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

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

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

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

Stateless servers are easier to scale because they don't store user session data locally.

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.

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

Dependency failures can disrupt applications, but resilience patterns can help manage them.

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.

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.

ZooKeeper coordinates distributed systems using an ensemble of servers and the ZAB protocol.