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

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

Graceful degradation allows apps to function partially during service failures.

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

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

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

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

Kafka uses retry topics and delayed retries to manage message failures efficiently.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

You can update Elasticsearch indices without downtime using alias switching.

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

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

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

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

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

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