update yearweek doc

.github/workflows/build-check.yml

@@ -68,5 +68,4 @@ jobs:
                   yarn cache clean
                   export NODE_OPTIONS=--max-old-space-size=8192
                   yarn && yarn build
-
                   rm -rf build

.github/workflows/cron-deploy-website.yml

@@ -37,7 +37,9 @@ jobs:
                   npm install -g yarn
                   yarn cache clean
                   export NODE_OPTIONS=--max-old-space-size=8192
-                  yarn && yarn build
+                  yarn
+                  node ./scripts/update_github_info.js
+                  yarn build
                   touch build/.dummy
                   cp .asf-site.yaml ./build/.asf.yaml
                   cp versions.json ./build/
@@ -62,6 +64,8 @@ jobs:
                   endpoint: ${{ secrets.ALIYUN_OSS_ENDPOINT }}
                   folder: build
                   onlyUpload: true
+                  otherCacheControl: 0
+                  skipSetting: true
 
             - name: Remove pdf
               run: rm -rf ./build/assets/files/*.pdf

.github/workflows/manual-deploy-website.yml

@@ -45,7 +45,7 @@ jobs:
                   touch build/.dummy
                   cp .asf-site.yaml ./build/.asf.yaml
                   cp versions.json ./build/
-                  cp worker.js ./build/
+                  cp .htaccess ./build/
                   ls build
                   export DORIS_COMMIT=`git rev-parse HEAD`
 
@@ -59,6 +59,8 @@ jobs:
                   endpoint: ${{ secrets.ALIYUN_OSS_ENDPOINT }}
                   folder: build
                   onlyUpload: true
+                  otherCacheControl: 0
+                  skipSetting: true
 
             - name: Deploy Website
               if: ${{ github.event.inputs.branch == 'master' }}

.htaccess

@@ -0,0 +1 @@
+ErrorDocument 404 /404.html

auto_correct_format.py

@@ -0,0 +1,24 @@
+import os
+import sys
+import subprocess
+
+def list_files(directory):
+    """Recursively traverse the directory and print all .md and .mdx file names, then run autocorrect"""
+    for root, _, files in os.walk(directory):
+        for file in files:
+            if file.endswith(('.md', '.mdx')):
+                file_path = os.path.join(root, file)
+                print(file_path)
+                subprocess.run(["autocorrect", "--fix", file_path], check=True)
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python script.py <directory_path>")
+        sys.exit(1)
+
+    dir_path = sys.argv[1]
+    if os.path.isdir(dir_path):
+        list_files(dir_path)
+    else:
+        print("The entered path is invalid. Please enter a valid directory path.")
+

blog/Log-Analysis-How-to-Digest-15-Billion-Logs-Per-Day-and-Keep-Big-Queries-Within-1-Second.md

@@ -5,7 +5,7 @@
     'date': '2023-09-16',
     'author': 'Yuqi Liu',
     'tags': ['Best Practice'],
-    "image": '/images/china-unicom.png'
+    "image": '/images/telegram.jpeg'
 }
 ---
 
@@ -30,7 +30,7 @@ under the License.
 
 
 
-This data warehousing use case is about **scale**. The user is [China Unicom](https://en.wikipedia.org/wiki/China_Unicom), one of the world's biggest telecommunication service providers. Using Apache Doris, they deploy multiple petabyte-scale clusters on dozens of machines to support their 15 billion daily log additions from their over 30 business lines. Such a gigantic log analysis system is part of their cybersecurity management. For the need of real-time monitoring, threat tracing, and alerting, they require a log analytic system that can automatically collect, store, analyze, and visualize logs and event records.
+This data warehousing use case is about **scale**. The user is one of the world's biggest telecommunication service providers. Using Apache Doris, they deploy multiple petabyte-scale clusters on dozens of machines to support their 15 billion daily log additions from their over 30 business lines. Such a gigantic log analysis system is part of their cybersecurity management. For the need of real-time monitoring, threat tracing, and alerting, they require a log analytic system that can automatically collect, store, analyze, and visualize logs and event records.
 
 From an architectural perspective, the system should be able to undertake real-time analysis of various formats of logs, and of course, be scalable to support the huge and ever-enlarging data size. The rest of this post is about what their log processing architecture looks like, and how they realize stable data ingestion, low-cost storage, and quick queries with it.
 

blog/ai-unicorn-minimax-from-loki-and-built-a-pb-scale-logging-system-with-doris.md

@@ -6,6 +6,8 @@
     'date': '2024-08-29',
     'author': 'Apache Doris',
     'tags': ['Best Practice'],
+    'picked': "true",
+    'order': "4",
     "image": '/images/minimax-creator-of-talkie.jpeg'
 }
 

blog/ortege-studio-1-scaling-bitcoin-data-to-billions-of-records.md

@@ -7,7 +7,7 @@
     'author': 'Justin Trollip',
     'tags': ['Best Practice'],
     'picked': "true",
-    'order': "1",
+    'order': "2",
     "image": '/images/ortege-1.jpg'
 }
 

blog/ortege-studio-2-fine-tuning-apache-doris-for-maximum-performance-and-resilience.md

@@ -7,7 +7,7 @@
     'author': 'Justin Trollip',
     'tags': ['Best Practice'],
     'picked': "true",
-    'order': "2",
+    'order': "3",
     "image": '/images/ortege-2.jpg'
 }