concurrency-maximizer

A simple utility to manage and maximize concurrency across independent nodes without overloading external resources.

$3

Image you have a system that will process a list of a million tasks you would like to complete as fast as possible. Imagine that each task will take some amount of CPU and hit one or more external services, such as a database, ElasticSearch or other APIs.

Now imagine that there might be several other independent nodes running similar (but not identical) tasks.

What concurrency do you run these tasks to maximize efficiency and not overload your resources?

Normally you would put a rough limit on your nodes and a rough limit on the concurrency of your tasks and keep an eye on things.

This library attempts to solve this by starting slowly and monitoring the speed of the tasks as it increases concurrency. If it notices a sudden increase or decrease in speed, it'll scale up or down concurrency as is appropriate. So if an external resource suddenly slows up, it'll reduce concurrency to give that resource a chance to recover.

$3

``
const ConcurrencyMaximizer = require('concurrency-maximizer');

let maximizer = new ConcurrencyMaximizer(5, 0.25);

maximizer.map(someBigList, task => {
return Promise.delay(100).then(() => 5);
}).then(results => {
// results have been solve
});
`

Alternatively, if a package supports dynamic concurrency, you can do this via token directly:

`
const ConcurrencyMaximizer = require('concurrency-maximizer');

let maximizer = new ConcurrencyMaximizer(5, 0.25);

let doTask = task => {
let finishToken = maximizer.startItem();
// do work (possibly async)
finishToken();
};

etl.map(someBigList, doTask, { concurrency: () => maximizer.concurrency });
`

$3

The maximizer has 3 inputs:
* window size: The number of samples to take before making adjustments. Defaults to 4.
* flexibility: A multiplier that dictates how far you want to allow the system to deviate from the fastest window size that it has seen. Defaults to 0.25.
* maximumDuration: If the tasks being measured have timeouts, the maximizer can get into a pathological state where it finds that adding concurrency won't slow stuff down because it is always hitting a timeout of X seconds. If the task being measured does have a timeout on it, it is recommended to set a maximumDuration of the task to be some percent of the timeout. If a task starts taking over this value, it will always back off concurrency.

To start processing one task, simply call startItem and get back a function that you execute when the task is done executing.

Whatever is controlling concurrency of tasks should check for the latest concurrency property on the maximizer. etl supports dynamic concurrency, but most promise libraries do not.

You can use map on your ConcurrencyMaximizer` to maximize promise solving over that array.