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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.

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

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

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

Transactions ensure database operations are reliable using ACID properties.

Database indexing speeds up data retrieval by allowing quick lookups.

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

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.

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

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

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

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

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.

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

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

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

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

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

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

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

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

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

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