Context and Problem
When systems are under heavy load, they can become overwhelmed by a sudden influx of tasks or messages.
- High system load causes delays and performance degradation.
- Immediate task processing puts a strain on resources.
- Risk of system failure or crashes due to overburdened components.
- Difficulty in managing task bursts or unpredictable workloads.
Solution
The Queue-Based Load Leveling pattern helps manage spikes in load by placing tasks in a queue and processing them gradually.
- Offload incoming tasks into a queue for sequential processing.
- Use consumers to pick tasks from the queue and process them at a manageable rate.
- Dynamically adjust the rate at which tasks are processed to match available system resources.
- Scale consumers based on system load to ensure efficient task processing.
- Monitor queue depth and adjust consumer throughput as necessary.
Benefits
- Load smoothing
- Helps distribute system load evenly, preventing spikes from overloading the system.
- Increased system stability
- Prevents crashes or slowdowns by controlling task processing rate.
- Scalability
- Consumers can be added or removed based on system load.
- Resilience
- Queues help buffer tasks during high load, ensuring they are processed later without system failure.
Trade-offs
- Latency
- Tasks are delayed until processed from the queue.
- Queue management complexity
- Requires monitoring and managing the queue to avoid issues like backlog or failure.
- Storage cost
- Storing queued tasks can incur additional resource costs.
Issues and Considerations
- Queue overflow
- Ensure the queue doesn’t get overwhelmed, causing delays or failures in task processing.
- Consumer scaling
- Dynamically adjusting consumers to handle load spikes without overloading the system.
- Task prioritization
- Decide whether tasks in the queue should be processed in a specific order.
When to Use This Pattern
- When processing workloads that come in bursts.
- When you need to smooth out the impact of high system load.
- When you need to decouple heavy tasks from immediate system processing.
- When maintaining system stability during peak usage times is critical.