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

Transactions ensure database operations are reliable using ACID properties.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

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

Distributed locks ensure only one server performs a task, avoiding duplication.

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

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

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

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

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

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

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

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

Choosing between a shared database and a database per service affects system design significantly.

The CAP Theorem explains the trade-offs in distributed systems during network failures.

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

Database isolation levels determine how transactions interact and affect data consistency.

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

Distributed tracing helps track requests across microservices to find delays.

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