Any concept.
clear in 60 seconds.

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

Database indexing speeds up data retrieval by allowing quick lookups.

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

Transactions ensure database operations are reliable using ACID properties.

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

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

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

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

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

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

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

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

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

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

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

Database replication enhances data availability and read performance but has challenges like replication lag.

Database replication helps keep data available and allows systems to handle more read requests.

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

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

Write-Ahead Log ensures data is safely recorded before changes are made.

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

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