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Disaster recovery ensures systems can restore services and data after major disruptions.

Active-passive architecture ensures a standby system takes over if the main system fails.

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

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

Point-in-Time Recovery allows databases to be restored to a specific moment before data loss.

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

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

Disaster Recovery Testing verifies that your recovery plan works effectively in real situations.

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

High Availability ensures your application remains operational even during server failures.

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

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

Global failover ensures applications remain available by redirecting traffic from failed regions to functioning ones.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

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

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

A single component's failure can take down your entire application, even with redundancy.

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

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

Late-arriving events can be dropped, updated, or routed based on system needs.

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

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

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

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