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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.

Logs, metrics, and traces help engineers debug issues in software systems.

Observability helps engineers understand system behavior to debug production issues effectively.

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

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

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.

Kafka uses heartbeats and timeouts to check if consumers are alive.

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

Kafka uses state stores to remember previous events in stateful stream processing.

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

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Kafka Streams uses local state for quick data access and changelogs for recovery.

Consistency models determine what data reads in distributed systems can see.

Kafka's subscribe() lets the group manage partitions, while assign() gives control to the application.

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

Kafka uses retention to keep historical data and compaction to keep the latest state.

Elasticsearch aggregations help summarize data using metrics for calculations and buckets for grouping.

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

Kafka consumers have three important states: Position, Committed Offset, and Application State.

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

Kafka commit strategies determine how offsets are managed during message processing.

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.

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

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.

Kafka Rebalance Listeners help manage state during partition changes in consumer groups.

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

Distributed tracing helps track requests across microservices to find delays.

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.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

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.

Elasticsearch aggregations help analyze data by grouping, calculating, and estimating values.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

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.