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

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.

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

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

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

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

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

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

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

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

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

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

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

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

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

Graceful degradation allows apps to function partially during service 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.

Classifying failures and using controlled retries in Kafka prevents outages.

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

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

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

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

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

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

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

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

Graceful shutdown in Kafka ensures no data loss during consumer termination.

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

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

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

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

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

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

RPC allows one service to call a function in another service over the network.

Availability is about access; reliability is about correct performance.

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

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

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

The Circuit Breaker pattern controls service calls to prevent failures from spreading.

Cache stampede occurs when many requests hit the database due to a cache miss.

Kafka rebalances consumer groups to manage partition assignments effectively.

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

Kafka consumers can pause and resume message processing to handle backpressure effectively.

Read-after-write consistency ensures users see their latest updates immediately.

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

Eager rebalancing revokes all assignments, while cooperative allows incremental changes.

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

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

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

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

Raft handles leader failure by allowing followers to elect a new leader through voting.

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

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

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

Caching helps applications respond faster by serving data from memory instead of hitting the database.