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

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

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

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

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

Kafka uses state stores to remember previous events in stateful stream processing.

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

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

Physical time tells when events happen, while logical time tells their order.

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

Logs, metrics, and traces help engineers debug issues in software systems.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

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

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

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

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

The TCP 3-way handshake ensures reliable connection establishment between a client and server.

Latency is the time for one request, while throughput is how many requests are processed.