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Transactions ensure database operations are reliable using ACID properties.

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

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

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

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.

Read-after-write consistency ensures users see their latest updates immediately.

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

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

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

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

Availability is about access; reliability is about correct performance.

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

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

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

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

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

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

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

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

The CAP Theorem explains the trade-offs in distributed systems during network failures.