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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.

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.

Classifying failures and using controlled retries in Kafka prevents outages.

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

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.

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

You can update Elasticsearch indices without downtime using alias switching.

Jitter helps spread out client retries to avoid traffic spikes.

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

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