copied from: https://claude.ai/share/e4bed98a-9049-44b3-9aee-173bba941120
When a Kafka producer sets partitions explicitly, there are several important trade-offs to consider:
- Guaranteed Message Ordering: Messages sent to the same partition are guaranteed to maintain their order. This is crucial for use cases where sequence matters, like financial transactions or event sourcing.
- Predictable Data Locality: You can ensure related messages always go to the same partition, which helps with consumer processing efficiency and stateful operations.
- Load Distribution Control: You have fine-grained control over how messages are distributed across partitions, allowing you to optimize for your specific access patterns.
- Deterministic Behavior: Your application's behavior becomes more predictable since you know exactly where each message will land.
- Increased Complexity: Your producer code becomes more complex as you need to implement partitioning logic and handle edge cases like partition changes.
- Potential Hot Partitions: Poor partitioning logic can create uneven load distribution, causing some partitions to become bottlenecks while others remain underutilized.
- Reduced Flexibility: Kafka's built-in partitioning strategies (like round-robin or key-based hashing) are battle-tested and automatically adapt to topology changes.
- Operational Overhead: When partitions are added or removed, you may need to update your partitioning logic, whereas Kafka's default partitioners handle this automatically.
- Error-Prone: Manual partition assignment requires careful consideration of partition counts and can lead to out-of-bounds errors if not properly validated.
Use explicit partitioning when you need strict ordering guarantees or have specific data locality requirements. Rely on Kafka's default partitioning for most other cases, as it provides good load distribution with less complexity.