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

Availability is about access; reliability is about correct performance.

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

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

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

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

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

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

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

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

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

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

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

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

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

Cache penetration occurs when requests for non-existent data overload the database.

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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

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

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

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

Kafka uses retention to keep historical data and compaction to keep the latest state.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

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

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

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

In distributed systems, timestamps don't always reflect the true order of events.

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

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