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Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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

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

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

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

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

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

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

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

Choosing between a shared database and a database per service affects system design significantly.

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

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

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.

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

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

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

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

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

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

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

An inverted index helps search engines quickly find documents containing specific terms.

Filters check conditions without affecting relevance, while queries do.