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Cache invalidation is about knowing when to refresh cached data.

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

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

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

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

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

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

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

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

A CDN speeds up web applications by caching content closer to users.

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

Optimizing Elasticsearch involves using filters, caching, and measuring performance.

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

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

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

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

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

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

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

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

Consistent hashing helps distribute data across servers with minimal movement.

Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

API pagination helps manage large datasets by breaking them into smaller parts.

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

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

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

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.

A document in Elasticsearch isn't searchable right after it's saved due to the refresh process.

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

Graceful degradation allows apps to function partially during service failures.

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

Elasticsearch ILM automates data management through defined lifecycle phases.

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

Database indexing speeds up data retrieval by allowing quick lookups.

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

You can update Elasticsearch indices without downtime using alias switching.

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

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