๐ฆ๐๐ฟ๐ฒ๐ฎ๐บ๐ถ๐ป๐ด - sequential movements, processed in real-time.
๐ค๐๐ฒ๐๐ถ๐ป๐ด - stored in a queue, processed sequentially.
Choosing between streaming and queuing isnโt just about picking Kafka over RabbitMQ. Itโs about making an architectural decision that will define how your system scales, evolves, and handles data over time.
Pick the wrong one, and youโll feel the consequences for years.
๐ช๐ต๐ฒ๐ป ๐ง๐ผ ๐๐ต๐ผ๐ผ๐๐ฒ ๐ฆ๐ง๐ฅ๐๐๐ ๐๐ก๐:
- You need historical data replay (e.g., debugging or analytics).
- Your system requires event order guarantees (e.g., processing transactions sequentially).
- Multiple consumers need to read the same event independently.
- Youโre handling high-throughput data flows that need efficient processing.
๐ช๐ต๐ฒ๐ป ๐ง๐ผ ๐๐ต๐ผ๐ผ๐๐ฒ ๐ค๐จ๐๐จ๐๐ก๐:
- You need guaranteed task completion (e.g., order processing or background jobs).
- Each task must be processed by only one consumer (e.g., no need for data replay).
- The message should be consumed once and discarded after processing.
- Built-in retries and dead-letter queues offer automatic failure handling.
๐๐ผ๐บ๐ฏ๐ถ๐ป๐ฒ ๐๐ผ๐๐ต: ๐ ๐ผ๐๐ ๐ฆ๐๐ฐ๐ฐ๐ฒ๐๐๐ณ๐๐น ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ๐ ๐๐ผ
The most powerful systems leverage bothโstreaming for real-time processing and queuing for task completion. An example might be:
Real-time tracking systems (using streaming for events like user activity or sensor data).
Task-based systems (using queuing to ensure reliable processing, such as background jobs or transactional workflows).
๐๐ฒ๐ ๐ฅ๐ฒ๐ฎ๐๐ผ๐ป๐ ๐ณ๐ผ๐ฟ ๐ ๐ถ๐๐๐๐ฒ
โ Events โ Tasks โ Queues handle tasks (e.g., payments), while streams handle continuous data (e.g., market prices).
โ Latency Matters โ Queues add delays; streams process in real-time.
โ No Replay โ Queues discard messages; streams allow reprocessing.
โTooling Bias โ Teams stick to familiar queues instead of streaming solutions like Kafka or Pulsar.
This awesome diagram by @boyney123
