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

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

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

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

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.

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

Cache invalidation is about knowing when to refresh cached data.

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

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

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

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

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

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

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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

Composite indexes speed up database queries that filter or sort by multiple columns.

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

Elasticsearch offers two pagination methods: from & size for shallow pages and search_after for deep pages.

Sharding splits databases into smaller parts to improve performance and scalability.

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

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

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

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

Search engines collect, index, retrieve, and rank documents to provide relevant results.

Jitter helps spread out client retries to avoid traffic spikes.

Elasticsearch ILM automates data management through defined lifecycle phases.

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

Choose Elasticsearch for complex text searches, but use databases for structured queries.

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

B-Trees help databases find data quickly without scanning every row.

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

Kafka commit strategies determine how offsets are managed during message processing.