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Structured logging helps in quickly identifying issues by organizing log data consistently.

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

Write-Ahead Log ensures data is safely recorded before changes are made.

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

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

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

matchIndex tracks what each follower has, while commitIndex shows what is committed.

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

Raft consensus helps distributed systems elect leaders and replicate logs reliably.

Alerting helps notify the right people about production issues quickly.

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

Raft's safety rules guarantee that committed entries are preserved even after leader failures.

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

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

HLD provides an overview of system architecture, while LLD details implementation specifics.

Stateless servers are easier to scale because they don't store user session data locally.

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

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

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

Lamport Timestamps help order events in distributed systems while preserving causality.

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

Classifying failures and using controlled retries in Kafka prevents outages.