Any concept.
clear in 60 seconds.

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.

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

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

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

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

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.

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.

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

N+1 redundancy means having one extra server to maintain capacity during a failure.

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

Availability is about access; reliability is about correct performance.

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

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

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.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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.

Container data can be lost unless stored in persistent volumes.

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

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