diff --git a/docs/materialized-view/refreshable-materialized-view.md b/docs/materialized-view/refreshable-materialized-view.md
index 1d797380ac7..9b2362b868d 100644
--- a/docs/materialized-view/refreshable-materialized-view.md
+++ b/docs/materialized-view/refreshable-materialized-view.md
@@ -1,6 +1,6 @@
---
slug: /materialized-view/refreshable-materialized-view
-title: 'Refreshable Materialized View'
+title: 'Refreshable materialized view'
description: 'How to use materialized views to speed up queries'
keywords: ['refreshable materialized view', 'refresh', 'materialized views', 'speed up queries', 'query optimization']
---
diff --git a/docs/migrations/bigquery/equivalent-concepts.md b/docs/migrations/bigquery/equivalent-concepts.md
index f12bab2bb28..48352b93c42 100644
--- a/docs/migrations/bigquery/equivalent-concepts.md
+++ b/docs/migrations/bigquery/equivalent-concepts.md
@@ -9,7 +9,7 @@ show_related_blogs: true
import bigquery_1 from '@site/static/images/migrations/bigquery-1.png';
import Image from '@theme/IdealImage';
-# BigQuery vs ClickHouse Cloud: Equivalent and different concepts
+# BigQuery vs ClickHouse Cloud: equivalent and different concepts
## Resource organization {#resource-organization}
@@ -21,7 +21,7 @@ The way resources are organized in ClickHouse Cloud is similar to [BigQuery's re
Similar to BigQuery, organizations are the root nodes in the ClickHouse cloud resource hierarchy. The first user you set up in your ClickHouse Cloud account is automatically assigned to an organization owned by the user. The user may invite additional users to the organization.
-### BigQuery Projects vs ClickHouse Cloud Services {#bigquery-projects-vs-clickhouse-cloud-services}
+### BigQuery projects vs ClickHouse Cloud services {#bigquery-projects-vs-clickhouse-cloud-services}
Within organizations, you can create services loosely equivalent to BigQuery projects because stored data in ClickHouse Cloud is associated with a service. There are [several service types available](/cloud/manage/cloud-tiers) in ClickHouse Cloud. Each ClickHouse Cloud service is deployed in a specific region and includes:
@@ -29,15 +29,15 @@ Within organizations, you can create services loosely equivalent to BigQuery pro
2. An object storage folder where the service stores all the data.
3. An endpoint (or multiple endpoints created via ClickHouse Cloud UI console) - a service URL that you use to connect to the service (for example, `https://dv2fzne24g.us-east-1.aws.clickhouse.cloud:8443`)
-### BigQuery Datasets vs ClickHouse Cloud Databases {#bigquery-datasets-vs-clickhouse-cloud-databases}
+### BigQuery datasets vs ClickHouse Cloud databases {#bigquery-datasets-vs-clickhouse-cloud-databases}
ClickHouse logically groups tables into databases. Like BigQuery datasets, ClickHouse databases are logical containers that organize and control access to table data.
-### BigQuery Folders {#bigquery-folders}
+### BigQuery folders {#bigquery-folders}
ClickHouse Cloud currently has no concept equivalent to BigQuery folders.
-### BigQuery Slot reservations and Quotas {#bigquery-slot-reservations-and-quotas}
+### BigQuery slot reservations and quotas {#bigquery-slot-reservations-and-quotas}
Like BigQuery slot reservations, you can [configure vertical and horizontal autoscaling](/manage/scaling#configuring-vertical-auto-scaling) in ClickHouse Cloud. For vertical autoscaling, you can set the minimum and maximum size for the memory and CPU cores of the compute nodes for a service. The service will then scale as needed within those bounds. These settings are also available during the initial service creation flow. Each compute node in the service has the same size. You can change the number of compute nodes within a service with [horizontal scaling](/manage/scaling#manual-horizontal-scaling).
@@ -78,7 +78,7 @@ When presented with multiple options for ClickHouse types, consider the actual r
## Query acceleration techniques {#query-acceleration-techniques}
-### Primary and Foreign keys and Primary index {#primary-and-foreign-keys-and-primary-index}
+### Primary and foreign keys and primary index {#primary-and-foreign-keys-and-primary-index}
In BigQuery, a table can have [primary key and foreign key constraints](https://cloud.google.com/bigquery/docs/information-schema-table-constraints). Typically, primary and foreign keys are used in relational databases to ensure data integrity. A primary key value is normally unique for each row and is not `NULL`. Each foreign key value in a row must be present in the primary key column of the primary key table or be `NULL`. In BigQuery, these constraints are not enforced, but the query optimizer may use this information to optimize queries better.
diff --git a/docs/migrations/bigquery/migrating-to-clickhouse-cloud.md b/docs/migrations/bigquery/migrating-to-clickhouse-cloud.md
index a146df70e1d..8c89305f4ac 100644
--- a/docs/migrations/bigquery/migrating-to-clickhouse-cloud.md
+++ b/docs/migrations/bigquery/migrating-to-clickhouse-cloud.md
@@ -66,7 +66,7 @@ Before trying the following examples, we recommend users review the [permissions
Change Data Capture (CDC) is the process by which tables are kept in sync between two databases. This is significantly more complex if updates and deletes are to be handled in near real-time. One approach is to simply schedule a periodic export using BigQuery's [scheduled query functionality](https://cloud.google.com/bigquery/docs/scheduling-queries). Provided you can accept some delay in the data being inserted into ClickHouse, this approach is easy to implement and maintain. An example is given in [this blog post](https://clickhouse.com/blog/clickhouse-bigquery-migrating-data-for-realtime-queries#using-scheduled-queries).
-## Designing Schemas {#designing-schemas}
+## Designing schemas {#designing-schemas}
The Stack Overflow dataset contains a number of related tables. We recommend focusing on migrating the primary table first. This may not necessarily be the largest table but rather the one on which you expect to receive the most analytical queries. This will allow you to familiarize yourself with the main ClickHouse concepts. This table may require remodeling as additional tables are added to fully exploit ClickHouse features and obtain optimal performance. We explore this modeling process in our [Data Modeling docs](/data-modeling/schema-design#next-data-modeling-techniques).
@@ -515,7 +515,7 @@ MaxViewCount: 66975
Peak memory usage: 377.26 MiB.
```
-## Conditionals and Arrays {#conditionals-and-arrays}
+## Conditionals and arrays {#conditionals-and-arrays}
Conditional and array functions make queries significantly simpler. The following query computes the tags (with more than 10000 occurrences) with the largest percentage increase from 2022 to 2023. Note how the following ClickHouse query is succinct thanks to conditionals, array functions, and the ability to reuse aliases in the `HAVING` and `SELECT` clauses.
diff --git a/docs/migrations/postgres/appendix.md b/docs/migrations/postgres/appendix.md
index 99e0286be0c..6e4b762cfbd 100644
--- a/docs/migrations/postgres/appendix.md
+++ b/docs/migrations/postgres/appendix.md
@@ -13,7 +13,7 @@ import Image from '@theme/IdealImage';
Users coming from OLTP systems who are used to ACID transactions should be aware that ClickHouse makes deliberate compromises in not fully providing these in exchange for performance. ClickHouse semantics can deliver high durability guarantees and high write throughput if well understood. We highlight some key concepts below that users should be familiar with prior to working with ClickHouse from Postgres.
-### Shards vs Replicas {#shards-vs-replicas}
+### Shards vs replicas {#shards-vs-replicas}
Sharding and replication are two strategies used for scaling beyond one Postgres instance when storage and/or compute become a bottleneck to performance. Sharding in Postgres involves splitting a large database into smaller, more manageable pieces across multiple nodes. However, Postgres does not support sharding natively. Instead, sharding can be achieved using extensions such as [Citus](https://www.citusdata.com/), in which Postgres becomes a distributed database capable of scaling horizontally. This approach allows Postgres to handle higher transaction rates and larger datasets by spreading the load across several machines. Shards can be row or schema-based in order to provide flexibility for workload types, such as transactional or analytical. Sharding can introduce significant complexity in terms of data management and query execution as it requires coordination across multiple machines and consistency guarantees.
diff --git a/docs/migrations/postgres/data-modeling-techniques.md b/docs/migrations/postgres/data-modeling-techniques.md
index 3e34f294cf1..f864bd8fb3e 100644
--- a/docs/migrations/postgres/data-modeling-techniques.md
+++ b/docs/migrations/postgres/data-modeling-techniques.md
@@ -71,7 +71,7 @@ PARTITION BY toYear(CreationDate)
For a full description of partitioning see ["Table partitions"](/partitions).
-### Applications of Partitions {#applications-of-partitions}
+### Applications of partitions {#applications-of-partitions}
Partitioning in ClickHouse has similar applications as in Postgres but with some subtle differences. More specifically:
@@ -114,7 +114,7 @@ Ok.
- **Query optimization** - While partitions can assist with query performance, this depends heavily on the access patterns. If queries target only a few partitions (ideally one), performance can potentially improve. This is only typically useful if the partitioning key is not in the primary key and you are filtering by it. However, queries that need to cover many partitions may perform worse than if no partitioning is used (as there may possibly be more parts as a result of partitioning). The benefit of targeting a single partition will be even less pronounced to non-existence if the partitioning key is already an early entry in the primary key. Partitioning can also be used to [optimize GROUP BY queries](/engines/table-engines/mergetree-family/custom-partitioning-key#group-by-optimisation-using-partition-key) if values in each partition are unique. However, in general, users should ensure the primary key is optimized and only consider partitioning as a query optimization technique in exceptional cases where access patterns access a specific predictable subset of the day, e.g., partitioning by day, with most queries in the last day.
-### Recommendations for Partitions {#recommendations-for-partitions}
+### Recommendations for partitions {#recommendations-for-partitions}
Users should consider partitioning a data management technique. It is ideal when data needs to be expired from the cluster when operating with time series data e.g. the oldest partition can [simply be dropped](/sql-reference/statements/alter/partition#drop-partitionpart).
diff --git a/docs/migrations/postgres/overview.md b/docs/migrations/postgres/overview.md
index ed3173ef9ee..ca1d195b914 100644
--- a/docs/migrations/postgres/overview.md
+++ b/docs/migrations/postgres/overview.md
@@ -23,7 +23,7 @@ When migrating from PostgreSQL to ClickHouse, the right strategy depends on your
Below section describes the two main strategies for migration: **Real-Time CDC** and **Manual Bulk Load + Periodic Updates**.
-### Real-Time replication (CDC) {#real-time-replication-cdc}
+### Real-time replication (CDC) {#real-time-replication-cdc}
Change Data Capture (CDC) is the process by which tables are kept in sync between two databases. It is the most efficient approach for most migration from PostgreSQL, but yet more complex as it handles insert, updates and deletes from PostgreSQL to ClickHouse in near real-time. It is ideal for use cases where real-time analytics are important.
diff --git a/docs/native-protocol/columns.md b/docs/native-protocol/columns.md
index 4f1b1bf960d..e0b65368ac0 100644
--- a/docs/native-protocol/columns.md
+++ b/docs/native-protocol/columns.md
@@ -5,7 +5,7 @@ title: 'Column types'
description: 'Column types for the native protocol'
---
-# Column Types
+# Column types
See [Data Types](/sql-reference/data-types/) for general reference.
@@ -77,7 +77,7 @@ Alias of `FixedString(16)`, UUID value represented as binary.
