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

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

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

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

Redis is a fast in-memory data store used for caching and low-latency applications.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

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

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

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

Message queues allow services to communicate asynchronously, improving scalability and reliability.

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

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

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

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

Fallbacks allow systems to handle failures safely and maintain user experience.

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

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.