💻 IT & Coding concepts,
clear in 60 seconds.

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

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

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

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

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

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

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

Graceful degradation allows apps to function partially during service failures.

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

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

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

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.

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.

Availability is about access; reliability is about correct performance.

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

The Circuit Breaker pattern controls service calls to prevent failures from spreading.

Read-after-write consistency ensures users see their latest updates immediately.

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

Kafka consumer offsets help track message processing to avoid loss and duplication.

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

Older offsets can overwrite newer ones in Kafka if not handled properly.

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

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