I'm writing a crawler module which is calling it-self recursively to download more and more links depending on a depth option parameter passed.
Besides that, I'm doing more tasks on the returned resources I've downloaded (enrich/change it depending on the configuration passed to the crawler). This process is going on recursively until it's done which might take a-lot of time (or not) depending on the configurations used.
I wish to optimize it to be as fast as possible and not to hinder on any Node.js application that will use it.I've set up an express server that one of its routes launch the crawler for a user defined (query string) host. After launching a few crawling sessions for different hosts, I've noticed that I can sometimes get real slow responses from other routes that only return simple text.The delay can be anywhere from a few milliseconds to something like 30 seconds, and it's seems to be happening at random times (well nothing is random but I can't pinpoint the cause).I've read an article of Jetbrains about CPU profiling using V8 profiler functionality that is integrated with Webstorm, but unfortunately it only shows on how to collect the information and how to view it, but it doesn't give me any hints on how to find such problems, so I'm pretty much stuck here.
Could anyone help me with this matter and guide me, any tips on what could hinder the express server that my crawler might do (A lot of recursive calls), or maybe how to find those hotspots I'm looking for and optimize them?
It's hard to say anything more specific on how to optimize code that is not shown, but I can give some advice that is relevant to the described situation.
One thing that comes to mind is that you may be running some blocking code. Never use deep recursion without using setTimeout or process.nextTick to break it up and give the event loop a chance to run once in a while.
Related
I have a simple node app with 1 function that defines 1000+ functions inside it (without running them).
When I call this function (the wrapper) around 200 times the RSS memory of the process spikes from 100MB to 1000MB and immediately goes down. (The memory spike only happens after around 200~ calls, before that all the calls do not cause a memory spike, and all the calls after do not cause a memory spike)
This issue is happening to us in our node server in production, and I was able to reproduce it in a simple node app here:
https://github.com/gileck/node-v8-memory-issue
When I use --jitless pr --no-opt the issue does not happen (no spikes). but obviously we do not want to remove all the v8 optimizations in production.
This issue must be some kind of a specific v8 optimization, I tried a few other v8 flags but non of them fix the issue (only --jitless and --no-opt fix it)
Anyone knows which v8 optimization could cause this?
Update:
We found that --no-concurrent-recompilation fix this issue (No memory spikes at all).
but still, we can't explain it.
We are not sure why it happens and which code changes might fix it (without the flag).
As one of the answers suggests, moving all the 1000+ function definitions out of the main function will solve it, but then those functions will not be able to access the context of the main function which is why they are defined inside it.
Imagine that you have a server and you want to handle a request.
Obviously, The request handler is going to run many times as the server gets a lot of requests from the client.
Would you define functions inside the request handler (so you can access the request context in those functions) or define them outside of the request handler and pass the request context as a parameter to all of them? We chose the first option... what do you think?
anyone knows which v8 optimization could cause this?
Load Elimination.
I guess it's fair to say that any optimization could cause lots of memory consumption in pathological cases (such as: a nearly 14 MB monster of a function as input, wow!), but Load Elimination is what causes it in this particular case.
You can see for yourself when your run with --turbo-stats (and optionally --turbo-filter=foo to zoom in on just that function).
You can disable Load Elimination if you feel that you must. A preferable approach would probably be to reorganize your code somewhat: defining 2,000 functions is totally fine, but the function defining all these other functions probably doesn't need to be run in a loop long enough until it gets optimized? You'll avoid not only this particular issue, but get better efficiency in general, if you define functions only once each.
There may or may not be room for improving Load Elimination in Turbofan to be more efficient for huge inputs; that's a longer investigation and I'm not sure it's worth it (compared to working on other things that likely show up more frequently in practice).
I do want to emphasize for any future readers of this that disabling optimization(s) is not generally a good rule of thumb for improving performance (or anything else), on the contrary; nor are any other "secret" flags needed to unlock "secret" performance: the default configuration is very carefully optimized to give you what's (usually) best. It's a very rare special case that a particular optimization pass interacts badly with a particular code pattern in an input function.
