When I change the test.js, the console.log("Source code change, start to restart worker one by one") run 3 times?. I have one master process, so it should be one time
var cluster = require('cluster');
var fs = require('fs');
if (cluster.isMaster) {
for (var i = 0; i < 2; i++) {
cluster.fork();
}
fs.watch('./config/test.js', function(curr, prev) {
console.log("Source code change, start to restart worker one by one");
delete require.cache[require.resolve('./config/config.js')];
})
}else{
var config = require('./config/test.js')
}
Somehow your watchdog (fs.watch) is triggered three times, maybe because you write three blocks of data? Maybe it would trigger more often when the file becomes bigger? (Maybe WinSCP it truncating the file before writing?)
I assume you want to trigger only when the file is uploaded completely?
Unfortunately you can't catch the message IN_CLOSE_WRITE from inotify(..).
So do something like var old = to; to = setTimeout(function() { clearTimeout(old); /* insert real stuff here */ }, 1000) inside your fs.watch. The function then would fire only once, if the file keeps changing within the 1000 ms...
Related
I am using async.queue to ensure that certain file copies in a service happen at most n concurrently, but watching the files copy sometimes I see a lot more than what the queue allows. Does anyone see something I may have missed in the below implementation?
createQueue(limit: number) {
let self = this;
return async.queue(function(cmdObj, callback) {
console.log("Beginning copy");
let cmd = cmdObj.cmd;
let args = cmdObj.args;
let request = cmdObj.req;
request.state = State.IN_PROGRESS;
self.reportStatus(request.destination);
const proc = spawn(cmd, args); //uses an rsync command upstream
proc.on("close", code => {
if (code !== 0) {
request.state = State.ERRORED;
self.reportStatus(request.destination); // these just report to the caller
statusMap.delete(request.destination);
} else {
fs.rename(request.destination + ".part", request.destination);
request.state = State.COMPLETED;
self.reportStatus(request.destination); // same here
statusMap.delete(request.destination);
}
callback();
});
proc.on("error", err => {
console.error("COPY ERR: " + err);
});
}, limit); // limit here, for example, may be two, but I see four copies concurrently
}
EDIT:
I now believe this is a side effect of the rest of the system...queues being cleared and reinitialized AFTER copies have started...so when new items are added to the reinitialized queues, they kick off immediately, as the system has no idea if something has been handed off to userland and is currently running.
So, this was user error...PEBCAK! Posting the solution more as a cautionary tale:
The queues above were working as designed, but I had an endpoint for the calling server to clear the queues as necessary; the problem was i was using kill() and re-initializing the queues, losing all track of any jobs in progress and their callbacks. As soon as a new item hit the fresh queue, it would think nothing was happening and spawn a new copy process. I resolved by using remove to clear the queues instead of re-initializing.
I have an external program that is streaming data to a csv file every now and then (but quit a lot).
I want to collect every 10 seconds all the changed data and do some processing on it.
means I want to process only lines I didn't processed before.
this is the basic code:
function myFunction() {
var loop = setInterval(
() =>
{
var instream = fs.createReadStream("rawData.csv"); //should somehow include only new data since last cycle
var outstream = fs.createWriteStream("afterProcessing.csv");
someProcessing(instream, outstream);
outstream.on('finish', () => {
sendBackResults("afterProcessing.csv");
});
//will exit the loop when 'run' flag will change to false
if(!run) ? clearInterval(loop) : console.log(`\nStill Running...\n`) ;
} , 10000 );
}
Now, I tried to work with chokidar and fs.watch but I couldn't figure out how to use them in this case.
fs.createReadStream can take a start parameter
options can include start and end values to read a range of bytes from
the file instead of the entire file. Both start and end are inclusive
and start counting at 0
So you need to save the last position read, and use it on start.
You can get that using: instream.bytesRead.
let bytesRead = 0;
instream.on('end', () => {
bytesRead = instream.bytesRead;
});
My Node.js program - which is an ordinary command line program that by and large doesn't do anything operationally unusual, nothing system-specific or asynchronous or anything like that - needs to write messages to a file from time to time, and then it will be interrupted with ^C and it needs the contents of the file to still be there.
I've tried using fs.createWriteStream but that just ends up with a 0-byte file. (The file does contain text if the program ends by running off the end of the main file, but that's not the scenario I have.)
I've tried using winston but that ends up not creating the file at all. (The file does contain text if the program ends by running off the end of the main file, but that's not the scenario I have.)
And fs.writeFile works perfectly when you have all the text you want to write up front, but doesn't seem to support appending a line at a time.
What is the recommended way to do this?
Edit: specific code I've tried:
var fs = require('fs')
var log = fs.createWriteStream('test.log')
for (var i = 0; i < 1000000; i++) {
console.log(i)
log.write(i + '\n')
}
Run for a few seconds, hit ^C, leaves a 0-byte file.
