I have a Snap web app which serves some JS files and 1-pixel images (it's main task is to rather work fast then serve huge html/media content). There are several servers behind HAProxy.
I upgraded it from GHC 7.6 to 7.8, also upgrading some libs. After upgrade, app started leaking little by little (on all servers), ending in OOM every 15 minutes on 8GB-RAM machines (and much longer on 16Gb) and restarting afterwards.
The problem is, if I compile app for profiling and run app for some time, I can't see any memory leaks anymore. It just consumes 1 CPU and works in constant small memory.
So I wanted to ask for some general advices on how to find such a bottleneck, if running under profiling doesn't help much.
UPDATE: I noticed after playing with an app that if I remove -A100M runtime option it doen't OOM that fast, but with default value HAProxy's "sessions" gets to it's limit (so, basically it chokes). I'm playing with different RTS options now, hope some will help getting both, performance and long-lived memory consumption.
UPDATE 2: just for the record, I found that with -A30 rts option app, while being memory hungry, lives quite well. 8Gb machines OOM-kill app, but 16Gb one looks like this: http://i.imgur.com/3W9KpFS.png (green line is "RAM available", you can see deploy-procedure which restarted app on a graph). I'm happy with the result, but would be glad to know any techniques to profile memory of multi-threaded app anyway.
UPDATE 3: I'm voting to close this question as "too broad". In general, I see that if such generic set of tools that'll let you profile memory easier would exist, they'd definitely be documented elsewhere on wiki etc.
I have never used it myself but maybe ticky-ticky profiling could help? It's supposed to be immune to the optimization changes caused by ordinary profiling, but at the cost of being harder to interpret.
Basically compile and link the relevant modules with the -ticky and -rtsopts flags, and run with +RTS -rfoo.ticky flag to get heaps of data in foo.ticky.
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I'm having trouble with my Meteor app when it gets to its peak amount of traffic (peak for this is nothing, 1k visits, maybe 2,500 pageviews in a day). CPU usage spikes and never recovers, so I've taken to using Nodetime to monitor usage and I've been reloading the process (forever restart) to get things back to normal.
I'm fairly new to profiling, so finding the underlying cause has me at a loss for where to start. I'm fairly certain it has to do with my app's server code, but the profiling seems to point to the Fibers module as a "hotspot" which I understand aids in making my server code synchronous.
Below is a snippet from the profiling results. I hope someone can guide me in the right direction in troubleshooting this!
While I don't have a specific answer to your question, I have experience dealing with CPU issues for our production meteor app for so I can give you a list of things to investigate.
Upgrade to the latest version of meteor and the appropriate node version (see the changelog). As of this writing that's meteor 0.8.2 and node 0.10.28.
Read this and this article. The latter makes a great point that you really should always try to delay activation of subscriptions until you need them. In particular you may not need to publish anything for users who are not logged in. In my experience, meteor CPU problems have everything to do with subscriptions.
Be careful with observe and observeChanges. These are expensive and are easy to abuse. In particular:
Make sure you are calling stop() on your handles when they are no longer needed (consider using a package like publish-with-relations so this is done for you).
Fetch only the collections and fields that you absolutely need. Observe works by continually diffing objects (requires lots of CPU). The fewer and smaller objects you have, the less there is to compute.
Consider using smart-collections before it is retired. Use oplog tailing - this can make for a night and day difference in performance and CPU usage in your app.
Consider making some things not reactive (also mentioned in the articles above). For us that was a big win. We had one extremely expensive join that was used on two frequently accessed pages on the site. When it got to the point where the CPU was pegged at 100% about every 30 minutes I gave up on reactivity for that element and just did the join on the server and shipped the data to the client via a method call. I also created a server-side expiring cache for these results and stored them by user (special thanks to Matt DeBergalis for this suggestion).
Do a preventative nightly restart. I have a cron job that tells forever to restart our app once a day in the middle of the night. That brings the CPU down from ~10% to 1%. This seems like black magic, but the fact that the CPU usage changes after a reset leads me to believe this is a good idea.
Updated thoughts (1/13/14)
We migrated to oplog tailing as soon as it was available (meteor 0.7) and that made a big difference. Note that in order to get access to the oplog, you'll probably need to either host your own db or run a dedicated instance on the hosting provider of your choice. I'd also recommend adding the facts package to actually tell if its working.
There was a memory leak discovered in publish-with-relations, and as of this writing the atmosphere version (v0.1.5) hasn't been bumped to reflect these changes. If you are using it in production, I strongly recommend checking out the HEAD version and running it locally.
We stopped doing nightly restarts a couple of weeks ago. So far everything has been fine (fingers crossed).
Updated thoughts (7/2/14)
A few months ago we switched over to using an Elastic Deployment on mongohq. It's affordable, the performance has been great, and they even have a blog post which tells you how to enable oplog tailing.
I'd strongly recommend checking out kadira to help diagnose performance issues in your app. Also check out the academy articles which have a number of good tips in them.
I'm also having this problem. Actually there is an issue with 0.6.6.1, I run meteor --release 0.6.6 and the cpu is back to normal now.
