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

Network partitions occur when servers are operational but can't communicate, affecting system performance.

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.

Lamport Timestamps help order events in distributed systems while preserving causality.

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

The CAP Theorem explains the trade-offs in distributed systems during network failures.

Consistent hashing helps distribute data across servers with minimal movement.

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.

Physical time tells when events happen, while logical time tells their order.

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

Causality in distributed systems helps understand how events influence each other, beyond just timestamps.

Message delivery semantics define how messages are sent in distributed systems and handle failures.

Consensus ensures that distributed systems can agree on decisions even during failures.

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

Leader election helps distributed systems select one coordinator from many nodes.

Crash failures stop a service, while network failures disrupt communication.

Vector clocks help track the order and concurrency of events in distributed systems.

Graceful degradation allows apps to function partially during service failures.

Load shedding helps systems reject excess requests to maintain performance during high demand.

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

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

Paxos is a protocol that helps distributed systems agree on one value despite failures.

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

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

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.

Leader election ensures a single node coordinates tasks in distributed systems, even after failures.

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.

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Request-Response asks for a result, while Events notify that something has happened.

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

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.

A timeout indicates a deadline was exceeded, not necessarily a failure.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

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

Synchronous communication waits for a response, while asynchronous continues without waiting.

Raft and Paxos are protocols that help computers agree on values in distributed systems.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

Choosing between a shared database and a database per service affects system design significantly.

Kafka uses state stores to remember previous events in stateful stream processing.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

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

Bully, Raft, and ZooKeeper are distinct algorithms for leader election in distributed systems.

Quorum and majority help distributed systems maintain data consistency and availability.

Availability is about access; reliability is about correct performance.

Sharding splits databases into smaller parts to improve performance and scalability.

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

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

Kafka Streams uses local state for quick data access and changelogs for recovery.

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

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