Any concept.
clear in 60 seconds.

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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

The choice between SQL and NoSQL databases depends on your application's specific needs.

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

Partition pruning helps databases skip unnecessary data scans, speeding up queries.

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

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

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

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

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

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

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

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