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

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

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

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

Jitter helps spread out client retries to avoid traffic spikes.

Backpressure helps manage message flow in Kafka during traffic spikes.

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Latency is the time for one request, while throughput is how many requests are processed.

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

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

The USE Method helps find and analyze infrastructure bottlenecks in software systems.