Alias of `Int8` or `Int16`, but each integer is mapped to some `String` value.
-## Low Cardinality {#low-cardinality}
+## `LowCardinality` type {#low-cardinality}
`LowCardinality(T)` consists of `Index T, Keys K`,
where `K` is one of (UInt8, UInt16, UInt32, UInt64) depending on size of `Index`.
diff --git a/docs/native-protocol/hash.md b/docs/native-protocol/hash.md
index 16a89325ab6..3de9ac70315 100644
--- a/docs/native-protocol/hash.md
+++ b/docs/native-protocol/hash.md
@@ -7,12 +7,13 @@ description: 'Native protocol hash'
# CityHash
-ClickHouse uses **one of previous** versions of [CityHash from Google](https://github.com/google/cityhash).
+ClickHouse uses **one of the previous** versions of [CityHash from Google](https://github.com/google/cityhash).
:::info
CityHash has changed the algorithm after we have added it into ClickHouse.
-CityHash documentation specifically notes that the user should not rely to specific hash values and should not save it anywhere or use it as sharding key.
+CityHash documentation specifically notes that the user should not rely on
+specific hash values and should not save it anywhere or use it as a sharding key.
But as we exposed this function to the user, we had to fix the version of CityHash (to 1.0.2). And now we guarantee that the behaviour of CityHash functions available in SQL will not change.
diff --git a/docs/use-cases/observability/build-your-own/grafana.md b/docs/use-cases/observability/build-your-own/grafana.md
index 33ab7bf899c..0cb500048b4 100644
--- a/docs/use-cases/observability/build-your-own/grafana.md
+++ b/docs/use-cases/observability/build-your-own/grafana.md
@@ -83,7 +83,7 @@ This query returns the column names expected by Grafana, rendering a table of tr
Users wishing to write more complex queries can switch to the `SQL Editor`.
-### View Trace details {#view-trace-details}
+### View trace details {#view-trace-details}
As shown above, Trace ids are rendered as clickable links. On clicking on a trace Id, a user can choose to view the associated spans via the link `View Trace`. This issues the following query (assuming OTel columns) to retrieve the spans in the required structure, rendering the results as a waterfall.
diff --git a/docs/use-cases/observability/build-your-own/integrating-opentelemetry.md b/docs/use-cases/observability/build-your-own/integrating-opentelemetry.md
index ea8bce63995..00517e5c3ae 100644
--- a/docs/use-cases/observability/build-your-own/integrating-opentelemetry.md
+++ b/docs/use-cases/observability/build-your-own/integrating-opentelemetry.md
@@ -15,7 +15,7 @@ import observability_8 from '@site/static/images/use-cases/observability/observa
import observability_9 from '@site/static/images/use-cases/observability/observability-9.png';
import Image from '@theme/IdealImage';
-# Integrating OpenTelemetry for Data Collection
+# Integrating OpenTelemetry for data collection
Any Observability solution requires a means of collecting and exporting logs and traces. For this purpose, ClickHouse recommends [the OpenTelemetry (OTel) project](https://opentelemetry.io/).
@@ -83,7 +83,7 @@ This approach requires users to instrument their code with their [appropriate la
[`otelbin.io`](https://www.otelbin.io/) is useful to validate and visualize configurations.
:::
-## Structured vs Unstructured {#structured-vs-unstructured}
+## Structured vs unstructured {#structured-vs-unstructured}
Logs can either be structured or unstructured.
@@ -202,7 +202,7 @@ The above messages don't have a `TraceID` or `SpanID` field. If present, e.g. in
For users needing to collect local or Kubernetes log files, we recommend users become familiar with the configuration options available for the [filelog receiver](https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/main/receiver/filelogreceiver/README.md#configuration) and how [offsets](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/filelogreceiver#offset-tracking) and [multiline log parsing is handled](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/filelogreceiver#example---multiline-logs-parsing).
-## Collecting Kubernetes Logs {#collecting-kubernetes-logs}
+## Collecting Kubernetes logs {#collecting-kubernetes-logs}
For the collection of Kubernetes logs, we recommend the [OpenTelemetry documentation guide](https://opentelemetry.io/docs/kubernetes/). The [Kubernetes Attributes Processor](https://opentelemetry.io/docs/kubernetes/collector/components/#kubernetes-attributes-processor) is recommended for enriching logs and metrics with pod metadata. This can potentially produce dynamic metadata e.g. labels, stored in the column `ResourceAttributes`. ClickHouse currently uses the type `Map(String, String)` for this column. See [Using Maps](/use-cases/observability/schema-design#using-maps) and [Extracting from maps](/use-cases/observability/schema-design#extracting-from-maps) for further details on handling and optimizing this type.
@@ -602,7 +602,7 @@ From the collector's perspective, (1) and (2) can be hard to distinguish. Howeve
We recommend users use the [batch processor](https://github.com/open-telemetry/opentelemetry-collector/blob/main/processor/batchprocessor/README.md) shown in earlier configurations to satisfy the above. This ensures inserts are sent as consistent batches of rows satisfying the above requirements. If a collector is expected to have high throughput (events per second), and at least 5000 events can be sent in each insert, this is usually the only batching required in the pipeline. In this case the collector will flush batches before the batch processor's `timeout` is reached, ensuring the end-to-end latency of the pipeline remains low and batches are of a consistent size.
-### Use Asynchronous inserts {#use-asynchronous-inserts}
+### Use asynchronous inserts {#use-asynchronous-inserts}
Typically, users are forced to send smaller batches when the throughput of a collector is low, and yet they still expect data to reach ClickHouse within a minimum end-to-end latency. In this case, small batches are sent when the `timeout` of the batch processor expires. This can cause problems and is when asynchronous inserts are required. This case typically arises when **collectors in the agent role are configured to send directly to ClickHouse**. Gateways, by acting as aggregators, can alleviate this problem - see [Scaling with Gateways](#scaling-with-gateways).
@@ -624,7 +624,7 @@ Finally, the previous deduplication behavior associated with synchronous inserts
Full details on configuring this feature can be found [here](/optimize/asynchronous-inserts#enabling-asynchronous-inserts), with a deep dive [here](https://clickhouse.com/blog/asynchronous-data-inserts-in-clickhouse).
-## Deployment Architectures {#deployment-architectures}
+## Deployment architectures {#deployment-architectures}
Several deployment architectures are possible when using the OTel collector with Clickhouse. We describe each below and when it is likely applicable.
@@ -642,7 +642,7 @@ Users should consider migrating to a Gateway-based architecture once the number
- **Processing at the edge** - Any transformations or event processing has to be performed at the edge or in ClickHouse in this architecture. As well as being restrictive this can either mean complex ClickHouse materialized views or pushing significant computation to the edge - where critical services may be impacted and resources scarce.
- **Small batches and latencies** - Agent collectors may individually collect very few events. This typically means they need to be configured to flush at a set interval to satisfy delivery SLAs. This can result in the collector sending small batches to ClickHouse. While a disadvantage, this can be mitigated with Asynchronous inserts - see [Optimizing inserts](#optimizing-inserts).
-### Scaling with Gateways {#scaling-with-gateways}
+### Scaling with gateways {#scaling-with-gateways}
OTel collectors can be deployed as Gateway instances to address the above limitations. These provide a standalone service, typically per data center or per region. These receive events from applications (or other collectors in the agent role) via a single OTLP endpoint. Typically a set of gateway instances are deployed, with an out-of-the-box load balancer used to distribute the load amongst them.
diff --git a/docs/use-cases/observability/build-your-own/introduction.md b/docs/use-cases/observability/build-your-own/introduction.md
index 6862cf3da06..ef2cacbcb33 100644
--- a/docs/use-cases/observability/build-your-own/introduction.md
+++ b/docs/use-cases/observability/build-your-own/introduction.md
@@ -10,7 +10,7 @@ import observability_1 from '@site/static/images/use-cases/observability/observa
import observability_2 from '@site/static/images/use-cases/observability/observability-2.png';
import Image from '@theme/IdealImage';
-# Using ClickHouse for Observability
+# Using ClickHouse for observability
## Introduction {#introduction}
@@ -84,7 +84,7 @@ We currently recommend ClickHouse for storing two types of observability data:
While ClickHouse can be used to store metrics data, this pillar is less mature in ClickHouse with pending support for features such as support for the Prometheus data format and PromQL.
:::
-### Distributed Tracing {#distributed-tracing}
+### Distributed tracing {#distributed-tracing}
Distributed tracing is a critical feature of Observability. A distributed trace, simply called a trace, maps the journey of a request through a system. The request will originate from an end user or application and proliferate throughout a system, typically resulting in a flow of actions between microservices. By recording this sequence, and allowing the subsequent events to be correlated, it allows an observability user or SRE to be able to diagnose issues in an application flow irrespective of how complex or serverless the architecture is.
diff --git a/docs/use-cases/observability/build-your-own/managing-data.md b/docs/use-cases/observability/build-your-own/managing-data.md
index e8c2fe6d6b1..840ac333880 100644
--- a/docs/use-cases/observability/build-your-own/managing-data.md
+++ b/docs/use-cases/observability/build-your-own/managing-data.md
@@ -10,7 +10,7 @@ import observability_14 from '@site/static/images/use-cases/observability/observ
import Image from '@theme/IdealImage';
-# Managing Data
+# Managing data
Deployments of ClickHouse for Observability invariably involve large datasets, which need to be managed. ClickHouse offers a number of features to assist with data management.
@@ -153,7 +153,7 @@ The above illustrates how data can be efficiently moved and manipulated by parti
We explore both of these in detail below.
-### Query Performance {#query-performance}
+### Query performance {#query-performance}
While partitions can assist with query performance, this depends heavily on the access patterns. If queries target only a few partitions (ideally one), performance can potentially improve. This is only typically useful if the partitioning key is not in the primary key and you are filtering by it. However, queries which need to cover many partitions may perform worse than if no partitioning is used (as there may possibly be more parts). The benefit of targeting a single partition will be even less pronounced to non-existent if the partitioning key is already an early entry in the primary key. Partitioning can also be used to [optimize GROUP BY queries](/engines/table-engines/mergetree-family/custom-partitioning-key#group-by-optimisation-using-partition-key) if values in each partition are unique. However, in general, users should ensure the primary key is optimized and only consider partitioning as a query optimization technique in exceptional cases where access patterns access a specific predictable subset of the data, e.g., partitioning by day, with most queries in the last day. See [here](https://medium.com/datadenys/using-partitions-in-clickhouse-3ea0decb89c4) for an example of this behavior.
diff --git a/docs/use-cases/observability/build-your-own/schema-design.md b/docs/use-cases/observability/build-your-own/schema-design.md
index 67e9cd1e381..bba6f764bfc 100644
--- a/docs/use-cases/observability/build-your-own/schema-design.md
+++ b/docs/use-cases/observability/build-your-own/schema-design.md
@@ -225,7 +225,7 @@ Materialized columns will, by default, not be returned in a `SELECT *`. This is
[Materialized views](/materialized-views) provide a more powerful means of applying SQL filtering and transformations to logs and traces.
-Materialized Views allow users to shift the cost of computation from query time to insert time. A ClickHouse Materialized View is just a trigger that runs a query on blocks of data as they are inserted into a table. The results of this query are inserted into a second "target" table.
+Materialized Views allow users to shift the cost of computation from query time to insert time. A ClickHouse materialized view is just a trigger that runs a query on blocks of data as they are inserted into a table. The results of this query are inserted into a second "target" table.
