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

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

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

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.

Kafka maintains message order within partitions using keys for routing.

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

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

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

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

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

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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

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

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

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

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

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

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

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

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

Availability is about access; reliability is about correct performance.

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

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

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

The TCP 3-way handshake ensures reliable connection establishment between a client and server.

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

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

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

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

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

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

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

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

API Gateway, Proxy, and Load Balancer serve different roles in managing requests and traffic in systems.

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

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

HTTP methods tell the server what to do, and status codes tell us the result.

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

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

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

Kafka's subscribe() lets the group manage partitions, while assign() gives control to the application.

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

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

Kafka Streams uses local state for quick data access and changelogs for recovery.

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

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