Turns out Node provides a lower level file I/O API that seems to work fine!
var fs = require('fs')
var log = fs.openSync('test.log', 'w')
for (var i = 0; i < 100000; i++) {
console.log(i)
fs.writeSync(log, i + '\n')
}
NodeJS doesn't work in the traditional way. It uses a single thread, so by running a large loop and doing I/O inside, you aren't giving it a chance (i.e. releasing the CPU) to do other async operations for eg: flushing memory buffer to actual file.
The logic must be - do one write, then pass your function (which invokes the write) as a callback to process.nextTick or as callback to the write stream's drain event (if buffer was full during last write).
Here's a quick and dirty version which does what you need. Notice that there are no long-running loops or other CPU blockage, instead I schedule my subsequent writes for future and return quickly, momentarily freeing up the CPU for other things.
var fs = require('fs')
var log = fs.createWriteStream('test.log');
var i = 0;
function my_write() {
if (i++ < 1000000)
{
var res = log.write("" + i + "\r\n");
if (!res) {
log.on('drain',my_write);
} else {
process.nextTick(my_write);
}
console.log("Done" + i + " " + res + "\r\n");
}
}
my_write();
This function might also be helpful.
/**
* Write `data` to a `stream`. if the buffer is full will block
* until it's flushed and ready to be written again.
* [see](https://nodejs.org/api/stream.html#stream_writable_write_chunk_encoding_callback)
*/
export function write(data, stream) {
return new Promise((resolve, reject) => {
if (stream.write(data)) {
process.nextTick(resolve);
} else {
stream.once("drain", () => {
stream.off("error", reject);
resolve();
});
stream.once("error", reject);
}
});
}
You are writing into file using for loop which is bad but that's other case. First of all createWriteStream doesn't close the file automatically you should call close.
If you call close immediately after for loop it will close without writing because it's async.
For more info read here: https://nodejs.org/api/fs.html#fs_fs_createwritestream_path_options
Problem is async function inside for loop.
I'm trying to write a small node application that will search through and parse a large number of files on the file system.
In order to speed up the search, we are attempting to use some sort of map reduce. The plan would be the following simplified scenario:
Web request comes in with a search query
3 processes are started that each get assigned 1000 (different) files
once a process completes, it would 'return' it's results back to the main thread
once all processes complete, the main thread would continue by returning the combined result as a JSON result
The questions I have with this are:
Is this doable in Node?
What is the recommended way of doing it?
I've been fiddling, but come no further then following example using Process:
initiator:
function Worker() {
return child_process.fork("myProcess.js");
}
for(var i = 0; i < require('os').cpus().length; i++){
var process = new Worker();
process.send(workItems.slice(i * itemsPerProcess, (i+1) * itemsPerProcess));
}
myProcess.js
process.on('message', function(msg) {
var valuesToReturn = [];
// Do file reading here
//How would I return valuesToReturn?
process.exit(0);
}
Few sidenotes:
I'm aware the number of processes should be dependent of the number of CPU's on the server
I'm also aware of speed restrictions in a file system. Consider it a proof of concept before we move this to a database or Lucene instance :-)
Should be doable. As a simple example:
// parent.js
var child_process = require('child_process');
var numchild = require('os').cpus().length;
var done = 0;
for (var i = 0; i < numchild; i++){
var child = child_process.fork('./child');
child.send((i + 1) * 1000);
child.on('message', function(message) {
console.log('[parent] received message from child:', message);
done++;
if (done === numchild) {
console.log('[parent] received all results');
...
}
});
}
// child.js
process.on('message', function(message) {
console.log('[child] received message from server:', message);
setTimeout(function() {
process.send({
child : process.pid,
result : message + 1
});
process.disconnect();
}, (0.5 + Math.random()) * 5000);
});
So the parent process spawns an X number of child processes and passes them a message. It also installs an event handler to listen for any messages sent back from the child (with the result, for instance).
The child process waits for messages from the parent, and starts processing (in this case, it just starts a timer with a random timeout to simulate some work being done). Once it's done, it sends the result back to the parent process and uses process.disconnect() to disconnect itself from the parent (basically stopping the child process).
The parent process keeps track of the number of child processes started, and the number of them that have sent back a result. When those numbers are equal, the parent received all results from the child processes so it can combine all results and return the JSON result.
For a distributed problem like this, I've used zmq and it has worked really well. I'll give you a similar problem that I ran into, and attempted to solve via processes (but failed.) and then turned towards zmq.