@@ -490,7 +490,7 @@ We don't recommend using dots in Map column names and may deprecate its use. Use
:::
-## Using Aliases {#using-aliases}
+## Using aliases {#using-aliases}
Querying map types is slower than querying normal columns - see ["Accelerating queries"](#accelerating-queries). In addition, it's more syntactically complicated and can be cumbersome for users to write. To address this latter issue we recommend using Alias columns.
@@ -571,7 +571,7 @@ By default, `SELECT *` excludes ALIAS columns. This behavior can be disabled by
The [general Clickhouse best practices](/data-modeling/schema-design#optimizing-types) for optimizing types apply to the ClickHouse use case.
-## Using Codecs {#using-codecs}
+## Using codecs {#using-codecs}
In addition to type optimizations, users can follow the [general best practices for codecs](/data-compression/compression-in-clickhouse#choosing-the-right-column-compression-codec) when attempting to optimize compression for ClickHouse Observability schemas.
@@ -579,7 +579,7 @@ In general, users will find the `ZSTD` codec highly applicable to logging and tr
Furthermore, timestamps, while benefiting from delta encoding with respect to compression, have been shown to cause slow query performance if this column is used in the primary/ordering key. We recommend users assess the respective compression vs. query performance tradeoffs.
-## Using Dictionaries {#using-dictionaries}
+## Using dictionaries {#using-dictionaries}
[Dictionaries](/sql-reference/dictionaries) are a [key feature](https://clickhouse.com/blog/faster-queries-dictionaries-clickhouse) of ClickHouse providing in-memory [key-value](https://en.wikipedia.org/wiki/Key%E2%80%93value_database) representation of data from various internal and external [sources](/sql-reference/dictionaries#dictionary-sources), optimized for super-low latency lookup queries.
@@ -604,7 +604,7 @@ We recommend that users familiarize themselves with the basics of dictionaries.
For simple enrichment examples see the guide on Dictionaries [here](/dictionary). Below, we focus on common observability enrichment tasks.
-### Using IP Dictionaries {#using-ip-dictionaries}
+### Using IP dictionaries {#using-ip-dictionaries}
Geo-enriching logs and traces with latitude and longitude values using IP addresses is a common Observability requirement. We can achieve this using `ip_trie` structured dictionary.
@@ -825,7 +825,7 @@ Users are likely to want the ip enrichment dictionary to be periodically updated
The above countries and coordinates offer visualization capabilities beyond grouping and filtering by country. For inspiration see ["Visualizing geo data"](/observability/grafana#visualizing-geo-data).
-### Using Regex Dictionaries (User Agent parsing) {#using-regex-dictionaries-user-agent-parsing}
+### Using regex dictionaries (user agent parsing) {#using-regex-dictionaries-user-agent-parsing}
The parsing of [user agent strings](https://en.wikipedia.org/wiki/User_agent) is a classical regular expression problem and a common requirement in log and trace based datasets. ClickHouse provides efficient parsing of user agents using Regular Expression Tree Dictionaries.
@@ -1335,7 +1335,7 @@ The CTE here identifies the minimum and maximum timestamp for the trace id `ae92
This same approach can be applied for similar access patterns. We explore a similar example in Data Modeling [here](/materialized-view/incremental-materialized-view#lookup-table).
-### Using Projections {#using-projections}
+### Using projections {#using-projections}
ClickHouse projections allow users to specify multiple `ORDER BY` clauses for a table.
@@ -1444,7 +1444,7 @@ Peak memory usage: 27.85 MiB.
In the above example, we specify the columns used in the earlier query in the projection. This will mean only these specified columns will be stored on disk as part of the projection, ordered by Status. If alternatively, we used `SELECT *` here, all columns would be stored. While this would allow more queries (using any subset of columns) to benefit from the projection, additional storage will be incurred. For measuring disk space and compression, see ["Measuring table size & compression"](#measuring-table-size--compression).
-### Secondary/Data Skipping indices {#secondarydata-skipping-indices}
+### Secondary/data skipping indices {#secondarydata-skipping-indices}
No matter how well the primary key is tuned in ClickHouse, some queries will inevitably require full table scans. While this can be mitigated using Materialized views (and projections for some queries), these require additional maintenance and users to be aware of their availability in order to ensure they are exploited. While traditional relational databases solve this with secondary indexes, these are ineffective in column-oriented databases like ClickHouse. Instead, ClickHouse uses "Skip" indexes, which can significantly improve query performance by allowing the database to skip over large data chunks with no matching values.
diff --git a/docs/use-cases/observability/index.md b/docs/use-cases/observability/index.md
index 17c73a27d75..31b03f1448c 100644
--- a/docs/use-cases/observability/index.md
+++ b/docs/use-cases/observability/index.md
@@ -9,7 +9,7 @@ keywords: ['observability', 'logs', 'traces', 'metrics', 'OpenTelemetry', 'Grafa
ClickHouse offers unmatched speed, scale, and cost-efficiency for observability. This guide provides two paths depending on your needs:
-## ClickStack - The ClickHouse Observability Stack {#clickstack}
+## ClickStack - the ClickHouse observability stack {#clickstack}
The ClickHouse Observability Stack is our **recommended approach** for most users.
@@ -28,7 +28,7 @@ The ClickHouse Observability Stack is our **recommended approach** for most user
| [Production](/use-cases/observability/clickstack/production) | Best practices for production deployment |
-## Build-Your-Own Stack {#build-your-own-stack}
+## Build-your-own stack {#build-your-own-stack}
For users with **custom requirements** — such as highly specialized ingestion pipelines, schema designs, or extreme scaling needs — we provide guidance to build a custom observability stack with ClickHouse as the core database.
diff --git a/docs/use-cases/time-series/analysis-functions.md b/docs/use-cases/time-series/analysis-functions.md
index 9312f1d4376..64329b30817 100644
--- a/docs/use-cases/time-series/analysis-functions.md
+++ b/docs/use-cases/time-series/analysis-functions.md
@@ -7,7 +7,7 @@ keywords: ['time-series']
show_related_blogs: true
---
-# Time-Series analysis functions
+# Time-series analysis functions
Time series analysis in ClickHouse can be performed using standard SQL aggregation and window functions.
When working with time series data, you'll typically encounter three main types of metrics:
diff --git a/docs/use-cases/time-series/query-performance.md b/docs/use-cases/time-series/query-performance.md
index f0029675764..84151cbf642 100644
--- a/docs/use-cases/time-series/query-performance.md
+++ b/docs/use-cases/time-series/query-performance.md
@@ -7,7 +7,7 @@ keywords: ['time-series']
show_related_blogs: true
---
-# Time-Series query performance
+# Time-series query performance
After optimizing storage, the next step is improving query performance.
This section explores two key techniques: optimizing `ORDER BY` keys and using materialized views.
diff --git a/docs/use-cases/time-series/storage-efficiency.md b/docs/use-cases/time-series/storage-efficiency.md
index 6f3c76c1ed9..27cc735c7aa 100644
--- a/docs/use-cases/time-series/storage-efficiency.md
+++ b/docs/use-cases/time-series/storage-efficiency.md
@@ -7,7 +7,7 @@ keywords: ['time-series']
show_related_blogs: true
---
-# Time-Series storage efficiency
+# Time-series storage efficiency
After exploring how to query our Wikipedia statistics dataset, let's focus on optimizing its storage efficiency in ClickHouse.
This section demonstrates practical techniques to reduce storage requirements while maintaining query performance.
diff --git a/docs/whats-new/changelog/2021.md b/docs/whats-new/changelog/2021.md
index f107acb1791..391f8e08d1f 100644
--- a/docs/whats-new/changelog/2021.md
+++ b/docs/whats-new/changelog/2021.md
@@ -989,7 +989,7 @@ description: 'Changelog for 2021'
* Fix limit/offset settings for distributed queries (ignore on the remote nodes). [#24940](https://github.com/ClickHouse/ClickHouse/pull/24940) ([Azat Khuzhin](https://github.com/azat)).
* Fix possible heap-buffer-overflow in `Arrow` format. [#24922](https://github.com/ClickHouse/ClickHouse/pull/24922) ([Kruglov Pavel](https://github.com/Avogar)).
* Fixed possible error 'Cannot read from istream at offset 0' when reading a file from DiskS3 (S3 virtual filesystem is an experimental feature under development that should not be used in production). [#24885](https://github.com/ClickHouse/ClickHouse/pull/24885) ([Pavel Kovalenko](https://github.com/Jokser)).
-* Fix "Missing columns" exception when joining Distributed Materialized View. [#24870](https://github.com/ClickHouse/ClickHouse/pull/24870) ([Azat Khuzhin](https://github.com/azat)).
+* Fix "Missing columns" exception when joining distributed materialized view. [#24870](https://github.com/ClickHouse/ClickHouse/pull/24870) ([Azat Khuzhin](https://github.com/azat)).
* Allow `NULL` values in postgresql compatibility protocol. Closes [#22622](https://github.com/ClickHouse/ClickHouse/issues/22622). [#24857](https://github.com/ClickHouse/ClickHouse/pull/24857) ([Kseniia Sumarokova](https://github.com/kssenii)).
* Fix bug when exception `Mutation was killed` can be thrown to the client on mutation wait when mutation not loaded into memory yet. [#24809](https://github.com/ClickHouse/ClickHouse/pull/24809) ([alesapin](https://github.com/alesapin)).
* Fixed bug in deserialization of random generator state with might cause some data types such as `AggregateFunction(groupArraySample(N), T))` to behave in a non-deterministic way. [#24538](https://github.com/ClickHouse/ClickHouse/pull/24538) ([tavplubix](https://github.com/tavplubix)).
@@ -1000,7 +1000,7 @@ description: 'Changelog for 2021'
* When user authentication is managed by LDAP. Fixed potential deadlock that can happen during LDAP role (re)mapping, when LDAP group is mapped to a nonexistent local role. [#24431](https://github.com/ClickHouse/ClickHouse/pull/24431) ([Denis Glazachev](https://github.com/traceon)).
* In "multipart/form-data" message consider the CRLF preceding a boundary as part of it. Fixes [#23905](https://github.com/ClickHouse/ClickHouse/issues/23905). [#24399](https://github.com/ClickHouse/ClickHouse/pull/24399) ([Ivan](https://github.com/abyss7)).
* Fix drop partition with intersect fake parts. In rare cases there might be parts with mutation version greater than current block number. [#24321](https://github.com/ClickHouse/ClickHouse/pull/24321) ([Amos Bird](https://github.com/amosbird)).
-* Fixed a bug in moving Materialized View from Ordinary to Atomic database (`RENAME TABLE` query). Now inner table is moved to new database together with Materialized View. Fixes [#23926](https://github.com/ClickHouse/ClickHouse/issues/23926). [#24309](https://github.com/ClickHouse/ClickHouse/pull/24309) ([tavplubix](https://github.com/tavplubix)).
+* Fixed a bug in moving materialized view from Ordinary to Atomic database (`RENAME TABLE` query). Now inner table is moved to new database together with materialized view. Fixes [#23926](https://github.com/ClickHouse/ClickHouse/issues/23926). [#24309](https://github.com/ClickHouse/ClickHouse/pull/24309) ([tavplubix](https://github.com/tavplubix)).
