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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.

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

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

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

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.

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.

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.