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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.

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

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

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

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

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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

Distributed tracing helps track requests across microservices to find delays.

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

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

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

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

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

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

Failover ensures systems remain operational by managing leader changes during failures.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

Graceful degradation allows apps to function partially during service failures.

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

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

Availability is about access; reliability is about correct performance.

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

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

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

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

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

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

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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

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

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

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