$ show ZYPGITA/astra-flux
AstraFlux is a distributed service framework that provides dependency injection, task scheduling with DAG dependencies, RPC with circuit breaker, worker management, and a web dashboard. It uses MongoDB for task persistence, Redis for worker registry and capacity tracking, and RabbitMQ for messaging. The framework follows a modular architecture with clear separation of concerns: DI container (FixtureManager), configuration system, controllers (scheduler, monitor), exports (APIs), and providers (implementations). Services and workers are dynamically loaded and communicate via message queues. The system demonstrates solid engineering practices in its implementation of distributed systems patterns, particularly in its DI container, retryable executors, distributed scheduler, and worker management components.
The LLM identified 4 files worth highlighting for reuse:
The FixtureManager implements a dependency injection container with singleton, global, and thread scopes. It supports factory functions with automatic dependency injection, handles cleanup via finalizers, and is thread-safe. This component could be extracted as a standalone DI container for any Python application, demonstrating strong understanding of DI patterns and lifecycle management.
The RetryableThreadPool and RetryableProcessPool provide thread and process pool executors with built-in retry mechanisms using exponential backoff. They feature progress tracking, batch submission, and proper resource management. These components demonstrate excellent concurrent programming practices and could be extracted as reusable executor libraries for any Python application requiring reliable background task processing.
The AdvancedCronScheduler implements a second-level precision cron scheduler with timezone support and distributed locking to prevent duplicate execution across clusters. It supports both thread and process execution modes, uses MongoDB for coordination, and provides comprehensive job lifecycle management. This component showcases sophisticated distributed systems knowledge and could be extracted as a standalone scheduling library.
The WorkerRegistry provides a thread-safe Redis client for managing worker metadata including registration, capacity tracking (max processes vs running processes), and process monitoring. It efficiently uses Redis data structures (hashes for metadata, sets for service discovery, sorted sets for process tracking) and implements proper connection pooling. This component demonstrates excellent Redis usage patterns and could be extracted as a reusable worker management library.