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

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

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

The Token Bucket algorithm helps manage how many requests a server can handle.

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

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

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Kafka can help ensure messages are processed once, but requires careful design.

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

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

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

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

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

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

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

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

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

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

Jitter helps spread out client retries to avoid traffic spikes.

Distributed locks ensure only one server performs a task, avoiding duplication.

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

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

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

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

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

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

A timeout indicates a deadline was exceeded, not necessarily a failure.

Choosing the right distributed lock depends on your system's needs and existing tools.

Classifying failures and using controlled retries in Kafka prevents outages.

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

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

Kafka's `seek()` lets you replay only specific messages, saving time.

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

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

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

ZooKeeper uses ephemeral and sequential nodes for managing distributed services effectively.

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

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

Raft and Paxos are protocols that help computers agree on values in distributed systems.

Message delivery semantics define how messages are sent in distributed systems and handle failures.

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

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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