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

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

Latency, throughput, and response time are key metrics in system performance.

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

Keep-Alive allows reusing HTTP connections to improve efficiency and reduce overhead.

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

Crash failures stop a service, while network failures disrupt communication.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

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

Jitter helps spread out client retries to avoid traffic spikes.

L4 and L7 load balancers differ in how they route network traffic based on layers of the OSI model.

RPC allows one service to call a function in another service over the network.

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