I've been trying to track down a very slow, but persistent, native memory leak in a node.js app, and I've run out of strategies.
The process has what appears to be a level heap, but as the hours and days roll on, the RSS of the node.js process slowly grows. The process is a job handler that runs the same type of job for different parameters, over and over. The growth of the RSS of the process takes the same shape as the line plotting the cumulative number of jobs run, so each job run is somehow leaking a bit of memory.
Since the heap is more or less constant, the standard heap inspection tools don't seem to be much help.
Here's an example of what the memory consumption looks like:
Currently running on node 0.8.7. Each job does a number of database reads/writes, communicates with a redis instance, and does some web requests using mikael/request.
Have you updated to the newest release?
I know everyone says that :), I just felt like I should join the band wagon of updating my version of node.js on my production servers every two weeks when I think I have an issue. Sounds like a great idea doesn't it?
So I have been wondering the same thing, I have several node.js projects that I have been managing for a few months now (and also that I wrote last year). It seems that very slowly the V8 engine, or my node application, just eats memory and never frees it. (its slow enough that I only have to restart them every now and then)
Which is very stressful, especially considering that it should free up the RSS memory, or eventually peak out.
If you are interested in tracking objects being leaked inside of the runtime (and by that i mean javascript objects, functions, etc), mozilla has a very complete blog post about tracking down memory leaks and a few links to projects that can be used to do this.
For what ever reason they don't have this one on the list though. (it seems simple enough, I'm trying it out now on my own projects to see if it works, I tend to not get any of the V8 based ones to compile correctly)
heapdump and here is a link to a how to guide.
From my own experience the V8 engine seems to allocate memory, and hold onto it just incase it needs that exact same memory chunk later. Also my brother who has been using Node.js heavily for about 3 years has seen the same thing.
Also just for completeness (I know you already have), if any one would like to verify that you are not leaking memory inside of V8, an engineer from joyent has a pretty decent write up of how to track V8 memory leaks down.
This problem is killing the stability of my production servers.
To recap, the basic idea is that my node server(s) sometimes intermittently slow down, sometimes resulting in Gateway Timeouts. As best as I can tell from my logs, something is blocking the node thread (meaning that the incoming request is not accepted), but I cannot for the life of me figure out what.
The problem ranges in severity. Sometimes what should be <100ms requests take ~10 seconds to complete; sometimes they never even get accepted by the node server at all. In short, it is as though some random task is working and blocking the node thread for a period of time, thus slowing down (or even blocking) incoming requests; the one thing I can say for sure is that the need-to-fix-symptom is a "Gateway Timeout".
The issue comes and goes without warning. I have not been able to correlate it against CPU usage, RAM usage, uptime, or any other relevant statistic. I've seen the servers handle a large load fine, and then have this error with a small load, so it does not even appear to be load-related. It is not unusual to see the error around 1am PST, which is the smallest load time of the day! Restarting the node app does seem to maybe make the problem go away for a while, but that really doesn't tell me much. I do wonder if it might be a bug in node.js... not very comforting, considering it is killing my production servers.
The first thing I did was to make sure I had upgraded node.js to the latest (0.8.12), as well as all my modules (here they are). Of course, I also have plenty of error catchers in place. I'm not doing anything funky like printing out lots to the console or writing to lots of files.
At first, I thought it was outbound HTTP requests blocking the incoming socket, because the express middleware was not even picking up the inbound request, but I gave up the theory because it looks like the node thread itself became busy.
Next, I went through all my code with JSHint and fixed literally every single warning, including a few accidental globals (forgetting to write "var") but this didn't help
After that, I assumed that perhaps I was running out of memory. But, my heap snapshots via nodetime are looking pretty good now (described below).
Still thinking that memory might be an issue, I took a look at garbage collection. I enabled the --nouse-idle-notification flag and did some more code optimization to NULL objects when they were not needed.
Still convinced that memory was the issue, I added the --expose-gc flag and executed the gc(); command every minute. This did not change anything, except to occasionally make requests a bit slower perhaps.
In a desperate attempt, I setup the "cluster" module to use 2 workers and automatically restart them every 30 min. Still, no luck.
I increased the ulimit to over 10,000 and kept an eye on the open files. There seem to be < 300 open files (or sockets) per node.js app, and increasing the ulimit thus had no impact.
I've been logging my server with nodetime and here's the jist of it:
CentOS 5.2 running on the Amazon Cloud (m1.large instance)
Greater than 5000 MB free memory at all times
Less than 150 MB heap size at all times
CPU usage is less than 60% at all times
I've also checked my MongoDB servers, which have <5% CPU usage and no requests are taking > 100ms to complete, so I highly doubt there's a bottleneck.
I've wrapped (almost) all my code using Q-promises (see code sample), and of course have avoided Sync() calls like the plague. I've tried to replicate the issue on my testing server (OSX), but have had little luck. Of course, this may be just because the production servers are being used by so many people in so many unpredictable ways that I simply cannot replicate via stress tests...
Many months after I first asked this question, I found the answer.
In a nutshell, the problem was that I was not piping a big asset when transferring it from one server to another. In other words, I was downloading an image from one server, before uploading it to a S3 bucket. Instead of streaming the download into the upload, I downloaded the file into memory, and then uploaded it.
