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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.

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

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

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

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

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

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

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

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

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

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

Distributed locks ensure only one server performs a task, avoiding duplication.

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

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

REST API design focuses on creating clean, efficient, and scalable web services.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

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

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

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

The USE Method helps find and analyze infrastructure bottlenecks in software systems.