Git，确保 git clone的顺利执行 JDK，确保 jdk = 8 Maven, 确保项目的顺利打包（当然你也可以在本地打包以后上传至服务器）","s":"环境准备","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#环境准备","p":771},{"i":775,"t":"git clone https://github.com/datavane/datavines.gitcd datavines","s":"下载代码","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#下载代码","p":771},{"i":777,"t":"Datavines 的元数据是存储在关系型数据库中，目前支持 MySQL ，下面以MySQL为例说明安装步骤： 创建数据库 datavines 执行 script/sql/datavines-mysql.sql 脚本进行数据库的初始化 下面的项目构建也是以MySQL为例","s":"数据库准备","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#数据库准备","p":771},{"i":779,"t":"打包并解压 mvn clean package -Preleasecd datavines-dist/targettar -zxvf datavines-1.0.0-SNAPSHOT-bin.tar.gz 注意: 此时如果前端 datavines-ui 打包异常, 可能是网络问题, 可以尝试更换 npm 源 找到 datavines-ui 项目的 pom 文件, 在 npm install 添加 --registry https://registry.npmmirror.com, 如下所示 npm install npm generate-resources install --registry https://registry.npmmirror.com 解压完成以后进入目录 cd datavines-1.0.0-SNAPSHOT-bin 修改配置信息 cd confvi application.yaml 主要是修改数据库信息 spring: config: activate: on-profile: mysql datasource: driver-class-name: com.mysql.cj.jdbc.Driver url: jdbc:mysql://127.0.0.1:3306/datavines?useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai username: root password: 123456 如果你是使用Spark做为执行引擎，并且是提交到Yarn上面去执行的，那么需要在common.properties中配置yarn相关的信息 standalone 模式 yarn.mode=standaloneyarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%s #第一个%s需要被替换成yarn的ip地址yarn.resource.manager.http.address.port=8088 ha 模式 yarn.mode=hayarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%syarn.resource.manager.http.address.port=8088yarn.resource.manager.ha.ids=192.168.0.1,192.168.0.2","s":"项目构建","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#项目构建","p":771},{"i":781,"t":"cd binsh datavines-daemon.sh start mysql 查看日志，如果日志里面没有报错信息，并且能看到[INFO] 2022-04-10 12:29:05.447 io.datavines.server.DatavinesServer:[61] - Started DatavinesServer in 3.97 seconds (JVM running for 4.69)的时候，证明服务已经成功启动","s":"启动服务","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#启动服务","p":771},{"i":783,"t":"如果项目是 在 windows 中打包的 执行 sh datavines-daemon.sh start mysql 时可能会报以下异常 这是因为 Windows格式文件的换行符为\\r\\n , 而Unix&Linux文件的换行符为\\n 此时需要执行 dos2unix 来转换文件格式 安装 dos2unix yum install -y dos2unix 格式转换 cd binfind . -type f -print0 | xargs -0 dos2unix","s":"windows 打包问题","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#windows-打包问题","p":771},{"i":785,"t":"在浏览器输入：localhost:5600，就会跳转至登录界面，输入账号密码 admin/123456","s":"访问前端页面","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#访问前端页面","p":771},{"i":788,"t":"本篇将简述如何通过docker run或docker-compose命令快速运行Datavines。","s":"背景说明","u":"/datavines-website/docs/get-started/docker/deploy-docker-image","h":"#背景说明","p":786},{"i":790,"t":"软件 版本 Docker 20.0及以上 docker-compose 2.10 及以上","s":"软件环境","u":"/datavines-website/docs/get-started/docker/deploy-docker-image","h":"#软件环境","p":786},{"i":793,"t":"1.下载并修改docker-compose.yaml 修改镜像版本为本地编译版本或私仓镜像版本 image: datavines:dev 如出现外部网段与容器内部网段冲突，表现为容器同网段可访问，跨网段无法访问，可修改docker-compose.yaml文件中的network_mode。 networks: datavines: driver: bridge ipam: config: - subnet: 172.18.0.0/24(自定义网段) 下载环境配置文件.env 修改.env文件中的配置项，参考如下： # 数据库连接SPRING_DATASOURCE_URL=jdbc:mysql://127.0.0.1:3306/datavines?useSSL=false&useUnicode=true&characterEncoding=UTF-8&allowPublicKeyRetrieval=false&useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=GMT%2B8# 数据库用户名SPRING_DATASOURCE_USERNAME=root","s":"前期准备","u":"/datavines-website/docs/get-started/docker/deploy-docker-image","h":"#前期准备","p":786},{"i":795,"t":"docker compose up -d","s":"快速启动","u":"/datavines-website/docs/get-started/docker/deploy-docker-image","h":"#快速启动","p":786},{"i":797,"t":"环境参数需从启动命令中传入，如下。 docker run -it -d --name datavines -p 5600:5600 --privileged=true --restart always -e SPRING_PROFILES_ACTIVE=mysql -e SPRING_DATASOURCE_URL=jdbc:mysql://127.0.0.1:3306/datavines?useSSL=false&useUnicode=true&characterEncoding=UTF-8&allowPublicKeyRetrieval=false&useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=GMT%2B8 -e SPRING_DATASOURCE_USERNAME=root -e SPRING_DATASOURCE_PASSWORD=123456 -e SPRING_DATASOURCE_DRIVER-CLASS_NAME=com.mysql.cj.jdbc.Driver datavines:dev","s":"方法二：docker run","u":"/datavines-website/docs/get-started/docker/deploy-docker-image","h":"#方法二docker-run","p":786},{"i":800,"t":"点击创建数据源按钮，输入数据源的名称，然后选择数据源类型。以mysql为例，输入mysql的连接信息，点击测试连接按钮。如果成功，请单击保存","s":"创建数据源","u":"/datavines-website/docs/user-guide/quick-start","h":"#创建数据源","p":798},{"i":802,"t":"点击已创建好的数据源，进入数据源详情页面","s":"查看数据源信息","u":"/datavines-website/docs/user-guide/quick-start","h":"#查看数据源信息","p":798},{"i":804,"t":"例如，点击表dv_catalog_entity_instance的properties列，进入列的详情页面","s":"选择列","u":"/datavines-website/docs/user-guide/quick-start","h":"#选择列","p":798},{"i":806,"t":"点击 添加规则 按钮, 选择空值检查规则, 选择 固定值 期望值类型、输入期望值 10 , 选择 实际值 检查公式 、> 比较符并输入阈值 10, 这样选择的意思就是当实际值 > 10 ,那么检查结果为成功，否则是失败。配置完成以后可以直接保存进行保存或者点击保存并运行来执行检查作业。 返回到列的详情页面，你可以看到你刚刚所创建的规则作业列表。你可以在这里看到这个检查作业的运行趋势图。","s":"添加规则","u":"/datavines-website/docs/user-guide/quick-start","h":"#添加规则","p":798},{"i":808,"t":"点击规则名则可以进入到规则作业的详情页面。 点击 运行结果 页面, 你可以看到执行历史列表。 点击日志按钮，你可以看到规则执行的日志信息。 点击检查结果按钮，你可以看到规则执行的检查结果。 点击错误数据按钮，你可以看到规则执行的错误数据。","s":"查看规则作业的信息","u":"/datavines-website/docs/user-guide/quick-start","h":"#查看规则作业的信息","p":798},{"i":811,"t":"cd datavines-1.0.0-SNAPSHOT-bin/binvi metric_job_config.json","s":"创建规则配置文件","u":"/datavines-website/docs/user-guide/local-mode","h":"#创建规则配置文件","p":809},{"i":813,"t":"{ \"languageEn\":true, \"name\":\"test\", \"executePlatformType\":\"client\", \"parameter\":{ \"connectorParameter\":{ \"type\":\"mysql\", \"parameters\":{ \"database\":\"test\", \"password\":\"123456\", \"port\":\"3306\", \"host\":\"localhost\", \"user\":\"root\", \"properties\":\"useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai\" } }, \"metricParameterList\":[ { \"metricType\":\"column_duplicate\", \"metricParameter\":{ \"table\":\"test_table\", \"column\":\"test_column\", \"database\":\"test\" }, \"expectedType\":\"fix_value\", \"expectedParameter\":{ \"expected_value\":\"10\" }, \"resultFormula\":\"count\", \"operator\":\"lt\", \"threshold\":5.0 } ] }, \"errorDataStorageType\": \"file\", \"errorDataStorageParameter\": { \"data_dir\":\"/tmp/datavines/error-data\", \"column_separator\":\",\" }, \"validateResultDataStorageType\": \"file\", \"validateResultDataStorageParameter\":{ \"data_dir\":\"/tmp/datavines/validate-result-data\", \"column_separator\":\",\" }, \"notificationParameters\": [ { \"type\":\"email\", \"config\": { \"serverHost\":\"smtp.qq.com\", \"serverPort\":\"25\", \"sender\":\"1234567@qq.com\", // input your mail \"enableSmtpAuth\":\"true\", \"user\":\"123456@qq.com\", // input your username \"passwd\":\"123456\", // input your passwd \"starttlsEnable\":\"false\", \"sslEnable\":\"false\", \"smtpSslTrust\":\"true\" }, \"receiver\": { \"to\":\"12345566@qq.com\" // input receiver email } } ]}","s":"添加以下内容到配置文件中","u":"/datavines-website/docs/user-guide/local-mode","h":"#添加以下内容到配置文件中","p":809},{"i":815,"t":"./datavines-submit.sh metric_job_config.json","s":"以脚本形式执行数据质量检查作业","u":"/datavines-website/docs/user-guide/local-mode","h":"#以脚本形式执行数据质量检查作业","p":809},{"i":817,"t":"让您更容易了解您的数据！ Datavines是一个易于使用的数据可观测性平台，提供数据目录和数据概览让您对你的数据有全方位的了解，同时内置了多种数据质量检查规则来确保数据集成和数据处理过程中数据的准确性。","s":"介绍","u":"/datavines-website/docs/intro","h":"","p":816},{"i":819,"t":"支持血缘关系管理 支持 Checksum 算法的数据比对（开发中） 支持 Flink 引擎 支持 Spark on K8s 在 DolphinScheduler 中增加 Datavines 任务类型（开发中） 支持 Livy 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Code Conduct Code Conduct 下一页 贡献指南","s":"Code Conduct","u":"/datavines-website/community/contribution_guide/code-conduct","h":"","p":3},{"i":6,"t":"贡献指南 贡献指南 本页总览 贡献指南 您可以报告 bug，提交一个新的功能增强建议或者直接对以上内容提交改进补丁。 提交 issue​ 在提交 issue 之前，请经过充分的搜索，确定该 issue 不是通过简单的检索即可以解决的问题。 查看 issue 列表，确定该 issue 不是一个重复的问题。 新建一个 issue 并选择您的 issue 类型。 使用一个清晰并有描述性的标题来定义 issue。 根据模板填写必要信息。 请对自己提交的 issue 保持关注，在讨论中进一步提供必要信息。 开发流程​ 1. 准备仓库 到 Datavines GitHub Repo fork 仓库到你的 GitHub 账号。 克隆到本地。 git clone https://github.com/(your_github_name)/datavines.git 添加 Datavines 远程仓库。 git remote add upstream https://github.com/datavane/datavines.gitgit remote -v 2. 选择 issue 请在选择您要修改的 issue。如果是您新发现的问题或想提供 issue 中没有的功能增强，请先新建一个 issue 并设置正确的标签。 在选中相关的 issue 之后，请回复以表明您当前正在这个 issue 上工作。并在回复的时候为自己设置一个 deadline，添加至回复内容中。 3. 创建分支 切换到 fork 的 dev 分支，拉取最新代码，创建本次的分支。 git checkout devgit fetch upstreamgit rebase upstream/devgit push origin dev # 可选操作git checkout -b issueNo 4. 编码 请您在开发过程中遵循 Datavines 的开发规范。并在准备提交 pull request 之前完成相应的检查。 将修改的代码 push 到 fork 库的分支上。 git add .git commit -m 'commit log'git rebase upstream/dev # 必要操作git push origin issueNo 5. 提交 PR 发送一个 pull request 到 Datavines 的 dev 分支。 接着会有其他开发者做 CodeReview，然后他会与您讨论一些细节（包括设计，实现，性能等）。当相应的开发者对本次修改满意后，会将提交合并到当前开发版本的分支中。 最后，恭喜您已经成为了 Datavines 的贡献者！ 6. 删除分支 在将 pull request 合并到 Datavines 的 dev 分支中之后，您就可以将远程的分支（origin/issueNo）及与远程分支（origin/issueNo）关联的本地分支（issueNo）删除。 git checkout devgit branch -d issueNogit remote prune origin # 如果你已经在 GitHub PR 页面删除了分支，否则的话可以执行下面的命令删除git push origin --delete issueNo 注意: 为了让您的 id 显示在 contributor 列表中，别忘了以下设置： git config --global user.name \"username\"git config --global user.email \"username@mail.com\" 感谢 本贡献指南参考Apache ShardingSphere的贡献指南，感谢他们做出的优秀贡献 上一页 Code Conduct 下一页 文档贡献指南 提交 issue 开发流程","s":"贡献指南","u":"/datavines-website/community/contribution_guide/contributor","h":"","p":5},{"i":10,"t":"在搭建 Datavines 开发环境之前请确保你已经安装一下软件 Git: 版本控制系统 JDK: 后端开发 Maven: Java包管理系统","s":"前置条件","u":"/datavines-website/docs/development/environment-preparation","h":"#前置条件","p":8},{"i":12,"t":"通过你 git 管理工具下载 git 代码，下面以 git-core 为例 mkdir datavinescd datavinesgit clone https://github.com/datavane/datavines.git","s":"克隆代码库","u":"/datavines-website/docs/development/environment-preparation","h":"#克隆代码库","p":8},{"i":14,"t":"如果使用MySQL数据库，请注意修改pom.xml，将 mysql-connector-java 依赖的 scope 改为 compile，使用 PostgreSQL 则不需要。 运行 mvn clean install -Prelease -Dmaven.test.skip=true","s":"编译源码","u":"/datavines-website/docs/development/environment-preparation","h":"#编译源码","p":8},{"i":16,"t":"Datavines 的元数据存储在关系型数据库中，目前支持的关系型数据库包括 MySQL 以及 PostgreSQL。下面以MySQL为例，启动数据库并创建新 database 作为 Datavines 元数据库，这里以数据库名 datavines 为例 创建完新数据库后，将 script/sql/datavines-mysql.sql 下的 sql 文件直接在 MySQL 中运行，完成数据库初始化","s":"数据库","u":"/datavines-website/docs/development/environment-preparation","h":"#数据库","p":8},{"i":18,"t":"下面步骤将引导如何启动 Datavines 后端服务","s":"启动后端","u":"/datavines-website/docs/development/environment-preparation","h":"#启动后端","p":8},{"i":20,"t":"打开项目：使用开发工具打开项目，这里以 Intellij IDEA 为例，打开后需要一段时间，让 Intellij IDEA 完成以依赖的下载 必要的修改 如果使用 MySQL 作为元数据库，需要先修改 datavines/pom.xml，将 mysql-connector-java 依赖的 scope 改为 compile，使用 PostgreSQL 则不需要 修改数据库配置，修改 datavines-server/src/main/resources/application.yaml 文件中的数据库配置 本样例以 MySQL 为例，其中数据库名为 datavines，账户名密码为 root/123456 spring: datasource: driver-class-name: com.mysql.cj.jdbc.Driver url: jdbc:mysql://127.0.0.1:3306/datavines?useUnicode=true&characterEncoding=UTF-8 username: root password: 123456","s":"必要的准备工作","u":"/datavines-website/docs/development/environment-preparation","h":"#必要的准备工作","p":8},{"i":22,"t":"启动 DatavinesServer 在VM Options中添加 -Dspring.profiles.active=mysql 中 mysql 表示指定的配置文件 -Dlogging.config=classpath:server-logback.xml 当你在Console里面看到 [INFO] 2022-04-10 12:29:05.447 io.datavines.server.DatavinesServer:[61] - Started DatavinesServer in 3.97 seconds (JVM running for 4.69) 日志的时候，证明服务已经启动成功了","s":"启动服务","u":"/datavines-website/docs/development/environment-preparation","h":"#启动服务","p":8},{"i":24,"t":"前置条件：本地安装好 node(v16.9.0) 和 npm(8.19.3) 依次执行以下命令，即可完成本地前端服务的启动以进行本地调试。 cd datavines\\uinpm installnpm fundnpm run dev:prod","s":"启动前端服务","u":"/datavines-website/docs/development/environment-preparation","h":"#启动前端服务","p":8},{"i":27,"t":"Connector 是 Datavines 中一个核心概念，一个 Connector 表示一个连接器，用于连接数据源。Metric 采用插件化设计，用户可以根据自己的需求来实现一个 Connector。下面我们来详细讲解一下如何自定义Connector。","s":"数据源开发","u":"/datavines-website/docs/development/plugin/connector-develop","h":"","p":26},{"i":29,"t":"我们先了解下几个接口和抽象类，它们是实现自定义 Connector 的关键。 ConnectorFactory 接口​ ConnectorFactory接口中定义了 Connector 需要的Connector、Executor、Dialect、TypeConverter等接口。 @SPIpublic interface ConnectorFactory { String getCategory(); Connector getConnector(); ResponseConverter getResponseConverter(); Dialect getDialect(); ConnectorParameterConverter getConnectorParameterConverter(); Executor getExecutor(); TypeConverter getTypeConverter();} Connector 接口​ Connector 接口中主要定义了获取数据源中数据库、表和列、测试链接、配置参数和关键属性等接口。 public interface Connector { default ConnectorResponse getDatabases(GetDatabasesRequestParam param) throws SQLException { return null; } default ConnectorResponse getTables(GetTablesRequestParam param) throws SQLException { return null; } default ConnectorResponse getColumns(GetColumnsRequestParam param) throws SQLException { return null; } default ConnectorResponse getPartitions(ConnectorRequestParam param) { return null; } default String getConfigJson(boolean isEn) { return null; } default ConnectorResponse testConnect(TestConnectionRequestParam param) { return null; } List keyProperties();} Executor 接口​ Executor 接口中主要定义执行数据查询的接口。 public interface Connector { default ConnectorResponse queryForPage(ExecuteRequestParam param) throws Exception { return null; } default ConnectorResponse queryForList(ExecuteRequestParam param) throws Exception { return null; } default ConnectorResponse queryForOne(ExecuteRequestParam param) throws Exception { return null; } default ConnectorResponse deleteData(ExecuteRequestParam param) throws Exception { return null; }} Dialect 接口​ Dialect 接口中定义了数据源的各种方言、查询脚本和支持的特性。 public interface Dialect { String getDriver(); String getColumnPrefix(); String getColumnSuffix(); default Map getDialectKeyMap() { return new HashMap<>(); } List getExcludeDatabases(); default String invalidateItemCanOutput(){ return \"true\"; } default String invalidateItemCanOutputToSelf(){ return \"false\"; } default boolean supportToBeErrorDataStorage(){ return false; } default String getJDBCType(DataType dataType){ return dataType.toString(); } default DataType getDataType(String jdbcType) { return DataType.valueOf(jdbcType); } default String quoteIdentifier(String column) { return \"`\" + column + \"`\"; } default String getTableExistsQuery(String table) { return String.format(\"SELECT * FROM %s WHERE 1=0\", table); } default String getSchemaQuery(String table) { return String.format(\"SELECT * FROM %s WHERE 1=0\", table); } default String getCountQuery(String table) { return String.format(\"SELECT COUNT(1) FROM %s\", table); } default String getSelectQuery(String table) { return String.format(\"SELECT * FROM %s\", table); } default String getCreateTableAsSelectStatement(String srcTable, String targetDatabase, String targetTable) { return String.format(\"CREATE TABLE %s.%s AS SELECT * FROM %s\", quoteIdentifier(targetDatabase), quoteIdentifier(targetTable), quoteIdentifier(srcTable)); } default String getCreateTableStatement(String table, List fields, TypeConverter typeConverter) { if (CollectionUtils.isNotEmpty(fields)) { String columns = fields.stream().map(field -> { return quoteIdentifier(field.getName()) + \" \" + typeConverter.convertToOriginType(field.getDataType()); }).collect(Collectors.joining(\",\")); return String.format(\"CREATE TABLE IF NOT EXISTS %s (%s)\", table, columns); } return \"\"; } default String getInsertAsSelectStatement(String srcTable, String targetDatabase, String targetTable) { return String.format(\"INSERT INTO %s.%s SELECT * FROM %s\", quoteIdentifier(targetDatabase), quoteIdentifier(targetTable), quoteIdentifier(srcTable)); } String getErrorDataScript(Map configMap); String getValidateResultDataScript(Map configMap);} ConnectorParameterConverter 接口​ ConnectorParameterConverter 接口负责对输入的参数进行转换，转换成符合引擎执行的格式。 public interface ConnectorParameterConverter { Map converter(Map parameter); default String getConnectorUUID(Map parameter) { Map convertResult = converter(parameter); return DigestUtils.md5Hex( String.valueOf(convertResult.get(URL)) + convertResult.get(TABLE) + convertResult.get(USER) + convertResult.get(PASSWORD)); }} TypeConverter 接口​ TypeConverter 接口定义数据源中的字段类型转换方法，支持将原始类型与系统类型的互相转换。 public interface TypeConverter { DataType convert(String originType); String convertToOriginType(DataType dataType);} ConfigBuilder 接口​ ConfigBuilder 接口定义了连接器配置参数的构造，用于返回给给前端进行展示。 public interface ConfigBuilder { String build(boolean isEn);} datavines-connector-jdbc 模块​ datavines-connector-jdbc 模块是 JDBC 类型数据源的基础实现，实现了 JDBC 类型 的 Connector 的基本操作，如果要新增一个 JDBC 类型的 Connector，引入该模块再做一些修改即可。","s":"第一步","u":"/datavines-website/docs/development/plugin/connector-develop","h":"#第一步","p":26},{"i":31,"t":"了解完上面的接口和 datavines-connector-jdbc 模块以后，自定义一个JDBC类型 的Connector就变得格外简单了。 基础工作​ 在 datavines-connector-plugins 下创建一个新 Connector 的 module 在 pom.xml 中添加 io.datavines datavines-connector-jdbc ${project.version} 以 MySQL Connector 为例来讲解​ 新增一个JDBC类型的Connector 可以继承 datavines-connector-jdbc 模块中的各种实现类。 创建 MysqlConfigBuilder 继承 JdbcConfigBuilder，对 getPropertiesInput 进行重写，这里重写的目的是增加一些连接数据源的配置，如果不需要则无需重写。 public class MysqlConfigBuilder extends JdbcConfigBuilder { @Override protected InputParam getPropertiesInput(boolean isEn) { return getInputParam(\"properties\", isEn ? \"properties\" : \"参数\", isEn ? \"please enter properties,like key=value&key1=value1\" : \"请填入参数，格式为key=value&key1=value1\", 2, null, \"useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai\"); }} 创建 MysqlDataSourceInfo 继承 BaseJdbcDataSourceInfo，主要是实现数据源的连接URL、Driver等方法。 public class MysqlDataSourceInfo extends BaseJdbcDataSourceInfo { public MysqlDataSourceInfo(JdbcConnectionInfo jdbcConnectionInfo) { super(jdbcConnectionInfo); } @Override public String getAddress() { return \"jdbc:mysql://\"+getHost()+\":\"+getPort(); } @Override public String getDriverClass() { return \"com.mysql.cj.jdbc.Driver\"; } @Override public String getType() { return \"mysql\"; } @Override protected String getSeparator() { return \"?\"; } @Override protected String filterProperties(String other){ return other; }} 创建 MysqlConnector 继承 JdbcConnector， JdbcConnector里面已经默认实现了获取数据库列表、表列表、列列表、测试连接等接口，不同的数据源在通过 jdbc 连接时要传给 DatabaseMetaData 的参数可能都不太一样，需要根据不同的数据去实现。 public class MysqlConnector extends JdbcConnector { @Override public BaseJdbcDataSourceInfo getDatasourceInfo(JdbcConnectionInfo jdbcConnectionInfo) { return new MysqlDataSourceInfo(jdbcConnectionInfo); } @Override public ResultSet getMetadataColumns(DatabaseMetaData metaData, String catalog, String schema, String tableName, String columnName) throws SQLException { return metaData.getColumns(schema, null, tableName, columnName); } @Override public ResultSet getMetadataTables(DatabaseMetaData metaData, String catalog, String schema) throws SQLException { return metaData.getTables(schema, null, null, TABLE_TYPES); } @Override public ResultSet getMetadataDatabases(Connection connection) throws SQLException { DatabaseMetaData metaData = connection.getMetaData(); return metaData.getCatalogs(); } } 创建 MysqlConnectorParameterConverter 继承 JdbcConnectorParameterConverter， 该接口已经默认了参数的格式化转换，只需要对URL的构造做个性化的实现。 public class MysqlConnectorParameterConverter extends JdbcConnectorParameterConverter { @Override protected String getUrl(Map parameter) { String url = String.format(\"jdbc:mysql://%s:%s/%s\", parameter.get(HOST), parameter.get(PORT), parameter.get(DATABASE)); String properties = (String)parameter.get(PROPERTIES); if (StringUtils.isNotEmpty(properties)) { url += \"?\" + properties; } return url; }} 创建 MysqlDialect 继承 JdbcDialect， 针对不同的数据源的方言做一些个性化实现。 public class MysqlDialect extends JdbcDialect { @Override public Map getDialectKeyMap() { super.getDialectKeyMap(); dialectKeyMap.put(STRING_TYPE, \"char\"); return dialectKeyMap; } @Override public String getDriver() { return \"com.mysql.cj.jdbc.Driver\"; } @Override public String invalidateItemCanOutputToSelf() { return \"true\"; } @Override public boolean supportToBeErrorDataStorage() { return true; }} 创建 MysqlExecutor 继承 BaseJdbcExecutor， BaseJdbcExecutor中已经实现了各种数据查询的接口，如果数据源存在特殊的查询逻辑，可进行重写来实现自有的查询逻辑。 public class MysqlExecutor extends BaseJdbcExecutor { @Override public BaseJdbcDataSourceInfo getDatasourceInfo(JdbcConnectionInfo jdbcConnectionInfo) { return new MysqlDataSourceInfo(jdbcConnectionInfo); }} 最后创建 MysqlConnectorFactory 继承 AbstractJdbcConnectorFactory，将上面的实现类通过 Factory 接口进行实例构造。 public class MysqlConnectorFactory extends AbstractJdbcConnectorFactory { @Override public ConnectorParameterConverter getConnectorParameterConverter() { return new MysqlConnectorParameterConverter(); } @Override public Dialect getDialect() { return new MysqlDialect(); } @Override public Connector getConnector() { return new MysqlConnector(); } @Override public Executor getExecutor() { return new MysqlExecutor(); } @Override public ConfigBuilder getConfigBuilder() { return new MysqlConfigBuilder(); }}","s":"第二步","u":"/datavines-website/docs/development/plugin/connector-develop","h":"#第二步","p":26},{"i":33,"t":"非常重要的一步 在 resources 目录下创建META-INF目录，再创建plugins目录。 在 plugins 目录下创建文件并且命名为io.datavines.connector.api.ConnectorFactory。 在文件中添加mysql=io.datavines.connector.plugin.MysqlConnectorFactory。","s":"第三步","u":"/datavines-website/docs/development/plugin/connector-develop","h":"#第三步","p":26},{"i":35,"t":"打包成jar放到 datavines 目录下的libs目录下，重启服务即可。","s":"第四步","u":"/datavines-website/docs/development/plugin/connector-develop","h":"#第四步","p":26},{"i":37,"t":"贡献指南 Pull Request 须知 本页总览 Pull Request 须知 前言​ Pull Request 本质上是一种软件的合作方式，是将涉及不同功能的代码，纳入主干的一种流程。