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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.

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

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

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

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

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

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

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

Graceful degradation allows apps to function partially during service failures.

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

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

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

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

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

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

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

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

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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

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

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

You can update Elasticsearch indices without downtime using alias switching.

Jitter helps spread out client retries to avoid traffic spikes.

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

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

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

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

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

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

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