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

Keep-Alive allows reusing HTTP connections to improve efficiency and reduce overhead.

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

Distributed caching spreads data across multiple servers for better performance and reliability.

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

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

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

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

Cascading failures occur when one service's failure impacts others, causing widespread issues.

Choosing the right distributed lock depends on your system's needs and existing tools.

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

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

Network partitions occur when servers are operational but can't communicate, affecting system performance.

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

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

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

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Distributed backpressure helps manage the flow of work between services to prevent overload.

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

Failover ensures systems remain operational by managing leader changes during failures.