这个过程中，可以进行讨论、审核和修改代码。 在 Pull Request 中尽量不讨论代码的实现方案，代码及其逻辑的大体实现方案应该尽量在 Issue 或者邮件列表中被讨论确定，在 Pull Request 中我们尽量只关注代码的格式以及代码规范等信息，从而避免实现方式的意见不同而导致 waste time。 规范​ Pull Request 标题​ 标题格式：[Pull Request 类型-Issue 号][`模块名`] Pull Request 描述 其中Pull Request 类型和Issue 类型的对应关系如下： Issue 类型 Pull Request 类型 样例（假设 Issue 号为 1111） Feature Feature [Feature-1111][server] Implement xxx Bug Fix [Fix-1111][server] Fix xxx Improvement Improvement [Improvement-1111][alert] Improve the performance of xxx Test Test [Test-1111][api] Add the e2e test of xxx Sub-Task Sub-Task 对应的父类型 [Feature-1111][server] Implement xxx 其中 Issue 号是指当前 Pull Request 对应要解决的 Issue 号，模块名同 Issue 的模块名。 Pull Request 分支名​ 分支名格式：Pull Request 类型-Issue 号，举例：Feature-1111。 上一页 文档贡献指南 前言 规范 Pull Request 标题 Pull Request 分支名","s":"Pull Request 须知","u":"/datavines-website/community/contribution_guide/pull-request","h":"","p":36},{"i":39,"t":"Metric 是 Datavines 中一个核心概念，一个 Metric 表示一个数据质量检查规则，比如空值检查和表行数检查都是一个规则。Metric 采用插件化设计，用户可以根据自己的需求来实现一个 Metric。下面我们来详细讲解一下如何自定义Metric。","s":"检查规则开发","u":"/datavines-website/docs/development/plugin/metric-develop","h":"","p":38},{"i":41,"t":"我们先了解下几个接口和抽象类，它们是实现自定义 Metric 的关键。 SqlMetric 接口​ SqlMetric接口中定义了规则的各种属性和操作的接口。 @SPIpublic interface SqlMetric { // 中文名 String getName(); // 英文名 String getZhName(); // 根据系统的语言进行名字返回 default String getNameByLanguage(boolean isEn) { return isEn ? getName() : getZhName(); } // 规则属于哪个维度，比如准确性、唯一性等等 MetricDimension getDimension(); // 规则的类型，包括单表检查、单表自定义检查 MetricType getType(); // 规则的级别，比如表级别、列级别 default MetricLevel getLevel() { return MetricLevel.NONE; } // 是否支持错误数据输出 boolean isInvalidateItemsCanOutput(); /** * 获取不符合规则的数据的SQL语句 * @return ExecuteSql */ ExecuteSql getInvalidateItems(String uniqueKey); /** * 计算实际值的SQL语句 * @return ExecuteSql */ ExecuteSql getActualValue(String uniqueKey); /** * 实际值的字段名 */ default String getActualName() { return \"actual_value\"; } // 实际值的类型，比如数字，百分比或者列表 default String getActualValueType() { return MetricActualValueType.COUNT.getDescription(); } // 对参数进行检查并输出检查结果 CheckResult validateConfig(Map config); //规则所需要的参数 Map getConfigMap(); //构造规则前需要做的检查 void prepare(Map config); default String getIssue() { return \"\"; } // 适合哪些字段类型 List suitableType(); // 是否支持多选，比如表行数检查支持多张表 default boolean supportMultiple() { return false; } // 对规则参数的重新构造，配合表行数多张表检查 default List> getMetricParameter(Map metricParameter) { return Collections.singletonList(metricParameter); }} BaseSingleTable 抽象类​ BaseSingleTable是实现了 SqlMetric 接口的抽象类，实现了表级别检查规则中所需要参数的添加、错误数据SQL语句构造和实际值计算SQL语句构造和对过滤条件的处理等。 这里定义了获取不符合规则的数据的基础SQL语句，判断类型的规则比如正则表达式检查和枚举值检查，只需要在基础SQL语句后面添加过滤条件即可。 protected StringBuilder invalidateItemsSql = new StringBuilder(\"select * from ${table}\"); 实际值计算SQL语句默认是计算不符合规则数据的行数 String actualValueSql = \"select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}\"; 计算平均值、汇总值等统计类型的规则需要重新实现getActualValue()中的ExecuteSql。 public abstract class BaseSingleTable implements SqlMetric { // 这里定义了获取不符合规则的数据的基础 SQL 语句，判断类的规则比如正则表达式和枚举值检查，只需要在基础SQL后面添加过滤条件即可。 protected StringBuilder invalidateItemsSql = new StringBuilder(\"select * from ${table}\"); protected List filters = new ArrayList<>(); protected HashMap configMap = new HashMap<>(); protected Set requiredOptions = new HashSet<>(); public BaseSingleTable() { configMap.put(\"table\",new ConfigItem(\"table\", \"表名\", \"table\")); configMap.put(\"filter\",new ConfigItem(\"filter\", \"过滤条件\", \"filter\")); requiredOptions.add(\"table\"); } @Override public ExecuteSql getInvalidateItems(String uniqueKey) { ExecuteSql executeSql = new ExecuteSql(); executeSql.setResultTable(\"invalidate_items_\" + uniqueKey); executeSql.setSql(invalidateItemsSql.toString()); executeSql.setErrorOutput(isInvalidateItemsCanOutput()); return executeSql; } @Override public ExecuteSql getActualValue(String uniqueKey) { ExecuteSql executeSql = new ExecuteSql(); executeSql.setResultTable(\"invalidate_count_\" + uniqueKey); String actualValueSql = \"select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}\"; executeSql.setSql(actualValueSql); executeSql.setErrorOutput(false); return executeSql; } @Override public CheckResult validateConfig(Map config) { return ConfigChecker.checkConfig(config, requiredOptions); } @Override public void prepare(Map config) { if (config.containsKey(\"filter\")) { filters.add(config.get(\"filter\")); } addFiltersIntoInvalidateItemsSql(); } private void addFiltersIntoInvalidateItemsSql() { if (filters.size() > 0) { invalidateItemsSql.append(\" where \").append(String.join(\" and \", filters)); } } @Override public MetricLevel getLevel() { return MetricLevel.TABLE; }} BaseSingleTableColumn 抽象类​ BaseSingleTableColumn是列级别的抽象实现类，主要是添加列级别规则的通用参数。 public abstract class BaseSingleTableColumn extends BaseSingleTable { public BaseSingleTableColumn() { super(); configMap.put(\"column\",new ConfigItem(\"column\", \"列名\", \"column\")); requiredOptions.add(\"column\"); } @Override public Map getConfigMap() { return configMap; } @Override public MetricLevel getLevel() { return MetricLevel.COLUMN; } @Override public boolean isInvalidateItemsCanOutput() { return false; }}","s":"第一步","u":"/datavines-website/docs/development/plugin/metric-develop","h":"#第一步","p":38},{"i":43,"t":"了解完上面的三个基础类以后，自定义一个Metric就变得格外简单了。 基础工作​ 在 datavines-metric-plugins 下创建一个新规则的 module 在 pom.xml 中添加 io.datavines datavines-metric-base ${project.version} 以 枚举值检查 规则为例来讲解​ 判断要实现的规则的级别，因为枚举值检查是列级别，所以继承 BaseSingleTableColumn 即可。 在构造函数中的configMap添加enum_list参数用于返回给前端进行展示，在requiredOptions添加enum_list用于参数的检查。 实现英文名、中文名、规则维度、规则类型这些基础的属性。 因为枚举值检查规则是为了找出在枚举值列表中的数据，所以只需要在fileters这个数组里面加入(${column} in ( ${enum_list} ))，prepare()方法会自动进行不符合规则的SQL语句构造。 实现suitableType()方法添加规则适用的字段类型。 public class ColumnInEnums extends BaseSingleTableColumn { public ColumnInEnums(){ super(); configMap.put(\"enum_list\",new ConfigItem(\"enum_list\", \"枚举值列表\", \"enum_list\")); requiredOptions.add(\"enum_list\"); } @Override public String getName() { return \"column_in_enums\"; } @Override public String getZhName() { return \"枚举值检查\"; } @Override public MetricDimension getDimension() { return MetricDimension.EFFECTIVENESS; } @Override public MetricType getType() { return MetricType.SINGLE_TABLE; } @Override public boolean isInvalidateItemsCanOutput() { return true; } @Override public void prepare(Map config) { if (config.containsKey(\"enum_list\") && config.containsKey(\"column\")) { filters.add(\" (${column} in ( ${enum_list} )) \"); } super.prepare(config); } @Override public List suitableType() { return Arrays.asList(DataVinesDataType.NUMERIC_TYPE, DataVinesDataType.STRING_TYPE, DataVinesDataType.DATE_TIME_TYPE); }}","s":"第二步","u":"/datavines-website/docs/development/plugin/metric-develop","h":"#第二步","p":38},{"i":45,"t":"非常重要的一步 在 resources 目录下创建META-INF/plugins目录。 在 plugins 目录下创建文件并且命名为io.datavines.metric.api.SqlMetric。 在文件中添加column_in_enums=io.datavines.metric.plugin.ColumnInEnums。 在datavines-metric-all模块下的pom.xml文件中添加新建的插件模块的依赖。 io.datavines datavines-metric-column-in-enums ${project.version}","s":"第三步","u":"/datavines-website/docs/development/plugin/metric-develop","h":"#第三步","p":38},{"i":47,"t":"打包成jar放到 datavines 目录下的libs目录下，重启服务即可。","s":"第四步","u":"/datavines-website/docs/development/plugin/metric-develop","h":"#第四步","p":38},{"i":51,"t":"本文针对 Datavines 中各个核心模块进行较为详细的介绍","s":"Datavines 代码架构解析","u":"/datavines-website/docs/development/source-module-explanation","h":"","p":50},{"i":53,"t":"datavines-server 是 datavines 中最核心的模块，核心功能包括对外提供 api 接口， 整个系统的数据管理， 数据源的元数据获取和管理， 数据质量规则的管理、调度和执行。它是无中心化设计，支持水平扩容提升性能，同时支持各种作业的容错处理。","s":"datavines-server","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-server","p":50},{"i":55,"t":"datavines-spi 是 datavines 中的 SPI 模块，主要功能是设计了SPI接口和PluginLoader，PluginLoader是负责扫描有@SPI的插件，把具体的插件加载到系统中。","s":"datavines-spi","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-spi","p":50},{"i":57,"t":"datavines-connector 是 datavines 中的数据源模块，datavines-connector-api 中定义了数据源的各种接口，比如 Connector 接口中定义了数据源各种元数据相关的接口，Executor 接口则是定义了数据源数据查询相关的接口，Dialect 接口里定义了数据源方言相关的接口。datavines-connector-plugins 中是各种数据源的具体实现，内置的数据源包括MySQL、Doris、Starrocks、Trino、Presto等10种数据源。","s":"datavines-connector","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-connector","p":50},{"i":59,"t":"datavines-metric 是 datavines 中的规则模块，datavines-metric-api 中定义了规则、期望值、结果计算公式的接口。SqlMetric接口主要是定义规则的类型、实际值计算逻辑等相关方法。ExpectedValue接口主要是定义了期望值的计算逻辑相关方法。ResultFormula接口主要是定义了检查公式的计算逻辑相关方法。","s":"datavines-metric","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-metric","p":50},{"i":61,"t":"datavines-engine 是 datavines 中的作业执行引擎模块。datavines-engine-api 定义了 engine 相关的接口，Execution 接口主要是定义了作业执行的相关方法，execute 方法主要执行 source 的读取，对数据进行 transform，最后把数据 sink 到目的地。datavines-engine-config 模块主要是定义了作业配置文件的构造逻辑和相关方法。datavines-engine-executor 模块是定义了作业执行器相关逻辑","s":"datavines-engine","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-engine","p":50},{"i":63,"t":"datavines-registry 是 datavines 中的注册中心。 datavines-registry-api 定义了 获取锁、释放锁、获取当前活跃节点、节点上下线通知等接口。datavines-registry-plugins 中内置了三个类型的注册中心，包括 MySQL、PostgreSQL 和 ZooKeeper。","s":"datavines-registry","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-registry","p":50},{"i":65,"t":"datavines-notification 是 datavines 中的通知通道管理。 datavines-notification-api 定义了通知通道的消息通知、配置参数等接口。datavines-notification-plugins 中内置了 email 类型的通知通道实现。","s":"datavines-notification","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-notification","p":50},{"i":67,"t":"datavines-runner 是 datavines 中负责脚本模式运行数据质量检查的模块，主要功能是读取配置文件、解析配置文件，执行数据质量检查，判断检查结果，运行告警处理。","s":"datavines-runner","u":"/datavines-website/docs/development/source-module-explanation","h":"#datavines-runner","p":50},{"i":71,"t":"ClickHouse 连接器用于与 ClickHouse 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-clickhouse","h":"#介绍","p":69},{"i":74,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-clickhouse","h":"#options","p":69},{"i":76,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-clickhouse","h":"#属性","p":69},{"i":79,"t":"Databend 连接器用于与 Databend 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-databend","h":"#介绍","p":77},{"i":82,"t":"name type required default value host string yes - port int yes - database string no - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-databend","h":"#options","p":77},{"i":84,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-databend","h":"#属性","p":77},{"i":88,"t":"Hive 连接器用于与 Hive 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-hive","h":"#介绍","p":86},{"i":91,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP, 支持ZooKeeper的高可用连接，ZooKeeper地址通过 , 隔开 port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： hive.resultset.use.unique.column.names=false 若采用ZooKeeper高可用方式，需要输入以下配置： serviceDiscoveryMode=zooKeeper;zooKeeperNamespace={your_namespace}","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-hive","h":"#options","p":86},{"i":93,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 false invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-hive","h":"#属性","p":86},{"i":96,"t":"Impala 连接器用于与 Impala 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-impala","h":"#介绍","p":94},{"i":99,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-impala","h":"#options","p":94},{"i":101,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-impala","h":"#属性","p":94},{"i":104,"t":"Doris 连接器用于与 Doris 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-doris","h":"#介绍","p":102},{"i":107,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-doris","h":"#options","p":102},{"i":109,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-doris","h":"#属性","p":102},{"i":112,"t":"MySQL 连接器用于与 MySQL 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-mysql","h":"#介绍","p":110},{"i":115,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-mysql","h":"#options","p":110},{"i":117,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 true","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-mysql","h":"#属性","p":110},{"i":120,"t":"PostgreSQL 连接器用于与 PostgreSQL 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-postgresql","h":"#介绍","p":118},{"i":123,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-postgresql","h":"#options","p":118},{"i":125,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-postgresql","h":"#属性","p":118},{"i":128,"t":"Presto 连接器用于与 Presto 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-presto","h":"#介绍","p":126},{"i":131,"t":"name type required default value host string yes - port int yes - catalog string yes - database string no - user string yes - password string no - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 catalog [string]​ catalog database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-presto","h":"#options","p":126},{"i":133,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-presto","h":"#属性","p":126},{"i":136,"t":"全局参数管理是 Datavines 中用于管理系统运行过程使用到的各种参数，支持实时的增删改查，默认参数不允许删除。","s":"介绍","u":"/datavines-website/docs/features/config","h":"#介绍","p":134},{"i":138,"t":"点击右上角的创建参数按钮，在对话框中输入参数的参数名和参数值，点击保存即可。","s":"创建参数","u":"/datavines-website/docs/features/config","h":"#创建参数","p":134},{"i":141,"t":"Trino 连接器用于与 Trino 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-trino","h":"#介绍","p":139},{"i":144,"t":"name type required default value host string yes - port int yes - catalog string yes - database string no - user string yes - password string no - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 catalog [string]​ catalog database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，用&隔开","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-trino","h":"#options","p":139},{"i":146,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-trino","h":"#属性","p":139},{"i":149,"t":"StarRocks 连接器用于与 StarRocks 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/docs/features/catalog/connector/connector-starrocks","h":"#介绍","p":147},{"i":152,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/docs/features/catalog/connector/connector-starrocks","h":"#options","p":147},{"i":154,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/docs/features/catalog/connector/connector-starrocks","h":"#属性","p":147},{"i":157,"t":"Livy 引擎是基于 Livy 开发的数据质量检查作业执行引擎，使得Datavines可以将 Spark 应用提交到 Livy 上去执行。","s":"原理解释","u":"/datavines-website/docs/features/engine/livy-engine","h":"#原理解释","p":155},{"i":159,"t":"在数据质量检查作业和数据比对作业中的引擎配置中选择 Livy 引擎，填好各种 Spark 相关的基础配置信息","s":"使用方法","u":"/datavines-website/docs/features/engine/livy-engine","h":"#使用方法","p":155},{"i":161,"t":"如果想要使用Livy引擎，需要完成Livy相关的配置 全局参数中配置livy开头的相关信息 将/lib/datavines-engine-spark-core-1.0.0-SNAPSHOT.jar上传到参数livy.task.jar.lib.path指定的位置 将作业任务相关jar (plugins目录下的jar) 上传到参数livy.task.jar.lib.path指定的位置，如果需要额外的jar，请自行上传 在作业配置jar具体为--jar xx.jar,zz.jar，若不指定--jar会默认按照livy.task.jars配置来执行,需要将相关文件上传","s":"注意事项","u":"/datavines-website/docs/features/engine/livy-engine","h":"#注意事项","p":155},{"i":164,"t":"Local 引擎是通过 Jdbc 的方式连接数据源，执行根据数据质量检查规则生成的 SQL 语句，得到相应的错误数据视图、实际值和期望值，最后将实际值和期望值写到 Datavines 系统的执行结果表以及将错误数据视图中的数据写到错误数据存储引擎。","s":"原理解释","u":"/datavines-website/docs/features/engine/local-engine","h":"#原理解释","p":162},{"i":166,"t":"Local 引擎的使用非常简单，创建数据质量检查作业时默认使用的是 Local 引擎，无需做任何额外的配置，保存好配置执行即可，需要关注一下注意事项。","s":"使用方法","u":"/datavines-website/docs/features/engine/local-engine","h":"#使用方法","p":162},{"i":168,"t":"用于创建数据源的用户需要有创建视图和删除视图的权限，如果你想要将错误数据写入到检查的数据源中某个库中，那么你的用户需要有创建表的权限。","s":"注意事项","u":"/datavines-website/docs/features/engine/local-engine","h":"#注意事项","p":162},{"i":171,"t":"错误数据存储引擎是用于存储数据质量检查中不符合规则的数据，便于用户可以查看有问题的数据来进行下一步的修复或者剔除操作，Datavines 目前支持写入至外部存储引擎和写入至检查数据源两种方式。 外部存储引擎目前只支持MySQL、写入至检查数据源支持MySQL、Doris、StarRocks。","s":"介绍","u":"/datavines-website/docs/features/error-data-storage","h":"#介绍","p":169},{"i":174,"t":"在错误数据管理页面创建存储引擎，填写上引擎的配置参数，点击测试连接，测试成功则保存存储引擎。 在作业配置页面进行配置，在错误数据存储配置里选择你所创建的引擎 写入外部存储引擎需要进行数据的读取和写入，为了保证系统的稳定，目前只支持前10000条错误数据的存储。","s":"写入外部存储引擎","u":"/datavines-website/docs/features/error-data-storage","h":"#写入外部存储引擎","p":169},{"i":176,"t":"无需进行额外的配置 在作业配置页面进行配置，在错误数据存储配置里, 开启 是否输出至源数据源 配置，并且输入用于存储错误数据的数据库名。","s":"写入检查数据源","u":"/datavines-website/docs/features/error-data-storage","h":"#写入检查数据源","p":169},{"i":178,"t":"点击数据质量检查作业的 查看执行记录，在执行记录中点击 错误数据 即可查看错误数据。","s":"错误数据的查看","u":"/datavines-website/docs/features/error-data-storage","h":"#错误数据的查看","p":169},{"i":181,"t":"Spark 引擎是基于 Spark 开发的数据质量检查作业执行引擎，SparkDataVinesBootstrap 是应用的执行入。Datavines 会根据用户所配置各种信息构造成配置文件并传入到 SparkDataVinesBootstrap中，SparkDataVinesBootstrap 会解析配置文件并选择不同的 source、transform 和 sink 插件来执行连接数据源、质量检查语句和将执行结果写入到相应的存储引擎中的操作。","s":"原理解释","u":"/datavines-website/docs/features/engine/spark-engine","h":"#原理解释","p":179},{"i":183,"t":"在数据质量检查作业和数据比对作业中的引擎配置中选择 Spark 引擎，填好各种 Spark 相关的基础配置信息","s":"使用方法","u":"/datavines-website/docs/features/engine/spark-engine","h":"#使用方法","p":179},{"i":185,"t":"如果想要使用 Spark 引擎，那么需要把 Datavines 部署在能执行 spark-submit 的服务器上 需要配置可以执行 spark-submit 的服务器用户 和 环境配置 服务器用户指的是 root、hadoop、ods等等这些服务器创建的用户 环境配置指的是 在服务器上 export 的各种配置，比如下面的SPARK_HOME2 export SPARK_HOME2=/usr/hdp/2.6.3.0-235/spark-2.4.8-bin-hadoop2.7 需要在全局配置那里进行yarn配置的修改 standalone 模式 yarn.mode=standaloneyarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%s #第一个%s需要被替换成yarn的ip地址yarn.resource.manager.http.address.port=8088 ha 模式 yarn.mode=hayarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%syarn.resource.manager.http.address.port=8088yarn.resource.manager.ha.ids=192.168.0.1,192.168.0.2","s":"注意事项","u":"/datavines-website/docs/features/engine/spark-engine","h":"#注意事项","p":179},{"i":189,"t":"最近7天均值 期望值类型表示在数据质量检查过程中使用的期望值是根据最近7天运行该规则得到的实际值所计算得出的最近7天的均值。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last7day-avg","h":"#概念解释","p":187},{"i":191,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 最近7天均值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last7day-avg","h":"#使用方法","p":187},{"i":193,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 unique_code：会根据每个规则的独有的配置信息生成一个唯一编码 Local 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_sub(date_format(${data_time},'%Y-%m-%d'),interval 7 DAY) and data_time < date_add(date_format(${data_time},'%Y-%m-%d'),interval 1 DAY) and unique_code = ${unique_code} Spark&Livy 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_add(date_format(${data_time}, 'yyyy-MM-dd'),-7) and data_time < date_add(date_format(${data_time}, 'yyyy-MM-dd'),1) and unique_code = ${unique_code}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last7day-avg","h":"#计算方式","p":187},{"i":196,"t":"固定值 期望值类型表示在数据质量检查过程中使用的是用户输入的期望值，内置使用到的参数是expected_value。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-fix-value","h":"#概念解释","p":194},{"i":198,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 固定值，在固定值输入框中输入期望值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-fix-value","h":"#使用方法","p":194},{"i":201,"t":"日均值 期望值类型表示在数据质量检查过程中使用的期望值是根据今日运行该规则得到的实际值所计算得出的当日的均值。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-daily-avg","h":"#概念解释","p":199},{"i":203,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 日均值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-daily-avg","h":"#使用方法","p":199},{"i":205,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 unique_code：会根据每个规则的独有的配置信息生成一个唯一编码 Local 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >=date_format(${data_time},'%Y-%m-%d') and data_time < date_add(date_format(${data_time},'%Y-%m-%d'), interval 1 DAY) and unique_code = ${unique_code} Spark&Livy 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >=date_format(${data_time}, 'yyyy-MM-dd') and data_time < date_add(date_format(${data_time}, 'yyyy-MM-dd'),1) and unique_code = ${unique_code}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-daily-avg","h":"#计算方式","p":199},{"i":208,"t":"最近30天均值 期望值类型表示在数据质量检查过程中使用的期望值是根据最近30天运行该规则得到的实际值所计算得出的最近30天的均值。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last30day-avg","h":"#概念解释","p":206},{"i":210,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 最近30天均值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last30day-avg","h":"#使用方法","p":206},{"i":212,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 unique_code：会根据每个规则的独有的配置信息生成一个唯一编码 Local 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_sub(date_format(${data_time},'%Y-%m-%d'),interval 30 DAY) and data_time < date_add(date_format(${data_time},'%Y-%m-%d'),interval 1 DAY) and unique_code = ${unique_code} Spark&Livy 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_add(date_format(${data_time}, 'yyyy-MM-dd'),-30) and data_time < date_add(date_format(${data_time}, 'yyyy-MM-dd'),1) and unique_code = ${unique_code}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-last30day-avg","h":"#计算方式","p":206},{"i":215,"t":"月均值 期望值类型表示在数据质量检查过程中使用的期望值是根据本月运行该规则得到的实际值所计算得出的当月的均值。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-monthly-avg","h":"#概念解释","p":213},{"i":217,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 月均值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-monthly-avg","h":"#使用方法","p":213},{"i":219,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 unique_code：会根据每个规则的独有的配置信息生成一个唯一编码 Local 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_format(curdate(), '%Y-%m-01') and data_time < date_add(date_format(${data_time},'%Y-%m-%d'),interval 1 DAY)\" + and unique_code = ${unique_code} Spark&Livy 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_format(${data_time}, 'yyyy-MM-01') and data_time < date_add(date_format(${data_time}, 'yyyy-MM-dd'),1) and unique_code = ${unique_code}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-monthly-avg","h":"#计算方式","p":213},{"i":222,"t":"目标表总行数 期望值类型表示在数据质量检查过程中使用的期望值是计算检查的目标表的总行数。