PouchDB Benchmark

pouchdb-benchmark.js

var dbName = 'http://localhost:5984/couch-test',
	nDocs = 10000,
	batchSize = 1000,
	scrapFactor = 0,
	docs = [],
	testQuery = 'entries/sumTime',
	destroyDb = false,
	_log = console.log,
	db;

console.log = function() {
	var e = document.getElementById('log'),
		i;

	for (i = 0; i < arguments.length; i++) {
		e.innerHTML = e.innerHTML + arguments[i] + '\n';
	}

	_log.apply(console, arguments);
};

function setUp() {
	db = new PouchDB({name:dbName});
}

function tearDown() {
	if (destroyDb) {
		db.destroy(function(err, info) {
			if (err) {
				console.log(err);
			}
		});
	} else {
		db.bulkDocs({docs: docs.map(function(d) { return {_id: d.id, _rev: d.rev, _deleted: true}; })});
	}
}

function range(lo, hi) {
	var r = [],
		i;
	for (i = lo; i < hi; i++) {
		r.push(i);
	}
	return r;
}

function time(fn, times, sequential) {
	var i,
		start = Date.now(),
		result;

	function logStats() {
		var t = Date.now() - start;
		console.log(fn.name + ': ' + t + ' ms, ' + (t / times) + ' ms/run.');
	}

	if (!sequential){
		result = new Promise(function(resolve, reject) {
			Promise.all(range(0, times).map(fn)).
			then(function() {
				logStats();
				resolve();
			}, function(err) {
				reject(err);
			});
		});
	} else {
		result = new Promise(function(resolve, reject) {
			function next(i) {
				if (i < times) {
					fn(i).
					then(function() { next(i + 1); }).
					catch(reject);
				} else {
					logStats();
					resolve();
				}
			}

			next(0);
		});
	}

	return result;
}

function createScrap(n) {
	return Promise.all(range(0, Math.ceil(n / batchSize)).map(function(i) {
		return db.bulkDocs({
			docs: range(i * batchSize, Math.min((i + 1) * batchSize, n)).map(function(j) {
				return {
					type: 'scrap',
					data: j
				};
			})
		});
	}));
}

function createDocuments(i) {
	var activities = [
			'Planering', 'Möte', 'Sprängning', 'Grävarbete',
			'Transport'
		],
		projects = [
			'Lilla Torget', 'Västlänken', 'Kruthusgatan'
		],
		customers = [
			'Skanska', 'Sweco', 'ÅF', 'Privat'
		],
		descriptions = [
			'', '', '', 'Kunden galen', 'Grus i maskineriet', 'OBS! Fakturera först nästa månad'
		],
		entries = range(0, batchSize).map(function() {
			return {
				type: 'entry',
				activity: activities[i % activities.length],
				referencenumber: i % projects.length,
				project: projects[i % projects.length],
				customer: customers[i % customers.length],
				time: (((i % 9) + '').replace(/,/g, '.') * 1),
				description: descriptions[i % descriptions.length],
				date: new Date(i * 86400)
			};
		});

	return db.bulkDocs({docs: entries}, function(err, response) {
		var i;
		if (response) {
			for (i = response.length - 1; i >= 0; i--) {
				docs.push({id: response[i].id, rev: response[i].rev});
			}
		}
	});
}

function getDocuments() {
	var id = docs[Math.floor(Math.random() * docs.length)].id;
	return db.get(id, function(err, response) {
		if (err) {
			console.log(err);
		}
	});
}

function query(i) {
	return db.query(testQuery,
	function(err, response) {
		if (err) {
			console.log(err);
		}
	});
}

console.log('Pouch-Couch Benchmark');
console.log('=====================');
console.log(dbName);
console.log('nDocs: ' + nDocs + ', scrap: ' + nDocs * scrapFactor);

setUp();

createScrap(nDocs * scrapFactor).
then(function() { return time(createDocuments, nDocs / batchSize); }).
then(function() { return time(getDocuments, 1000); }).
then(function() { return time(query, 1000, true); }, function(err) {
	console.log(err);
}).
then(tearDown).
catch(function(e) { console.log(e); tearDown(); });

results.md

PouchDB Benchmarks

IndexedDB

Operations over all documents

# docs	Create	Get	Sum
1	649	0.65	1.35
10	51	0.4	3
100	8.02	0.4	20.85
1000	2.32	0.5	172.5
10000	4.31	0.5	3198

Operations over a subset of all documents

Using 10% of all docs

# docs	Create	Get	Sum	Total # docs
1	2	0.45	2.8	11
10	2.1	0.4	21.8	110
100	1.73	0.5	182.2	1100
1000	2.32	0.45	3163	11000

Tests for 10000 docs omitted, since it takes unreasonable amounts of time to complete.

CouchDB

Operations over all documents

# docs	Create	Get	Sum (temp)	Sum (view)
1	29	3	1.9	3.1
10	8.2	3.4	2.3	2.6
100	6.0	3.5	3.7	3.1
1000	5.8	3.5	8.5	3.2

Test for 10000 docs omitted, since inserting docs one by one stops working here (my guess: too many concurrent requests for Couch to handle). Switching to bulk insert, which changes performance profile for the create operation.

# docs	Create	Get	Sum (temp)	Sum (view)
10000	9.7	3.5	60	3.7

Operations over a subset of all documents

Again, logic must be changed to avoid absurd concurrency - query is now run sequentially instead of in paralell, possibly reducing performance somewhat (all cores not necessarily used efficiently).

# docs	Create	Get	Sum (view)	Total # docs
1	13.4	2.7	6.9	~100,000
10	11.0	2.7	7.2	~100,000
100	13.8	2.7	7.0	~100,000
1000	13.6	2.7	7.2	~101,000
10000	8.5	2.6	8.4	~110,000
10000	7.0	2.8	8.4	~1,000,000

What do these numbers represent? Is it milliseconds, seconds, minutes?