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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.

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.

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.

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

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

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

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

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.

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.

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.

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

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

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

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

Read-after-write consistency ensures users see their latest updates immediately.

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

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

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

Apache Kafka is a system for managing large volumes of events efficiently.

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

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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.

Raft consensus helps distributed systems elect leaders and replicate logs reliably.

matchIndex tracks what each follower has, while commitIndex shows what is committed.

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.