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

Database replication helps keep data available and allows systems to handle more read requests.

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.

In distributed systems, timestamps don't always reflect the true order of events.

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

Consistency models determine what data reads in distributed systems can see.

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

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

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

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

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.

Kafka uses replication and in-sync replicas to ensure data is always available.

Partial failure means some services fail while others keep running, impacting system reliability.

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

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

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

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

Kafka uses retries and Dead Letter Queues to manage message processing failures.

FAANG engineers use high-level design principles to create scalable systems.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Kafka can help ensure messages are processed once, but requires careful design.