Implement Phase 1: Security and Monitoring Features#15
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kommunication wants to merge 2 commits intoGhurtchu:mainfrom
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Implement Phase 1: Security and Monitoring Features#15kommunication wants to merge 2 commits intoGhurtchu:mainfrom
kommunication wants to merge 2 commits intoGhurtchu:mainfrom
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This commit implements critical security and monitoring improvements for the distributed code execution engine. Security Enhancements: - Add API key-based authentication for all code execution endpoints - Implement rate limiting (100 requests/hour per API key, configurable) - Add comprehensive input validation: * Code size limits (100KB/50k chars) * Language validation * Dangerous pattern detection (rm -rf, wget, curl, etc.) - Remove security vulnerabilities (seccomp=unconfined from docker-compose) Monitoring & Observability: - Add Prometheus metrics collection: * Request counts by language and status * Execution duration histograms * Active execution gauges * Authentication failure tracking * Rate limit violation tracking * Input validation error tracking - Implement health check endpoint (/health) - Implement readiness check endpoint (/ready) - Add JVM metrics (memory, GC, threads) New Components: - monitoring/Metrics.scala: Prometheus metrics collection - security/Authentication.scala: API key authentication middleware - security/RateLimiter.scala: Actor-based rate limiting - security/InputValidator.scala: Multi-stage input validation Configuration: - Add security.rate-limit.max-requests config option - Add RATE_LIMIT_MAX_REQUESTS environment variable support Documentation: - Update README with authentication examples - Document rate limiting behavior - Document monitoring endpoints and available metrics - Add architecture improvements section Files Modified: - build.sbt: Add Prometheus client dependencies - ClusterSystem.scala: Integrate auth, rate limiting, validation, and metrics - docker-compose.yaml: Remove insecure seccomp=unconfined - application.conf: Add security configuration - README.md: Comprehensive documentation of new features
This commit implements async job execution capabilities and advanced resource management for the distributed code execution engine. Async Job Execution: - Add JobManager actor for centralized job state management - Implement job lifecycle tracking (Queued → Running → Completed/Failed) - Add REST API endpoints for async job operations: * POST /jobs - Submit jobs for async execution * GET /jobs/:id - Retrieve job status and results * GET /jobs - List all jobs with pagination - Add automatic job cleanup with configurable TTL (default: 1 hour) - Maintain backward compatibility with synchronous /lang/:language endpoint Advanced Resource Management: - Create ResourceConfig for per-language resource profiles - Implement configurable CPU, memory, and timeout limits per language: * Java: 2 CPUs, 256MB, 10s timeout * Python: 1 CPU, 50MB, 5s timeout * JavaScript: 1 CPU, 50MB, 5s timeout * Ruby: 1 CPU, 30MB, 5s timeout * Perl: 1 CPU, 20MB, 3s timeout * PHP: 1 CPU, 40MB, 5s timeout - Update CodeExecutor to use configurable resource limits - Update FileHandler and Worker to pass resource limits through execution chain Enhanced Monitoring: - Add job queue depth metrics (braindrill_queue_depth) - Add queued jobs gauge (braindrill_queued_jobs) - Add total jobs submitted counter (braindrill_jobs_submitted_total) - Track job state transitions in metrics New Components: - jobs/Job.scala: Job model with state management - jobs/JobManager.scala: Actor-based job queue and lifecycle management - jobs/JobJsonSupport.scala: JSON serialization for job API responses - config/ResourceConfig.scala: Per-language resource configuration Configuration: - Add jobs.ttl config option for job cleanup TTL - Add per-language resource profiles Documentation: - Add comprehensive async API documentation to README - Document per-language resource limits - Add job API usage examples - Update metrics documentation with new job-related metrics - Update TODO list to reflect Phase 2 completion Files Modified: - ClusterSystem.scala: Add JobManager, async job endpoints, and dual execution modes - Metrics.scala: Add job queue tracking metrics - Worker.scala: Integrate ResourceConfig for dynamic resource allocation - CodeExecutor.scala: Use configurable resource limits in docker execution - FileHandler.scala: Pass resource limits to CodeExecutor - application.conf: Add jobs configuration section - README.md: Extensive documentation of Phase 2 features Benefits: - Non-blocking job submission for long-running code execution - Job history and result retrieval - Optimized resource allocation per programming language - Better observability with job queue metrics - Foundation for future auto-scaling implementation
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This commit implements critical security and monitoring improvements for the distributed code execution engine.
Security Enhancements:
Monitoring & Observability:
New Components:
Configuration:
Documentation:
Files Modified: