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

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

RPO and RTO are key metrics for planning disaster recovery strategies.

Fail Fast means stopping quickly on errors, while Fail Safe ensures safety in failures.

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

Choosing between single and multi-region architecture affects system resilience and cost.

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

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

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

Data residency is crucial for compliance with laws about where data can be stored.

Graceful degradation allows apps to function partially during service failures.

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

Fault domains help prevent multiple servers from failing together in an architecture.

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

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

The CAP Theorem explains the trade-offs in distributed systems during network failures.

Failover ensures systems remain operational by managing leader changes during failures.

Crash failures stop a service, while network failures disrupt communication.

Active-Active Architecture uses multiple regions to serve traffic simultaneously for better resilience.

Availability is about access; reliability is about correct performance.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Raft's safety rules guarantee that committed entries are preserved even after leader failures.

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

Classifying failures and using controlled retries in Kafka prevents outages.

Retention policies help manage how long data is kept in databases.

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

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

Load shedding helps systems reject excess requests to maintain performance during high demand.