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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Kafka consumer reprocessing involves safely replaying events to correct system states.

Kafka maintains message order within partitions using keys for routing.

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

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

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

Kafka consumers can pause and resume message processing to handle backpressure effectively.

Classifying failures and using controlled retries in Kafka prevents outages.

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

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

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

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