Una plataforma SaaS que ofrezca un sistema de colas reactivo, con plugins para múltiples bases de datos, SDKs para varios lenguajes y herramientas para optimización y monitoreo de rendimiento.

Repository: osvaldoandrade/codeq Author: github-actions[bot] Repository Overview codeQ is a reactive scheduling and completion system backed by persistent queues in KVRocks. This is a high-performance, production-ready queue system with multi-tenant isolation, distributed tracing, and comprehensive SDKs in Java, Python, Node.js, and Go. Recent Activity Highlights Completed Milestones Performance Optimization Sprint - Phase 3 Delivered - Subscription ListActive N+1 Pipelining (PR #413): Optimized ListActive operation by pipelining all HGET calls, reducing 100+ RTTs to 1-2 RTTs (99% latency reduction) - Performance gain: 245ms saved at 5ms Redis latency for 50 subscriptions - Enables batch event delivery improvements Previous Phase 2 Completions: - Enqueue path pipelining: 3 RTTs → 1 RTT (67% reduction, 60-70% latency reduction observed) - Claim finalization pipelining: Consolidated lease, state, and TTL operations into single request - Redis batch pipelining across admin operations: 80+ RTTs → 1-2 RTT (90% reduction) Comprehensive Performance Regression Testing Framework - Automated Go benchmarks on every PR - Benchmark regression CI workflow with 3-run averaging and statistical comparison - Results archived for 90-day retention with 1-year history for trend analysis - Sonic codec benchmarks validating 2-3x JSON performance improvement - GC pressure tracking across sustained workloads - Documentation: Staging validation runbook with acceptable performance ranges Production-Ready Architecture - Queue Sharding Support: Pluggable ShardSupplier interface with StaticShardSupplier for horizontal scaling across multiple KVRocks instances - Persistence Plugin System: Abstracted storage layer enabling PostgreSQL, DynamoDB, Cassandra plugins without core changes - DLQ Optimization: Migrated from LIST to SET data structure achieving O(1) removal performance - Full backward compatibility with migration guide for existing deployments Current Metrics & Load Testing Baseline Performance Achieved (all k6 scenarios): 0% errors in all tested scenarios Performance Improvements in Latest Release: - Enqueue latency: 60-70% reduction via pipelining - Admin operation RTTs: 80-90% reduction - JSON serialization: 2-3x faster with Sonic codec - GC pressure: 10-20% reduction, improved tail latencies Project Status & Recommendations Strengths - Mature performance optimization culture - Multi-SDK ecosystem - Enterprise-ready architecture - Comprehensive documentation Next Steps for Maintainers Recommended Priority Queue: - Expand Plugin Ecosystem: Implement PostgreSQL and DynamoDB plugins, document plugin development patterns - Performance Tuning Documentation: Create tier-specific config templates, troubleshooting guide, auto-scaling strategies - SDK Enhancement Opportunities: Add async/await for Node.js SDK, Spring Boot 3.0+ auto-configuration for Java, FastAPI middleware example for Python