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-target-table-rows","h":"#概念解释","p":220},{"i":224,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 目标表总行数。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-target-table-rows","h":"#使用方法","p":220},{"i":226,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 Local&Spark&Livy 引擎 select count(1) as expected_value_${uniqueKey} from ${target_table}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-target-table-rows","h":"#计算方式","p":220},{"i":229,"t":"表总行数 期望值类型表示在数据质量检查过程中使用的期望值是计算检查的表的总行数。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-table-rows","h":"#概念解释","p":227},{"i":231,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 表总行数。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-table-rows","h":"#使用方法","p":227},{"i":233,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 Local&Spark&Livy 引擎 select count(1) as expected_value_${uniqueKey} from ${table}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-table-rows","h":"#计算方式","p":227},{"i":236,"t":"无 期望值类型表示在数据质量检查过程不需要使用到期望值，默认设置null","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-none","h":"#概念解释","p":234},{"i":238,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 无","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-none","h":"#使用方法","p":234},{"i":241,"t":"跨表准确性是用于检查源表与目标表指定字段的数据差异，举个例子 表：test1 C1 C2 a 1 b 2 表：test2 C21 C22 a 1 b 3 如果对比c1和c21中的数据，则表test1和test2完全一致。 如果对比c2和c22则表test1和表test2中的数据则存在不一致了。","s":"概念解释","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#概念解释","p":239},{"i":243,"t":"通过 【源表 LEFT JOIN 目标表 on 源表.列1 = 目标表.列21 where 源表.列1 不等于 NULL 且 目标表.列21 等于 NULL】 SQL 语句来筛选出源表中存在的数据而目标表中不存在的数据.","s":"实现原理","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#实现原理","p":239},{"i":245,"t":"点击创建规则作业，选择数据比对作业 进入作业页面选择 跨表准确性 规则 进行源表和目标表的配置信息，选择要检查的字段","s":"使用方法","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#使用方法","p":239},{"i":248,"t":"name type required default value database string yes - table string yes - database2 string yes - table2 string yes - mappingColumns string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 database2 [string]​ 目标表数据库名 table2 [string]​ 目标表数据库中的表名 mappingColumns [string]​ 要进行检查的字段，column 为 源表的字段，column2 为目标表的字段，格式如下： [{\\\"column\\\":\\\"job_execution_id\\\",\\\"operator\\\":\\\"=\\\",\\\"column2\\\":\\\"job_execution_id\\\"}]","s":"Options","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#options","p":239},{"i":250,"t":"{ \"metricType\": \"multi_table_value_comparison\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_actual_values\", \"database2\": \"datavines\", \"table2\": \"dv_actual_values1\", \"mappingColumns\": \"[{\\\"column\\\":\\\"job_execution_id\\\",\\\"operator\\\":\\\"=\\\",\\\"column2\\\":\\\"job_execution_id\\\"}\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#配置文件例子","p":239},{"i":252,"t":"如果你进行检查的源表和目标表都在同一个数据库实例中，那么可以使用 Local 引擎来执行，如果源表和目标表在不同的数据库实例中，那么只能使用 Spark 引擎。","s":"注意事项","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#注意事项","p":239},{"i":255,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#场景","p":239},{"i":257,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#思路","p":239},{"i":259,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-accuracy","h":"#步骤","p":239},{"i":262,"t":"点击创建规则作业，选择数据比对作业 进入作业页面选择 两表值比对 规则 进行源表和目标表的配置信息，编写各自的检查SQL语句","s":"使用方法","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#使用方法","p":260},{"i":265,"t":"name type required default value database string yes - table string yes - actual_execute_sql string yes - database2 string yes - table2 string yes - expected_execute_sql string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 actual_execute_sql [string]​ 实际值执行SQL，注意 as 后面的别名一定要是 actual_value，否则统计会出错。 database2 [string]​ 目标表数据库名 table2 [string]​ 目标表数据库中的表名 expected_execute_sql [string]​ 期望值执行SQL，注意 as 后面的别名一定要是 expected_value，否则统计会出错。","s":"Options","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#options","p":260},{"i":267,"t":"{ \"metricType\": \"multi_table_value_comparison\", \"metricParameter\": { \"database\": \"cbs\", \"table\": \"cbs_ratio\", \"actual_execute_sql\": \"select count(1) as actual_value from ${table}\", \"database2\": \"cbs\", \"table2\": \"cbs_ratio\", \"expected_execute_sql\": \"select count(1) as expected_value from ${table2}\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#配置文件例子","p":260},{"i":270,"t":"比较某张表(有时间分区列)今天的数据量和昨天的数据量，如果今天的数据量小于昨天的数据量就需要告警。","s":"场景","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#场景","p":260},{"i":272,"t":"使用两表值比对规则配合内置时间参数。 使用两表值比对的规则，通过编写 SQL 语句来统计今天和昨天的数据量，然后比较两个值，如果今天的值大于昨天的值则为真，否则为假。","s":"思路","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#思路","p":260},{"i":274,"t":"选择数据比对作业中的两表值比对规则 选择对应的数据库和表，并编写计算实际值 SQL 语句。 语句里面用到了$[today]时间变量，系统会自动替换成今天的日期，格式为yyyy-MM-dd，也可以自己配置格式 $[today(yyyyMMdd)]。 注意 as 后面的别名一定要是 actual_value，否则统计会出错。 表名可以用${table}，系统会自动替换，也可以直接写正确的表名。 select count(1) as actual_value from ${table} where data_date='$[today]' 选择对应的数据库和表，并编写计算期望值 SQL 语句 语句里面用到了$[yesterday]时间变量，系统会自动替换成昨天的日期，格式为yyyy-MM-dd，也可以自己配置格式 $[yesterday(yyyyMMdd)]。 注意 as 后面的别名一定要是 expected_value，否则统计会出错。 表名可以用${table2}，系统会自动替换，也可以直接写正确的表名。 select count(1) as expected_value from ${table2} where data_date='$[yesterday]' 配置结果判断公式 结果公式选择：实际值-期望值 比较符选择：>= 阈值：0 如果公式实际值-期望值 >= 0的结果为真, 那么就证明今天的表行数大于昨天的表行数，否则就证明今天的表行数小于昨天的表行数，结果是异常的，需要告警。","s":"步骤","u":"/datavines-website/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#步骤","p":260},{"i":277,"t":"周均值 期望值类型表示在数据质量检查过程中使用的期望值是根据本周运行该规则得到的实际值所计算得出的当周的均值。","s":"概念解释","u":"/datavines-website/docs/features/metric/expected-value/expected-value-weekly-avg","h":"#概念解释","p":275},{"i":279,"t":"点击创建规则作业，选择数据质量作业 在期望值中选择 周均值。","s":"使用方法","u":"/datavines-website/docs/features/metric/expected-value/expected-value-weekly-avg","h":"#使用方法","p":275},{"i":281,"t":"相关参数 uniqueKey：会根据每个规则的配置信息生成一个唯一键值,为8位长度的字符串 unique_code：会根据每个规则的独有的配置信息生成一个唯一编码 Local 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_sub(${data_time},interval weekday(${data_time}) + 0 day) and data_time < date_add(date_format(${data_time},'%Y-%m-%d'),interval 1 DAY) and unique_code = ${unique_code} Spark&Livy 引擎 select round(avg(actual_value),2) as expected_value_${uniqueKey}from dv_actual_values where data_time >= date_sub(date_format(${data_time},'yyyy-MM-dd'), (7- datediff(next_day(date_format(${data_time}, 'yyyy-MM-dd'),'Sunday'),date_format(${data_time}, 'yyyy-MM-dd')))-1) and data_time < date_add(date_format(${data_time}, 'yyyy-MM-dd'),1) and unique_code = ${unique_code}","s":"计算方式","u":"/datavines-website/docs/features/metric/expected-value/expected-value-weekly-avg","h":"#计算方式","p":275},{"i":285,"t":"|实际值-期望值| 比对公式表示在进行结果判断时所使用的是实际值和期望值的差的绝对值。","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff","h":"#概念解释","p":283},{"i":287,"t":"点击创建规则作业，选择数据质量作业 在比对公式中选择 |实际值-期望值|","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff","h":"#使用方法","p":283},{"i":289,"t":"@Overridepublic Double getResult(Double actualValue, Double expectedValue) { return Math.abs(actualValue - expectedValue);}","s":"计算公式","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff","h":"#计算公式","p":283},{"i":292,"t":"实际值-期望值 比对公式表示在进行结果判断时所使用的是实际值和期望值的差值。","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-actual-expected","h":"#概念解释","p":290},{"i":294,"t":"点击创建规则作业，选择数据质量作业 在比对公式中选择 实际值-期望值","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-actual-expected","h":"#使用方法","p":290},{"i":296,"t":"@Overridepublic Double getResult(Double actualValue, Double expectedValue) { BigDecimal av = new BigDecimal(Double.toString(actualValue)); BigDecimal bv = new BigDecimal(Double.toString(expectedValue)); return av.subtract(bv).doubleValue();}","s":"计算公式","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-actual-expected","h":"#计算公式","p":290},{"i":299,"t":"实际值 比对公式表示在进行结果判断时所使用的是实际值和期望值的差的绝对值。","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-count","h":"#概念解释","p":297},{"i":301,"t":"点击创建规则作业，选择数据质量作业 在比对公式中选择 实际值","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-count","h":"#使用方法","p":297},{"i":303,"t":"@Overridepublic Double getResult(Double actualValue, Double expectedValue) { return actualValue;}","s":"计算公式","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-count","h":"#计算公式","p":297},{"i":306,"t":"|实际值-期望值|/期望值*100% 比对公式表示在进行结果判断时所使用的是实际值和期望值的差值的绝对值除以期望值的百分比。","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-percentage","h":"#概念解释","p":304},{"i":308,"t":"点击创建规则作业，选择数据质量作业 在比对公式中选择 |实际值-期望值|/期望值*100%","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-percentage","h":"#使用方法","p":304},{"i":310,"t":"@Overridepublic Double getResult(Double actualValue, Double expectedValue) { double result = 0; if (expectedValue > 0) { result = Math.abs(expectedValue - actualValue) / expectedValue * 100; } return result;}","s":"计算公式","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-diff-percentage","h":"#计算公式","p":304},{"i":313,"t":"实际值/期望值*100% 比对公式表示在进行结果判断时所使用的是实际值除以期望值的百分比。","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-percentage","h":"#概念解释","p":311},{"i":315,"t":"点击创建规则作业，选择数据质量作业 在比对公式中选择 实际值/期望值*100%","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-percentage","h":"#使用方法","p":311},{"i":317,"t":"@Overridepublic Double getResult(Double actualValue, Double expectedValue) { double result = 0; if (expectedValue > 0) { result = actualValue / expectedValue * 100; } return result;}","s":"计算公式","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-percentage","h":"#计算公式","p":311},{"i":320,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 平均值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#使用方法","p":318},{"i":323,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#options","p":318},{"i":325,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#配置文件例子","p":318},{"i":327,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select avg(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#检查过程中自动生成的-sql-语句","p":318},{"i":330,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#场景","p":318},{"i":332,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#思路","p":318},{"i":334,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg","h":"#步骤","p":318},{"i":337,"t":"比对公式 指的是在进行结果判断时所使用的计算公式，一般配合比较符（comparator）和阈值（threshold）一起使用，完整的判断公式为 [比对公式][比较符] [阈值]","s":"概念解释","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-intro","h":"#概念解释","p":335},{"i":339,"t":"检查结果的判断公式为：[比对公式][比较符] [阈值]，如果结果为真，就是成功，否则为失败 举个例子：[实际值-期望值] [>] [0]， 实际值为10，期望值为9，代入公式以后就是 [10-9][>][0]，这个结果为真，那么检查结果就是成功。","s":"使用方法","u":"/datavines-website/docs/features/metric/result-validate-formula/formula-intro","h":"#使用方法","p":335},{"i":342,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 无值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#使用方法","p":340},{"i":345,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#options","p":340},{"i":347,"t":"{ \"metricType\": \"column_blank\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#配置文件例子","p":340},{"i":349,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (${column} is null or ${column} = '') and ${filter} 计算实际值的 SQL select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#检查过程中自动生成的-sql-语句","p":340},{"i":352,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#场景","p":340},{"i":354,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#思路","p":340},{"i":356,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-blank","h":"#步骤","p":340},{"i":359,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 枚举值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#使用方法","p":357},{"i":362,"t":"name type required default value database string yes - table string yes - column string yes - enum_list string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 enum_list [string]​ 枚举值列表，用,隔开","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#options","p":357},{"i":364,"t":"{ \"metricType\": \"column_in_enums\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" \"enum_list\":\"'database','table'\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#配置文件例子","p":357},{"i":366,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (${column} in (${enum_list})) and ${filter} 计算实际值的 SQL，输出的实际值是列的值在枚举值列表中的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#检查过程中自动生成的-sql-语句","p":357},{"i":369,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#场景","p":357},{"i":371,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#思路","p":357},{"i":373,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-in-enums","h":"#步骤","p":357},{"i":376,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 Distinct检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#使用方法","p":374},{"i":379,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#options","p":374},{"i":381,"t":"{ \"metricType\": \"column_distinct\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#配置文件例子","p":374},{"i":383,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL， 输出不重复的行数 select count(distinct(${column})) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#检查过程中自动生成的-sql-语句","p":374},{"i":386,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#场景","p":374},{"i":388,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#思路","p":374},{"i":390,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-distinct","h":"#步骤","p":374},{"i":393,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 平均长度检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#使用方法","p":391},{"i":396,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#options","p":391},{"i":398,"t":"{ \"metricType\": \"column_avg_length\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#配置文件例子","p":391},{"i":400,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select avg(length(${column})) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#检查过程中自动生成的-sql-语句","p":391},{"i":403,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#场景","p":391},{"i":405,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#思路","p":391},{"i":407,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-avg-length","h":"#步骤","p":391},{"i":410,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 重复值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#使用方法","p":408},{"i":413,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#options","p":408},{"i":415,"t":"{ \"metricType\": \"column_duplicate\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#配置文件例子","p":408},{"i":417,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select ${column} from ${table} where ${filter} group by ${column} having count(${column}) > 1 计算实际值的 SQL，输出的实际值是 重复值>1 的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#检查过程中自动生成的-sql-语句","p":408},{"i":420,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#场景","p":408},{"i":422,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#思路","p":408},{"i":424,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-duplicate","h":"#步骤","p":408},{"i":427,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 正则表达式[不匹配]检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#使用方法","p":425},{"i":430,"t":"name type required default value database string yes - table string yes - column string yes - regexp string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 regexp [string]​ 正则表达式","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#options","p":425},{"i":432,"t":"{ \"metricType\": \"column_in_enums\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" \"regexp\":\"\\d\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#配置文件例子","p":425},{"i":434,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (${column} not regexp '${regexp}') and ${filter} 计算实际值的 SQL，输出的实际值是列的值没有匹配上正则表达式的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#检查过程中自动生成的-sql-语句","p":425},{"i":437,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#场景","p":425},{"i":439,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#思路","p":425},{"i":441,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-not-regex","h":"#步骤","p":425},{"i":444,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 及时性检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#使用方法","p":442},{"i":447,"t":"name type required default value database string yes - table string yes - column string yes - begin_time string yes - deadline_time string yes - datetime_format string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 begin_time [string]​ 开始时间 deadline_time [string]​ 结束时间 datetime_format [string]​ 日期格式","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#options","p":442},{"i":449,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"update_time\", \"begin_time\":\"2023-10-01 08:00:00\", \"deadline_time\":\"2023-10-01 09:00:00\", \"datetime_format\":\"yyyy-MM-dd HH:mm:ss\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#配置文件例子","p":442},{"i":451,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select ${column} from ${table} where (DATE_FORMAT(${column}, '${datetime_format}') <= DATE_FORMAT('${deadline_time}', '${datetime_format}') ) AND (DATE_FORMAT(${column}, '${datetime_format}') >= DATE_FORMAT('${begin_time}', '${datetime_format}')) and ${filter} 计算实际值的 SQL，输出的实际值是 列值在时间范围内 的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#检查过程中自动生成的-sql-语句","p":442},{"i":454,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#场景","p":442},{"i":456,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#思路","p":442},{"i":458,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-freshness","h":"#步骤","p":442},{"i":461,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 正则表达式[匹配]检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#使用方法","p":459},{"i":464,"t":"name type required default value database string yes - table string yes - column string yes - regexp string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 regexp [string]​ 正则表达式","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#options","p":459},{"i":466,"t":"{ \"metricType\": \"column_in_enums\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" \"regexp\":\"\\d\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#配置文件例子","p":459},{"i":468,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (${column} regexp '${regexp}') and ${filter} 计算实际值的 SQL，输出的实际值是列的值匹配上正则表达式的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#检查过程中自动生成的-sql-语句","p":459},{"i":471,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#场景","p":459},{"i":473,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#思路","p":459},{"i":475,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-match-regex","h":"#步骤","p":459},{"i":478,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 字段长度检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#使用方法","p":476},{"i":481,"t":"name type required default value database string yes - table string yes - column string yes - comparator string yes - length string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 comparator [string]​ 比较符，>,>=.<,<=,=,!= length [string]​ 用于比较的长度","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#options","p":476},{"i":483,"t":"{ \"metricType\": \"column_in_enums\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\", \"comparator\":\">\", \"length\":\"5\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#配置文件例子","p":476},{"i":485,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (length(${column}) ${comparator} ${length})) and ${filter} 计算实际值的 SQL，输出的实际值是列的长度符合判断规则的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#检查过程中自动生成的-sql-语句","p":476},{"i":488,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#场景","p":476},{"i":490,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#思路","p":476},{"i":492,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-length","h":"#步骤","p":476},{"i":495,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 最大值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#使用方法","p":493},{"i":498,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#options","p":493},{"i":500,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#配置文件例子","p":493},{"i":502,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select max(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#检查过程中自动生成的-sql-语句","p":493},{"i":505,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#场景","p":493},{"i":507,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#思路","p":493},{"i":509,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-max","h":"#步骤","p":493},{"i":512,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 最大长度检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#使用方法","p":510},{"i":515,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#options","p":510},{"i":517,"t":"{ \"metricType\": \"column_avg_length\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#配置文件例子","p":510},{"i":519,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select max(length(${column})) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#检查过程中自动生成的-sql-语句","p":510},{"i":522,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#场景","p":510},{"i":524,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#思路","p":510},{"i":526,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-max-length","h":"#步骤","p":510},{"i":529,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 最小值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#使用方法","p":527},{"i":532,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#options","p":527},{"i":534,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#配置文件例子","p":527},{"i":536,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select min(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#检查过程中自动生成的-sql-语句","p":527},{"i":539,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#场景","p":527},{"i":541,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#思路","p":527},{"i":543,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-min","h":"#步骤","p":527},{"i":545,"t":"贡献指南 文档贡献指南 文档贡献指南 上一页 贡献指南 下一页 Pull Request 须知","s":"文档贡献指南","u":"/datavines-website/community/contribution_guide/document-contributor","h":"","p":544},{"i":548,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 枚举值[不在]检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#使用方法","p":546},{"i":551,"t":"name type required default value database string yes - table string yes - column string yes - enum_list string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 enum_list [string]​ 枚举值列表，用,隔开","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#options","p":546},{"i":553,"t":"{ \"metricType\": \"column_in_enums\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" \"enum_list\":\"'database','table'\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#配置文件例子","p":546},{"i":555,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where (${column} not in ( ${enum_list} ) or ${column} is null) and ${filter} 计算实际值的 SQL，输出的实际值是列的值不在枚举值列表中的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#检查过程中自动生成的-sql-语句","p":546},{"i":558,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#场景","p":546},{"i":560,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#思路","p":546},{"i":562,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-in-enums","h":"#步骤","p":546},{"i":565,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 非空检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#使用方法","p":563},{"i":568,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#options","p":563},{"i":570,"t":"{ \"metricType\": \"column_not_null\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#配置文件例子","p":563},{"i":572,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where ${column} is not null and ${filter} 计算实际值的 SQL select count(1) as actual_value_\" + uniqueKey + \" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#检查过程中自动生成的-sql-语句","p":563},{"i":575,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#场景","p":563},{"i":577,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#思路","p":563},{"i":579,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-not-null","h":"#步骤","p":563},{"i":582,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 总值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#使用方法","p":580},{"i":585,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#options","p":580},{"i":587,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#配置文件例子","p":580},{"i":589,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select sum(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#检查过程中自动生成的-sql-语句","p":580},{"i":592,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#场景","p":580},{"i":594,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#思路","p":580},{"i":596,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-sum","h":"#步骤","p":580},{"i":599,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 值范围检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#使用方法","p":597},{"i":602,"t":"name type required default value database string yes - table string yes - column string yes - min number yes - max number yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列 min [number]​ 最小值 max [number]​ 最大值","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#options","p":597},{"i":604,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\", \"min\":\"0\", \"max\":\"10\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#配置文件例子","p":597},{"i":606,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select ${column} from ${table} where ${column} >= ${min} and ${column} <= ${max} and ${filter} 计算实际值的 SQL，输出的实际值是 列值的重复数=1 的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#检查过程中自动生成的-sql-语句","p":597},{"i":609,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#场景","p":597},{"i":611,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#思路","p":597},{"i":613,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-value-between","h":"#步骤","p":597},{"i":616,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 方差检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#使用方法","p":614},{"i":619,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#options","p":614},{"i":621,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#配置文件例子","p":614},{"i":623,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select variance(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#检查过程中自动生成的-sql-语句","p":614},{"i":626,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#场景","p":614},{"i":628,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#思路","p":614},{"i":630,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-variance","h":"#步骤","p":614},{"i":633,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 最小长度检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#使用方法","p":631},{"i":636,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#options","p":631},{"i":638,"t":"{ \"metricType\": \"column_avg_length\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#配置文件例子","p":631},{"i":640,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select min(length(${column})) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#检查过程中自动生成的-sql-语句","p":631},{"i":643,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#场景","p":631},{"i":645,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#思路","p":631},{"i":647,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-min-length","h":"#步骤","p":631},{"i":650,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 自定义聚合SQL 规则 选择要检查的数据源信息，编写自定义聚合 SQL 语句","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#使用方法","p":648},{"i":653,"t":"name type required default value database string yes - table string yes - actual_aggregate_sql string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 actual_aggregate_sql [string]​ 自定义聚合SQL，注意 as 后面的别名一定要是 actual_value，否则统计会出错。","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#options","p":648},{"i":655,"t":"{ \"metricType\": \"custom_aggregate_sql\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_actual_values\", \"actual_aggregate_sql\": \"select count(1) as actual_value from ${table}\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#配置文件例子","p":648},{"i":658,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#场景","p":648},{"i":660,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#思路","p":648},{"i":662,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/custom-aggregate-sql","h":"#步骤","p":648},{"i":664,"t":"SLA 是系统的核心功能，也是通知用户的通道。用户可以给每个规则配置多个SLA来通知不同的用户群，一个SLA也可以给多个规则检查作业使用，目前系统只支持邮件。","s":"SLAs介绍","u":"/datavines-website/docs/features/slas/slas-intro","h":"","p":663},{"i":666,"t":"在sla中创建sla， 在通知里面创建一个通道 进去sla后，在sla中创建个通知关联上这个通道","s":"配置 SLA","u":"/datavines-website/docs/features/slas/slas-intro","h":"#配置-sla","p":663},{"i":668,"t":"创建数据质量检查作业时可以在配置页面选择SLA，当检查结果为失败时会通过该SLA里面所配置的告警通道进行告警","s":"SLA 使用","u":"/datavines-website/docs/features/slas/slas-intro","h":"#sla-使用","p":663},{"i":671,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 空值检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#使用方法","p":669},{"i":674,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#options","p":669},{"i":676,"t":"{ \"metricType\": \"column_null\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#配置文件例子","p":669},{"i":678,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select * from ${table} where ${column} is null and ${filter} 计算实际值的 SQL select count(1) as actual_value_\" + uniqueKey + \" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#检查过程中自动生成的-sql-语句","p":669},{"i":681,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#场景","p":669},{"i":683,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#思路","p":669},{"i":685,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-null","h":"#步骤","p":669},{"i":688,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 唯一性检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#使用方法","p":686},{"i":691,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#options","p":686},{"i":693,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#配置文件例子","p":686},{"i":695,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 invalidate_items_table 会创建一个视图用于存储中间表数据，中间表数据一般为命中规则的数据，即为错误数据，该视图的名字生成规则为 invalidate_items_uniqueKey 中间表 invalidate_items_uniqueKey select ${column} from ${table} where ${filter} group by ${column} having count(${column}) = 1 计算实际值的 SQL，输出的实际值是 列值的重复数=1 的列的行数 select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#检查过程中自动生成的-sql-语句","p":686},{"i":698,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#场景","p":686},{"i":700,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#思路","p":686},{"i":702,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-unique","h":"#步骤","p":686},{"i":705,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 标准差检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#使用方法","p":703},{"i":708,"t":"name type required default value database string yes - table string yes - column string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 column [string]​ 要检查的列","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#options","p":703},{"i":710,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", \"column\": \"type\" }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#配置文件例子","p":703},{"i":712,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select stddev(${column}) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#检查过程中自动生成的-sql-语句","p":703},{"i":715,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#场景","p":703},{"i":717,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#思路","p":703},{"i":719,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/column-std-dev","h":"#步骤","p":703},{"i":722,"t":"支持用企业微信机器人通知","s":"介绍","u":"/datavines-website/docs/features/slas/slas-notification/notification-wecombot","h":"#介绍","p":720},{"i":724,"t":"参数 描述 webhook 企业微信机器人 webhook 地址. 如 https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=12345678-xxxx-xxxx-xxxx-123456789999","s":"属性配置","u":"/datavines-website/docs/features/slas/slas-notification/notification-wecombot","h":"#属性配置","p":720},{"i":728,"t":"点击创建规则作业，选择数据质量作业 进入作业页面选择 表行数检查 规则 选择要检查的数据源信息","s":"使用方法","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#使用方法","p":726},{"i":731,"t":"name type required default value database string yes - table string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名","s":"Options","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#options","p":726},{"i":733,"t":"{ \"metricType\": \"column_avg\", \"metricParameter\": { \"database\": \"datavines\", \"table\": \"dv_catalog_entity_instance\", }}","s":"配置文件例子","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#配置文件例子","p":726},{"i":735,"t":"检查过程会用到的一些自动生成的参数，用于区分各个检查规则。 uniqueKey 会根据每个规则的配置信息生成一个唯一键值 计算实际值的 SQL select count(1) as actual_value_${uniqueKey} from ${table} where ${filter}","s":"检查过程中自动生成的 SQL 语句","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#检查过程中自动生成的-sql-语句","p":726},{"i":738,"t":"...","s":"场景","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#场景","p":726},{"i":740,"t":"...","s":"思路","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#思路","p":726},{"i":742,"t":"...","s":"步骤","u":"/datavines-website/docs/features/metric/single-table-metric/table-row-count","h":"#步骤","p":726},{"i":745,"t":"时间变量是Datavines系统内置的变量，可用于检查作业中的过滤条件和自定义SQL中，","s":"介绍","u":"/datavines-website/docs/features/time-variable","h":"#介绍","p":743},{"i":747,"t":"在过滤条件或者自定义SQL中可以使用 $[表达式] 的形式来使用，假设今天是 2023-10-01，目前支持的表达式如下： $[today]: 获取今天的日期，格式为yyyy-MM-dd，例如2023-10-01 $[yesterday]: 获取昨天的日期，格式为yyyy-MM-dd，例如2023-09-30 $[tomorrow]: 获取明天的日期，格式为yyyy-MM-dd，例如2023-10-02 $[yyyy-MM-dd HH:mm:ss]: 获取当前格式的日期或者时间 $[(时间格式)(-/+)n]: 获取当前格式的日期或者时间加减n天 $[yyyy-MM-dd-2]: 获取当前格式的日期的前两天，就是2023-09-29 $[add_months(时间格式, (-/+)n)]: 获取当前格式的日期或者时间加减n个月 $[add_months(yyyy-MM-dd, 2)]: 获取当前格式的日期的后两个月，就是2023-12-01 $[month_begin(时间格式, (-/+)n)]: 获取当前格式的日期所在月的第一天加减n天 $[month_begin(yyyy-MM-dd, +2)]: 获取当前格式的日期所在月的第一天的后两天，就是2023-10-03 $[month_end(时间格式, (-/+)n)]: 获取当前格式的日期所在月的最后一天加减n天 $[add_months(yyyy-MM-dd, -2)]: 获取当前格式的日期所在月的最后一天的前两天，就是2023-10-29 $[week_begin(时间格式, (-/+)n)]: 获取当前格式的日期所在周的第一天加减n天 $[week_begin(yyyy-MM-dd, -2)]: 获取当前格式的日期所在周的第一天的前两天，就是2023-09-23 $[week_end(时间格式, (-/+)n)]: 获取当前格式的日期所在周的最后一天加减n天 $[add_months(yyyy-MM-dd, -2)]: 获取当前格式的日期所在周的最后一天的前两天，就是2023-09-29","s":"使用方法","u":"/datavines-website/docs/features/time-variable","h":"#使用方法","p":743},{"i":750,"t":"支持用邮件通知","s":"介绍","u":"/datavines-website/docs/features/slas/slas-notification/notification-email","h":"#介绍","p":748},{"i":753,"t":"参数 描述 mail.smtp.host SMTP 服务器地址 mail.smtp.port SMTP 服务器端口 mail.sender 邮件发送者 mail.smtp.auth 是否需要认证 mail.user 验证用户名 mail.passwd 验证密码 mail.smtp.starttls.enable 如果为true，则在发出任何登录命令之前，允许使用STARTTLS命令（如果服务器支持）将连接切换到 TLS-protected 的连接。 如果服务器不支持STARTTLS，则在不使用TLS的情况下继续连接。 mail.smtp.ssl.enable 如果设置为true，则默认情况下使用SSL进行连接并使用SSL端口。对于“smtp”协议，默认值为false，对于“smtps”协议，则默认值为true。 mail.smtp.ssl.trust 如果已设置，并且尚未指定套接字工厂，则启用 MailSSLSocketFactory。如果设置为“*”，则所有主机都是受信任的。如果设置为以空格分隔的主机列表，则这些主机是受信任的。否则，信任取决于服务器提供的证书。","s":"通知发件人配置","u":"/datavines-website/docs/features/slas/slas-notification/notification-email","h":"#通知发件人配置","p":748},{"i":756,"t":"该文档主要介绍了如何通过 Dockerfile 来制作 DataVines 的运行镜像，以便于在容器化编排工具或者快速测试过程中可迅速拉取一个 Datavines Image 来完成集群的创建。","s":"构建Docker Image","u":"/datavines-website/docs/get-started/docker/build-docker-image","h":"#构建docker-image","p":754},{"i":758,"t":"Dockerfile编写需注意以下几点： 基础父镜像选用经过 Docker-Hub 认证的 OpenJDK 官方镜像，版本用 JDK 1.8 版本。 应用所需二进制包需自行打包,参考 用源码部署 章节。 应用在Docker内启动时应使用 start_container 参数 构建平台应与运行平台保持一致。","s":"准备 Dockerfile","u":"/datavines-website/docs/get-started/docker/build-docker-image","h":"#准备-dockerfile","p":754},{"i":760,"t":"编写 Dockerfile 文件，可参考如下Dockerfile,可通过wget进行下载。 复制打包好的二进制包到 Dockerfile 所在目录。 校验 Dockerfile 文件中二进制包名一致性。 通过如下命令进行打包。 docker build -t datavines:dev .","s":"构建步骤","u":"/datavines-website/docs/get-started/docker/build-docker-image","h":"#构建步骤","p":754},{"i":762,"t":"登录 DockerHub 账号 docker login -u 登录成功会提示 Success 相关提示。 更改镜像标签 docker tag datavines:dev //datavines:dev 推送镜像 docker push //datavines:dev","s":"推送镜像至 DockerHub 或私有仓库","u":"/datavines-website/docs/get-started/docker/build-docker-image","h":"#推送镜像至-dockerhub-或私有仓库","p":754},{"i":765,"t":"本篇将简述如何通过docker run或docker-compose命令快速运行Datavines。","s":"背景说明","u":"/datavines-website/docs/get-started/docker/deploy-docker-image","h":"#背景说明","p":763},{"i":767,"t":"软件 版本 Docker 20.0及以上 docker-compose 2.10 及以上","s":"软件环境","u":"/datavines-website/docs/get-started/docker/deploy-docker-image","h":"#软件环境","p":763},{"i":770,"t":"1.下载并修改docker-compose.yaml 修改镜像版本为本地编译版本或私仓镜像版本 image: datavines:dev 如出现外部网段与容器内部网段冲突，表现为容器同网段可访问，跨网段无法访问，可修改docker-compose.yaml文件中的network_mode。 networks: datavines: driver: bridge ipam: config: - subnet: 172.18.0.0/24(自定义网段) 下载环境配置文件.env 修改.env文件中的配置项，参考如下： # 数据库连接SPRING_DATASOURCE_URL=jdbc:mysql://127.0.0.1:3306/datavines?useSSL=false&useUnicode=true&characterEncoding=UTF-8&allowPublicKeyRetrieval=false&useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=GMT%2B8# 数据库用户名SPRING_DATASOURCE_USERNAME=root","s":"前期准备","u":"/datavines-website/docs/get-started/docker/deploy-docker-image","h":"#前期准备","p":763},{"i":772,"t":"docker compose up -d","s":"快速启动","u":"/datavines-website/docs/get-started/docker/deploy-docker-image","h":"#快速启动","p":763},{"i":774,"t":"环境参数需从启动命令中传入，如下。 docker run -it -d --name datavines -p 5600:5600 --privileged=true --restart always -e SPRING_PROFILES_ACTIVE=mysql -e SPRING_DATASOURCE_URL=jdbc:mysql://127.0.0.1:3306/datavines?useSSL=false&useUnicode=true&characterEncoding=UTF-8&allowPublicKeyRetrieval=false&useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=GMT%2B8 -e SPRING_DATASOURCE_USERNAME=root -e SPRING_DATASOURCE_PASSWORD=123456 -e SPRING_DATASOURCE_DRIVER-CLASS_NAME=com.mysql.cj.jdbc.Driver datavines:dev","s":"方法二：docker run","u":"/datavines-website/docs/get-started/docker/deploy-docker-image","h":"#方法二docker-run","p":763},{"i":777,"t":"安装k8s 构建datavines镜像 创建或下载datavines on k8s的配置文件 https://raw.githubusercontent.com/datavane/datavines/dev/deploy/k8s/datavines.yaml","s":"环境准备","u":"/datavines-website/docs/get-started/kubernetes-deploy","h":"#环境准备","p":775},{"i":779,"t":"指定命名空间执行如下命令。 kubectl apply -f datavines.yaml -n ","s":"部署","u":"/datavines-website/docs/get-started/kubernetes-deploy","h":"#部署","p":775},{"i":781,"t":"获取命名空间下的pod，查看datavines是否正常运行。 kubectl get pod -n ","s":"验证","u":"/datavines-website/docs/get-started/kubernetes-deploy","h":"#验证","p":775},{"i":783,"t":"让您更容易了解您的数据！ Datavines是一个易于使用的数据可观测性平台，提供数据目录和数据概览让您对你的数据有全方位的了解，同时内置了多种数据质量检查规则来确保数据集成和数据处理过程中数据的准确性。","s":"介绍","u":"/datavines-website/docs/intro","h":"","p":782},{"i":785,"t":"支持血缘关系管理 支持 Checksum 算法的数据比对（开发中） 支持 Flink 引擎 支持 Spark on K8s 在 DolphinScheduler 中增加 Datavines 任务类型（开发中） 支持 Livy 引擎","s":"未来规划","u":"/datavines-website/docs/roadmap","h":"","p":784},{"i":788,"t":"点击创建数据源按钮，输入数据源的名称，然后选择数据源类型。以mysql为例，输入mysql的连接信息，点击测试连接按钮。如果成功，请单击保存","s":"创建数据源","u":"/datavines-website/docs/user-guide/quick-start","h":"#创建数据源","p":786},{"i":790,"t":"点击已创建好的数据源，进入数据源详情页面","s":"查看数据源信息","u":"/datavines-website/docs/user-guide/quick-start","h":"#查看数据源信息","p":786},{"i":792,"t":"例如，点击表dv_catalog_entity_instance的properties列，进入列的详情页面","s":"选择列","u":"/datavines-website/docs/user-guide/quick-start","h":"#选择列","p":786},{"i":794,"t":"点击 添加规则 按钮, 选择空值检查规则, 选择 固定值 期望值类型、输入期望值 10 , 选择 实际值 检查公式 、> 比较符并输入阈值 10, 这样选择的意思就是当实际值 > 10 ,那么检查结果为成功，否则是失败。配置完成以后可以直接保存进行保存或者点击保存并运行来执行检查作业。 返回到列的详情页面，你可以看到你刚刚所创建的规则作业列表。你可以在这里看到这个检查作业的运行趋势图。","s":"添加规则","u":"/datavines-website/docs/user-guide/quick-start","h":"#添加规则","p":786},{"i":796,"t":"点击规则名则可以进入到规则作业的详情页面。 点击 运行结果 页面, 你可以看到执行历史列表。 点击日志按钮，你可以看到规则执行的日志信息。 点击检查结果按钮，你可以看到规则执行的检查结果。 点击错误数据按钮，你可以看到规则执行的错误数据。","s":"查看规则作业的信息","u":"/datavines-website/docs/user-guide/quick-start","h":"#查看规则作业的信息","p":786},{"i":799,"t":"在安装Datavines之前请确保你的服务器上已经安装下面软件 Git，确保 git clone的顺利执行 JDK，确保 jdk = 8 Maven, 确保项目的顺利打包（当然你也可以在本地打包以后上传至服务器）","s":"环境准备","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#环境准备","p":797},{"i":801,"t":"git clone https://github.com/datavane/datavines.gitcd datavines","s":"下载代码","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#下载代码","p":797},{"i":803,"t":"Datavines 的元数据是存储在关系型数据库中，目前支持 MySQL ，下面以MySQL为例说明安装步骤： 创建数据库 datavines 执行 script/sql/datavines-mysql.sql 脚本进行数据库的初始化 下面的项目构建也是以MySQL为例","s":"数据库准备","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#数据库准备","p":797},{"i":805,"t":"打包并解压 mvn clean package -Preleasecd datavines-dist/targettar -zxvf datavines-1.0.0-SNAPSHOT-bin.tar.gz 注意: 此时如果前端 datavines-ui 打包异常, 可能是网络问题, 可以尝试更换 npm 源 找到 datavines-ui 项目的 pom 文件, 在 npm install 添加 --registry https://registry.npmmirror.com, 如下所示 npm install npm generate-resources install --registry https://registry.npmmirror.com 解压完成以后进入目录 cd datavines-1.0.0-SNAPSHOT-bin 修改配置信息 cd confvi application.yaml 主要是修改数据库信息 spring: config: activate: on-profile: mysql datasource: driver-class-name: com.mysql.cj.jdbc.Driver url: jdbc:mysql://127.0.0.1:3306/datavines?useUnicode=true&characterEncoding=UTF-8&serverTimezone=Asia/Shanghai username: root password: 123456 如果你是使用Spark做为执行引擎，并且是提交到Yarn上面去执行的，那么需要在common.properties中配置yarn相关的信息 standalone 模式 yarn.mode=standaloneyarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%s #第一个%s需要被替换成yarn的ip地址yarn.resource.manager.http.address.port=8088 ha 模式 yarn.mode=hayarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%syarn.resource.manager.http.address.port=8088yarn.resource.manager.ha.ids=192.168.0.1,192.168.0.2","s":"项目构建","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#项目构建","p":797},{"i":807,"t":"cd binsh datavines-daemon.sh start mysql 查看日志，如果日志里面没有报错信息，并且能看到[INFO] 2022-04-10 12:29:05.447 io.datavines.server.DatavinesServer:[61] - Started DatavinesServer in 3.97 seconds (JVM running for 4.69)的时候，证明服务已经成功启动","s":"启动服务","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#启动服务","p":797},{"i":809,"t":"如果项目是 在 windows 中打包的 执行 sh datavines-daemon.sh start mysql 时可能会报以下异常 这是因为 Windows格式文件的换行符为\\r\\n , 而Unix&Linux文件的换行符为\\n 此时需要执行 dos2unix 来转换文件格式 安装 dos2unix yum install -y dos2unix 格式转换 cd binfind . -type f -print0 | xargs -0 dos2unix","s":"windows 打包问题","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#windows-打包问题","p":797},{"i":811,"t":"在浏览器输入：localhost:5600，就会跳转至登录界面，输入账号密码 admin/123456","s":"访问前端页面","u":"/datavines-website/docs/get-started/deploy-from-source","h":"#访问前端页面","p":797},{"i":814,"t":"cd datavines-1.0.0-SNAPSHOT-bin/binvi metric_job_config.json","s":"创建规则配置文件","u":"/datavines-website/docs/user-guide/local-mode","h":"#创建规则配置文件","p":812},{"i":816,"t":"{ \"languageEn\":true, \"name\":\"test\", \"executePlatformType\":\"client\", \"parameter\":{ \"connectorParameter\":{ \"type\":\"mysql\", \"parameters\":{ \"database\":\"test\", \"password\":\"123456\", \"port\":\"3306\", \"host\":\"localhost\", \"user\":\"root\", \"properties\":\"useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai\" } }, \"metricParameterList\":[ { \"metricType\":\"column_duplicate\", \"metricParameter\":{ \"table\":\"test_table\", \"column\":\"test_column\", \"database\":\"test\" }, \"expectedType\":\"fix_value\", \"expectedParameter\":{ \"expected_value\":\"10\" }, \"resultFormula\":\"count\", \"operator\":\"lt\", \"threshold\":5.0 } ] }, \"errorDataStorageType\": \"file\", \"errorDataStorageParameter\": { \"data_dir\":\"/tmp/datavines/error-data\", \"column_separator\":\",\" }, \"validateResultDataStorageType\": \"file\", \"validateResultDataStorageParameter\":{ \"data_dir\":\"/tmp/datavines/validate-result-data\", \"column_separator\":\",\" }, \"notificationParameters\": [ { \"type\":\"email\", \"config\": { \"serverHost\":\"smtp.qq.com\", \"serverPort\":\"25\", \"sender\":\"1234567@qq.com\", // input your mail \"enableSmtpAuth\":\"true\", \"user\":\"123456@qq.com\", // input your username \"passwd\":\"123456\", // input your passwd \"starttlsEnable\":\"false\", \"sslEnable\":\"false\", \"smtpSslTrust\":\"true\" }, \"receiver\": { \"to\":\"12345566@qq.com\" // input receiver email } } ]}","s":"添加以下内容到配置文件中","u":"/datavines-website/docs/user-guide/local-mode","h":"#添加以下内容到配置文件中","p":812},{"i":818,"t":"./datavines-submit.sh metric_job_config.json","s":"以脚本形式执行数据质量检查作业","u":"/datavines-website/docs/user-guide/local-mode","h":"#以脚本形式执行数据质量检查作业","p":812},{"i":822,"t":"在 Datavines 中申请令牌，这个令牌是调用 Datavines OpenApi 时用的，步骤如下： 创建令牌： 查看令牌：","s":"第一步：申请令牌","u":"/datavines-website/docs/integration/dolphin-scheduler","h":"#第一步申请令牌","p":819},{"i":824,"t":"在 Datavines 中创建数据质量检查作业，拿到规则作业ID","s":"第二步：创建作业","u":"/datavines-website/docs/integration/dolphin-scheduler","h":"#第二步创建作业","p":819},{"i":826,"t":"创建任务​ 进入项目以后，创建或选择一个工作流，进入工作流编辑页面 新增一个 Task，选择 Shell 任务类型 输入脚本模版​ 将下面脚本复制到脚本输入框里面，这个脚本是我们提供的脚本模版，用户可以根据自己的需要进行修改，当前脚本的运行结果是根据检查结果来输出的，结果为成功则 exit 0 ，失败则 exit 1。 #!/bin/bashjob_execute_url=\"${datavines_address}/api/v1/openapi/job/execute\"job_execution_status_url=\"${datavines_address}/api/v1/openapi/job/execution/status/\"job_execution_result_url=\"${datavines_address}/api/v1/openapi/job/execution/result/\"repsonse_job_execute=$(curl -s -X POST \"$job_execute_url/${job_id}\" -H \"Authorization: Bearer ${token}\")if [ -z \"$repsonse_job_execute\" ]; thenecho \"[DATAVINES][ERROR]: job execute response is null\"exit 1ficode_job_execute=$(echo \"$repsonse_job_execute\" | sed -n 's/.*\"code\":\\([0-9]*\\).*/\\1/p')job_execution_id=$(echo \"$repsonse_job_execute\" | sed -n 's/.*\"data\":\\([0-9]*\\).*/\\1/p')# 判断 job_execute 接口返回的 code 是否为 200if [ \"$code_job_execute\" -ne 200 ]; thenecho \"[DATAVINES][ERROR]: job execute response : $repsonse_job_execute\"exit 1fiecho \"[DATAVINES][INFO]: job execution id is $job_execution_id\"# 检查 id 是否为空if [ -z \"$job_execution_id\" ]; thenecho \"[DATAVINES][ERROR]: job execute repsonse id is null, exit\"exit 1fi# 轮询 job_execution_status 接口while true; do# 调用 job_execution_status 接口（GET）response_job_execution_status=$(curl -s -X GET \"$job_execution_status_url/$job_execution_id\" -H \"Authorization: Bearer ${token}\")# 提取 job_execution_status 接口返回的 code 和 data 值code_job_execution_status=$(echo \"$response_job_execution_status\" | sed -n 's/.*\"code\":\\([0-9]*\\).*/\\1/p')data_job_execution_status=$(echo \"$response_job_execution_status\" | sed -n 's/.*\"data\":\"\\([^\"]*\\)\".*/\\1/p')# 判断 job_execution_status 接口返回的 code 是否为 200if [ \"$code_job_execution_status\" -ne 200 ]; thenecho \"[DATAVINES][ERROR]: job execution status repsonse is error: $response_job_execution_result\"exit 1fi# 检查返回的 data 值if [ \"$data_job_execution_status\" == \"FAILURE\" ]; thenecho \"[DATAVINES][ERROR]: job execution status is $data_job_execution_status , exit\"exit 1elif [ \"$data_job_execution_status\" == \"SUCCESS\" ]; thenecho \"[DATAVINES][INFO]: job execution status is $data_job_execution_status , to get job execution result\" # 调用 job_execution_result 接口（GET） repsonse_job_execution_result=$(curl -s -X GET \"$job_execution_result_url/$job_execution_id\" -H \"Authorization: Bearer ${token}\") # 提取 job_execution_result 接口返回的 code 和 data code_job_execution_result=$(echo \"$repsonse_job_execution_result\" | sed -n 's/.*\"code\":\\([0-9]*\\).*/\\1/p') data_job_execution_result=$(echo \"$repsonse_job_execution_result\" | sed -n 's/.*\"data\":\"\\([^\"]*\\)\".*/\\1/p') # 判断 job_execution_result 接口返回的 code 是否为 200 if [ \"$code_job_execution_result\" -ne 200 ]; then echo \"[DATAVINES][ERROR]: job execution result is error: $repsonse_job_execution_result\" exit 1 fi # 检查 job_execution_result 接口的返回 data 值 if [ \"$data_job_execution_result\" == \"SUCCESS\" ]; then echo \"[DATAVINES][INFO]: job execution result is $data_job_execution_result\" exit 0 else echo \"[DATAVINES][INFO]: job execution result is $data_job_execution_result\" exit 1 fielse# data_job_execution_status 为其他值时记录日志，但不中断执行echo \"[DATAVINES][INFO]: job execution status is $data_job_execution_status , continute\"fi# 等待一段时间再进行下一次轮询sleep 2done 添加参数​ 这里需要输入三个参数 datavines_address: datavines 服务地址，比如 http://localhost:5600 job_id：作业ID，可以从作业列表上进行复制 token：在令牌管理页面可以找到","s":"第三步：在 DS 中创建任务","u":"/datavines-website/docs/integration/dolphin-scheduler","h":"#第三步在-ds-中创建任务","p":819},{"i":828,"t":"完成上面几个步骤以后，运行 DolphinScheduler 的工作流，查看任务实例日志如下：","s":"第四步：运行工作流","u":"/datavines-website/docs/integration/dolphin-scheduler","h":"#第四步运行工作流","p":819}],"index":{"version":"2.3.9","fields":["t"],"fieldVectors":[["t/4",[0,7.571,1,7.571,2,6.46,3,8.598,4,6.278]],["t/6",[0,3.934,1,3.583,2,0.742,3,0.987,4,1.594,5,1.686,6,0.987,7,0.987,8,1.84,9,1.09,10,0.987,11,4.787,12,1.917,13,1.594,14,1.458,15,1.998,16,1.09,17,0.767,18,0.869,19,1.323,20,0.987,21,1.998,22,1.09,23,1.09,24,6.672,25,1.594,26,3.279,27,0.987,28,1.09,29,1.09,30,1.81,31,1.998,32,0.681,33,0.92,34,1.09,35,0.987,36,2.334,37,0.767,38,1.19,39,0.796,40,1.81,41,0.975,42,0.473,43,0.486,44,1.09,45,1.09,46,0.987,47,0.742,48,0.414,49,1.09,50,0.987,51,2.505,52,0.552,53,1.52,54,1.09,55,0.92,56,1.81,57,1.114,58,1.09,59,1.594,60,3.038,61,1.81,62,0.444,63,1.998,64,2.765,65,1.396,66,3.791,67,1.09,68,2.765,69,0.987,70,1.09,71,1.594,72,5.147,73,0.869,74,1.36,75,1.775,76,1.09,77,0.697,78,1.282,79,2.765,80,2.765,81,1.282,82,2.765,83,0.869,84,1.09,85,1.09,86,0.665,87,0.373,88,1.826,89,1.054,90,1.09,91,1.09,92,0.92,93,0.869,94,2.505,95,0.987,96,0.987,97,1.09,98,0.609,99,1.998,100,2.765,101,1.09,102,1.52,103,1.09,104,0.742,105,0.829,106,0.92,107,0.869,108,0.796,109,0.562,110,5.915,111,0.987,112,2.81,113,0.987,114,0.987,115,1.09,116,1.686,117,1.998,118,2.765,119,2.765,120,1.09,121,1.09,122,1.998,123,3.101,124,4.491,125,0.987,126,1.594,127,0.987,128,4.491,129,0.869,130,0.796,131,0.444,132,1.09,133,0.987,134,2.334,135,2.334,136,0.72,137,1.686,138,0.265,139,1.912,140,0.987,141,1.998,142,0.796,143,1.09,144,0.92,145,1.998,146,1.09,147,1.09,148,1.088,149,0.987,150,1.998,151,0.796,152,1.09,153,0.92,154,0.514,155,1.09,156,0.796,157,0.829,158,0.699,159,0.987,160,0.72,161,1.09,162,1.998,163,0.829,164,0.829,165,1.09,166,1.458,167,1.09,168,1.09,169,0.987,170,2.889,171,0.796,172,0.987,173,1.09,174,0.699,175,1.09,176,1.09,177,0.455,178,0.742,179,1.09,180,0.467,181,0.699,182,0.767,183,1.09,184,0.796,185,0.92,186,0.767,187,1.09,188,1.09,189,1.09,190,0.869,191,1.282,192,1.998,193,1.163,194,1.406,195,0.92,196,0.92,197,1.998,198,0.869,199,1.09,200,1.09,201,1.09,202,1.09,203,1.09,204,0.869]],["t/10",[25,5.268,26,4.821,60,6.472,65,1.732,72,5.024,163,5.024,166,4.821,205,6.603,206,5.573,207,5.573,208,4.821,209,5.268,210,5.268,211,6.603,212,5.573,213,5.982,214,5.982,215,5.573,216,5.573,217,5.982]],["t/12",[32,4.183,65,1.756,66,5.649,72,7.6,73,5.339,74,4.556,75,4.295,83,5.339,116,5.649,181,4.295,218,6.693,219,5.092,220,6.063,221,4.556,222,6.693,223,6.693,224,6.693]],["t/14",[26,4.353,43,3.544,88,3.937,89,3.147,139,3.331,184,4.353,215,5.033,225,4.277,226,2.17,227,4.353,228,4.353,229,4.196,230,5.033,231,5.402,232,5.402,233,5.402,234,4.537,235,3.147,236,3.266,237,5.402,238,5.402,239,5.033,240,5.963,241,5.963,242,5.033,243,5.963,244,2.943]],["t/16",[13,5.316,18,3.685,57,1.506,65,2.244,109,1.873,136,3.05,139,2.58,156,3.373,181,2.965,221,4.536,225,4.599,226,3.546,234,3.515,236,2.53,245,3.799,246,0.956,247,2.086,248,5.624,249,6.037,250,3.373,251,2.21,252,3.899,253,3.251,254,1.408,255,2.816,256,3.373,257,1.718,258,3.899,259,4.185,260,3.11,261,3.05,262,4.185]],["t/18",[65,2.064,139,4.396,181,5.051,213,7.13,253,5.538,263,6.278,264,7.87,265,5.988,266,5.538]],["t/20",[5,2.705,43,2.788,51,2.904,57,0.724,62,1.306,65,1.864,75,2.057,77,1.119,88,4.691,89,1.692,93,2.557,136,2.116,139,1.79,194,2.256,215,2.705,219,2.439,221,4.26,225,4.156,226,2.586,227,2.34,228,2.34,229,2.256,230,4.267,231,2.904,232,2.904,233,2.904,234,2.439,235,3.304,244,1.582,245,1.827,255,1.954,256,2.34,257,1.192,261,2.116,267,6.259,268,3.69,269,3.205,270,5.055,271,5.055,272,2.904,273,1.615,274,2.439,275,3.205,276,3.205,277,2.439,278,2.705,279,2.439,280,2.904,281,2.904,282,3.205,283,3.205,284,1.954,285,3.69,286,3.846,287,2.557,288,2.557,289,3.846,290,2.34,291,2.557,292,3.557,293,2.256,294,2.557,295,2.622,296,2.439,297,2.116,298,2.116,299,2.116,300,1.662,301,1.827]],["t/22",[57,1.506,65,1.212,78,2.965,106,3.899,108,3.373,129,3.685,166,3.373,191,2.965,225,3.582,228,3.373,253,4.689,266,3.251,274,5.07,302,7.081,303,4.62,304,4.62,305,4.62,306,3.899,307,3.899,308,2.53,309,3.515,310,3.373,311,4.62,312,4.62,313,4.62,314,4.62,315,3.144,316,3.685,317,3.899,318,4.185,319,4.185,320,2.965,321,3.899,322,3.899,323,4.185,324,4.185,325,3.373,326,4.185,327,3.685,328,4.185,329,4.185,330,4.185,331,3.515,332,4.185,333,3.515,334,3.899,335,3.144]],["t/24",[65,1.509,71,7.015,108,4.201,112,4.049,136,3.799,180,2.464,182,4.049,190,4.59,208,4.201,253,4.049,266,4.049,295,2.984,300,2.984,331,4.378,336,5.754,337,5.754,338,5.754,339,4.856,340,5.213,341,5.754,342,5.754,343,3.799,344,4.59,345,2.635,346,5.754,347,4.378,348,5.754,349,5.754,350,5.754,351,5.754]],["t/27",[8,2.988,12,3.609,48,2.113,53,4.229,57,1.256,65,1.458,157,4.229,158,3.568,181,3.568,209,4.435,229,6.532,265,4.229,308,3.045,352,4.229,353,5.036,354,3.389,355,2.113,356,2.934,357,1.845,358,3.784,359,4.692,360,4.229,361,4.692,362,3.67,363,5.036,364,4.058,365,4.692,366,4.435,367,3.474]],["t/29",[12,0.333,19,0.41,42,1.444,47,1.92,57,0.855,62,0.916,65,0.419,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Guide Code Conduct Code Conduct Next Contributor","s":"Code Conduct","u":"/datavines-website/en/community/contribution_guide/code-conduct","h":"","p":822},{"i":825,"t":"Contribution Guide Contributor On this page Contributor You can report a bug, submit a new function enhancement suggestion, or submit a pull request directly. Submit an Issue​ Before submitting an issue, please go through a comprehensive search to make sure the problem cannot be solved just by searching. Check the Issue List to make sure the problem is not repeated. Create a new issue and choose the type of issue. Define the issue with a clear and descriptive title. Fill in necessary information according to the template. Please pay attention for your issue, you may need provide more information during discussion. Developer Flow​ 1. Prepare repository Go to Datavines GitHub Repo and fork repository to your account. Clone repository to local machine. git clone https://github.com/(your_github_name)/datavines.git Add Datavines remote repository. git remote add upstrem https://github.com/datavane/datavines.gitgit remote -v 2. Choose Issue Please choose the issue to be edited. If it is a new issue discovered or a new function enhancement to offer, please create an issue and set the right label for it. After choosing the relevant issue, please reply with a deadline to indicate that you are working on it. 3. Create Branch Switch to forked master branch, update local branch, then create a new branch. git checkout devgit fetch upstreamgit rebase upstream/devgit push origin dev # optionalgit checkout -b issueNo 4. Coding Please obey the Code of Conduct during the process of development and finish the check before submitting the pull request. push code to your fork repo. git add .git commit -m 'commit log'git push origin issueNo 5. Submit Pull Request Send a pull request to the master branch. The other developer will do code review before discussing some details (including the design, the implementation and the performance) with you. The request will be merged into the branch of current development version after the edit is well enough. At last, congratulations on being an contributor of Datavines 6. Delete Branch You can delete the remote branch (origin/issueNo) and the local branch (issueNo) associated with the remote branch (origin/issueNo) after the mentor merged the pull request into the master branch of Datavines. git checkout devgit branch -d issueNogit remote prune origin # If you delete branch on GitHub PR page, else you could delete origin branch with following commandgit push origin --delete issueNo Notice: Please note that in order to show your id in the contributor list, don't forget the configurations below: git config --global user.name \"username\"git config --global user.email \"username@mail.com\" Thanks This contribution guide refers to the contribution guide of Apache ShardingSphere, thanks to them for their excellent contributions. Previous Code Conduct Next Document Contributor Submit an Issue Developer Flow","s":"Contributor","u":"/datavines-website/en/community/contribution_guide/contributor","h":"","p":824},{"i":828,"t":"Contribution Guide Pull Request On this page Pull Request Notice Preface​ Pull Request is a way of software cooperation, which is a process of bringing code involving different functions into the trunk. During this process, the code can be discussed, reviewed, and modified. In Pull Request, we try not to discuss the implementation of the code. The general implementation of the code and its logic should be determined in Issue. In the Pull Request, we only focus on the code format and code specification, so as to avoid wasting time caused by different opinions on implementation. Specification​ Pull Request Title​ Title Format: [Pull Request Type-Issue No][`Module Name`] Pull Request Description The corresponding relationship between Pull Request Type and Issue Type is as follows: Issue Type Pull Request Type Example(Suppose Issue No is 1111) Feature Feature [Feature-1111][server] Implement xxx Bug Fix [Fix-1111][server] Fix xxx Improvement Improvement [Improvement-1111][alert] Improve the performance of xxx Test Test [Test-1111][api] Add the e2e test of xxx Sub-Task (Parent type corresponding to Sub-Task) [Feature-1111][server] Implement xxx Issue No refers to the Issue number corresponding to the current Pull Request to be resolved, Module Name is the same as the Module Name of Issue. Pull Request Branch​ Branch name format: Pull Request type-Issue number. e.g. Feature-3333 Previous Document Contributor Preface Specification Pull Request Title Pull Request Branch","s":"Pull Request Notice","u":"/datavines-website/en/community/contribution_guide/pull-request","h":"","p":827},{"i":830,"t":"Contribution Guide Document Contributor Document Contributor Previous Contributor Next Pull Request","s":"Document Contributor","u":"/datavines-website/en/community/contribution_guide/document-contributor","h":"","p":829},{"i":833,"t":"Please make sure you have installed the software as follow Git: JDK Maven","s":"Software Requests","u":"/datavines-website/en/docs/development/environment-preparation","h":"#software-requests","p":831},{"i":835,"t":"mkdir datavinescd datavinesgit clone https://github.com/datavane/datavines.git","s":"Clone Git Repository","u":"/datavines-website/en/docs/development/environment-preparation","h":"#clone-git-repository","p":831},{"i":837,"t":"If you use MySQL as your metadata database, you need to modify datavines/pom.xml and change the scope of the mysql-connector-java dependency to compile. This step is not necessary to use PostgreSQL Run mvn clean install -Prelease -Dmaven.test.skip=true","s":"Compile source code","u":"/datavines-website/en/docs/development/environment-preparation","h":"#compile-source-code","p":831},{"i":839,"t":"The Datavines's metadata is stored in relational database. Currently supported MySQL and Postgresql. We use MySQL as an example. Start the database and create a new database named datavines as Datavines metabase After creating the new database, run the sql file underscript/sql/datavines-mysql.sql to complete the database initialization","s":"Database","u":"/datavines-website/en/docs/development/environment-preparation","h":"#database","p":831},{"i":841,"t":"Following steps will guide how to start the DatavinesServer","s":"Start DatavinesServer","u":"/datavines-website/en/docs/development/environment-preparation","h":"#start-datavinesserver","p":831},{"i":843,"t":"Open project: Use IDE open the project, here we use Intellij IDEA as an example Configure database related information If you use MySQL as your metadata database, you need to modify datavines/pom.xml and change the scope of the mysql-connector-java dependency to compile. This step is not necessary to use PostgreSQL change database config in datavines-server/src/main/resources/application.yaml For example spring: datasource: driver-class-name: com.mysql.cj.jdbc.Driver url: jdbc:mysql://127.0.0.1:3306/datavines?useUnicode=true&characterEncoding=UTF-8 username: root password: 123456","s":"Prepare","u":"/datavines-website/en/docs/development/environment-preparation","h":"#prepare","p":831},{"i":845,"t":"Start DatavinesServer add options in VM Options -Dspring.profiles.active=mysql -Dlogging.config=classpath:server-logback.xml When you see the log that [INFO] 2022-04-10 12:29:05.447 io.datavines.server.DatavinesServer:[61] - Started DatavinesServer in 3.97 seconds (JVM running for 4.69) in console，DatavinesServer started successfully.","s":"Start Service","u":"/datavines-website/en/docs/development/environment-preparation","h":"#start-service","p":831},{"i":850,"t":"Metric 是 Datavines 中一个核心概念，一个 Metric 表示一个数据质量检查规则，比如空值检查和表行数检查都是一个规则。Metric 采用插件化设计，用户可以根据自己的需求来实现一个 Metric。下面我们来详细讲解一下如何自定义Metric。","s":"Metric Develop Guide","u":"/datavines-website/en/docs/development/plugin/metric-develop","h":"","p":849},{"i":852,"t":"我们先了解下几个接口和抽象类，它们是实现自定义 Metric 的关键。 SqlMetric 接口​ SqlMetric接口中定义了规则的各种属性和操作的接口。 @SPIpublic interface SqlMetric { // 中文名 String getName(); // 英文名 String getZhName(); // 根据系统的语言进行名字返回 default String getNameByLanguage(boolean isEn) { return isEn ? getName() : getZhName(); } // 规则属于哪个维度，比如准确性、唯一性等等 MetricDimension getDimension(); // 规则的类型，包括单表检查、单表自定义检查 MetricType getType(); // 规则的级别，比如表级别、列级别 default MetricLevel getLevel() { return MetricLevel.NONE; } // 是否支持错误数据输出 boolean isInvalidateItemsCanOutput(); /** * 获取不符合规则的数据的SQL语句 * @return ExecuteSql */ ExecuteSql getInvalidateItems(String uniqueKey); /** * 计算实际值的SQL语句 * @return ExecuteSql */ ExecuteSql getActualValue(String uniqueKey); /** * 实际值的字段名 */ default String getActualName() { return \"actual_value\"; } // 实际值的类型，比如数字，百分比或者列表 default String getActualValueType() { return MetricActualValueType.COUNT.getDescription(); } // 对参数进行检查并输出检查结果 CheckResult validateConfig(Map config); //规则所需要的参数 Map getConfigMap(); //构造规则前需要做的检查 void prepare(Map config); default String getIssue() { return \"\"; } // 适合哪些字段类型 List suitableType(); // 是否支持多选，比如表行数检查支持多张表 default boolean supportMultiple() { return false; } // 对规则参数的重新构造，配合表行数多张表检查 default List> getMetricParameter(Map metricParameter) { return Collections.singletonList(metricParameter); }} BaseSingleTable 抽象类​ BaseSingleTable是实现了 SqlMetric 接口的抽象类，实现了表级别检查规则中所需要参数的添加、错误数据SQL语句构造和实际值计算SQL语句构造和对过滤条件的处理等。 这里定义了获取不符合规则的数据的基础SQL语句，判断类型的规则比如正则表达式检查和枚举值检查，只需要在基础SQL语句后面添加过滤条件即可。 protected StringBuilder invalidateItemsSql = new StringBuilder(\"select * from ${table}\"); 实际值计算SQL语句默认是计算不符合规则数据的行数 String actualValueSql = \"select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}\"; 计算平均值、汇总值等统计类型的规则需要重新实现getActualValue()中的ExecuteSql。 public abstract class BaseSingleTable implements SqlMetric { // 这里定义了获取不符合规则的数据的基础 SQL 语句，判断类的规则比如正则表达式和枚举值检查，只需要在基础SQL后面添加过滤条件即可。 