feat(docker): optimize concurrency performance and memory management #1689
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This commit consolidates several optimizations for crawl4ai in high-concurrency environments: 1. Browser Pool Optimization: - Implemented a tiered browser pool (Hot, Cold, Retired). - Added a browser retirement mechanism based on usage count (MAX_USAGE_COUNT) and memory pressure (MEMORY_RETIRE_THRESHOLD). - Added reference counting (active_requests) to ensure browser instances are not closed while in use. - Enhanced the pool janitor with adaptive cleanup intervals based on system memory. 2. Resource Loading Optimization: - Integrated optional CSS and Ad blocking to reduce memory footprint and improve QPS. - Decoupled resource filtering from text_mode to allow granular control. 3. Stability and Scalability: - Added mandatory release_crawler calls in API/Server handlers to prevent resource leaks. - Introduced environment variables to toggle these new features (defaulting to False for safe community adoption). - Added optional 5-minute pool audit logs for better observability. Co-authored-by: dylan.min <dylan.min@example.com>
…eanup docs - Refactor BrowserManager to dynamically block resources based on avoid_css and text_mode - Align text_mode behavior with community standards (no forced CSS blocking) - Add Top 20 curated ad and tracker patterns for performance - Restore and translate permanent browser logs in crawler_pool.py - Clean up models.py schema annotations and server.py docstrings - Add unit and functional tests for filtering flags
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@unclecode @ntohidi please review this MR |
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@mzyfree +1, better supported in high concurrence envs is needed |
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Look forward to it as well! The current performance is very POOR. The QPS is <1 for 2 CPU + 4GB RAM, tried to fetch 3 URLs in one request |
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@ntohidi @aravindkarnam Pls help... |
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Any update pls? |
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Anyone is looking at this issue pls? |
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Summary
This PR introduces a comprehensive optimization suite for
crawl4aiin high-concurrency Docker environments. It focuses on improving QPS (Queries Per Second) and ensuring long-term memory stability by re-engineering the browser pooling mechanism and introducing optional resource filtering.Key Design Principle: All new features are opt-in. By default, the system behaves exactly as before, ensuring zero impact on existing community users.
Core Enhancements:
active_requeststracking to prevent browsers from being closed while still processing requests, fixing common "Target closed" errors under load.New Configuration Options
These new features can be enabled via
BrowserConfigor Environment Variables:Engine Layer (
BrowserConfig)avoid_ads(bool, default:False): Enable intercepting and blocking ad/tracker network requests.avoid_css(bool, default:False): Enable blocking CSS resource loading to save CPU/Memory.Docker Layer (Environment Variables)
CRAWL4AI_BROWSER_RETIREMENT_ENABLED(default:false): Enable the usage/memory-based retirement mechanism.CRAWL4AI_PERMANENT_BROWSER_DISABLED(default:false): If true, disables the always-on permanent browser instance.CRAWL4AI_POOL_AUDIT_ENABLED(default:false): Enable detailed pool status logging every 5 minutes.CRAWL4AI_BROWSER_MAX_USAGE(default:100): Max requests per instance before retirement.CRAWL4AI_MEMORY_RETIRE_THRESHOLD(default:75): System memory % to trigger aggressive retirement.List of files changed and why
crawl4ai/async_configs.py: Added new parameters toBrowserConfig.crawl4ai/browser_manager.py: Implemented the network interception logic for resource filtering.deploy/docker/crawler_pool.py: Implemented the tiered pool, retirement, and audit logic.deploy/docker/api.py&deploy/docker/server.py: Updated withtry...finallyfor accurate reference counting.How Has This Been Tested?
False) values.Checklist:
Stress test performance
QPS increased by 40%
Resource with no OOM