Any concept.
clear in 60 seconds.

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

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

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 retries and Dead Letter Queues to manage message processing failures.

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

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

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

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

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

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

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

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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

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

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

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

Kafka maintains message order within partitions using keys for routing.

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

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

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

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

Graceful shutdown in Kafka ensures no data loss during consumer termination.

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

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

Kafka uses heartbeats and timeouts to check if consumers are alive.

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

Kafka consumers resume processing from the last committed offset after a crash.

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

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

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

Kafka consumer lag shows how far behind consumers are from new messages.

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

Kafka consumer offsets help track message processing to avoid loss and duplication.

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