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

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

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

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

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

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

Observability helps engineers understand system behavior to debug production issues effectively.

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

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

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.

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

Retry storms occur when too many retries overload a failing service.

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

Distributed tracing helps track requests across microservices to find delays.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

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

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

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

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

Availability is about access; reliability is about correct performance.

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

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

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

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

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