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

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

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

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

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

Poison messages in Kafka can cause infinite retries, risking system stability.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

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

Kafka uses partitions to allow multiple consumers to process messages in parallel.

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

Kafka maintains message order within partitions using keys for routing.

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

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

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

Kafka commit strategies determine how offsets are managed during message processing.

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

gRPC supports four communication patterns: Unary, Server Streaming, Client Streaming, and Bidirectional Streaming.

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

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

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

Older offsets can overwrite newer ones in Kafka if not handled properly.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

Logs, metrics, and traces help engineers debug issues in software systems.

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

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

REST, RPC, and gRPC are different methods for communication in software systems.

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

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

Availability is about access; reliability is about correct performance.

gRPC allows efficient communication between services using a structured request-response model.

Latency, throughput, and response time are key metrics in system performance.

You can replay Kafka events safely by separating live and replay processes.

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.

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

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

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

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

Backpressure prevents system overload by managing message processing rates in Kafka.

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

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

Kafka consumers have three important states: Position, Committed Offset, and Application State.

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

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

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

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Kafka's `seek()` lets you replay only specific messages, saving time.