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

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

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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.

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.

Availability is about access; reliability is about correct performance.

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

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

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

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