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The Token Bucket algorithm helps manage how many requests a server can handle.

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

API pagination helps manage large datasets by breaking them into smaller parts.

An API Gateway simplifies client access to backend services in microservices.

REST API design focuses on creating clean, efficient, and scalable web services.

Backpressure helps manage message flow in Kafka during traffic spikes.

Connection pooling reuses database connections but can cause issues if misconfigured.

API Gateway, Proxy, and Load Balancer serve different roles in managing requests and traffic in systems.

Poison messages in Kafka can cause infinite retries, risking system stability.

Backpressure prevents system overload by managing message processing rates in Kafka.

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Distributed backpressure helps manage the flow of work between services to prevent overload.

A timeout indicates a deadline was exceeded, not necessarily a failure.

The CAP Theorem explains the trade-offs in distributed systems during network failures.

Fetch limits control data size from Kafka, while poll records limit processed records.

Load shedding helps systems reject excess requests to maintain performance during high demand.