Backpressure Handling in Distributed Systems: Techniques, Implementations, and Workflows

Backpressure handling in distributed systems is crucial for managing the load efficiently and ensuring the system doesn’t fail under heavy traffic. When a system or service cannot process incoming requests as quickly as they arrive, backpressure occurs, potentially leading to system overload, failures, or data loss.

https://www.geeksforgeeks.org/back-pressure-in-distributed-systems/

Rate Limiting (Token Bucket)

The Token Bucket Algorithm is a common technique to control the rate of request processing. A “bucket” is filled with tokens at a constant rate. Each incoming request consumes a token. If the bucket is empty (no tokens left), the system either delays or rejects the request.

Use Cases:

• API Gateways: Prevent API abuse by limiting the number of requests a user can make in a specific time.
• Microservices: Regulate traffic between services to avoid cascading failures.

Implementation Considerations:

• Configure token generation rate based on system capacity.
• Decide whether to reject or queue excess requests.
• Use libraries like Guava’s RateLimiter for implementation in Java.

Workflow:

1. A token bucket is initialized with a fixed capacity.
2. Tokens are added to the bucket at a steady rate.
3. When a request arrives:

• If tokens are available, consume one and process the request.
• If no tokens are left, delay or reject the request.

Queue-Based Backpressure

Queues act as buffers between services. When a service processes requests slower than they arrive, the excess requests are temporarily held in the queue.

Use Cases:

• Message Brokers: Kafka, RabbitMQ, and others use queues to decouple producers and consumers.
• Task Processing: Systems like Celery or SQS-based workers handle asynchronous tasks.

Implementation Considerations:

• Set a maximum queue size to avoid memory overflow.
• Implement retry mechanisms for failed requests.
• Monitor queue length to trigger alerts or scaling events.

Workflow:

1. Incoming requests are added to a queue.
2. A worker service processes requests from the queue at its own pace.
3. If the queue exceeds a threshold, apply strategies like load shedding or scaling up consumers.

Load Shedding

Load shedding involves dropping requests when the system is overloaded, prioritizing important tasks over less critical ones.

Use Cases:

• E-commerce Sites: Prioritize checkout transactions over browsing requests during high traffic.
• Streaming Services: Ensure smooth playback for existing users by rejecting new connections.

Implementation Considerations:

• Use circuit breakers to detect and respond to overload.
• Classify requests into priority levels.
• Implement service-specific fallback mechanisms.

Workflow:

1. Monitor system metrics like CPU, memory, or request latency.
2. When metrics cross a threshold, start rejecting low-priority requests.
3. Allow high-priority requests to continue.

Backpressure Propagation

Backpressure propagation involves signaling upstream services to slow down when downstream services are overwhelmed.

Use Cases:

• Event Streaming: Kafka’s consumer-lag metrics can propagate backpressure to producers.
• Microservices Pipelines: Services in a pipeline communicate their processing capacity.

Implementation Considerations:

• Implement flow-control mechanisms like reactive streams (e.g., Project Reactor, RxJava).
• Ensure upstream services can handle throttling or delays.

Workflow:

1. Downstream services monitor their processing rate and queue sizes.
2. If overwhelmed, they send signals to upstream services.
3. Upstream services reduce their request rates accordingly.

Dynamic Scaling

Dynamic scaling adds or removes system resources (e.g., servers, containers) based on traffic.

Use Cases:

• Cloud Applications: AWS Auto Scaling, Kubernetes Horizontal Pod Autoscaler.
• Data Processing Pipelines: Scale up workers during batch processing.

Implementation Considerations:

• Define scaling policies and thresholds.
• Account for the time delay in resource provisioning.

Workflow:

1. Monitor metrics like request rate, CPU, and memory usage.
2. When metrics exceed thresholds, spin up additional resources.
3. Scale down resources during periods of low traffic.

Windowed Batch Processing

Group requests into batches and process them in fixed-size windows to control load.

Use Cases:

• Data Aggregation: Batch incoming telemetry data.
• Database Writes: Reduce the overhead of frequent, small writes.

Implementation Considerations:

• Optimize batch size for system capacity.
• Use frameworks like Apache Flink or Spark for streaming data.

Workflow:

1. Incoming requests are grouped into batches based on size or time window.
2. Each batch is processed together.
3. If batches accumulate, adjust batch size or processing frequency.

Timeouts and Retries

Define time limits for request processing and retry failed requests with exponential back-off.

Use Cases:

• APIs: Prevent client-side timeout issues.
• Distributed Systems: Retry failed RPC calls.

Implementation Considerations:

• Set reasonable timeout values to avoid premature failures.
• Use retry libraries (e.g., Resilience4j) for exponential back-off.

Workflow:

1. A request is sent with a defined timeout.
2. If the request fails, retry it after a delay (increasing exponentially).
3. Stop retrying after a maximum number of attempts.

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