Speed up your IO bound async specs by running them at the same time. Compatible with node 0.10+, and Mocha 2.3.5 - 5.2.x.
npm install --save-dev mocha.parallel
/**
* Generates a suite for parallel execution of individual specs. While each
* spec is ran in parallel, specs resolve in series, leading to deterministic
* output. Compatible with both callbacks and promises. Supports hooks, pending
* or skipped specs/suites via parallel.skip() and it.skip(), but not nested
* suites. parallel.only() and it.only() may be used to only wait on the
* specified specs and suites. Runnable contexts are bound, so this.skip()
* and this.timeout() may be used from within a spec. parallel.disable()
* may be invoked to use mocha's default test behavior, and parallel.enable()
* will re-enable the module. parallel.limit(n) can be used to limit the number
* of specs running simultaneously.
*
* @example
* parallel('setTimeout', function() {
* it('test1', function(done) {
* setTimeout(done, 500);
* });
* it('test2', function(done) {
* setTimeout(done, 500);
* });
* });
*
* @param {string} name Name of the function
* @param {function} fn The test suite's body
*/
In the examples below, imagine that setTimeout
is a function that performs
some async IO with the specified delay. This could include requests to your
http server using a module like supertest
or request
. Or maybe a headless
browser using zombie
or nightmare
.
Rather than taking 1.5s, the specs below run in parallel, completing in just over 500ms.
var parallel = require('mocha.parallel');
var Promise = require('bluebird');
parallel('delays', function() {
it('test1', function(done) {
setTimeout(done, 500);
});
it('test2', function(done) {
setTimeout(done, 500);
});
it('test3', function() {
return Promise.delay(500);
});
});
delays
✓ test1 (500ms)
✓ test2
✓ test3
3 passing (512ms)
Individual parallel suites run in series and in isolation from each other. In the example below, the two specs in suite1 run in parallel, followed by those in suite2.
var parallel = require('mocha.parallel');
parallel('suite1', function() {
it('test1', function(done) {
setTimeout(done, 500);
});
it('test2', function(done) {
setTimeout(done, 500);
});
});
parallel('suite2', function() {
it('test1', function(done) {
setTimeout(done, 500);
});
it('test2', function(done) {
setTimeout(done, 500);
});
});
suite1
✓ test1 (503ms)
✓ test2
suite2
✓ test1 (505ms)
✓ test2
4 passing (1s)
Uncaught exceptions are associated with the spec that threw them, despite them all running at the same time. So debugging doesn't need to be too difficult!
var parallel = require('mocha.parallel');
parallel('uncaught', function() {
it('test1', function(done) {
setTimeout(done, 500);
});
it('test2', function(done) {
setTimeout(function() {
// Thrown while test1 is executing
throw new Error('test');
}, 100);
});
it('test3', function(done) {
setTimeout(done, 500);
});
});
uncaught
✓ test1 (501ms)
1) test2
✓ test3
2 passing (519ms)
1 failing
1) uncaught test2:
Error: test
at null._onTimeout (fixtures/uncaughtException.js:11:13)
Hook behavior may not be as intuitive when ran using this library.
var parallel = require('mocha.parallel');
var assert = require('assert');
describe('suite', function() {
var i = 0;
beforeEach(function(done) {
// Invoked twice, before either spec starts
i++;
done();
});
parallel('hooks', function() {
beforeEach(function(done) {
// Invoked twice, before either spec starts
i++;
done();
});
it('test1', function(done) {
// Incremented by 4x beforeEach
setTimeout(function() {
assert.equal(i, 4);
done();
}, 1000);
});
it('test2', function(done) {
// Incremented by 4x beforeEach
setTimeout(function() {
assert.equal(i, 4);
done();
}, 1000);
});
});
});
Debugging parallel execution can be more difficult as exceptions may be thrown from any of the running specs. Also, the use of the word "parallel" is in the same spirit as other nodejs async control flow libraries, such as https://github.com/caolan/async#parallel, https://github.com/creationix/step and https://github.com/tj/co#yieldables This library does not offer true parallelism using multiple threads/workers/fibers, or by spawning multiple processes.