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

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

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

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

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

Primary shards partition data, while replica shards duplicate it for redundancy.

Distributed locks ensure only one server performs a task, avoiding duplication.

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

Consistency models determine what data reads in distributed systems can see.

Redis is a fast in-memory data store used for caching and low-latency applications.

Kafka can help ensure messages are processed once, but requires careful design.

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

Choosing the right distributed lock depends on your system's needs and existing tools.

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

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

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

Distributed caching spreads data across multiple servers for better performance and reliability.

Stateless servers are easier to scale because they don't store user session data locally.

Classifying failures and using controlled retries in Kafka prevents outages.

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

You can update Elasticsearch indices without downtime using alias switching.

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

Database per Service ensures each microservice owns its data, reducing dependencies.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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