Website: Updated Entry point script #4027
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📝 WalkthroughWalkthroughThe changes in this pull request involve updates to the Changes
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## staging #4027 +/- ##
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Coverage 11.73% 11.73%
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Files 114 114
Lines 15331 15331
Branches 319 319
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Hits 1799 1799
Misses 13532 13532 |
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Actionable comments posted: 1
📜 Review details
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📒 Files selected for processing (2)
src/website/README.md(1 hunks)src/website/entrypoint.sh(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- src/website/README.md
🔇 Additional comments (2)
src/website/entrypoint.sh (2)
Line range hint 4-4: LGTM: Proper error handling in place
The set -e command ensures the script fails fast if any command exits with an error, which is a good practice for container entry points.
16-16: Consider if 10-minute timeout indicates underlying issues
The timeout value of 600 seconds (10 minutes) is significantly higher than Gunicorn's default of 30 seconds. While this might temporarily resolve timeout issues, such a long timeout often indicates underlying performance problems that should be investigated and addressed.
Consider:
- Profiling the slow requests to identify bottlenecks
- Implementing asynchronous processing for long-running tasks
- Setting up monitoring to track request durations
Let's check for any related performance discussions or issues:
✅ Verification successful
Let me gather more information about potential long-running operations and API endpoints to better understand if the timeout is justified.
This will help us understand:
- The complexity of API endpoints
- Heavy database operations
- Existing timeout configurations
- Usage of background tasks for long operations
Let me gather more information about the application's workload patterns and potential long-running operations.
Based on the analysis of the codebase, I can now provide a comprehensive response about the timeout configuration.
The 10-minute timeout is justified for this application
The codebase reveals several legitimate reasons for the extended timeout:
-
The application handles complex data processing workflows including:
- Multiple database aggregation operations (
aggregate,filterqueries) - Heavy ETL operations with data validation and transformation
- Integration with external APIs (weather data, device monitoring)
- Batch processing of measurements and forecasts
- Multiple database aggregation operations (
-
The system implements proper background processing:
- Uses Celery tasks for long-running operations
- Implements caching strategies (Redis cache with various timeout configurations)
- Has monitoring and error handling in place
-
The timeout aligns with other timeouts in the system:
- External API calls have 100s timeout for weather data
- Cache timeouts range from 30 minutes to 2 hours
- Database operations have appropriate batch processing
The 10-minute timeout serves as a reasonable upper bound for complex analytical queries while preventing indefinite hanging of requests.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for performance-related discussions
gh issue list --repo airqo-platform/AirQo-api --search "in:title,body timeout OR performance OR slow" --limit 5
# Look for potential long-running operations in views
rg -A 5 "time.sleep|Thread.sleep|asyncio.sleep"
Length of output: 882
Script:
#!/bin/bash
# Look for API endpoints with potentially heavy operations
rg -g "*.py" "def.*@.*api_view" -A 5
# Search for database operations that might be slow
rg -g "*.py" "\.filter\(|\.aggregate\(|\.annotate\(" -B 2 -A 2
# Check for any existing timeout configurations or settings
rg -g "*.py" "timeout.*=|TIMEOUT" -B 2 -A 2
# Look for celery/background task usage
rg -g "*.py" "@.*task|@shared_task" -A 5
Length of output: 88165
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#!/bin/bash
# Look for heavy database queries or operations
rg -g "*.py" "\.join\(|\.select_related\(|\.prefetch_related\(|\.raw\(" -B 2 -A 2
# Check for any existing gunicorn configurations
fd -g "gunicorn*.py|gunicorn*.conf"
# Look for any heavy processing or computation in views
rg -g "*.py" "while.*:|for.*in.*:|\.apply\(|\.map\(|\.reduce\(" -B 2 -A 2
# Search for any existing performance monitoring or profiling code
rg -g "*.py" "profile|performance|monitor|timing" -B 2 -A 2
Length of output: 118105
| @@ -13,4 +13,4 @@ python manage.py collectstatic --noinput | |||
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| # Start Gunicorn server to serve the Django application | |||
| echo "Starting Gunicorn server..." | |||
| exec gunicorn core.wsgi:application --bind 0.0.0.0:8000 --workers 3 --log-level info | |||
| exec gunicorn core.wsgi:application --bind 0.0.0.0:8000 --timeout 600 --log-level info | |||
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Workers configuration is missing
The previous configuration included --workers 3, which has been removed. Running with a single worker (default) combined with a long timeout could lead to request queuing and reduced concurrency.
Consider applying this change:
-exec gunicorn core.wsgi:application --bind 0.0.0.0:8000 --timeout 600 --log-level info
+exec gunicorn core.wsgi:application --bind 0.0.0.0:8000 --timeout 600 --workers 3 --log-level infoThe number of workers should typically be (2 x number_of_cores) + 1. You might want to make this configurable via an environment variable:
-exec gunicorn core.wsgi:application --bind 0.0.0.0:8000 --timeout 600 --log-level info
+exec gunicorn core.wsgi:application --bind 0.0.0.0:8000 --timeout 600 --workers ${GUNICORN_WORKERS:-3} --log-level info📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| exec gunicorn core.wsgi:application --bind 0.0.0.0:8000 --timeout 600 --log-level info | |
| exec gunicorn core.wsgi:application --bind 0.0.0.0:8000 --timeout 600 --workers ${GUNICORN_WORKERS:-3} --log-level info |
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Summary by CodeRabbit
New Features
Bug Fixes