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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.

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.

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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Jitter helps spread out client retries to avoid traffic spikes.

Consistency models determine what data reads in distributed systems can see.

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

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

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.