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

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

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

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

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

Database indexing speeds up data retrieval by allowing quick lookups.

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

Transactions ensure database operations are reliable using ACID properties.

Kafka maintains message order within partitions using keys for routing.

Database isolation levels determine how transactions interact and affect data consistency.

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

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

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

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

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