Any concept.
clear in 60 seconds.

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

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

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

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

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

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

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

Distributed tracing helps track requests across microservices to find delays.

Kafka uses replication and in-sync replicas to ensure data is always available.

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

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

FAANG engineers use high-level design principles to create scalable systems.

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

Kafka uses retention to keep historical data and compaction to keep the latest state.

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

ZooKeeper coordinates distributed systems using an ensemble of servers and the ZAB protocol.

Search engines collect, index, retrieve, and rank documents to provide relevant results.

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

Observability helps engineers understand system behavior to debug production issues effectively.

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