* Allow empty HTTP headers. Fixes [#23901](https://github.com/ClickHouse/ClickHouse/issues/23901). [#24285](https://github.com/ClickHouse/ClickHouse/pull/24285) ([Ivan](https://github.com/abyss7)).
* Correct processing of mutations (ALTER UPDATE/DELETE) in Memory tables. Closes [#24274](https://github.com/ClickHouse/ClickHouse/issues/24274). [#24275](https://github.com/ClickHouse/ClickHouse/pull/24275) ([flynn](https://github.com/ucasfl)).
* Make column LowCardinality property in JOIN output the same as in the input, close [#23351](https://github.com/ClickHouse/ClickHouse/issues/23351), close [#20315](https://github.com/ClickHouse/ClickHouse/issues/20315). [#24061](https://github.com/ClickHouse/ClickHouse/pull/24061) ([Vladimir](https://github.com/vdimir)).
@@ -1109,7 +1109,7 @@ description: 'Changelog for 2021'
* Fixed the behavior when query `SYSTEM RESTART REPLICA` or `SYSTEM SYNC REPLICA` is being processed infinitely. This was detected on server with extremely little amount of RAM. [#24457](https://github.com/ClickHouse/ClickHouse/pull/24457) ([Nikita Mikhaylov](https://github.com/nikitamikhaylov)).
* Fix incorrect monotonicity of `toWeek` function. This fixes [#24422](https://github.com/ClickHouse/ClickHouse/issues/24422) . This bug was introduced in [#5212](https://github.com/ClickHouse/ClickHouse/pull/5212), and was exposed later by smarter partition pruner. [#24446](https://github.com/ClickHouse/ClickHouse/pull/24446) ([Amos Bird](https://github.com/amosbird)).
* Fix drop partition with intersect fake parts. In rare cases there might be parts with mutation version greater than current block number. [#24321](https://github.com/ClickHouse/ClickHouse/pull/24321) ([Amos Bird](https://github.com/amosbird)).
-* Fixed a bug in moving Materialized View from Ordinary to Atomic database (`RENAME TABLE` query). Now inner table is moved to new database together with Materialized View. Fixes [#23926](https://github.com/ClickHouse/ClickHouse/issues/23926). [#24309](https://github.com/ClickHouse/ClickHouse/pull/24309) ([tavplubix](https://github.com/tavplubix)).
+* Fixed a bug in moving materialized view from Ordinary to Atomic database (`RENAME TABLE` query). Now inner table is moved to new database together with materialized view. Fixes [#23926](https://github.com/ClickHouse/ClickHouse/issues/23926). [#24309](https://github.com/ClickHouse/ClickHouse/pull/24309) ([tavplubix](https://github.com/tavplubix)).
* Allow empty HTTP headers in client requests. Fixes [#23901](https://github.com/ClickHouse/ClickHouse/issues/23901). [#24285](https://github.com/ClickHouse/ClickHouse/pull/24285) ([Ivan](https://github.com/abyss7)).
* Set `max_threads = 1` to fix mutation fail of `Memory` tables. Closes [#24274](https://github.com/ClickHouse/ClickHouse/issues/24274). [#24275](https://github.com/ClickHouse/ClickHouse/pull/24275) ([flynn](https://github.com/ucasFL)).
* Fix typo in implementation of `Memory` tables, this bug was introduced at [#15127](https://github.com/ClickHouse/ClickHouse/issues/15127). Closes [#24192](https://github.com/ClickHouse/ClickHouse/issues/24192). [#24193](https://github.com/ClickHouse/ClickHouse/pull/24193) ([张中南](https://github.com/plugine)).
@@ -1245,7 +1245,7 @@ description: 'Changelog for 2021'
* Correct aliases handling if subquery was optimized to constant. Fixes [#22924](https://github.com/ClickHouse/ClickHouse/issues/22924). Fixes [#10401](https://github.com/ClickHouse/ClickHouse/issues/10401). [#23191](https://github.com/ClickHouse/ClickHouse/pull/23191) ([Maksim Kita](https://github.com/kitaisreal)).
* Server might fail to start if `data_type_default_nullable` setting is enabled in default profile, it's fixed. Fixes [#22573](https://github.com/ClickHouse/ClickHouse/issues/22573). [#23185](https://github.com/ClickHouse/ClickHouse/pull/23185) ([tavplubix](https://github.com/tavplubix)).
* Fixed a crash on shutdown which happened because of wrong accounting of current connections. [#23154](https://github.com/ClickHouse/ClickHouse/pull/23154) ([Vitaly Baranov](https://github.com/vitlibar)).
-* Fixed `Table .inner_id... doesn't exist` error when selecting from Materialized View after detaching it from Atomic database and attaching back. [#23047](https://github.com/ClickHouse/ClickHouse/pull/23047) ([tavplubix](https://github.com/tavplubix)).
+* Fixed `Table .inner_id... doesn't exist` error when selecting from materialized view after detaching it from Atomic database and attaching back. [#23047](https://github.com/ClickHouse/ClickHouse/pull/23047) ([tavplubix](https://github.com/tavplubix)).
* Fix error `Cannot find column in ActionsDAG result` which may happen if subquery uses `untuple`. Fixes [#22290](https://github.com/ClickHouse/ClickHouse/issues/22290). [#22991](https://github.com/ClickHouse/ClickHouse/pull/22991) ([Nikolai Kochetov](https://github.com/KochetovNicolai)).
* Fix usage of constant columns of type `Map` with nullable values. [#22939](https://github.com/ClickHouse/ClickHouse/pull/22939) ([Anton Popov](https://github.com/CurtizJ)).
* fixed `formatDateTime()` on `DateTime64` and "%C" format specifier fixed `toDateTime64()` for large values and non-zero scale. [#22937](https://github.com/ClickHouse/ClickHouse/pull/22937) ([Vasily Nemkov](https://github.com/Enmk)).
@@ -1764,7 +1764,7 @@ description: 'Changelog for 2021'
* Uninitialized memory read was possible in encrypt/decrypt functions if empty string was passed as IV. This closes [#19391](https://github.com/ClickHouse/ClickHouse/issues/19391). [#19397](https://github.com/ClickHouse/ClickHouse/pull/19397) ([alexey-milovidov](https://github.com/alexey-milovidov)).
* Fix possible buffer overflow in Uber H3 library. See https://github.com/uber/h3/issues/392. This closes [#19219](https://github.com/ClickHouse/ClickHouse/issues/19219). [#19383](https://github.com/ClickHouse/ClickHouse/pull/19383) ([alexey-milovidov](https://github.com/alexey-milovidov)).
* Fix system.parts _state column (LOGICAL_ERROR when querying this column, due to incorrect order). [#19346](https://github.com/ClickHouse/ClickHouse/pull/19346) ([Azat Khuzhin](https://github.com/azat)).
-* Fixed possible wrong result or segfault on aggregation when Materialized View and its target table have different structure. Fixes [#18063](https://github.com/ClickHouse/ClickHouse/issues/18063). [#19322](https://github.com/ClickHouse/ClickHouse/pull/19322) ([tavplubix](https://github.com/tavplubix)).
+* Fixed possible wrong result or segfault on aggregation when materialized view and its target table have different structure. Fixes [#18063](https://github.com/ClickHouse/ClickHouse/issues/18063). [#19322](https://github.com/ClickHouse/ClickHouse/pull/19322) ([tavplubix](https://github.com/tavplubix)).
* Fix error `Cannot convert column now64() because it is constant but values of constants are different in source and result`. Continuation of [#7156](https://github.com/ClickHouse/ClickHouse/issues/7156). [#19316](https://github.com/ClickHouse/ClickHouse/pull/19316) ([Nikolai Kochetov](https://github.com/KochetovNicolai)).
* Fix bug when concurrent `ALTER` and `DROP` queries may hang while processing ReplicatedMergeTree table. [#19237](https://github.com/ClickHouse/ClickHouse/pull/19237) ([alesapin](https://github.com/alesapin)).
* Fixed `There is no checkpoint` error when inserting data through http interface using `Template` or `CustomSeparated` format. Fixes [#19021](https://github.com/ClickHouse/ClickHouse/issues/19021). [#19072](https://github.com/ClickHouse/ClickHouse/pull/19072) ([tavplubix](https://github.com/tavplubix)).
@@ -1803,7 +1803,7 @@ description: 'Changelog for 2021'
* Add functions `countMatches`/`countMatchesCaseInsensitive`. [#17459](https://github.com/ClickHouse/ClickHouse/pull/17459) ([Azat Khuzhin](https://github.com/azat)).
* Implement `countSubstrings()`/`countSubstringsCaseInsensitive()`/`countSubstringsCaseInsensitiveUTF8()` (Count the number of substring occurrences). [#17347](https://github.com/ClickHouse/ClickHouse/pull/17347) ([Azat Khuzhin](https://github.com/azat)).
* Add information about used databases, tables and columns in system.query_log. Add `query_kind` and `normalized_query_hash` fields. [#17726](https://github.com/ClickHouse/ClickHouse/pull/17726) ([Amos Bird](https://github.com/amosbird)).
-* Add a setting `optimize_on_insert`. When enabled, do the same transformation for INSERTed block of data as if merge was done on this block (e.g. Replacing, Collapsing, Aggregating...). This setting is enabled by default. This can influence Materialized View and MaterializeMySQL behaviour (see detailed description). This closes [#10683](https://github.com/ClickHouse/ClickHouse/issues/10683). [#16954](https://github.com/ClickHouse/ClickHouse/pull/16954) ([Kruglov Pavel](https://github.com/Avogar)).
+* Add a setting `optimize_on_insert`. When enabled, do the same transformation for INSERTed block of data as if merge was done on this block (e.g. Replacing, Collapsing, Aggregating...). This setting is enabled by default. This can influence materialized view and MaterializeMySQL behaviour (see detailed description). This closes [#10683](https://github.com/ClickHouse/ClickHouse/issues/10683). [#16954](https://github.com/ClickHouse/ClickHouse/pull/16954) ([Kruglov Pavel](https://github.com/Avogar)).
* Kerberos Authenticaiton for HDFS. [#16621](https://github.com/ClickHouse/ClickHouse/pull/16621) ([Ilya Golshtein](https://github.com/ilejn)).
* Support `SHOW SETTINGS` statement to show parameters in system.settings. `SHOW CHANGED SETTINGS` and `LIKE/ILIKE` clause are also supported. [#18056](https://github.com/ClickHouse/ClickHouse/pull/18056) ([Jianmei Zhang](https://github.com/zhangjmruc)).
* Function `position` now supports `POSITION(needle IN haystack)` synax for SQL compatibility. This closes [#18701](https://github.com/ClickHouse/ClickHouse/issues/18701). ... [#18779](https://github.com/ClickHouse/ClickHouse/pull/18779) ([Jianmei Zhang](https://github.com/zhangjmruc)).
diff --git a/docs/whats-new/changelog/2022.md b/docs/whats-new/changelog/2022.md
index 1b9fc200cc1..4d7dcf4d498 100644
--- a/docs/whats-new/changelog/2022.md
+++ b/docs/whats-new/changelog/2022.md
@@ -95,7 +95,7 @@ Refer to this issue on GitHub for more details: https://github.com/ClickHouse/Cl
* Fix functions `arrayFirstOrNull` and `arrayLastOrNull` or null when the array contains `Nullable` elements. [#43274](https://github.com/ClickHouse/ClickHouse/pull/43274) ([Duc Canh Le](https://github.com/canhld94)).
