Cluster Computing with Node.js
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Join For FreeA single instance of Node runs in a single thread. To take advantage of multi-core systems we may want to launch a cluster of Node processes to handle the load!
That is to say if a system has 8 cores in it a single instance of node would use only one core, but to make the most of it, we can make use of all the cores with the wonderful concept of workers and more interestingly they can share the same port!
This module is still in Stability: 1 - Experimental phase, check out the code below to enjoy the awesomeness of the cluster!
var cluster = require('cluster'); var http = require('http'); var numCPUs = require('os').cpus().length; if (cluster.isMaster) { // Fork workers. require('os').cpus().forEach(function(){ cluster.fork(); }); // In case the worker dies! cluster.on('exit', function(worker, code, signal) { console.log('worker ' + worker.process.pid + ' died'); }); // As workers come up. cluster.on('listening', function(worker, address) { console.log("A worker with #"+worker.id+" is now connected to " +\ address.address +\ ":" + address.port); }); // When the master gets a msg from the worker increment the request count. var reqCount = 0; Object.keys(cluster.workers).forEach(function(id) { cluster.workers[id].on('message',function(msg){ if(msg.info && msg.info == 'ReqServMaster'){ reqCount += 1; } }); }); // Track the number of request served. setInterval(function() { console.log("Number of request served = ",reqCount); }, 1000); } else { // Workers can share the same port! require('http').Server(function(req, res) { res.writeHead(200); res.end("Hello from Cluster!"); // Notify the master about the request. process.send({ info : 'ReqServMaster' }); }).listen(8000); }
On a quad core machine, the output would be as below, for each hit two works are responding.
Number of request served = 0 A worker with #2 is now connected to 0.0.0.0:8000 A worker with #4 is now connected to 0.0.0.0:8000 A worker with #1 is now connected to 0.0.0.0:8000 A worker with #3 is now connected to 0.0.0.0:8000 Number of request served = 0 ... Number of request served = 2 .. Number of request served = 4 ... Number of request served = 6
One can also try : ab -n 1000 -c 5 http://127.0.0.1:8000 to benchmark this!
Benchmarking 127.0.0.1 (be patient) Completed 100 requests Completed 200 requests Completed 300 requests Completed 400 requests Completed 500 requests Completed 600 requests Completed 700 requests Completed 800 requests Completed 900 requests Completed 1000 requests Finished 1000 requests Server Software: Server Hostname: 127.0.0.1 Server Port: 8000 Document Path: / Document Length: 19 bytes Concurrency Level: 5 Time taken for tests: 0.171 seconds Complete requests: 1000 Failed requests: 0 Write errors: 0 Total transferred: 94000 bytes HTML transferred: 19000 bytes Requests per second: 5841.57 [#/sec] (mean) Time per request: 0.856 [ms] (mean) Time per request: 0.171 [ms] (mean, across all concurrent requests) Transfer rate: 536.24 [Kbytes/sec] received Percentage of the requests served within a certain time (ms) 50% 1 66% 1 75% 1 80% 1 90% 1 95% 2 98% 4 99% 5 100% 8 (longest request)
Published at DZone with permission of Hemanth HM, DZone MVB. See the original article here.
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