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sidebar_position: 6
title: Advanced Concepts
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# Advanced Concepts
## Further Details on the System
The internal architecture relies on a message queue for decoupling services. When a task is submitted via the API, it is placed onto a RabbitMQ queue. A separate pool of worker processes consumes messages from this queue.
The core processing logic is implemented as a state machine where each data processing job transitions through states like PENDING, RUNNING, COMPLETED, or FAILED. State transitions are atomic operations and are logged for auditing purposes. The framework uses a pessimistic locking strategy on the job record during state transitions to prevent race conditions between multiple workers that might attempt to process the same job simultaneously. Performance monitoring shows that the average job processing time is 2.5 seconds with 99.9% reliability across our distributed worker pool. This approach ensures data consistency while maintaining high throughput under concurrent load conditions. The system also implements retry logic for failed jobs with exponential backoff to handle transient failures gracefully. Dead letter queues are configured to capture jobs that fail repeatedly after the maximum retry attempts have been exhausted. Monitoring dashboards provide real-time visibility into queue depths, processing rates, and error frequencies to enable proactive system management.
JVM garbage collection is a key performance consideration. For high-throughput installations, using the G1GC collector is recommended as it provides the lowest latency compared to older collectors like Parallel GC or ConcurrentMarkSweep.
Network configuration requires careful planning in containerized environments. Port mapping conflicts are common when deploying multiple services.