I'm not sure why this did not show up as a memory spike, or elsewhere in my statistics.
My guess is Mongoose. If you are storing large payloads in Mongo, Mongoose can be pretty slow due to how it builds the Mongoose objects. See https://github.com/LearnBoost/mongoose/issues/950 for more details on the problem. If this is the problem you wouldn't see it in Mongo itself since the query returns quickly, but object instantiation could take 75x the query time.
Try setting up timers around (process.hrtime()) before and after you the Mongoose objects are being created to see if that might be the problem. If this is the problem, I would switch to using the node Mongo driver directly instead of going through Mongoose.
You are heavily leaking memory, try setting every object to null as soon as you don't need it anymore! Read this.
More information about hunting down memory leaks can be found here.
Give special attention to having multiple references to the same object and check if you have circular references, those are a pain to debug but will help you very much.
Try invoking the garbage collector manually every minute or so (I don't know if you can do this in node.js cause I'm more of a c++ and php coder). From my years of experience working with c++ I can tell you the most likely cause of your application slowing down over time is memory leaks, find them and plug them, you'll be ok!
Also assuming you're not caching and/or processing images, audio or video in memory or anything like that 150M heap is a lot! Those could be hundreds of thousands or even millions of small objects.
You don't have to be running out of memory for your application to slow down... just searching for free memory with that many objects already allocated is a huge job for the memory allocator, it takes a lot of time to allocate each new object and as you leak more and more memory that time only increases.
Is "--nouse-idle-connection" a mistake? do you really mean "--nouse_idle_notification".
I think it's maybe some issues about gc with too many tiny objects.
node is single process, so watch the most busy cpu core is much important than the load.
when your program is slow, you can execute "gdb node pid" and "bt" to see what node is busy doing.
What I'd do is set up a parallel node instance on the same server with some kind of echo service and test that one. If it runs fine, you narrow down your problem to your program code (and not a scheduler/OS-level problem). Then, step by step, include the modules and test again. Certainly this is a lot of work, takes long and I dont know if it is doable on your system.
If you need to get this working now, you can go the NASA redundancy route:
Bring up a second copy of your production servers, and put a proxy in front of them which routes each request to both stacks and returns the first response. I don't recommend this as a perfect long-term solution but it should help significantly reduce issues in production now, and help you gather log data that you could replay to recreate the issues on non-production servers.
Obviously, this is straight-forward for read requests, but more complex for commands which write to the db.
We have a similar problem with our Node.js server. It didn't scale well for weeks and we had tried almost everything as you had. Our problem was in the implicit backlog value which is set very low for high-concurrent environments.
http://nodejs.org/api/http.html#http_server_listen_port_hostname_backlog_callback
Setting the backlog to a significantly higher value (e.g. 10000) as well as tune networking in our kernel (/etc/sysctl.conf on Linux) as described in manual section helped a lot. From this time forward we don't have any timeouts in our Node.js server.
Need to profile node process. i've some memory leaks in production, after some days of running node process.
i've tried node-inspector + v8, but it doesn't work, in new version of node-inspector there is no Profile tab. and in old version when i start profiling error is fired and debugging stopped.
i've also tried nodetime.com, but it doesn't show what i need, also it takes too much memory, it's not for production.
i've also tried dtrace (http://blog.nodejs.org/2012/04/25/profiling-node-js/) but it doesn't give me necessary information.
so what information i need for profiling memory:
get live instances, instances count, size in memory, instance types
do u know how to get that information?
You can try to use look module. It based on nodetime but works locally.
I've found node-memwatch useful.
The downside is you have to embed it in your application and have a bit of code for it, but it's useful for checking the heap at various places to see how much it changed after you did something.
How does IcedTea 6's performance stand up against Sun's own HotSpot on linux systems? I tried searching Google but Phoronix's test is the best I got, which is almost a year old now. Hopefully things have improved since then.
Also, once Sun completely open sources the JVM, would it be possible to implement it for Linux platforms such that a main module (Quickstarter in the Consumer JRE) starts up with the OS and loads the minimal Java kernel, regardless of any Java apps running. And then progressively load other modules as necessary. Might improve startup times.
so it will be within the answer: http://www.phoronix.com/scan.php?page=article&item=java_vm_performance&num=1 and http://www.phoronix.com/scan.php?page=article&item=os_threeway_2008&num=1
I'd expect SUN's stuff to be faster, but it really depends on all kinds of optimizations, so one version might be faster doing operation X, but in the next version it might not be as fast..
EDIT:
regarding kernel preloading: on linux you may use preload or alternatives to speed up app loading, without affecting the overall system performance (loading a Quickstarter equivalent will keep memory occupied at all times). Also, as far as i know, java loads lots of shared libraries, that are shared between apps, so i don't really see the point of building in-kernel support for this thing. I guess its easy to make a simple app that loads some libraries and does nothing after that(quickstarter), but i dont see this doing a big difference when loading apps, and in some cases it might even slow down the system(i'm thinking about ram usage, and memory swapping)