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Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Connection pooling reuses database connections but can cause issues if misconfigured.

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

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

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

Vector clocks help track the order and concurrency of events in distributed systems.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Choosing the right distributed lock depends on your system's needs and existing tools.

The Token Bucket algorithm helps manage how many requests a server can handle.

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

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

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

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

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

Read-after-write consistency ensures users see their latest updates immediately.

Transactions ensure database operations are reliable using ACID properties.

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