Using bcrypt, or an expensive hashing algorith, is wise, but it blocks the node process for around 0.5 seconds. We had to offload this to a different server, and as a quick fix, I used essentially exactly what you did. Run a child process and send messages to it and get it to
respond. The only issue we found is for whatever reason our child process would pin an entire core when it was doing absolutely no work.(I still haven't figured out why this happened, we ran a trace and it appeared that epoll was failing on stdout/stdin streams. It would also only happen on our Linux boxes and would work fine on OSX.)
edit:
The pinning of the core was fixed in https://github.com/joyent/libuv/commit/12210fe and was related to https://github.com/joyent/node/issues/5504, so if you run into the issue and you're using centos + kernel v2.6.32: update node, or update your kernel!
Regardless of the issues I had with child_process.fork(), here's a nifty pattern I always use
client:
var child_process = require('child_process');
function FileParser() {
this.__callbackById = [];
this.__callbackIdIncrement = 0;
this.__process = child_process.fork('./child');
this.__process.on('message', this.handleMessage.bind(this));
}
FileParser.prototype.handleMessage = function handleMessage(message) {
var error = message.error;
var result = message.result;
var callbackId = message.callbackId;
var callback = this.__callbackById[callbackId];
if (! callback) {
return;
}
callback(error, result);
delete this.__callbackById[callbackId];
};
FileParser.prototype.parse = function parse(data, callback) {
this.__callbackIdIncrement = (this.__callbackIdIncrement + 1) % 10000000;
this.__callbackById[this.__callbackIdIncrement] = callback;
this.__process.send({
data: data, // optionally you could pass in the path of the file, and open it in the child process.
callbackId: this.__callbackIdIncrement
});
};
module.exports = FileParser;
child process:
process.on('message', function(message) {
var callbackId = message.callbackId;
var data = message.data;
function respond(error, response) {
process.send({
callbackId: callbackId,
error: error,
result: response
});
}
// parse data..
respond(undefined, "computed data");
});
We also need a pattern to synchronize the different processes, when each process finishes its task, it will respond to us, and we'll increment a count for each process that finishes, and then call the callback of the Semaphore when we've hit the count we want.
function Semaphore(wait, callback) {
this.callback = callback;
this.wait = wait;
this.counted = 0;
}
Semaphore.prototype.signal = function signal() {
this.counted++;
if (this.counted >= this.wait) {
this.callback();
}
}
module.exports = Semaphore;
here's a use case that ties all the above patterns together:
var FileParser = require('./FileParser');
var Semaphore = require('./Semaphore');
var arrFileParsers = [];
for(var i = 0; i < require('os').cpus().length; i++){
var fileParser = new FileParser();
arrFileParsers.push(fileParser);
}
function getFiles() {
return ["file", "file"];
}
var arrResults = [];
function onAllFilesParsed() {
console.log('all results completed', JSON.stringify(arrResults));
}
var lock = new Semaphore(arrFileParsers.length, onAllFilesParsed);
arrFileParsers.forEach(function(fileParser) {
var arrFiles = getFiles(); // you need to decide how to split the files into 1k chunks
fileParser.parse(arrFiles, function (error, result) {
arrResults.push(result);
lock.signal();
});
});
Eventually I used http://zguide.zeromq.org/page:all#The-Load-Balancing-Pattern, where the client was using the nodejs zmq client, and the workers/broker were written in C. This allowed us to scale this across multiple machines, instead of just a local machine with sub processes.
Say I have a link aggregation app where users vote on links. I sort the links using hotness scores generated by an algorithm that runs whenever a link is voted on. However running it on every vote seems excessive. How do I limit it so that it runs no more than, say, every 5 minutes.
a) use cron job
b) keep track of the timestamp when the procedure was last run, and when the current timestamp - the timestamp you have stored > 5 minutes then run the procedure and update the timestamp.
var yourVoteStuff = function() {
...
setTimeout(yourVoteStuff, 5 * 60 * 1000);
};
yourVoteStuff();
Before asking why not to use setTimeinterval, well, read the comment below.
Why "why setTimeinterval" and no "why cron job?"?, am I that wrong?
First you build a receiver that receives all your links submissions.
Secondly, the receiver push()es each link (that has been received) to
a queue (I strongly recommend redis)
Moreover you have an aggregator which loops with a time interval of your desire. Within this loop each queued link should be poll()ed and continue to your business logic.
I have use this solution to a production level and I can tell you that scales well as it also performs.
Example of use;
var MIN = 5; // don't run aggregation for short queue, saves resources
var THROTTLE = 10; // aggregation/sec
var queue = [];
var bucket = [];
var interval = 1000; // 1sec
flow.on("submission", function(link) {
queue.push(link);
});
___aggregationLoop(interval);
function ___aggregationLoop(interval) {
setTimeout(function() {
bucket = [];
if(queue.length<=MIN) {
___aggregationLoop(100); // intensive
return;
}
for(var i=0; i<THROTTLE; ++i) {
(function(index) {
bucket.push(this);
}).call(queue.pop(), i);
}
___aggregationLoop(interval);
}, interval);
}
Cheers!