Point-to-Point (Queue): One producer sends to a queue. One of potentially many consumers reads from it. Each message is processed exactly once. Use case: distributing work across a pool of workers. AWS SQS, RabbitMQ queues.

Publish-Subscribe (Topic): One producer publishes to a topic. ALL subscribed consumers receive a copy. Use case: broadcasting an event to multiple services (order_placed event → notify inventory service, billing service, email service simultaneously). AWS SNS, Kafka topics.

Dead Letter Queue (DLQ): When a message fails processing N times (after retries), it is moved to a DLQ for manual inspection instead of blocking the main queue. Essential for production reliability - a malformed message ("poison pill") can otherwise block an entire queue forever.

Competing Consumers: Multiple consumer instances read from the same queue, each processing different messages concurrently. This is how you horizontally scale message processing - run 50 workers instead of 1 to consume faster.

Message Ordering: Standard queues (SQS Standard, Kafka with multiple partitions) do NOT guarantee ordering. FIFO queues (SQS FIFO, Kafka with a single partition keyed by ID) guarantee order for a given partition key but limit throughput. For most use cases, exact ordering is not required and adds unnecessary cost.

Idempotency: At-least-once delivery (the safe default in most queues) means a message might be delivered multiple times due to network retries. Your consumer MUST be idempotent - processing the same message twice should have the same effect as processing it once. Use unique message IDs and check-before-process.