* Fix incorrect `UserTimeMicroseconds`/`SystemTimeMicroseconds` accounting related to Kafka tables. [#42791](https://github.com/ClickHouse/ClickHouse/pull/42791) ([Azat Khuzhin](https://github.com/azat)).
* Do not suppress exceptions in `web` disks. Fix retries for the `web` disk. [#42800](https://github.com/ClickHouse/ClickHouse/pull/42800) ([Azat Khuzhin](https://github.com/azat)).
-* Fixed (logical) race condition between inserts and dropping materialized views. A race condition happened when a Materialized View was dropped at the same time as an INSERT, where the MVs were present as a dependency of the insert at the begining of the execution, but the table has been dropped by the time the insert chain tries to access it, producing either an `UNKNOWN_TABLE` or `TABLE_IS_DROPPED` exception, and stopping the insertion. After this change, we avoid these exceptions and just continue with the insert if the dependency is gone. [#43161](https://github.com/ClickHouse/ClickHouse/pull/43161) ([AlfVII](https://github.com/AlfVII)).
+* Fixed (logical) race condition between inserts and dropping materialized views. A race condition happened when a materialized view was dropped at the same time as an INSERT, where the MVs were present as a dependency of the insert at the begining of the execution, but the table has been dropped by the time the insert chain tries to access it, producing either an `UNKNOWN_TABLE` or `TABLE_IS_DROPPED` exception, and stopping the insertion. After this change, we avoid these exceptions and just continue with the insert if the dependency is gone. [#43161](https://github.com/ClickHouse/ClickHouse/pull/43161) ([AlfVII](https://github.com/AlfVII)).
* Fix undefined behavior in the `quantiles` function, which might lead to uninitialized memory. Found by fuzzer. This closes [#44066](https://github.com/ClickHouse/ClickHouse/issues/44066). [#44067](https://github.com/ClickHouse/ClickHouse/pull/44067) ([Alexey Milovidov](https://github.com/alexey-milovidov)).
* Additional check on zero uncompressed size is added to `CompressionCodecDelta`. [#43255](https://github.com/ClickHouse/ClickHouse/pull/43255) ([Nikita Taranov](https://github.com/nickitat)).
* Flatten arrays from Parquet to avoid an issue with inconsistent data in arrays. These incorrect files can be generated by Apache Iceberg. [#43297](https://github.com/ClickHouse/ClickHouse/pull/43297) ([Arthur Passos](https://github.com/arthurpassos)).
@@ -1789,7 +1789,7 @@ Refer to this issue on GitHub for more details: https://github.com/ClickHouse/Cl
* Out of band `offset` and `limit` settings may be applied incorrectly for views. Close [#33289](https://github.com/ClickHouse/ClickHouse/issues/33289) [#33518](https://github.com/ClickHouse/ClickHouse/pull/33518) ([hexiaoting](https://github.com/hexiaoting)).
* Fix an exception `Block structure mismatch` which may happen during insertion into table with default nested `LowCardinality` column. Fixes [#33028](https://github.com/ClickHouse/ClickHouse/issues/33028). [#33504](https://github.com/ClickHouse/ClickHouse/pull/33504) ([Nikolai Kochetov](https://github.com/KochetovNicolai)).
* Fix dictionary expressions for `range_hashed` range min and range max attributes when created using DDL. Closes [#30809](https://github.com/ClickHouse/ClickHouse/issues/30809). [#33478](https://github.com/ClickHouse/ClickHouse/pull/33478) ([Maksim Kita](https://github.com/kitaisreal)).
-* Fix possible use-after-free for INSERT into Materialized View with concurrent DROP ([Azat Khuzhin](https://github.com/azat)).
+* Fix possible use-after-free for INSERT into materialized view with concurrent DROP ([Azat Khuzhin](https://github.com/azat)).
* Do not try to read pass EOF (to workaround for a bug in the Linux kernel), this bug can be reproduced on kernels (3.14..5.9), and requires `index_granularity_bytes=0` (i.e. turn off adaptive index granularity). [#33372](https://github.com/ClickHouse/ClickHouse/pull/33372) ([Azat Khuzhin](https://github.com/azat)).
* The commands `SYSTEM SUSPEND` and `SYSTEM ... THREAD FUZZER` missed access control. It is fixed. Author: Kevin Michel. [#33333](https://github.com/ClickHouse/ClickHouse/pull/33333) ([alexey-milovidov](https://github.com/alexey-milovidov)).
* Fix when `COMMENT` for dictionaries does not appear in `system.tables`, `system.dictionaries`. Allow to modify the comment for `Dictionary` engine. Closes [#33251](https://github.com/ClickHouse/ClickHouse/issues/33251). [#33261](https://github.com/ClickHouse/ClickHouse/pull/33261) ([Maksim Kita](https://github.com/kitaisreal)).
diff --git a/docs/whats-new/changelog/2024.md b/docs/whats-new/changelog/2024.md
index d74962ebe45..1f32611026e 100644
--- a/docs/whats-new/changelog/2024.md
+++ b/docs/whats-new/changelog/2024.md
@@ -1239,7 +1239,7 @@ description: 'Changelog for 2024'
* Fix analyzer: only interpolate expression should be used for DAG [#64096](https://github.com/ClickHouse/ClickHouse/pull/64096) ([Yakov Olkhovskiy](https://github.com/yakov-olkhovskiy)).
* Fix azure backup writing multipart blocks by 1 MiB (read buffer size) instead of `max_upload_part_size` (in non-native copy case) [#64117](https://github.com/ClickHouse/ClickHouse/pull/64117) ([Kseniia Sumarokova](https://github.com/kssenii)).
* Correctly fallback during backup copy [#64153](https://github.com/ClickHouse/ClickHouse/pull/64153) ([Antonio Andelic](https://github.com/antonio2368)).
-* Prevent LOGICAL_ERROR on CREATE TABLE as Materialized View [#64174](https://github.com/ClickHouse/ClickHouse/pull/64174) ([Raúl Marín](https://github.com/Algunenano)).
+* Prevent LOGICAL_ERROR on CREATE TABLE as materialized view [#64174](https://github.com/ClickHouse/ClickHouse/pull/64174) ([Raúl Marín](https://github.com/Algunenano)).
* Query Cache: Consider identical queries against different databases as different [#64199](https://github.com/ClickHouse/ClickHouse/pull/64199) ([Robert Schulze](https://github.com/rschu1ze)).
* Ignore `text_log` for Keeper [#64218](https://github.com/ClickHouse/ClickHouse/pull/64218) ([Antonio Andelic](https://github.com/antonio2368)).
* Fix Logical error: Bad cast for Buffer table with prewhere. [#64388](https://github.com/ClickHouse/ClickHouse/pull/64388) ([Nikolai Kochetov](https://github.com/KochetovNicolai)).
@@ -1588,7 +1588,7 @@ description: 'Changelog for 2024'
* Fix for the materialized view security issue, which allowed a user to insert into a table without required grants for that. Fix validates that the user has permission to insert not only into a materialized view but also into all underlying tables. This means that some queries, which worked before, now can fail with `Not enough privileges`. To address this problem, the release introduces a new feature of SQL security for views https://clickhouse.com/docs/sql-reference/statements/create/view#sql_security. [#54901](https://github.com/ClickHouse/ClickHouse/pull/54901) [#60439](https://github.com/ClickHouse/ClickHouse/pull/60439) ([pufit](https://github.com/pufit)).
#### New Feature {#new-feature-10}
-* Added new syntax which allows to specify definer user in View/Materialized View. This allows to execute selects/inserts from views without explicit grants for underlying tables. So, a View will encapsulate the grants. [#54901](https://github.com/ClickHouse/ClickHouse/pull/54901) [#60439](https://github.com/ClickHouse/ClickHouse/pull/60439) ([pufit](https://github.com/pufit)).
+* Added new syntax which allows to specify definer user in view/materialized view. This allows to execute selects/inserts from views without explicit grants for underlying tables. So, a View will encapsulate the grants. [#54901](https://github.com/ClickHouse/ClickHouse/pull/54901) [#60439](https://github.com/ClickHouse/ClickHouse/pull/60439) ([pufit](https://github.com/pufit)).
* Try to detect file format automatically during schema inference if it's unknown in `file/s3/hdfs/url/azureBlobStorage` engines. Closes [#50576](https://github.com/ClickHouse/ClickHouse/issues/50576). [#59092](https://github.com/ClickHouse/ClickHouse/pull/59092) ([Kruglov Pavel](https://github.com/Avogar)).
* Implement auto-adjustment for asynchronous insert timeouts. The following settings are introduced: async_insert_poll_timeout_ms, async_insert_use_adaptive_busy_timeout, async_insert_busy_timeout_min_ms, async_insert_busy_timeout_max_ms, async_insert_busy_timeout_increase_rate, async_insert_busy_timeout_decrease_rate. [#58486](https://github.com/ClickHouse/ClickHouse/pull/58486) ([Julia Kartseva](https://github.com/jkartseva)).
* Allow to set up a quota for maximum sequential login failures. [#54737](https://github.com/ClickHouse/ClickHouse/pull/54737) ([Alexey Gerasimchuck](https://github.com/Demilivor)).
