💻 IT & Coding concepts,
clear in 60 seconds.

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

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

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.

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

Fallbacks allow systems to handle failures safely and maintain user experience.

Jitter helps spread out client retries to avoid traffic spikes.

Retry storms occur when too many retries overload a failing service.

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

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

Cascading failures occur when one service's failure impacts others, causing widespread issues.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.