It addresses a diversity of market applications. The Lightspeed Live C4 server has up to 50% more encoding and processing power when compared to the current C3 server and is capable of running Telestream’s Live Capture and Live Stream applications simultaneously across up to eight SDI channels and IP inputs of live video data. Support for Avid MediaCentral | Production (interplay) check-in.Supports UHD/4K codecs including XAVC 300/480.8 concurrent HD captures for most available HD codecs or 2 concurrent UHD/4K up to 60p.We do need to find the root cause and fix it soon but this will keep the app afloat in the meantime. This is good as we have made sure we have the application running regardless of any crash. server listening on 3000 and worker 288097Įvery time a worker exits, a new one is being spun up. server listening on 3000 and worker 288086 Worker 10 has exited. server listening on 3000 and worker 288075 Worker 14 has exited. server listening on 3000 and worker 288064 Worker 7 has exited. server listening on 3000 and worker 288053 Worker 1 has exited. When we run it, we see server listening on 3000 and worker 287932 server listening on 3000 and worker 287921 server listening on 3000 and worker 287928 server listening on 3000 and worker 287922 server listening on 3000 and worker 287940 server listening on 3000 and worker 287959 server listening on 3000 and worker 287967 server listening on 3000 and worker 287984 server listening on 3000 and worker 287951 server listening on 3000 and worker 287981 server listening on 3000 and worker 287973 server listening on 3000 and worker 287952 Worker 5 has exited. We add condition before forking to make sure it was a crash, not killed or disconnected by the master. But for the child process, we can fork again when we see a crash. We still have to restart the application if the master process crashes. Here, we simulate a random crash and make sure the crash is happening in one of the worker processes not in the master process. This is a real gain, without any external mechanism, just with built in tools.Īnd now you have a NodeJS application scaled to run in all cores of a machine! We are now able to serve 100 thousand requests a second almost 4 times the previous performance. We see a drastic performance improvement over a single threaded NodeJS server. The master distributes the load in round robin fashion among the workers. When we now hit the web server multiple times, the requests will start to get handled by different worker processes with different process ids. I have 12 cores in my system, so it runs 12 processes. The output will vary from machine to machine depending on the number of cores in the system. The output looks like server listening on 3000 and worker 280474 server listening on 3000 and worker 280473 server listening on 3000 and worker 280483 server listening on 3000 and worker 280480 server listening on 3000 and worker 280492 server listening on 3000 and worker 280503 server listening on 3000 and worker 280510 server listening on 3000 and worker 280517 server listening on 3000 and worker 280504 server listening on 3000 and worker 280533 server listening on 3000 and worker 280526 server listening on 3000 and worker 280536 The master process listens at our HTTP server’s port and load balances all requests among the workers. When the child process executes, the cluster module’s isMaster returns false and it runs the program as usual. What fork does is really just runs another node process of the same program similar to running node index.js. It then loops over the number of CPUs of the machine and forks the current process using the cluster.fork() method. If it has more cores, it detects if the running process is the Master process with the help of cluster module. If the number of cores is 1, it just simply runs the application as it ran previously. Here we use the os module to detect the number of CPU cores the system has.
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