protected StringBuilder invalidateItemsSql = new StringBuilder(\"select * from ${table}\"); protected List filters = new ArrayList<>(); protected HashMap configMap = new HashMap<>(); protected Set requiredOptions = new HashSet<>(); public BaseSingleTable() { configMap.put(\"table\",new ConfigItem(\"table\", \"表名\", \"table\")); configMap.put(\"filter\",new ConfigItem(\"filter\", \"过滤条件\", \"filter\")); requiredOptions.add(\"table\"); } @Override public ExecuteSql getInvalidateItems(String uniqueKey) { ExecuteSql executeSql = new ExecuteSql(); executeSql.setResultTable(\"invalidate_items_\" + uniqueKey); executeSql.setSql(invalidateItemsSql.toString()); executeSql.setErrorOutput(isInvalidateItemsCanOutput()); return executeSql; } @Override public ExecuteSql getActualValue(String uniqueKey) { ExecuteSql executeSql = new ExecuteSql(); executeSql.setResultTable(\"invalidate_count_\" + uniqueKey); String actualValueSql = \"select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}\"; executeSql.setSql(actualValueSql); executeSql.setErrorOutput(false); return executeSql; } @Override public CheckResult validateConfig(Map config) { return ConfigChecker.checkConfig(config, requiredOptions); } @Override public void prepare(Map config) { if (config.containsKey(\"filter\")) { filters.add(config.get(\"filter\")); } addFiltersIntoInvalidateItemsSql(); } private void addFiltersIntoInvalidateItemsSql() { if (filters.size() > 0) { invalidateItemsSql.append(\" where \").append(String.join(\" and \", filters)); } } @Override public MetricLevel getLevel() { return MetricLevel.TABLE; }} BaseSingleTableColumn 抽象类​ BaseSingleTableColumn是列级别的抽象实现类，主要是添加列级别规则的通用参数。 public abstract class BaseSingleTableColumn extends BaseSingleTable { public BaseSingleTableColumn() { super(); configMap.put(\"column\",new ConfigItem(\"column\", \"列名\", \"column\")); requiredOptions.add(\"column\"); } @Override public Map getConfigMap() { return configMap; } @Override public MetricLevel getLevel() { return MetricLevel.COLUMN; } @Override public boolean isInvalidateItemsCanOutput() { return false; }}","s":"第一步","u":"/datavines-website/en/docs/development/plugin/metric-develop","h":"#第一步","p":849},{"i":854,"t":"了解完上面的三个基础类以后，自定义一个Metric就变得格外简单了。 基础工作​ 在 datavines-metric-plugins 下创建一个新规则的 module 在 pom.xml 中添加 io.datavines datavines-metric-base ${project.version} 以 枚举值检查 规则为例来讲解​ 判断要实现的规则的级别，因为枚举值检查是列级别，所以继承 BaseSingleTableColumn 即可。 在构造函数中的configMap添加enum_list参数用于返回给前端进行展示，在requiredOptions添加enum_list用于参数的检查。 实现英文名、中文名、规则维度、规则类型这些基础的属性。 因为枚举值检查规则是为了找出在枚举值列表中的数据，所以只需要在fileters这个数组里面加入(${column} in ( ${enum_list} ))，prepare()方法会自动进行不符合规则的SQL语句构造。 实现suitableType()方法添加规则适用的字段类型。 public class ColumnInEnums extends BaseSingleTableColumn { public ColumnInEnums(){ super(); configMap.put(\"enum_list\",new ConfigItem(\"enum_list\", \"枚举值列表\", \"enum_list\")); requiredOptions.add(\"enum_list\"); } @Override public String getName() { return \"column_in_enums\"; } @Override public String getZhName() { return \"枚举值检查\"; } @Override public MetricDimension getDimension() { return MetricDimension.EFFECTIVENESS; } @Override public MetricType getType() { return MetricType.SINGLE_TABLE; } @Override public boolean isInvalidateItemsCanOutput() { return true; } @Override public void prepare(Map config) { if (config.containsKey(\"enum_list\") && config.containsKey(\"column\")) { filters.add(\" (${column} in ( ${enum_list} )) \"); } super.prepare(config); } @Override public List suitableType() { return Arrays.asList(DataVinesDataType.NUMERIC_TYPE, DataVinesDataType.STRING_TYPE, DataVinesDataType.DATE_TIME_TYPE); }}","s":"第二步","u":"/datavines-website/en/docs/development/plugin/metric-develop","h":"#第二步","p":849},{"i":856,"t":"非常重要的一步 在 resources 目录下创建META-INF/plugins目录。 在 plugins 目录下创建文件并且命名为io.datavines.metric.api.SqlMetric。 在文件中添加column_in_enums=io.datavines.metric.plugin.ColumnInEnums。","s":"第三步","u":"/datavines-website/en/docs/development/plugin/metric-develop","h":"#第三步","p":849},{"i":858,"t":"打包成jar放到 datavines 目录下的libs目录下，重启服务即可。","s":"第四步","u":"/datavines-website/en/docs/development/plugin/metric-develop","h":"#第四步","p":849},{"i":860,"t":"This article gives a detailed introduction to each core module in Datavines","s":"Datavines Code Architecture Analysis","u":"/datavines-website/en/docs/development/source-module-explanation","h":"","p":859},{"i":862,"t":"datavines-server is the core module of datavines. Its core functions include providing API interface externally, data management of the whole system, metadata acquisition and management of data sources, management, scheduling and execution of data quality rules. It is a decentralized design, supports horizontal expansion to improve performance, and supports fault-tolerant processing of various jobs.","s":"datavines-server","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-server","p":859},{"i":864,"t":"datavines-spi is the SPI module in datavines. Its main function is to design the SPI interface and PluginLoader. PluginLoader is responsible for scanning the plugins with @SPI and loading the specific plugins into the system.","s":"datavines-spi","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-spi","p":859},{"i":866,"t":"datavines-connector is the data source module in datavines. datavines-connector-api defines various interfaces of data sources. For example, Connector interface defines various metadata-related interfaces of data sources, and Executor interface defines data sources. The interface related to data query, the interface related to the dialect of the data source is defined in the Dialect interface. datavines-connector-plugins is the specific implementation of various data sources, and the built-in data sources include MySQL, Doris, Starrocks, Trino, Presto and other 10 data sources.","s":"datavines-connector","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-connector","p":859},{"i":868,"t":"datavines-metric is a rule module in datavines, and datavines-metric-api defines the interfaces of rules, expected values, and result calculation formulas. The SqlMetric interface mainly defines the type of rules, actual value calculation logic and other related methods. The ExpectedValue interface mainly defines the calculation logic related methods of the expected value. The ResultFormula interface mainly defines methods related to the calculation logic of the check formula.","s":"datavines-metric","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-metric","p":859},{"i":870,"t":"datavines-engine is the job execution engine module in datavines. datavines-engine-api defines the engine-related interfaces, and the Execution interface mainly defines the related methods of job execution. The execute method mainly executes the reading of the source, transforms the data, and finally sinks the data to the destination. The datavines-engine-config module mainly defines the construction logic and related methods of job configuration files. The datavines-engine-executor module defines the logic related to the job executor","s":"datavines-engine","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-engine","p":859},{"i":872,"t":"datavines-registry is the registry in datavines. datavines-registry-api defines interfaces such as acquiring locks, releasing locks, acquiring current active nodes, and notifying nodes when they go offline. There are three types of registries built into datavines-registry-plugins, including MySQL, PostgreSQL, and ZooKeeper.","s":"datavines-registry","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-registry","p":859},{"i":874,"t":"datavines-notification is the notification channel management in datavines. datavines-notification-api defines the notification channel's message notification, configuration parameters and other interfaces. The email type notification channel implementation is built into datavines-notification-plugins.","s":"datavines-notification","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-notification","p":859},{"i":876,"t":"datavines-runner is a module in datavines responsible for running data quality checks in script mode. Its main functions are to read configuration files, parse configuration files, perform data quality checks, judge check results, and run alarm processing.","s":"datavines-runner","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-runner","p":859},{"i":880,"t":"ClickHouse 连接器用于与 ClickHouse 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-clickhouse","h":"#介绍","p":878},{"i":883,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-clickhouse","h":"#options","p":878},{"i":885,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-clickhouse","h":"#属性","p":878},{"i":888,"t":"Databend 连接器用于与 Databend 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-databend","h":"#介绍","p":886},{"i":891,"t":"name type required default value host string yes - port int yes - database string no - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-databend","h":"#options","p":886},{"i":893,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-databend","h":"#属性","p":886},{"i":897,"t":"Doris 连接器用于与 Doris 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-doris","h":"#介绍","p":895},{"i":900,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-doris","h":"#options","p":895},{"i":902,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-doris","h":"#属性","p":895},{"i":905,"t":"Hive 连接器用于与 Hive 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-hive","h":"#介绍","p":903},{"i":908,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP, 支持ZooKeeper的高可用连接，ZooKeeper地址通过 , 隔开 port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： hive.resultset.use.unique.column.names=false 若采用ZooKeeper高可用方式，需要输入以下配置： serviceDiscoveryMode=zooKeeper;zooKeeperNamespace={your_namespace}","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-hive","h":"#options","p":903},{"i":910,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 false invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-hive","h":"#属性","p":903},{"i":913,"t":"MySQL 连接器用于与 MySQL 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-postgresql","h":"#介绍","p":911},{"i":916,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-postgresql","h":"#options","p":911},{"i":918,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-postgresql","h":"#属性","p":911},{"i":921,"t":"Impala 连接器用于与 Impala 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-impala","h":"#介绍","p":919},{"i":924,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-impala","h":"#options","p":919},{"i":926,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-impala","h":"#属性","p":919},{"i":929,"t":"StarRocks 连接器用于与 StarRocks 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-starrocks","h":"#介绍","p":927},{"i":932,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-starrocks","h":"#options","p":927},{"i":934,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-starrocks","h":"#属性","p":927},{"i":938,"t":"MySQL 连接器用于与 MySQL 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-mysql","h":"#介绍","p":936},{"i":941,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-mysql","h":"#options","p":936},{"i":943,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 true","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-mysql","h":"#属性","p":936},{"i":946,"t":"Presto 连接器用于与 Presto 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-presto","h":"#介绍","p":944},{"i":949,"t":"name type required default value host string yes - port int yes - catalog string yes - database string no - user string yes - password string no - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 catalog [string]​ catalog database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-presto","h":"#options","p":944},{"i":951,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-presto","h":"#属性","p":944},{"i":954,"t":"Trino 连接器用于与 Trino 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-trino","h":"#介绍","p":952},{"i":957,"t":"name type required default value host string yes - port int yes - catalog string yes - database string no - user string yes - password string no - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 catalog [string]​ catalog database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，用&隔开","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-trino","h":"#options","p":952},{"i":959,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-trino","h":"#属性","p":952},{"i":978,"t":"点击创建规则作业，选择数据比对作业 进入作业页面选择 两表值比对 规则 进行源表和目标表的配置信息，编写各自的检查SQL语句","s":"使用方法","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#使用方法","p":976},{"i":981,"t":"name type required default value database string yes - table string yes - actual_execute_sql string yes - database2 string yes - table2 string yes - expected_execute_sql string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 actual_execute_sql [string]​ 实际值执行SQL，注意 as 后面的别名一定要是 actual_value，否则统计会出错。 database2 [string]​ 目标表数据库名 table2 [string]​ 目标表数据库中的表名 expected_execute_sql [string]​ 期望值执行SQL，注意 as 后面的别名一定要是 expected_value，否则统计会出错。","s":"Options","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#options","p":976},{"i":983,"t":"{ \"metricType\": \"multi_table_value_comparison\", \"metricParameter\": { \"database\": \"cbs\", \"table\": \"cbs_ratio\", \"actual_execute_sql\": \"select count(1) as actual_value from ${table}\", \"database2\": \"cbs\", \"table2\": \"cbs_ratio\", \"expected_execute_sql\": \"select count(1) as expected_value from ${table2}\" }}","s":"配置文件例子","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#配置文件例子","p":976},{"i":986,"t":"比较某张表(有时间分区列)今天的数据量和昨天的数据量，如果今天的数据量小于昨天的数据量就需要告警。","s":"场景","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#场景","p":976},{"i":988,"t":"使用两表值比对规则配合内置时间参数。 使用两表值比对的规则，通过编写 SQL 语句来统计今天和昨天的数据量，然后比较两个值，如果今天的值大于昨天的值则为真，否则为假。","s":"思路","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#思路","p":976},{"i":990,"t":"选择数据比对作业中的两表值比对规则 选择对应的数据库和表，并编写计算实际值 SQL 语句。 语句里面用到了$[today]时间变量，系统会自动替换成今天的日期，格式为yyyy-MM-dd，也可以自己配置格式 $[today(yyyyMMdd)]。 注意 as 后面的别名一定要是 actual_value，否则统计会出错。 表名可以用${table}，系统会自动替换，也可以直接写正确的表名。 select count(1) as actual_value from ${table} where data_date='$[today]' 选择对应的数据库和表，并编写计算期望值 SQL 语句 语句里面用到了$[yesterday]时间变量，系统会自动替换成昨天的日期，格式为yyyy-MM-dd，也可以自己配置格式 $[yesterday(yyyyMMdd)]。 注意 as 后面的别名一定要是 expected_value，否则统计会出错。 表名可以用${table2}，系统会自动替换，也可以直接写正确的表名。 select count(1) as expected_value from ${table2} where data_date='$[yesterday]' 配置结果判断公式 结果公式选择：实际值-期望值 比较符选择：>= 阈值：0 如果公式实际值-期望值 >= 0的结果为真, 那么就证明今天的表行数大于昨天的表行数，否则就证明今天的表行数小于昨天的表行数，结果是异常的，需要告警。","s":"步骤","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#步骤","p":976},{"i":1028,"t":"Before installing Datavines, please make sure the following software is installed on your server Git to ensure smooth execution of git clone JDK, make sure jdk = 8 Maven, to ensure the smooth packaging of the project (of course, you can also upload it to the server after packaging locally)","s":"Environment preparation","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#environment-preparation","p":1026},{"i":1030,"t":"git clone https://github.com/datavane/datavines.gitcd datavines","s":"Download code","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#download-code","p":1026},{"i":1032,"t":"The metadata of Datavines is stored in a relational database. Currently, MySQL are supported. The following uses MySQL as an example to illustrate the installation steps: Create database datavines Execute the script/sql/datavines-mysql.sql script to initialize the database The following building also uses MySQL as an example","s":"Database preparation","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#database-preparation","p":1026},{"i":1034,"t":"Build and unpack mvn clean package -Preleasecd datavines-dist/targettar -zxvf datavines-1.0.0-SNAPSHOT-bin.tar.gz After the decompression is complete, enter the directory cd datavines-1.0.0-SNAPSHOT-bin Modify configuration information cd confvi application.yaml Mainly modify database information spring: datasource: driver-class-name: com.mysql.cj.jdbc.Driver url: jdbc:mysql://127.0.0.1:3306/datavines?useUnicode=true&characterEncoding=UTF-8 username: root password: 123456 If you use Spark as the execution engine and submit it to Yarn for execution, you need the yarn-related configuration information in common.properties standalone mode yarn.mode=standaloneyarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%s #The first %s needs to be replaced the ip address of yarnyarn.resource.manager.http.address.port=8088 ha mode yarn.mode=hayarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%syarn.resource.manager.http.address.port=8088yarn.resource.manager.ha.ids=192.168.0.1,192.168.0.2","s":"Build Project","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#build-project","p":1026},{"i":1036,"t":"cd binsh datavines-daemon.sh start mysql Check the log, if there is no error message in the log, and you can see [INFO] 2022-04-10 12:29:05.447 io.datavines.server.DatavinesServer:[61] - Started DatavinesServer in 3.97 seconds (JVM running for 4.69 ), it proves that the service has been successfully started","s":"Start service","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#start-service","p":1026},{"i":1038,"t":"Enter localhost:5600 in the browser, and you will see the login interface. Enter the username & password admin / 123456","s":"Start exploring","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#start-exploring","p":1026},{"i":1042,"t":"This article will briefly describe how to quickly run Datavines using the 'Docker run' or 'Docker compile' commands.","s":"Description","u":"/datavines-website/en/docs/get-started/docker/deploy-docker-image","h":"#description","p":1040},{"i":1044,"t":"software Version Docker 20.0 and above docker-compose 2.10 and above","s":"Environment","u":"/datavines-website/en/docs/get-started/docker/deploy-docker-image","h":"#environment","p":1040},{"i":1047,"t":"1.Download and modifydocker-compose.yaml Modify the image version to a locally compiled version or a private repository image version image: datavines:dev If there is a conflict between the external network segment and the internal network segment of the container, which means that the container can be accessed within the same network segment, but cannot be accessed across different network segments,Can modify the network_mode in the docker-compose.yaml file.。 networks: datavines: driver: bridge ipam: config: - subnet: 172.18.0.0/24(Custom network segment) Download environment configuration file.env Modify the configuration items in the . env file as follows: # Database ConnectionSPRING_DATASOURCE_URL=jdbc:mysql://127.0.0.1:3306/datavines?useSSL=false&useUnicode=true&characterEncoding=UTF-8&allowPublicKeyRetrieval=false&useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=GMT%2B8# User nameSPRING_DATASOURCE_USERNAME=root","s":"Preparation in advance","u":"/datavines-website/en/docs/get-started/docker/deploy-docker-image","h":"#preparation-in-advance","p":1040},{"i":1049,"t":"docker compose up -d","s":"Quick Start","u":"/datavines-website/en/docs/get-started/docker/deploy-docker-image","h":"#quick-start","p":1040},{"i":1051,"t":"The environmental parameters need to be passed in from the startup command, as shown below. docker run -it -d --name datavines -p 5600:5600 --privileged=true --restart always -e SPRING_PROFILES_ACTIVE=mysql -e SPRING_DATASOURCE_URL=jdbc:mysql://127.0.0.1:3306/datavines?useSSL=false&useUnicode=true&characterEncoding=UTF-8&allowPublicKeyRetrieval=false&useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=GMT%2B8 -e SPRING_DATASOURCE_USERNAME=root -e SPRING_DATASOURCE_PASSWORD=123456 -e SPRING_DATASOURCE_DRIVER-CLASS_NAME=com.mysql.cj.jdbc.Driver datavines:dev","s":"Method 2 ：docker run","u":"/datavines-website/en/docs/get-started/docker/deploy-docker-image","h":"#method-2-docker-run","p":1040},{"i":1054,"t":"Installation k8s Build datavines image Create or download the yml file for datavines on k8s https://raw.githubusercontent.com/datavane/datavines/dev/deploy/k8s/datavines.yaml","s":"Environmental Preparation","u":"/datavines-website/en/docs/get-started/kubernetes-deploy","h":"#environmental-preparation","p":1052},{"i":1056,"t":"Execute the following command by specifying the namespace. kubectl apply -f datavines.yaml -n ","s":"Starting a cluster","u":"/datavines-website/en/docs/get-started/kubernetes-deploy","h":"#starting-a-cluster","p":1052},{"i":1058,"t":"Get the pod under the namespace and check if the datavines are running properly. kubectl get pod -n ","s":"Test and verify","u":"/datavines-website/en/docs/get-started/kubernetes-deploy","h":"#test-and-verify","p":1052},{"i":1060,"t":"Know your data easier! Datavines is an easy-to-use data observability service platform that supports multiple metric.Data quality is used to ensure the accuracy of data in the process of integration and processing. It is also the core component of DataOps.","s":"Introduction","u":"/datavines-website/en/docs/intro","h":"","p":1059},{"i":1062,"t":"Support lineage management Data comparison supporting Checksum algorithm (under development) Support Flink engine Support Spark on K8s Adding DatavinesTask in DolphinScheduler (under development) Support Livy engine","s":"Roadmap","u":"/datavines-website/en/docs/roadmap","h":"","p":1061},{"i":1065,"t":"This document mainly introduces how to use Dockerfile to create a running image of DataVines, so that a DataVines Image can be quickly pulled to complete cluster creation in containerized orchestration tools or rapid testing processes.","s":"Build Docker Image","u":"/datavines-website/en/docs/get-started/docker/build-docker-image","h":"#build-docker-image","p":1064},{"i":1067,"t":"The following points should be noted when writing Dockerfile: The basic parent image uses the official OpenJDK image certified by Docker Hub, with JDK version 1.8 used. The binary packages required for the application need to be packaged by oneself. Please refer to the Deploy with Source Code When starting an application within Docker, use start_container parameter The construction platform should be consistent with the operation platform.","s":"Prepare Dockerfile","u":"/datavines-website/en/docs/get-started/docker/build-docker-image","h":"#prepare-dockerfile","p":1064},{"i":1069,"t":"Write a Dockerfile file, which can be referenced as follows Dockerfile,Can be downloaded through wget。 