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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.

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

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

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

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

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

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

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

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.

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

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

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

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

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.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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