diff --git a/docs/whats-new/roadmap.md b/docs/whats-new/roadmap.md
index d30f4c68a49..93f7d5313dd 100644
--- a/docs/whats-new/roadmap.md
+++ b/docs/whats-new/roadmap.md
@@ -5,13 +5,13 @@ sidebar_position: 50
description: 'Present and past ClickHouse road maps'
---
-## Current Roadmap {#current-roadmap}
+## Current roadmap {#current-roadmap}
The current roadmap is published for open discussion:
- [2025](https://github.com/ClickHouse/ClickHouse/issues/74046)
-## Previous Roadmaps {#previous-roadmaps}
+## Previous roadmaps {#previous-roadmaps}
- [2024](https://github.com/ClickHouse/ClickHouse/issues/58392)
- [2023](https://github.com/ClickHouse/ClickHouse/issues/44767)
diff --git a/docs/whats-new/security-changelog.md b/docs/whats-new/security-changelog.md
index 1c79dca658c..2f02469a7e5 100644
--- a/docs/whats-new/security-changelog.md
+++ b/docs/whats-new/security-changelog.md
@@ -1,12 +1,12 @@
---
slug: /whats-new/security-changelog
sidebar_position: 20
-sidebar_label: 'Security Changelog'
-title: 'Security Changelog'
+sidebar_label: 'Security changelog'
+title: 'Security changelog'
description: 'Security changelog detailing security related updates and changes'
---
-# Security Changelog
+# Security changelog
## Fixed in ClickHouse v25.1.5.5, 2025-01-05 {#fixed-in-clickhouse-release-2025-01-05}
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/chdb/install/bun.md b/i18n/jp/docusaurus-plugin-content-docs/current/chdb/install/bun.md
index 59e5bac14db..058cd1c3c64 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/chdb/install/bun.md
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/chdb/install/bun.md
@@ -42,7 +42,9 @@ var result = query("SELECT version()", "CSV");
console.log(result); // 23.10.1.1
```
+
### Session.Query(query, *format) {#sessionqueryquery-format}
+
```javascript
const sess = new Session('./chdb-bun-tmp');
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/get-started/cloud-quick-start.mdx b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/get-started/cloud-quick-start.mdx
index 02b9c0a8828..e1afdf787f2 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/get-started/cloud-quick-start.mdx
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/get-started/cloud-quick-start.mdx
@@ -26,8 +26,6 @@ import client_details from '@site/static/images/_snippets/client_details.png';
import new_rows_from_csv from '@site/static/images/_snippets/new_rows_from_csv.png';
import SQLConsoleDetail from '@site/i18n/jp/docusaurus-plugin-content-docs/current/_snippets/_launch_sql_console.md';
-```md
-
# ClickHouse Cloud クイックスタート
ClickHouse を始める最も迅速で簡単な方法は、[ClickHouse Cloud](https://console.clickhouse.cloud) に新しいサービスを作成することです。
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/manage/billing/marketplace/index.md b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/manage/billing/marketplace/index.md
index 41113920eed..b9c1b2dd71a 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/manage/billing/marketplace/index.md
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/manage/billing/marketplace/index.md
@@ -8,8 +8,6 @@
- 'GCP'
---
-
-
このセクションでは、マーケットプレイスに関連する請求トピックについて詳しく説明します。
| ページ | 説明 |
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
index 1dc21f9d681..a266bd73f75 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
@@ -146,7 +146,7 @@ ClickHouse Cloudの安定した使用を確保し、ベストプラクティス
[Golang](https://github.com/ClickHouse/clickhouse-go/releases/tag/v2.30.1)、[Python](https://github.com/ClickHouse/clickhouse-connect/releases/tag/v0.8.11)、および[NodeJS](https://github.com/ClickHouse/clickhouse-js/releases/tag/1.10.1)クライアントが、Dynamic、Variant、およびJSONタイプリクエストをサポートしました。
-### DBT support for Refreshable Materialized Views {#dbt-support-for-refreshable-materialized-views}
+### DBT support for refreshable materialized views {#dbt-support-for-refreshable-materialized-views}
DBTは、`1.8.7`リリースで[リフレッシュ可能なマテリアライズドビュー](https://github.com/ClickHouse/dbt-clickhouse/releases/tag/v1.8.7)をサポートしています。
@@ -297,7 +297,7 @@ ClickHouse Cloudは、いくつかの請求およびスケーリングイベン
多要素認証を使用している顧客は、電話を失ったりトークンを誤って削除した場合に使用できる回復コードを取得できるようになりました。初めてMFAに登録する顧客には、設定時にコードが提供されます。既存のMFAを持っている顧客は、既存のMFAトークンを削除し新しいトークンを追加することで回復コードを取得できます。
-### ClickPipes Update: Custom Certificates, Latency Insights, and More! {#clickpipes-update-custom-certificates-latency-insights-and-more}
+### ClickPipes update: custom certificates, latency insights, and more! {#clickpipes-update-custom-certificates-latency-insights-and-more}
ClickPipes、データをClickHouseサービスに取り込むための最も簡単な方法に関する最新の更新情報をお知らせできることを嬉しく思います！これらの新機能は、データ取り込みの制御を強化し、パフォーマンスメトリクスへの可視化を提供することを目的としています。
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/security/personal-data-access.md b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/security/personal-data-access.md
index 1ea5b38d0dd..6e72e55f9b9 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/cloud/security/personal-data-access.md
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/cloud/security/personal-data-access.md
@@ -39,7 +39,7 @@ ClickHouseが収集する個人データやその使用方法については、C
注意: `OrgID`を含むURLは、特定のアカウントの`OrgID`を反映するように更新する必要があります。
-### Current Customers {#current-customers}
+### Current customers {#current-customers}
弊社とアカウントをお持ちで、セルフサービスオプションで個人データの問題が解決しない場合、プライバシーポリシーに基づきデータ主体アクセス要求を提出できます。そのためには、ClickHouseアカウントにログインし、[サポートケース](https://console.clickhouse.cloud/support)を開いてください。これにより、あなたの身元を確認し、リクエストに対応するプロセスをスムーズに進めることができます。
diff --git a/i18n/jp/docusaurus-plugin-content-docs/current/managing-data/core-concepts/shards.md b/i18n/jp/docusaurus-plugin-content-docs/current/managing-data/core-concepts/shards.md
index 689171ef683..9072126e4e5 100644
--- a/i18n/jp/docusaurus-plugin-content-docs/current/managing-data/core-concepts/shards.md
+++ b/i18n/jp/docusaurus-plugin-content-docs/current/managing-data/core-concepts/shards.md
@@ -52,6 +52,7 @@ CREATE TABLE uk.uk_price_paid_simple_dist ON CLUSTER test_cluster
price UInt32
)
ENGINE = Distributed('test_cluster', 'uk', 'uk_price_paid_simple', rand())
+```
`ON CLUSTER` 句により、DDL ステートメントは [分散 DDL ステートメント](/sql-reference/distributed-ddl) となり、ClickHouse に `test_cluster` [クラスター定義](/architecture/horizontal-scaling#replication-and-sharding-configuration) にリストされているすべてのサーバーでテーブルを作成するよう指示します。分散 DDL には、[クラスターアーキテクチャ](/architecture/horizontal-scaling#architecture-diagram) において追加の [Keeper](https://clickhouse.com/clickhouse/keeper) コンポーネントが必要です。
diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md b/i18n/zh/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
index 567c07d020c..0d0e7992055 100644
--- a/i18n/zh/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
+++ b/i18n/zh/docusaurus-plugin-content-docs/current/cloud/reference/changelog.md
@@ -201,7 +201,7 @@ Users can schedule upgrades for their services. This feature is supported for En
[Golang](https://github.com/ClickHouse/clickhouse-go/releases/tag/v2.30.1), [Python](https://github.com/ClickHouse/clickhouse-connect/releases/tag/v0.8.11), and [NodeJS](https://github.com/ClickHouse/clickhouse-js/releases/tag/1.10.1) clients added support for Dynamic, Variant, and JSON types.
-### DBT support for Refreshable Materialized Views {#dbt-support-for-refreshable-materialized-views}
+### DBT support for refreshable materialized views {#dbt-support-for-refreshable-materialized-views}
DBT now [supports Refreshable Materialized Views](https://github.com/ClickHouse/dbt-clickhouse/releases/tag/v1.8.7) in the `1.8.7` release.
@@ -360,7 +360,7 @@ Compute-compute separation allows you to designate specific services as read-wri
Customers using multi-factor authentication can now obtain recovery codes that can be used in the event of a lost phone or accidentally deleted token. Customers enrolling in MFA for the first time will be provided the code on set up. Customers with existing MFA can obtain a recovery code by removing their existing MFA token and adding a new one.
-### ClickPipes Update: Custom Certificates, Latency Insights, and More! {#clickpipes-update-custom-certificates-latency-insights-and-more}
+### ClickPipes update: custom certificates, latency insights, and more! {#clickpipes-update-custom-certificates-latency-insights-and-more}
We're excited to share the latest updates for ClickPipes, the easiest way to ingest data into your ClickHouse service! These new features are designed to enhance your control over data ingestion and provide greater visibility into performance metrics.
@@ -414,7 +414,7 @@ ClickPipes is the easiest way to ingest data into ClickHouse Cloud. We're happy
## July 18, 2024 {#july-18-2024}
-### Prometheus Endpoint for Metrics is now Generally Available {#prometheus-endpoint-for-metrics-is-now-generally-available}
+### Prometheus endpoint for metrics is now generally available {#prometheus-endpoint-for-metrics-is-now-generally-available}
In our last cloud changelog, we announced the Private Preview for exporting [Prometheus](https://prometheus.io/) metrics from ClickHouse Cloud. This feature allows you to use the [ClickHouse Cloud API](/cloud/manage/api/api-overview) to get your metrics into tools like [Grafana](https://grafana.com/) and [Datadog](https://www.datadoghq.com/) for visualization. We're happy to announce that this feature is now **Generally Available**. Please see [our docs](/integrations/prometheus) to learn more about this feature.
@@ -449,13 +449,13 @@ This release also includes support for subscriptions via the [Microsoft Azure Ma
If you'd like any specific region to be supported, please [contact us](https://clickhouse.com/support/program).
-### Query Log Insights {#query-log-insights}
+### Query log insights {#query-log-insights}
Our new Query Insights UI in the Cloud Console makes ClickHouse's built-in query log a lot easier to use. ClickHouse's `system.query_log` table is a key source of information for query optimization, debugging, and monitoring overall cluster health and performance. There's just one caveat: with 70+ fields and multiple records per query, interpreting the query log represents a steep learning curve. This initial version of query insights provides a blueprint for future work to simplify query debugging and optimization patterns. We'd love to hear your feedback as we continue to iterate on this feature, so please reach out—your input will be greatly appreciated!
-### Prometheus Endpoint for Metrics (Private Preview) {#prometheus-endpoint-for-metrics-private-preview}
+### Prometheus endpoint for metrics (private preview) {#prometheus-endpoint-for-metrics-private-preview}
Perhaps one of our most requested features: you can now export [Prometheus](https://prometheus.io/) metrics from ClickHouse Cloud to [Grafana](https://grafana.com/) and [Datadog](https://www.datadoghq.com/) for visualization. Prometheus provides an open-source solution to monitor ClickHouse and set up custom alerts. Access to Prometheus metrics for your ClickHouse Cloud service is available via the [ClickHouse Cloud API](/integrations/prometheus). This feature is currently in Private Preview. Please reach out to the [support team](https://clickhouse.com/support/program) to enable this feature for your organization.
diff --git a/styles/ClickHouse/Headings.yml b/styles/ClickHouse/Headings.yml
index fbc5c83da1b..df83f9a0cef 100644
--- a/styles/ClickHouse/Headings.yml
+++ b/styles/ClickHouse/Headings.yml
@@ -7,38 +7,464 @@ match: $sentence
indicators:
- ":"
exceptions:
- - ClickHouse
- - Cloud
+ - "^[a-z]+[A-Z][a-zA-Z]*$" # Matches camelCase pattern
+ - "^[A-Z]+$" # Matches words that are entirely uppercase
+ - "^[A-Z][a-zA-Z]*[A-Z][a-zA-Z]*$" # Matches PascalCase system metrics
+ - "^[A-Z][a-zA-Z]*[A-Z][a-zA-Z]*_.*$" # Matches system metrics with underscores
+ - "^[a-z][a-z_]*[a-z]$" # Matches configuration parameter names with underscores
+ - API
+ - ANN
+ - APPEND
+ - DELETE FROM
+ - ALTER DELETE
+ - DROP PARTITION
+ - TRUNCATE
+ - Arrow
+ - AWS
+ - AWS
+ - Apache
+ - AggregatingMergeTree
+ - Amazon
+ - Amazon Web Services
- Azure
+ - Azure Blob Storage
+ - B2B
+ - BigQuery
+ - Bring Your Own Cloud
+ - Bun
+ - BYOC
+ - BYOC
+ - CPU
+ - CSV
+ - CDC
+ - CMEK
- CLI
+ - ClickHouse
+ - ClickHouse Cloud
+ - ClickHouse Keeper
+ - ClickPipe
+ - ClickPipes
+ - ClickPipes Connector
+ - ClickStack
+ - ClickStack
+ - CloudFormation
- Cosmos
+ - Customer Managed Encryption Keys
+ - DBT
+ - DBT
+ - DDL
+ - DNS
+ - Docker
- Docker
+ - Docker Compose
+ - DigitalOcean Spaces
+ - Duo SAML
+ - EDOT
+ - Elastic Agent
- Emmet
- - gRPC
+ - FAQ
+ - Filebeat
+ - Frequently Asked Questions
+ - GA
+ - GCS
+ - GET
+ - GitHub
+ - Go
+ - Google
+ - Google Cloud
+ - Google Cloud Platform
+ - Google Cloud Storage
+ - HTTP
+ - Helm
+ - HyperDX
+ - HyperDX
- I
+ - IdP
+ - JWT
+ - Java
+ - JSON
+ - JSON
+ - JSON format settings
+ - JSON settings
+ - Kafka
+ - Kafka
+ - Kafka Connect
+ - KMS
- Kubernetes
+ - LZ4
- Linux
- - macOS
- - Marketplace
+ - MERGETREE
+ - Microsoft
+ - Middle East
- MongoDB
+ - NPM
+ - NodeJS
+ - NodeJs
+ - OLAP
+ - OLTP
+ - ORC
+ - OSS
+ - OTel
+ - Okta
+ - OpenTelemetry
+ - Pandas
+ - Parquet
+ - PlanetScale
+ - Postgres
+ - PostgreSQL
+ - PSC
+ - Private Link
+ - Private Service Connect
+ - PrivateLink
+ - Prometheus
+ - Python
+ - RBAC
- REPL
+ - REPLACE
+ - REST
+ - Role-Based Access Control
+ - SAML
+ - SOC
+ - SQL
+ - SQL
+ - SSO
+ - S3
+ - SaaS
+ - SDK
- Studio
+ - TCP
+ - TDE
+ - TTL
+ - TiDB
+ - Time To Live
+ - Transparent Data Encryption
- TypeScript
+ - U.S.
