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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.

Change Data Capture allows real-time syncing of database changes to other systems.

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

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

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

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

Transactions ensure database operations are reliable using ACID properties.

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

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

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

MongoDB uses sharding to distribute data across multiple servers for better scalability.