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

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

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

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

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

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

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

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

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

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

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

Connection pooling reuses database connections but can cause issues if misconfigured.

Cache stampede occurs when many requests hit the database due to a cache miss.

Poison messages in Kafka can cause infinite retries, risking system stability.

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

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

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

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

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

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

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

Kafka uses state stores to remember previous events in stateful stream processing.

Cache invalidation is about knowing when to refresh cached data.

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

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

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

Keep-Alive allows reusing HTTP connections to improve efficiency and reduce overhead.

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

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

Graceful degradation allows apps to function partially during service failures.

Jitter helps spread out client retries to avoid traffic spikes.

Cache eviction policies determine which data to remove when caches are full.

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

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

DNS helps microservices find each other, but it has limitations that may require a service registry.