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

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

Elasticsearch rescoring improves search result rankings using a two-stage process.

Elasticsearch distributes search queries across shards to find and retrieve results quickly.

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

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

The number of Elasticsearch nodes depends on storage, workload, and performance needs.

Elasticsearch uses shards for data distribution and replicas for redundancy.

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.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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