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

Replicas keep your system available, while snapshots allow for data recovery.

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

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

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.

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

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

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.

Partial failure means some services fail while others keep running, impacting system reliability.

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.

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

Availability is about access; reliability is about correct performance.

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

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.

Jitter helps spread out client retries to avoid traffic spikes.

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

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

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

Kafka uses retention to keep historical data and compaction to keep the latest state.

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