+ - UDF
+ - UI
- URLs
- - Visual
+ - UAE
+ - User Defined Functions
+ - VLDB
- VS
+ - VPC
+ - Vitess
+ - Visual
- Windows
- - JSON
+ - Yandex.Metrica
+ - ZSTD
+ - chDB
+ - ch_bucket_us_east1
+ - ch_bucket_us_east4
+ - gRPC
+ - macOS
- MergeTree
+ - MySQL
- ReplacingMergeTree
+ - SLA
+ - SLAs
+ - Beta
+ - Preview
+ - Private Preview
+ - CDC
+ - DB
+ - URL
+ - Build
+ - Testing
+ - Packaging
+ - Tier
+ - Tiers
+ - Overview
+ - Console
+ - Endpoints
+ - Backups
+ - Thresholds
+ - Keys
+ - Routing
+ - Cloud
+ - Change Data Capture
+ - PostLinks
+ - PostHistory
+ - DateTime
+ - Stack Overflow
+ - Homebrew
+ - WSL
+ - London
+ - LowCardinality
+ - Query 2. Average price per year in London
+ - V24.5 changelog for Cloud
+ - V24.6 changelog for Cloud
+ - SELECT
+ - PARTITION BY
+ - Common Table Expressions
+ - FixedString(N)
+ - AvgMerge
+ - AvgMergeState
+ - AvgState
+ - CountResample
+ - Avgmap
+ - Avgmergestate
+ - Avgstate
+ - Countresample
+ - Grouparrayresample
+ - Grouparraydistinct
+ - Maxmap
+ - Minsimplestate
+ - Minmap
+ - Sumarray
+ - Summap
+ - Uniqarray
+ - Sumsimplestate
+ - CityHash
+ - FixedString(N)
+ - Avg merge
+ - Avg merge state
+ - Avg state
+ - Count resample
+ - avgMerge
+ - avgMergeState
+ - avgState
+ - countResample
+ - PREWHERE
+ - Splunk
+ - Retool
+ - Airbyte
+ - RawBLOB
+ - RowBinary
+ - MessagePack
+ - Protocol Buffers
+ - Cap'n Proto
+ - TabSeparated
+ - TabSeparatedRaw
+ - TabSeparatedWithNames
+ - TabSeparatedWithNamesAndTypes
+ - TabSeparatedRawWithNames
+ - TabSeparatedRawWithNamesAndTypes
+ - CustomSeparated
+ - CustomSeparatedWithNames
+ - CustomSeparatedWithNamesAndTypes
+ - BSONEachRow
+ - PrettyNoEscapes
+ - PrettyMonoBlock
+ - PrettyNoEscapesMonoBlock
+ - PrettyCompact
+ - PrettyCompactNoEscapes
+ - PrettyCompactMonoBlock
+ - PrettyCompactNoEscapesMonoBlock
+ - PrettySpace
+ - PrettySpaceNoEscapes
+ - PrettySpaceMonoBlock
+ - PrettySpaceNoEscapesMonoBlock
+ - CapnProto
+ - ProtobufSingle
+ - ProtobufList
+ - AvroConfluent
+ - MsgPack
+ - predefined_query_handler
+ - dynamic_query_handler
+ - static
+ - clickhouse-local
+ - agx
+ - ch-ui
+ - HouseOps
+ - LightHouse
+ - qryn
+ - clickhouse-cli
+ - clickhouse-flamegraph
+ - clickhouse-plantuml
+ - xeus-clickhouse
+ - ClickCat
+ - ClickHouse-Mate
+ - clickhouse-monitoring
+ - CKibana
+ - SeekTable
+ - chproxy
+ - KittenHouse
+ - ClickHouse-Bulk
+ - input_format_json_try_infer_numbers_from_strings
+ - input_format_json_try_infer_named_tuples_from_objects
+ - input_format_json_use_string_type_for_ambiguous_paths_in_named_tuples_inference_from_objects
+ - input_format_json_read_objects_as_strings
+ - input_format_json_read_numbers_as_strings
+ - input_format_json_read_bools_as_numbers
+ - input_format_json_read_bools_as_strings
+ - input_format_json_read_arrays_as_strings
+ - input_format_json_infer_incomplete_types_as_strings
+ - input_format_csv_try_infer_numbers_from_strings
+ - input_format_max_rows_to_read_for_schema_inference
+ - input_format_max_bytes_to_read_for_schema_inference
+ - column_names_for_schema_inference
+ - schema_inference_hints
+ - schema_inference_make_columns_nullable
+ - input_format_try_infer_integers
+ - input_format_try_infer_datetimes
+ - input_format_try_infer_datetimes_only_datetime64
+ - input_format_try_infer_dates
+ - input_format_try_infer_exponent_floats
+ - TSV
+ - TSKV
+ - format_template_row
+ - format_template_rows_between_delimiter
+ - format_template_resultset
+ - DataLens
+ - Avro
+ - Yandex
+ - Holistics
+ - ClickHouse Schema Flow Visualizer
+ - Holistics Software
+ - UPDATEs
+ - DELETEs
+ - INSERTs
+ - MERGEs
+ - DBT
+ - insert_overwrite
+ - Vector
+ - Nginx
+ - Dataflow
+ - QueryResult
+ - AsyncClient
+ - QueryContexts
+ - StreamContexts
+ - Easypanel
+ - Date32
+ - Node.js
+ - EventHubs
+ - Kafka Connect Sink
+ - String
+ - Map
+ - Nested
+ - flatten_nested
+ - Tableau Online
+ - SSL/TLS
+ - Delta Lake
+ - Embeddable
+ - Draxlr
+ - Rust
+ - Rockset
+ - Astrato
+ - Chartbrew
+ - Deepnote
+ - Zing Data
+ - Mitzu
+ - Tableau
+ - DataGrip
+ - Grafana
+ - Datadog
+ - Splunk Enterprise
+ - Explo
+ - Hashboard
+ - Luzmo
+ - QuickSight
+ - Metabase
+ - Superset
+ - clickhouse-static-files-disk-uploader
+ - OpenTelemetry
+ - Kubernetes
+ - Data Lake
+ - Unity
+ - Databricks
+ - Read Delta
+ - Google Cloud Run
+ - ClickStack
+ - Build-Your-Own
+ - Time-Series
+ - New Features
+ - Bug Fixes
+ - Bug Fix
+ - Backward Incompatible Changes
+ - Backward Incompatible Change
+ - Build Improvements
+ - Main Changes
+ - New Feature
+ - Experimental Feature
+ - Security Fix
+ - Performance Improvements
+ - Performance Improvement
+ - Documentation Updates
+ - Documentation Update
+ - Improved Workflow for Developing and Assembling ClickHouse
+ - Please Note When Upgrading
+ - Complete List of Changes
+ - Minor Changes
+ - ClickHouse Release
+ - Improvements
+ - Build Changes
+ - Experimental Features
+ - Table of Contents
+ - Upgrade Notes
+ - CVE-2021-42390
+ - CVE-2021-42391
+ - CVE-2021-25263
+ - CVE-2019-15024
+ - CVE-2019-16535
+ - CVE-2019-16536
+ - CVE-2019-18657
+ - CVE-2018-14672
+ - CVE-2018-14671
+ - CVE-2018-14668
+ - CVE-2018-14669
+ - CVE-2018-14670
+ - Performance Optimizations
+ - Code Cleanup
+ - Cloud Changelog
+ - CVE-2025-1385
+ - CVE-2024-6873
+ - CVE-2024-22412
+ - CVE-2023-47118
+ - CVE-2023-48298
+ - CVE-2023-48704
+ - CVE-2022-44011
+ - CVE-2022-44010
+ - CVE-2021-43304
+ - CVE-2021-43305
+ - CVE-2021-42387
+ - CVE-2021-42388
+ - CVE-2021-42389
+ - cmake-variants.yaml
+ - launch.json
+ - settings.json
+ - run-debug.sh
+ - tasks.json
+ - Third-Party Libraries
+ - Build Clickhouse with DEFLATE_QPL
+ - Run Benchmark with DEFLATE_QPL
+ - Rust Libraries
+ - DEFLATE_QPL
+ - Star Schema
+ - DockerHub
+ - cpp
+ - codespell
+ - aspell
+ - mypy
+ - AST
+ - "C\\+\\+"
+ - clang-tidy
+ - DataLakeCatalog
+ - MergeTree
+ - ReplacingMergeTree
+ - SummingMergeTree
- AggregatingMergeTree
- - DigitalOcean Spaces
- - Azure Blob Storage
- - VPC
- - BYOC
- - TiDB
- - PlanetScale
- - Vitess
+ - CollapsingMergeTree
+ - VersionedCollapsingMergeTree
+ - GraphiteMergeTree
+ - TinyLog
+ - StripeLog
+ - MaterializedView
+ - DROP/DETACH TABLE
+ - ReplicatedMergeTree
+ - AzureQueue
+ - DeltaLake
+ - AzureBlobStorage
+ - Hudi
+ - MinMax
+ - Bloom
+ - N-gram
+ - EmbeddedRocksDB
+ - HDFS
+ - Hive
+ - ORC
+ - Parquet
+ - NATS
- MySQL
- ClickPipe
- ClickPipes
@@ -69,3 +495,395 @@ exceptions:
- OTel
- SQL
- OSS
+ - URL
+ - RabbitMQ
+ - Iceberg
+ - TimeSeries
+ - AggregateFunction
+ - SimpleAggregateFunction
+ - FileLog
+ - system.asynchronous_insert_log
+ - system.asynchronous_loader
+ - system.asynchronous_metrics
+ - system.backups
+ - system.backup_log
+ - system.iceberg_history
+ - system.latency_buckets
+ - system.latency_log
+ - system.histogram_metrics
+ - system.licenses
+ - system.merges
+ - system.merge_tree_settings
+ - system.metric_log
+ - system.moves
+ - system.numbers
+ - system.numbers_mt
+ - system.one
+ - system.opentelemetry_span_log
+ - system.mutations
+ - system.parts_columns
+ - system.projections
+ - system.processors_profile_log
+ - system.query_cache
+ - system.query_condition_cache
+ - system.query_metric_log
+ - system.query_thread_log
+ - system.quota_limits
+ - system.query_views_log
+ - system.quotas_usage
+ - system.replicated_fetches
+ - system.role_grants
+ - system.replication_queue
+ - system.s3_queue_settings
+ - system.row_policies
+ - system.schema_inference_cache
+ - system.server_settings
