Natural Batching

Adaptive batching for increased throughput and low average latency

Also known as Smart Batching. There isn't much in the academic literature that I've found on this topic (perhaps there is another name?), but natural batching can be a highly effective mechanism to increase systems throughput while also keeping average latency low. The way it works is deceptively simple.

Let's consider the following scenario - we have a thread that is producing data, and another thread consuming data. The rate at which the producer adds data is random (for example, as a result of an external network process), and the consumer may take a varied amount of time to process the data as well. There are three mechanisms we can deploy for the consumer to accept data:

  • take one item at a time;
  • process n items at a time, waiting until either n items are available or a timer expires before processing;
  • and natural batching, in which the consumer takes up to n items without waiting.

What tends to happen with natural batching is that during normal steady state processing, batches are small - sometimes only a single item. As the producer bursts, or the consumer slows down, batch sizes elastically increase up to the maximum number of items. Once conditions return to normal, batch sizes drop back down to low numbers. So we're seeing the best of both worlds - no waits, and thus lower latency, with a smooth running system, and batches to increase throughput as conditions demand.

The Ring Buffer implementations in Agrona, Subscriptions in Aeron, LMAX's Disruptor and other libraries offer support for natural batching.

Note that Backpressure must be taken into account.


References

Martin Thompson: Smart Batching.

Change log

  • Added 13 December 2020
Metadata
🌿
reading time
2 min read
published
2020-12-13
last updated
2020-12-13
importance
low
review policy
continuous
Topics
Distributed Systems
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