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Alerting helps notify the right people about production issues quickly.

The RED Method helps monitor microservices using Rate, Errors, and Duration metrics.

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

SLIs measure service performance, SLOs set reliability targets, and SLAs are customer agreements.

Failover ensures systems remain operational by managing leader changes during failures.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Availability is about access; reliability is about correct performance.

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

Monolithic and microservices architectures each have unique advantages and challenges for software development.

Microservices and modular monoliths differ mainly in deployment and communication methods.

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

Choosing the right boundaries for microservices is crucial for effective architecture.

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

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

Kafka consumer offsets help track message processing to avoid loss and duplication.

Backpressure helps manage message flow in Kafka during traffic spikes.

Older offsets can overwrite newer ones in Kafka if not handled properly.

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

Fail Fast means stopping quickly on errors, while Fail Safe ensures safety in failures.

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

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

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

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