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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.

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.

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

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

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

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

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

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

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

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.

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.

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.

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.

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

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.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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