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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.

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

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.

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.

Consistent hashing helps distribute data across servers with minimal movement.

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

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

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.

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

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

Eager rebalancing revokes all assignments, while cooperative allows incremental changes.

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

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.

Service Discovery helps microservices locate each other dynamically without fixed IP addresses.

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

gRPC supports four communication patterns: Unary, Server Streaming, Client Streaming, and Bidirectional Streaming.

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

ZooKeeper uses ephemeral and sequential nodes for managing distributed services effectively.

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

RPC allows one service to call a function in another service over the network.