+ - system.settings_changes
+ - system.session_log
+ - system.settings_profile_elements
+ - system.settings_profiles
+ - system.stack_trace
+ - system.storage_policies
+ - system.table_engines
+ - system.user_processes
+ - system.view_refreshes
+ - system.zookeeper_connection
+ - system.zookeeper_log
+ - AsynchronousHeavyMetricsCalculationTimeSpent
+ - AsynchronousHeavyMetricsUpdateInterval
+ - AsynchronousMetricsCalculationTimeSpent
+ - AsynchronousMetricsUpdateInterval
+ - BlockActiveTime_
+ - BlockDiscardBytes_
+ - BlockDiscardMerges_
+ - BlockDiscardOps_
+ - BlockDiscardTime_
+ - BlockInFlightOps_
+ - BlockQueueTime_
+ - BlockReadBytes_
+ - BlockReadMerges_
+ - BlockReadOps_
+ - BlockReadTime_
+ - BlockWriteBytes_
+ - BlockWriteMerges_
+ - BlockWriteOps_
+ - BlockWriteTime_
+ - CPUFrequencyMHz_
+ - AggregatorThreads
+ - AggregatorThreadsActive
+ - TablesLoaderForegroundThreads
+ - TablesLoaderForegroundThreadsActive
+ - TablesLoaderBackgroundThreads
+ - TablesLoaderBackgroundThreadsActive
+ - AsynchronousReadWait
+ - BackgroundBufferFlushSchedulePoolSize
+ - BackgroundBufferFlushSchedulePoolTask
+ - BackgroundCommonPoolSize
+ - BackgroundCommonPoolTask
+ - BackgroundDistributedSchedulePoolSize
+ - BackgroundDistributedSchedulePoolTask
+ - BackgroundFetchesPoolSize
+ - BackgroundFetchesPoolTask
+ - BackgroundMergesAndMutationsPoolSize
+ - BackgroundMergesAndMutationsPoolTask
+ - BackgroundMessageBrokerSchedulePoolSize
+ - BackgroundMessageBrokerSchedulePoolTask
+ - BackgroundMovePoolSize
+ - BackgroundMovePoolTask
+ - BackgroundSchedulePoolSize
+ - BackgroundSchedulePoolTask
+ - CacheDetachedFileSegments
+ - CacheDictionaryThreads
+ - CacheDictionaryThreadsActive
+ - CacheDictionaryUpdateQueueBatches
+ - CacheFileSegments
+ - ContextLockWait
+ - DatabaseCatalogThreads
+ - DatabaseCatalogThreadsActive
+ - DatabaseOnDiskThreads
+ - DatabaseOnDiskThreadsActive
+ - DestroyAggregatesThreads
+ - DestroyAggregatesThreadsActive
+ - DictCacheRequests
+ - DiskObjectStorageAsyncThreads
+ - DiskObjectStorageAsyncThreadsActive
+ - DiskSpaceReservedForMerge
+ - DistributedSend
+ - EphemeralNode
+ - FilesystemCacheElements
+ - FilesystemCacheReadBuffers
+ - FilesystemCacheSize
+ - QueryCacheBytes
+ - QueryCacheEntries
+ - UncompressedCacheBytes
+ - UncompressedCacheCells
+ - CompiledExpressionCacheBytes
+ - CompiledExpressionCacheCount
+ - MarkCacheBytes
+ - MarkCacheFiles
+ - GlobalThread
+ - LocalThread
+ - MemoryCode
+ - MemoryShared
+ - NetworkSend
+ - OSGuestTime
+ - OSNiceTime
+ - OSOpenFiles
+ - OSUptime
+ - OSUserTime
+ - PartMutation
+ - PartsActive
+ - PartsCompact
+ - PartsWide
+ - QueryThread
+ - RemoteRead
+ - SendScalars
+ - keeper_response_time_ms_bucket
+ - KEY_COLUMN_USAGE
+ - user_name/password
+ - username/ssh-key
+ - access_management
+ - grants
+ - user_name/networks
+ - user_name/profile
+ - user_name/quota
+ - user_name/databases
+ - readonly
+ - allow_ddl
+ - max_pipeline_depth
+ - histogram_metrics
+ - latency_log
+ - system.part_log
+ - system.parts
+ - system.processes
+ - system.query_log
+ - backup_log
+ - backups
+ - clickhouse-benchmark
+ - clickhouse-format utility
+ - compression
+ - core_dump
+ - crash_log
+ - encryption
+ - error_log
+ - Example
+ - File System
+ - graphite
+ - hsts_max_age
+ - Huge Pages
+ - include_from
+ - ldap_servers
+ - Linux Kernel
+ - listen_host
+ - listen_try
+ - logger
+ - macros
+ - merge_tree
+ - metric_log
+ - mysql_port
+ - openSSL
+ - part_log
+ - path
+ - prometheus
+ - proxy
+ - query_cache
+ - query_log
+ - s3queue_log
+ - ssh_server
+ - system.metrics
+ - system.quotas
+ - system.replicas
+ - system.resources
+ - system.roles
+ - system.scheduler
+ - system.settings
+ - system.table_engine
+ - system.tables
+ - system.text_log
+ - system.time_zones
+ - system.trace_log
+ - system.users
+ - system.warnings
+ - system.workloads
+ - system.zookeeper
+ - tcp_port
+ - tcp_ssh_port
+ - text_log
+ - timezone
+ - trace_log
+ - users_config
+ - zookeeper
+ - clickhouse_backupview
+ - jemalloc.active
+ - jemalloc.allocated
+ - jemalloc.arenas.all.dirty_purged
+ - jemalloc.arenas.all.muzzy_purged
+ - jemalloc.arenas.all.pactive
+ - jemalloc.arenas.all.pdirty
+ - jemalloc.arenas.all.pmuzzy
+ - jemalloc.background_thread.num_runs
+ - jemalloc.background_thread.num_threads
+ - jemalloc.background_thread.run_intervals
+ - jemalloc.epoch
+ - jemalloc.mapped
+ - jemalloc.metadata
+ - jemalloc.metadata_thp
+ - jemalloc.resident
+ - jemalloc.retained
+ - jemalloc.prof.active
+ - clickhouse-keeper-client
+ - toBFloat16
+ - toFloat32
+ - toFloat64
+ - toDecimal32
+ - toDecimal64
+ - toDecimal128
+ - toDecimal256
+ - toFloat32OrZero
+ - toFloat32OrNull
+ - toFloat32OrDefault
+ - toFloat64OrZero
+ - toFloat64OrNull
+ - toFloat64OrDefault
+ - toBFloat16OrZero
+ - toBFloat16OrNull
+ - toDecimal32OrZero
+ - toDecimal32OrNull
+ - toDecimal32OrDefault
+ - toDecimal64OrZero
+ - toDecimal64OrNull
+ - toDecimal64OrDefault
+ - toDecimal128OrZero
+ - toDecimal128OrNull
+ - toDecimal128OrDefault
+ - toDecimal256OrZero
+ - toDecimal256OrNull
+ - toDecimal256OrDefault
+ - reinterpretAsFloat32
+ - reinterpretAsFloat64
+ - accurateCast
+ - accurateCastOrNull
+ - accurateCastOrDefault
+ - toUnixTimestamp64Second
+ - toUnixTimestamp64Milli
+ - toUnixTimestamp64Micro
+ - toUnixTimestamp64Nano
+ - fromUnixTimestamp64Second
+ - fromUnixTimestamp64Milli
+ - fromUnixTimestamp64Micro
+ - fromUnixTimestamp64Nano
+ - accurateCast.*
+ - accurateCastOrNull.*
+ - accurateCastOrDefault.*
+ - fromDaysSinceYearZero32
+ - now64
+ - getMacro
+ - blockSize
+ - currentUser
+ - transform.*
+ - runningDifference
+ - lengthUTF8
+ - leftUTF8
+ - leftPadUTF8
+ - rightUTF8
+ - rightPadUTF8
+ - lowerUTF8
+ - upperUTF8
+ - isValidUTF8
+ - toValidUTF8
+ - reverseUTF8
+ - substringUTF8
+ - endsWithUTF8
+ - startsWithUTF8
+ - normalizeUTF8NFC
+ - normalizeUTF8NFD
+ - normalizeUTF8NFKC
+ - normalizeUTF8NFKD
+ - editDistanceUTF8
+ - initcapUTF8
+ - sparseGramsUTF8
+ - sparseGramsHashesUTF8
+ - base32Encode
+ - base32Decode
+ - tryBase32Decode
+ - base58Encode
+ - base58Decode
+ - tryBase58Decode
+ - base64Encode
+ - base64Decode
+ - tryBase64Decode
+ - CRC32
+ - CRC64
+ - positionUTF8
+ - multiSearchAllPositionsUTF8
+ - multiSearchFirstPositionUTF8
+ - multiSearchAnyUTF8
+ - ngramDistanceUTF8
+ - ngramSearchUTF8
+ - hasSubsequenceUTF8
+ - halfMD5
+ - MD4
+ - MD5
+ - sipHash64
+ - sipHash64Keyed
+ - sipHash128
+ - sipHash128Keyed
+ - sipHash128Reference
+ - sipHash128ReferenceKeyed
+ - cityHash64
+ - intHash32
+ - intHash64
+ - SHA1
+ - SHA224
+ - SHA256
+ - SHA512
+ - SHA512_256
+ - SHA1, SHA224, SHA256, SHA512, SHA512_256
+ - BLAKE3
+ - farmFingerprint64
+ - farmHash64
+ - javaHashUTF16LE
+ - metroHash64
+ - murmurHash2_32
+ - murmurHash2_64
+ - murmurHash3_32
+ - murmurHash3_64
+ - murmurHash3_128
+ - xxh3
+ - xxHash32
+ - xxHash64
+ - ngramSimHashUTF8
+ - wordShingleSimHashUTF8
+ - wyHash64
+ - ngramMinHashUTF8
+ - ngramMinHashArgUTF8
+ - wordShingleMinHashUTF8
+ - wordShingleMinHashArgUTF8
+ - keccak256
+ - uniqUpTo.*
+ - quantileBFloat16Weighted
+ - quantiles
+ - quantileExact
+ - uniqHLL12
+ - uniqCombined64
+ - nonNegativeDerivative.*
+ - MySQL Docs
+ - Postgres Docs
+ - simpleJSON.*functions
+ - atan2
+ - e
+ - exp10
+ - exp2
+ - intExp10
+ - intExp2
+ - log10
+ - log1p
+ - log2
+ - bitmaskToList.*
+ - bitmaskToArray.*
+ - bitPositionsToArray.*
+ - h3kRing
+ - stringToH3
+ - h3PointDistM
+ - h3PointDistKm
+ - h3PointDistRads