Copy the packaged binary package to the directory where Dockerfile is located。 Verify the consistency of binary package names in Dockerfile files。 Pack and pack using the following command docker build -t datavines:dev .","s":"Build Steps","u":"/datavines-website/en/docs/get-started/docker/build-docker-image","h":"#build-steps","p":1064},{"i":1071,"t":"Login to DockerHub account docker login -u Successful login will prompt a Success related prompt. change the image tag docker tag datavines:dev //datavines:dev push image to DockerHub docker push //datavines:dev","s":"Push images to DockerHub or private warehouse","u":"/datavines-website/en/docs/get-started/docker/build-docker-image","h":"#push-images-to-dockerhub-or-private-warehouse","p":1064},{"i":1074,"t":"Click the Create DataSource button, enter the name of the datasource, and select the datasource type. Take mysql as an example, enter the connection information of mysql, and click to test connection button . If successful, click Save","s":"Connect DataSource","u":"/datavines-website/en/docs/user-guide/quick-start","h":"#connect-datasource","p":1072},{"i":1076,"t":"Click the datasource that created , enter the datasource profile page","s":"View Datasource","u":"/datavines-website/en/docs/user-guide/quick-start","h":"#view-datasource","p":1072},{"i":1078,"t":"For example, click the column properties of table dv_catalog_entity_instance, enter the column detail page","s":"Select Column","u":"/datavines-website/en/docs/user-guide/quick-start","h":"#select-column","p":1072},{"i":1080,"t":"Click the Add Metric button, select metric column_null, select expected_value_type fixed_value、input the expected_value and threshold 10, choose Actual formula and > comparator and input threshold 10, the check result will be true if actual value > 10, false otherwise.You can click Save button to save metric job or Save and Run to run the metric job. Retrun the column detail page, you can see metric list. You also can view the execute result dashboard in each metric item.","s":"Add Metric","u":"/datavines-website/en/docs/user-guide/quick-start","h":"#add-metric","p":1072},{"i":1082,"t":"Click the Metric Name will enter into metric detail info CLick the Runs tab, you can view execution list. CLick the Log button, you can view log info. CLick the ExecuteResult button, you can view execution result. CLick the Error button, you can view error data .","s":"View Metric Info","u":"/datavines-website/en/docs/user-guide/quick-start","h":"#view-metric-info","p":1072},{"i":1085,"t":"cd datavines-1.0.0-SNAPSHOT-bin/binvi metric_job_config.json","s":"Create config file","u":"/datavines-website/en/docs/user-guide/local-mode","h":"#create-config-file","p":1083},{"i":1087,"t":"{ \"languageEn\":true, \"name\":\"test\", \"executePlatformType\":\"client\", \"parameter\":{ \"connectorParameter\":{ \"type\":\"mysql\", \"parameters\":{ \"database\":\"test\", \"password\":\"123456\", \"port\":\"3306\", \"host\":\"localhost\", \"user\":\"root\", \"properties\":\"useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai\" } }, \"metricParameterList\":[ { \"metricType\":\"column_duplicate\", \"metricParameter\":{ \"table\":\"test_table\", \"column\":\"test_column\", \"metric_database\":\"test\" }, \"expectedType\":\"fix_value\", \"expectedParameter\":{ \"expected_value\":\"10\" }, \"resultFormula\":\"count\", \"operator\":\"lt\", \"threshold\":5.0 } ] }, \"errorDataStorageType\": \"file\", \"errorDataStorageParameter\": { \"data_dir\":\"/tmp/datavines/error-data\", \"column_separator\":\",\" }, \"validateResultDataStorageType\": \"file\", \"validateResultDataStorageParameter\":{ \"data_dir\":\"/tmp/datavines/validate-result-data\", \"column_separator\":\",\" }, \"notificationParameters\": [ { \"type\":\"email\", \"config\": { \"serverHost\":\"smtp.qq.com\", \"serverPort\":\"25\", \"sender\":\"1234567@qq.com\", // input your mail \"enableSmtpAuth\":\"true\", \"user\":\"123456@qq.com\", // input your username \"passwd\":\"123456\", // input your passwd \"starttlsEnable\":\"false\", \"sslEnable\":\"false\", \"smtpSslTrust\":\"true\" }, \"receiver\": { \"to\":\"12345566@qq.com\" // input receiver email } } ]}","s":"Add metric config","u":"/datavines-website/en/docs/user-guide/local-mode","h":"#add-metric-config","p":1083},{"i":1089,"t":"./datavines-submit.sh metric_job_config.json","s":"Run metric 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Guide Code Conduct Code Conduct Next Contributor","s":"Code Conduct","u":"/datavines-website/en/community/contribution_guide/code-conduct","h":"","p":831},{"i":834,"t":"Contribution Guide Document Contributor Document Contributor Previous Contributor Next Pull Request","s":"Document Contributor","u":"/datavines-website/en/community/contribution_guide/document-contributor","h":"","p":833},{"i":836,"t":"Contribution Guide Pull Request On this page Pull Request Notice Preface​ Pull Request is a way of software cooperation, which is a process of bringing code involving different functions into the trunk. During this process, the code can be discussed, reviewed, and modified. In Pull Request, we try not to discuss the implementation of the code. The general implementation of the code and its logic should be determined in Issue. In the Pull Request, we only focus on the code format and code specification, so as to avoid wasting time caused by different opinions on implementation. Specification​ Pull Request Title​ Title Format: [Pull Request Type-Issue No][`Module Name`] Pull Request Description The corresponding relationship between Pull Request Type and Issue Type is as follows: Issue Type Pull Request Type Example(Suppose Issue No is 1111) Feature Feature [Feature-1111][server] Implement xxx Bug Fix [Fix-1111][server] Fix xxx Improvement Improvement [Improvement-1111][alert] Improve the performance of xxx Test Test [Test-1111][api] Add the e2e test of xxx Sub-Task (Parent type corresponding to Sub-Task) [Feature-1111][server] Implement xxx Issue No refers to the Issue number corresponding to the current Pull Request to be resolved, Module Name is the same as the Module Name of Issue. Pull Request Branch​ Branch name format: Pull Request type-Issue number. e.g. Feature-3333 Previous Document Contributor Preface Specification Pull Request Title Pull Request Branch","s":"Pull Request Notice","u":"/datavines-website/en/community/contribution_guide/pull-request","h":"","p":835},{"i":838,"t":"Contribution Guide Contributor On this page Contributor You can report a bug, submit a new function enhancement suggestion, or submit a pull request directly. Submit an Issue​ Before submitting an issue, please go through a comprehensive search to make sure the problem cannot be solved just by searching. Check the Issue List to make sure the problem is not repeated. Create a new issue and choose the type of issue. Define the issue with a clear and descriptive title. Fill in necessary information according to the template. Please pay attention for your issue, you may need provide more information during discussion. Developer Flow​ 1. Prepare repository Go to Datavines GitHub Repo and fork repository to your account. Clone repository to local machine. git clone https://github.com/(your_github_name)/datavines.git Add Datavines remote repository. git remote add upstrem https://github.com/datavane/datavines.gitgit remote -v 2. Choose Issue Please choose the issue to be edited. If it is a new issue discovered or a new function enhancement to offer, please create an issue and set the right label for it. After choosing the relevant issue, please reply with a deadline to indicate that you are working on it. 3. Create Branch Switch to forked master branch, update local branch, then create a new branch. git checkout devgit fetch upstreamgit rebase upstream/devgit push origin dev # optionalgit checkout -b issueNo 4. Coding Please obey the Code of Conduct during the process of development and finish the check before submitting the pull request. push code to your fork repo. git add .git commit -m 'commit log'git push origin issueNo 5. Submit Pull Request Send a pull request to the master branch. The other developer will do code review before discussing some details (including the design, the implementation and the performance) with you. The request will be merged into the branch of current development version after the edit is well enough. At last, congratulations on being an contributor of Datavines 6. Delete Branch You can delete the remote branch (origin/issueNo) and the local branch (issueNo) associated with the remote branch (origin/issueNo) after the mentor merged the pull request into the master branch of Datavines. git checkout devgit branch -d issueNogit remote prune origin # If you delete branch on GitHub PR page, else you could delete origin branch with following commandgit push origin --delete issueNo Notice: Please note that in order to show your id in the contributor list, don't forget the configurations below: git config --global user.name \"username\"git config --global user.email \"username@mail.com\" Thanks This contribution guide refers to the contribution guide of Apache ShardingSphere, thanks to them for their excellent contributions. Previous Code Conduct Next Document Contributor Submit an Issue Developer Flow","s":"Contributor","u":"/datavines-website/en/community/contribution_guide/contributor","h":"","p":837},{"i":843,"t":"Please make sure you have installed the software as follow Git: JDK Maven","s":"Software Requests","u":"/datavines-website/en/docs/development/environment-preparation","h":"#software-requests","p":841},{"i":845,"t":"mkdir datavinescd datavinesgit clone https://github.com/datavane/datavines.git","s":"Clone Git Repository","u":"/datavines-website/en/docs/development/environment-preparation","h":"#clone-git-repository","p":841},{"i":847,"t":"If you use MySQL as your metadata database, you need to modify datavines/pom.xml and change the scope of the mysql-connector-java dependency to compile. This step is not necessary to use PostgreSQL Run mvn clean install -Prelease -Dmaven.test.skip=true","s":"Compile source code","u":"/datavines-website/en/docs/development/environment-preparation","h":"#compile-source-code","p":841},{"i":849,"t":"The Datavines's metadata is stored in relational database. Currently supported MySQL and Postgresql. We use MySQL as an example. Start the database and create a new database named datavines as Datavines metabase After creating the new database, run the sql file underscript/sql/datavines-mysql.sql to complete the database initialization","s":"Database","u":"/datavines-website/en/docs/development/environment-preparation","h":"#database","p":841},{"i":851,"t":"Following steps will guide how to start the DatavinesServer","s":"Start DatavinesServer","u":"/datavines-website/en/docs/development/environment-preparation","h":"#start-datavinesserver","p":841},{"i":853,"t":"Open project: Use IDE open the project, here we use Intellij IDEA as an example Configure database related information If you use MySQL as your metadata database, you need to modify datavines/pom.xml and change the scope of the mysql-connector-java dependency to compile. This step is not necessary to use PostgreSQL change database config in datavines-server/src/main/resources/application.yaml For example spring: datasource: driver-class-name: com.mysql.cj.jdbc.Driver url: jdbc:mysql://127.0.0.1:3306/datavines?useUnicode=true&characterEncoding=UTF-8 username: root password: 123456","s":"Prepare","u":"/datavines-website/en/docs/development/environment-preparation","h":"#prepare","p":841},{"i":855,"t":"Start DatavinesServer add options in VM Options -Dspring.profiles.active=mysql -Dlogging.config=classpath:server-logback.xml When you see the log that [INFO] 2022-04-10 12:29:05.447 io.datavines.server.DatavinesServer:[61] - Started DatavinesServer in 3.97 seconds (JVM running for 4.69) in console，DatavinesServer started successfully.","s":"Start Service","u":"/datavines-website/en/docs/development/environment-preparation","h":"#start-service","p":841},{"i":860,"t":"Metric 是 Datavines 中一个核心概念，一个 Metric 表示一个数据质量检查规则，比如空值检查和表行数检查都是一个规则。Metric 采用插件化设计，用户可以根据自己的需求来实现一个 Metric。下面我们来详细讲解一下如何自定义Metric。","s":"Metric Develop Guide","u":"/datavines-website/en/docs/development/plugin/metric-develop","h":"","p":859},{"i":862,"t":"我们先了解下几个接口和抽象类，它们是实现自定义 Metric 的关键。 SqlMetric 接口​ SqlMetric接口中定义了规则的各种属性和操作的接口。 @SPIpublic interface SqlMetric { // 中文名 String getName(); // 英文名 String getZhName(); // 根据系统的语言进行名字返回 default String getNameByLanguage(boolean isEn) { return isEn ? getName() : getZhName(); } // 规则属于哪个维度，比如准确性、唯一性等等 MetricDimension getDimension(); // 规则的类型，包括单表检查、单表自定义检查 MetricType getType(); // 规则的级别，比如表级别、列级别 default MetricLevel getLevel() { return MetricLevel.NONE; } // 是否支持错误数据输出 boolean isInvalidateItemsCanOutput(); /** * 获取不符合规则的数据的SQL语句 * @return ExecuteSql */ ExecuteSql getInvalidateItems(String uniqueKey); /** * 计算实际值的SQL语句 * @return ExecuteSql */ ExecuteSql getActualValue(String uniqueKey); /** * 实际值的字段名 */ default String getActualName() { return \"actual_value\"; } // 实际值的类型，比如数字，百分比或者列表 default String getActualValueType() { return MetricActualValueType.COUNT.getDescription(); } // 对参数进行检查并输出检查结果 CheckResult validateConfig(Map config); //规则所需要的参数 Map getConfigMap(); //构造规则前需要做的检查 void prepare(Map config); default String getIssue() { return \"\"; } // 适合哪些字段类型 List suitableType(); // 是否支持多选，比如表行数检查支持多张表 default boolean supportMultiple() { return false; } // 对规则参数的重新构造，配合表行数多张表检查 default List> getMetricParameter(Map metricParameter) { return Collections.singletonList(metricParameter); }} BaseSingleTable 抽象类​ BaseSingleTable是实现了 SqlMetric 接口的抽象类，实现了表级别检查规则中所需要参数的添加、错误数据SQL语句构造和实际值计算SQL语句构造和对过滤条件的处理等。 这里定义了获取不符合规则的数据的基础SQL语句，判断类型的规则比如正则表达式检查和枚举值检查，只需要在基础SQL语句后面添加过滤条件即可。 protected StringBuilder invalidateItemsSql = new StringBuilder(\"select * from ${table}\"); 实际值计算SQL语句默认是计算不符合规则数据的行数 String actualValueSql = \"select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}\"; 计算平均值、汇总值等统计类型的规则需要重新实现getActualValue()中的ExecuteSql。 public abstract class BaseSingleTable implements SqlMetric { // 这里定义了获取不符合规则的数据的基础 SQL 语句，判断类的规则比如正则表达式和枚举值检查，只需要在基础SQL后面添加过滤条件即可。 protected StringBuilder invalidateItemsSql = new StringBuilder(\"select * from ${table}\"); protected List filters = new ArrayList<>(); protected HashMap configMap = new HashMap<>(); protected Set requiredOptions = new HashSet<>(); public BaseSingleTable() { configMap.put(\"table\",new ConfigItem(\"table\", \"表名\", \"table\")); configMap.put(\"filter\",new ConfigItem(\"filter\", \"过滤条件\", \"filter\")); requiredOptions.add(\"table\"); } @Override public ExecuteSql getInvalidateItems(String uniqueKey) { ExecuteSql executeSql = new ExecuteSql(); executeSql.setResultTable(\"invalidate_items_\" + uniqueKey); executeSql.setSql(invalidateItemsSql.toString()); executeSql.setErrorOutput(isInvalidateItemsCanOutput()); return executeSql; } @Override public ExecuteSql getActualValue(String uniqueKey) { ExecuteSql executeSql = new ExecuteSql(); executeSql.setResultTable(\"invalidate_count_\" + uniqueKey); String actualValueSql = \"select count(1) as actual_value_\"+ uniqueKey +\" from ${invalidate_items_table}\"; executeSql.setSql(actualValueSql); executeSql.setErrorOutput(false); return executeSql; } @Override public CheckResult validateConfig(Map config) { return ConfigChecker.checkConfig(config, requiredOptions); } @Override public void prepare(Map config) { if (config.containsKey(\"filter\")) { filters.add(config.get(\"filter\")); } addFiltersIntoInvalidateItemsSql(); } private void addFiltersIntoInvalidateItemsSql() { if (filters.size() > 0) { invalidateItemsSql.append(\" where \").append(String.join(\" and \", filters)); } } @Override public MetricLevel getLevel() { return MetricLevel.TABLE; }} BaseSingleTableColumn 抽象类​ BaseSingleTableColumn是列级别的抽象实现类，主要是添加列级别规则的通用参数。 public abstract class BaseSingleTableColumn extends BaseSingleTable { public BaseSingleTableColumn() { super(); configMap.put(\"column\",new ConfigItem(\"column\", \"列名\", \"column\")); requiredOptions.add(\"column\"); } @Override public Map getConfigMap() { return configMap; } @Override public MetricLevel getLevel() { return MetricLevel.COLUMN; } @Override public boolean isInvalidateItemsCanOutput() { return false; }}","s":"第一步","u":"/datavines-website/en/docs/development/plugin/metric-develop","h":"#第一步","p":859},{"i":864,"t":"了解完上面的三个基础类以后，自定义一个Metric就变得格外简单了。 基础工作​ 在 datavines-metric-plugins 下创建一个新规则的 module 在 pom.xml 中添加 io.datavines datavines-metric-base ${project.version} 以 枚举值检查 规则为例来讲解​ 判断要实现的规则的级别，因为枚举值检查是列级别，所以继承 BaseSingleTableColumn 即可。 在构造函数中的configMap添加enum_list参数用于返回给前端进行展示，在requiredOptions添加enum_list用于参数的检查。 实现英文名、中文名、规则维度、规则类型这些基础的属性。 因为枚举值检查规则是为了找出在枚举值列表中的数据，所以只需要在fileters这个数组里面加入(${column} in ( ${enum_list} ))，prepare()方法会自动进行不符合规则的SQL语句构造。 实现suitableType()方法添加规则适用的字段类型。 public class ColumnInEnums extends BaseSingleTableColumn { public ColumnInEnums(){ super(); configMap.put(\"enum_list\",new ConfigItem(\"enum_list\", \"枚举值列表\", \"enum_list\")); requiredOptions.add(\"enum_list\"); } @Override public String getName() { return \"column_in_enums\"; } @Override public String getZhName() { return \"枚举值检查\"; } @Override public MetricDimension getDimension() { return MetricDimension.EFFECTIVENESS; } @Override public MetricType getType() { return MetricType.SINGLE_TABLE; } @Override public boolean isInvalidateItemsCanOutput() { return true; } @Override public void prepare(Map config) { if (config.containsKey(\"enum_list\") && config.containsKey(\"column\")) { filters.add(\" (${column} in ( ${enum_list} )) \"); } super.prepare(config); } @Override public List suitableType() { return Arrays.asList(DataVinesDataType.NUMERIC_TYPE, DataVinesDataType.STRING_TYPE, DataVinesDataType.DATE_TIME_TYPE); }}","s":"第二步","u":"/datavines-website/en/docs/development/plugin/metric-develop","h":"#第二步","p":859},{"i":866,"t":"非常重要的一步 在 resources 目录下创建META-INF/plugins目录。 在 plugins 目录下创建文件并且命名为io.datavines.metric.api.SqlMetric。 