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Replicas keep your system available, while snapshots allow for data recovery.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

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

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

Fallbacks allow systems to handle failures safely and maintain user experience.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

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

Graceful degradation allows apps to function partially during service failures.

Partial failure means some services fail while others keep running, impacting system reliability.

Kafka consumers resume processing from the last committed offset after a crash.

Database replication helps keep data available and allows systems to handle more read requests.

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

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

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

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

Availability is about access; reliability is about correct performance.

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

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

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

Kafka lag recovery time is how fast consumers can process backlogged messages.

Database replication enhances data availability and read performance but has challenges like replication lag.

Retry storms occur when too many retries overload a failing service.

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

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

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

Kafka consumer reprocessing involves safely replaying events to correct system states.

Kafka uses replication and in-sync replicas to ensure data is always available.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Classifying failures and using controlled retries in Kafka prevents outages.

Elasticsearch uses master election and quorum to prevent split brain scenarios.

You can replay Kafka events safely by separating live and replay processes.

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

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

Alerting helps notify the right people about production issues quickly.

An error budget shows how much downtime is acceptable while meeting reliability goals.

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

Poison messages in Kafka can cause infinite retries, risking system stability.

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

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

Redis is a fast in-memory data store used for caching and low-latency applications.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.