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

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

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

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

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

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

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

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

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

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

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

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

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

Microservices can communicate using five key patterns based on their needs.

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.

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

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

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

Microservices and modular monoliths differ mainly in deployment and communication methods.

Availability is about access; reliability is about correct performance.

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

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

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

The Token Bucket algorithm helps manage how many requests a server can handle.

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

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Kafka's parallel processing depends on partitions, not just consumers or threads.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

Jitter helps spread out client retries to avoid traffic spikes.

The Circuit Breaker pattern controls service calls to prevent failures from spreading.

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

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

Primary shards partition data, while replica shards duplicate it for redundancy.

The number of Elasticsearch nodes depends on storage, workload, and performance needs.

Elasticsearch has different node roles that help manage data and scale clusters effectively.