在文件中添加column_in_enums=io.datavines.metric.plugin.ColumnInEnums。","s":"第三步","u":"/datavines-website/en/docs/development/plugin/metric-develop","h":"#第三步","p":859},{"i":868,"t":"打包成jar放到 datavines 目录下的libs目录下，重启服务即可。","s":"第四步","u":"/datavines-website/en/docs/development/plugin/metric-develop","h":"#第四步","p":859},{"i":872,"t":"ClickHouse 连接器用于与 ClickHouse 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-clickhouse","h":"#介绍","p":870},{"i":875,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-clickhouse","h":"#options","p":870},{"i":877,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-clickhouse","h":"#属性","p":870},{"i":879,"t":"This article gives a detailed introduction to each core module in Datavines","s":"Datavines Code Architecture Analysis","u":"/datavines-website/en/docs/development/source-module-explanation","h":"","p":878},{"i":881,"t":"datavines-server is the core module of datavines. Its core functions include providing API interface externally, data management of the whole system, metadata acquisition and management of data sources, management, scheduling and execution of data quality rules. It is a decentralized design, supports horizontal expansion to improve performance, and supports fault-tolerant processing of various jobs.","s":"datavines-server","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-server","p":878},{"i":883,"t":"datavines-spi is the SPI module in datavines. Its main function is to design the SPI interface and PluginLoader. PluginLoader is responsible for scanning the plugins with @SPI and loading the specific plugins into the system.","s":"datavines-spi","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-spi","p":878},{"i":885,"t":"datavines-connector is the data source module in datavines. datavines-connector-api defines various interfaces of data sources. For example, Connector interface defines various metadata-related interfaces of data sources, and Executor interface defines data sources. The interface related to data query, the interface related to the dialect of the data source is defined in the Dialect interface. datavines-connector-plugins is the specific implementation of various data sources, and the built-in data sources include MySQL, Doris, Starrocks, Trino, Presto and other 10 data sources.","s":"datavines-connector","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-connector","p":878},{"i":887,"t":"datavines-metric is a rule module in datavines, and datavines-metric-api defines the interfaces of rules, expected values, and result calculation formulas. The SqlMetric interface mainly defines the type of rules, actual value calculation logic and other related methods. The ExpectedValue interface mainly defines the calculation logic related methods of the expected value. The ResultFormula interface mainly defines methods related to the calculation logic of the check formula.","s":"datavines-metric","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-metric","p":878},{"i":889,"t":"datavines-engine is the job execution engine module in datavines. datavines-engine-api defines the engine-related interfaces, and the Execution interface mainly defines the related methods of job execution. The execute method mainly executes the reading of the source, transforms the data, and finally sinks the data to the destination. The datavines-engine-config module mainly defines the construction logic and related methods of job configuration files. The datavines-engine-executor module defines the logic related to the job executor","s":"datavines-engine","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-engine","p":878},{"i":891,"t":"datavines-registry is the registry in datavines. datavines-registry-api defines interfaces such as acquiring locks, releasing locks, acquiring current active nodes, and notifying nodes when they go offline. There are three types of registries built into datavines-registry-plugins, including MySQL, PostgreSQL, and ZooKeeper.","s":"datavines-registry","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-registry","p":878},{"i":893,"t":"datavines-notification is the notification channel management in datavines. datavines-notification-api defines the notification channel's message notification, configuration parameters and other interfaces. The email type notification channel implementation is built into datavines-notification-plugins.","s":"datavines-notification","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-notification","p":878},{"i":895,"t":"datavines-runner is a module in datavines responsible for running data quality checks in script mode. Its main functions are to read configuration files, parse configuration files, perform data quality checks, judge check results, and run alarm processing.","s":"datavines-runner","u":"/datavines-website/en/docs/development/source-module-explanation","h":"#datavines-runner","p":878},{"i":898,"t":"Databend 连接器用于与 Databend 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-databend","h":"#介绍","p":896},{"i":901,"t":"name type required default value host string yes - port int yes - database string no - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-databend","h":"#options","p":896},{"i":903,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-databend","h":"#属性","p":896},{"i":906,"t":"Doris 连接器用于与 Doris 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-doris","h":"#介绍","p":904},{"i":909,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-doris","h":"#options","p":904},{"i":911,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-doris","h":"#属性","p":904},{"i":915,"t":"Hive 连接器用于与 Hive 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-hive","h":"#介绍","p":913},{"i":918,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP, 支持ZooKeeper的高可用连接，ZooKeeper地址通过 , 隔开 port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： hive.resultset.use.unique.column.names=false 若采用ZooKeeper高可用方式，需要输入以下配置： serviceDiscoveryMode=zooKeeper;zooKeeperNamespace={your_namespace}","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-hive","h":"#options","p":913},{"i":920,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 false invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-hive","h":"#属性","p":913},{"i":923,"t":"Impala 连接器用于与 Impala 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-impala","h":"#介绍","p":921},{"i":926,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-impala","h":"#options","p":921},{"i":928,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-impala","h":"#属性","p":921},{"i":931,"t":"MySQL 连接器用于与 MySQL 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-mysql","h":"#介绍","p":929},{"i":934,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-mysql","h":"#options","p":929},{"i":936,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 true","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-mysql","h":"#属性","p":929},{"i":939,"t":"MySQL 连接器用于与 MySQL 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-postgresql","h":"#介绍","p":937},{"i":942,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-postgresql","h":"#options","p":937},{"i":944,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-postgresql","h":"#属性","p":937},{"i":947,"t":"Presto 连接器用于与 Presto 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-presto","h":"#介绍","p":945},{"i":950,"t":"name type required default value host string yes - port int yes - catalog string yes - database string no - user string yes - password string no - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 catalog [string]​ catalog database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-presto","h":"#options","p":945},{"i":952,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-presto","h":"#属性","p":945},{"i":956,"t":"Trino 连接器用于与 Trino 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-trino","h":"#介绍","p":954},{"i":959,"t":"name type required default value host string yes - port int yes - catalog string yes - database string no - user string yes - password string no - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 catalog [string]​ catalog database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，用&隔开","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-trino","h":"#options","p":954},{"i":961,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 false supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-trino","h":"#属性","p":954},{"i":966,"t":"StarRocks 连接器用于与 StarRocks 类型的数据源建立连接，获取元数据和执行数据质量检查。","s":"介绍","u":"/datavines-website/en/docs/features/catalog/connector/connector-starrocks","h":"#介绍","p":964},{"i":969,"t":"name type required default value host string yes - port int yes - database string yes - user string yes - password string yes - properties string no - host [string]​ 数据库实例IP port [int]​ 数据库实例端口 database [string]​ 数据库名 user [string]​ 用户名 password [string]​ 密码 properties [string]​ 配置信息，默认为： useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai","s":"Options","u":"/datavines-website/en/docs/features/catalog/connector/connector-starrocks","h":"#options","p":964},{"i":971,"t":"name zh-name supported invalidateItemCanOutput 错误数据是否支持导出 true invalidateItemCanOutputToSelf 错误数据是否支持导出至数据源 true supportToBeErrorDataStorage 是否支持作为错误数据存储引擎 false","s":"属性","u":"/datavines-website/en/docs/features/catalog/connector/connector-starrocks","h":"#属性","p":964},{"i":989,"t":"点击创建规则作业，选择数据比对作业 进入作业页面选择 两表值比对 规则 进行源表和目标表的配置信息，编写各自的检查SQL语句","s":"使用方法","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#使用方法","p":987},{"i":992,"t":"name type required default value database string yes - table string yes - actual_execute_sql string yes - database2 string yes - table2 string yes - expected_execute_sql string yes - database [string]​ 源表数据库名 table [string]​ 源表数据库中的表名 actual_execute_sql [string]​ 实际值执行SQL，注意 as 后面的别名一定要是 actual_value，否则统计会出错。 database2 [string]​ 目标表数据库名 table2 [string]​ 目标表数据库中的表名 expected_execute_sql [string]​ 期望值执行SQL，注意 as 后面的别名一定要是 expected_value，否则统计会出错。","s":"Options","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#options","p":987},{"i":994,"t":"{ \"metricType\": \"multi_table_value_comparison\", \"metricParameter\": { \"database\": \"cbs\", \"table\": \"cbs_ratio\", \"actual_execute_sql\": \"select count(1) as actual_value from ${table}\", \"database2\": \"cbs\", \"table2\": \"cbs_ratio\", \"expected_execute_sql\": \"select count(1) as expected_value from ${table2}\" }}","s":"配置文件例子","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#配置文件例子","p":987},{"i":997,"t":"比较某张表(有时间分区列)今天的数据量和昨天的数据量，如果今天的数据量小于昨天的数据量就需要告警。","s":"场景","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#场景","p":987},{"i":999,"t":"使用两表值比对规则配合内置时间参数。 使用两表值比对的规则，通过编写 SQL 语句来统计今天和昨天的数据量，然后比较两个值，如果今天的值大于昨天的值则为真，否则为假。","s":"思路","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#思路","p":987},{"i":1001,"t":"选择数据比对作业中的两表值比对规则 选择对应的数据库和表，并编写计算实际值 SQL 语句。 语句里面用到了$[today]时间变量，系统会自动替换成今天的日期，格式为yyyy-MM-dd，也可以自己配置格式 $[today(yyyyMMdd)]。 注意 as 后面的别名一定要是 actual_value，否则统计会出错。 表名可以用${table}，系统会自动替换，也可以直接写正确的表名。 select count(1) as actual_value from ${table} where data_date='$[today]' 选择对应的数据库和表，并编写计算期望值 SQL 语句 语句里面用到了$[yesterday]时间变量，系统会自动替换成昨天的日期，格式为yyyy-MM-dd，也可以自己配置格式 $[yesterday(yyyyMMdd)]。 注意 as 后面的别名一定要是 expected_value，否则统计会出错。 表名可以用${table2}，系统会自动替换，也可以直接写正确的表名。 select count(1) as expected_value from ${table2} where data_date='$[yesterday]' 配置结果判断公式 结果公式选择：实际值-期望值 比较符选择：>= 阈值：0 如果公式实际值-期望值 >= 0的结果为真, 那么就证明今天的表行数大于昨天的表行数，否则就证明今天的表行数小于昨天的表行数，结果是异常的，需要告警。","s":"步骤","u":"/datavines-website/en/docs/features/metric/multi-table-metric/multi-table-value-comparison","h":"#步骤","p":987},{"i":1036,"t":"This document mainly introduces how to use Dockerfile to create a running image of DataVines, so that a DataVines Image can be quickly pulled to complete cluster creation in containerized orchestration tools or rapid testing processes.","s":"Build Docker Image","u":"/datavines-website/en/docs/get-started/docker/build-docker-image","h":"#build-docker-image","p":1035},{"i":1038,"t":"The following points should be noted when writing Dockerfile: The basic parent image uses the official OpenJDK image certified by Docker Hub, with JDK version 1.8 used. The binary packages required for the application need to be packaged by oneself. Please refer to the Deploy with Source Code When starting an application within Docker, use start_container parameter The construction platform should be consistent with the operation platform.","s":"Prepare Dockerfile","u":"/datavines-website/en/docs/get-started/docker/build-docker-image","h":"#prepare-dockerfile","p":1035},{"i":1040,"t":"Write a Dockerfile file, which can be referenced as follows Dockerfile,Can be downloaded through wget。 Copy the packaged binary package to the directory where Dockerfile is located。 Verify the consistency of binary package names in Dockerfile files。 Pack and pack using the following command docker build -t datavines:dev .","s":"Build Steps","u":"/datavines-website/en/docs/get-started/docker/build-docker-image","h":"#build-steps","p":1035},{"i":1042,"t":"Login to DockerHub account docker login -u Successful login will prompt a Success related prompt. change the image tag docker tag datavines:dev //datavines:dev push image to DockerHub docker push //datavines:dev","s":"Push images to DockerHub or private warehouse","u":"/datavines-website/en/docs/get-started/docker/build-docker-image","h":"#push-images-to-dockerhub-or-private-warehouse","p":1035},{"i":1046,"t":"This article will briefly describe how to quickly run Datavines using the 'Docker run' or 'Docker compile' commands.","s":"Description","u":"/datavines-website/en/docs/get-started/docker/deploy-docker-image","h":"#description","p":1044},{"i":1048,"t":"software Version Docker 20.0 and above docker-compose 2.10 and above","s":"Environment","u":"/datavines-website/en/docs/get-started/docker/deploy-docker-image","h":"#environment","p":1044},{"i":1051,"t":"1.Download and modifydocker-compose.yaml Modify the image version to a locally compiled version or a private repository image version image: datavines:dev If there is a conflict between the external network segment and the internal network segment of the container, which means that the container can be accessed within the same network segment, but cannot be accessed across different network segments,Can modify the network_mode in the docker-compose.yaml file.。 networks: datavines: driver: bridge ipam: config: - subnet: 172.18.0.0/24(Custom network segment) Download environment configuration file.env Modify the configuration items in the . env file as follows: # Database ConnectionSPRING_DATASOURCE_URL=jdbc:mysql://127.0.0.1:3306/datavines?useSSL=false&useUnicode=true&characterEncoding=UTF-8&allowPublicKeyRetrieval=false&useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=GMT%2B8# User nameSPRING_DATASOURCE_USERNAME=root","s":"Preparation in advance","u":"/datavines-website/en/docs/get-started/docker/deploy-docker-image","h":"#preparation-in-advance","p":1044},{"i":1053,"t":"docker compose up -d","s":"Quick Start","u":"/datavines-website/en/docs/get-started/docker/deploy-docker-image","h":"#quick-start","p":1044},{"i":1055,"t":"The environmental parameters need to be passed in from the startup command, as shown below. docker run -it -d --name datavines -p 5600:5600 --privileged=true --restart always -e SPRING_PROFILES_ACTIVE=mysql -e SPRING_DATASOURCE_URL=jdbc:mysql://127.0.0.1:3306/datavines?useSSL=false&useUnicode=true&characterEncoding=UTF-8&allowPublicKeyRetrieval=false&useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=GMT%2B8 -e SPRING_DATASOURCE_USERNAME=root -e SPRING_DATASOURCE_PASSWORD=123456 -e SPRING_DATASOURCE_DRIVER-CLASS_NAME=com.mysql.cj.jdbc.Driver datavines:dev","s":"Method 2 ：docker run","u":"/datavines-website/en/docs/get-started/docker/deploy-docker-image","h":"#method-2-docker-run","p":1044},{"i":1059,"t":"Installation k8s Build datavines image Create or download the yml file for datavines on k8s https://raw.githubusercontent.com/datavane/datavines/dev/deploy/k8s/datavines.yaml","s":"Environmental Preparation","u":"/datavines-website/en/docs/get-started/kubernetes-deploy","h":"#environmental-preparation","p":1057},{"i":1061,"t":"Execute the following command by specifying the namespace. kubectl apply -f datavines.yaml -n ","s":"Starting a cluster","u":"/datavines-website/en/docs/get-started/kubernetes-deploy","h":"#starting-a-cluster","p":1057},{"i":1063,"t":"Get the pod under the namespace and check if the datavines are running properly. kubectl get pod -n ","s":"Test and verify","u":"/datavines-website/en/docs/get-started/kubernetes-deploy","h":"#test-and-verify","p":1057},{"i":1065,"t":"Know your data easier! Datavines is an easy-to-use data observability service platform that supports multiple metric.Data quality is used to ensure the accuracy of data in the process of integration and processing. It is also the core component of DataOps.","s":"Introduction","u":"/datavines-website/en/docs/intro","h":"","p":1064},{"i":1067,"t":"Support lineage management Data comparison supporting Checksum algorithm (under development) Support Flink engine Support Spark on K8s Adding DatavinesTask in DolphinScheduler (under development) Support Livy engine","s":"Roadmap","u":"/datavines-website/en/docs/roadmap","h":"","p":1066},{"i":1070,"t":"Before installing Datavines, please make sure the following software is installed on your server Git to ensure smooth execution of git clone JDK, make sure jdk = 8 Maven, to ensure the smooth packaging of the project (of course, you can also upload it to the server after packaging locally)","s":"Environment preparation","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#environment-preparation","p":1068},{"i":1072,"t":"git clone https://github.com/datavane/datavines.gitcd datavines","s":"Download code","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#download-code","p":1068},{"i":1074,"t":"The metadata of Datavines is stored in a relational database. Currently, MySQL are supported. The following uses MySQL as an example to illustrate the installation steps: Create database datavines Execute the script/sql/datavines-mysql.sql script to initialize the database The following building also uses MySQL as an example","s":"Database preparation","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#database-preparation","p":1068},{"i":1076,"t":"Build and unpack mvn clean package -Preleasecd datavines-dist/targettar -zxvf datavines-1.0.0-SNAPSHOT-bin.tar.gz After the decompression is complete, enter the directory cd datavines-1.0.0-SNAPSHOT-bin Modify configuration information cd confvi application.yaml Mainly modify database information spring: datasource: driver-class-name: com.mysql.cj.jdbc.Driver url: jdbc:mysql://127.0.0.1:3306/datavines?useUnicode=true&characterEncoding=UTF-8 username: root password: 123456 If you use Spark as the execution engine and submit it to Yarn for execution, you need the yarn-related configuration information in common.properties standalone mode yarn.mode=standaloneyarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%s #The first %s needs to be replaced the ip address of yarnyarn.resource.manager.http.address.port=8088 ha mode yarn.mode=hayarn.application.status.address=http://%s:%s/ws/v1/cluster/apps/%syarn.resource.manager.http.address.port=8088yarn.resource.manager.ha.ids=192.168.0.1,192.168.0.2","s":"Build Project","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#build-project","p":1068},{"i":1078,"t":"cd binsh datavines-daemon.sh start mysql Check the log, if there is no error message in the log, and you can see [INFO] 2022-04-10 12:29:05.447 io.datavines.server.DatavinesServer:[61] - Started DatavinesServer in 3.97 seconds (JVM running for 4.69 ), it proves that the service has been successfully started","s":"Start service","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#start-service","p":1068},{"i":1080,"t":"Enter localhost:5600 in the browser, and you will see the login interface. Enter the username & password admin / 123456","s":"Start exploring","u":"/datavines-website/en/docs/get-started/deploy-from-source","h":"#start-exploring","p":1068},{"i":1083,"t":"cd datavines-1.0.0-SNAPSHOT-bin/binvi metric_job_config.json","s":"Create config file","u":"/datavines-website/en/docs/user-guide/local-mode","h":"#create-config-file","p":1081},{"i":1085,"t":"{ \"languageEn\":true, \"name\":\"test\", \"executePlatformType\":\"client\", \"parameter\":{ \"connectorParameter\":{ \"type\":\"mysql\", \"parameters\":{ \"database\":\"test\", \"password\":\"123456\", \"port\":\"3306\", \"host\":\"localhost\", \"user\":\"root\", \"properties\":\"useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai\" } }, \"metricParameterList\":[ { \"metricType\":\"column_duplicate\", \"metricParameter\":{ \"table\":\"test_table\", \"column\":\"test_column\", \"metric_database\":\"test\" }, \"expectedType\":\"fix_value\", \"expectedParameter\":{ \"expected_value\":\"10\" }, \"resultFormula\":\"count\", \"operator\":\"lt\", \"threshold\":5.0 } ] }, \"errorDataStorageType\": \"file\", \"errorDataStorageParameter\": { \"data_dir\":\"/tmp/datavines/error-data\", \"column_separator\":\",\" }, \"validateResultDataStorageType\": \"file\", \"validateResultDataStorageParameter\":{ \"data_dir\":\"/tmp/datavines/validate-result-data\", \"column_separator\":\",\" }, \"notificationParameters\": [ { \"type\":\"email\", \"config\": { \"serverHost\":\"smtp.qq.com\", \"serverPort\":\"25\", \"sender\":\"1234567@qq.com\", // input your mail \"enableSmtpAuth\":\"true\", \"user\":\"123456@qq.com\", // input your username \"passwd\":\"123456\", // input your passwd \"starttlsEnable\":\"false\", \"sslEnable\":\"false\", \"smtpSslTrust\":\"true\" }, \"receiver\": { \"to\":\"12345566@qq.com\" // input receiver email } } ]}","s":"Add metric config","u":"/datavines-website/en/docs/user-guide/local-mode","h":"#add-metric-config","p":1081},{"i":1087,"t":"./datavines-submit.sh metric_job_config.json","s":"Run metric job","u":"/datavines-website/en/docs/user-guide/local-mode","h":"#run-metric-job","p":1081},{"i":1090,"t":"Click the Create DataSource button, enter the name of the datasource, and select the datasource type. Take mysql as an example, enter the connection information of mysql, and click to test connection button . If successful, click Save","s":"Connect DataSource","u":"/datavines-website/en/docs/user-guide/quick-start","h":"#connect-datasource","p":1088},{"i":1092,"t":"Click the datasource that created , enter the datasource profile page","s":"View Datasource","u":"/datavines-website/en/docs/user-guide/quick-start","h":"#view-datasource","p":1088},{"i":1094,"t":"For example, click the column properties of table dv_catalog_entity_instance, enter the column detail page","s":"Select Column","u":"/datavines-website/en/docs/user-guide/quick-start","h":"#select-column","p":1088},{"i":1096,"t":"Click the Add Metric button, select metric column_null, select expected_value_type fixed_value、input the expected_value and threshold 10, choose Actual formula and > comparator and input threshold 10, the check result will be true if actual value > 10, false otherwise.You can click Save button to save metric job or Save and Run to run the metric job. Retrun the column detail page, you can see metric list. You also can view the execute result dashboard in each metric item.","s":"Add Metric","u":"/datavines-website/en/docs/user-guide/quick-start","h":"#add-metric","p":1088},{"i":1098,"t":"Click the Metric Name will enter into metric detail info CLick the Runs tab, you can view execution list. CLick the Log button, you can view log info. CLick the ExecuteResult button, you can view execution result. CLick the Error button, you can view error data .","s":"View Metric 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