I am trying to implement a Django web application (on Python 3.8.5) which allows a user to create “activities” where they define an activity duration and then set the activity status to “In progress”.
The POST action to the View writes the new status, the duration and the start time (end time, based on start time and duration is also possible to add here of course).
The back-end should then keep track of the duration and automatically change the status to “Finished”.
User actions can also change the status to “Finished” before the calculated end time (i.e. the timer no longer needs to be tracked).
I am fairly new to Python so I need some advice on the smartest way to implement such a concept?
It needs to be efficient and scalable – I’m currently using a Heroku Free account so have limited system resources, but efficiency would also be important for future production implementations of course.
I have looked at the Python threading Timer, and this seems to work on a basic level, but I’ve not been able to determine what kind of constraints this places on the system – e.g. whether the spawned Timer thread might prevent the main thread from finishing and releasing resources (i.e. Heroku Dyno threads), etc.
I have read that persistence might be a problem (if the server goes down), and I haven’t found a way to cancel the timer from another process (the .cancel() method seems to rely on having the original object to cancel, and I’m not sure if this is achievable from another process).
I was also wondering about a more “background” approach, i.e. a single process which is constantly checking the database looking for activity records which have reached their end time and swapping the status.
But what would be the best way of implementing such a server?
Is it practical to read the database every second to find records with an end time of “now”? I need the status to change in real-time when the end time is reached.
Is something like Celery a good option, or is it overkill for a single process like this?
As I said I’m fairly new to these technologies, so I may be missing other obvious solutions – please feel free to enlighten me!
Thanks in advance.
To achieve this you need some kind of scheduling tasks functionality. For a fast simpler implementation is a good solution to use the Timer object from the
Threading module.
A more complete solution is tu use Celery. If you are new, deeping in it will give you a good value start using celery as a queue manager distributing your work easily across several threads or process.
You mentioned that you want it to be efficient and scalable, so I guess you will want to implement similar functionalities that will require multiprocessing and schedule so for that reason my recommendation is to use celery.
You can integrate it into your Django application easily following the documentation Integrate Django with Celery.
I have inherited a website built on Expression Engine which is having a lot of trouble under load. Looking in the server console for the database I am seeing a lot of database writes (300-800/second)
Trying to track down why we are getting so much write activity compared to read activity and seeing things like
UPDATE `exp_snippets` SET `snippet_contents` = 'some content in here' WHERE `snippet_name` = 'member_login_form'
Why would EE be writing these to the database when no administrative changes are happening and how can I turn this behavior off?
Any other bottlenecks which could be avoided? The site is using an EE ad module so I cannot easily run it through Varnish since the ads need to change on each page load - looking to try and integrate DFP instead so they can be loaded asynchronously
There are a lot of front end operations that trigger INSERT and UPDATE operations. (Having to do with tracking users, hits, sessions, also generating hashes for forms etc.)
The snippets one tho seems very strange indeed I wouldn't think that snippets would call an UPDATE under normal circumstances. Perhaps the previous developer did something where the member_login_form (which has dynamic hash in it) is written to a snippet each time it is called? Not sure why you would do it, but there's a guess.
For general speed optimization see:
Optimizing ExpressionEngine
There are a number of configs in the "Extreme Traffic" section that will reduce the number of writes (tho not the snippet one which doesn't seem to be normal behavior).
For over a month I'm struggling with a very annoying memory leak issue and I have no clue how to solve it.
I'm writing a general purpose web crawler based on: http, async, cheerio and nano. From the very beginning I've been struggling with memory leak which was very difficult to isolate.
I know it's possible to do a heapdump and analyse it with Google Chrome but I can't understand the output. It's usually a bunch of meaningless strings and objects leading to some anonymous functions telling me exactly nothing (it might be lack of experience on my side).
Eventually I came to a conclusion that the library I had been using at the time (jQuery) had issues and I replaced it with Cheerio. I had an impression that Cheerio solved the problem but now I'm sure it only made it less dramatic.
You can find my code at: https://github.com/lukaszkujawa/node-web-crawler. I understand it might be lots of code to analyse but perhaps I'm doing something stupid which can be obvious strait away. I'm suspecting the main agent class which does HTTP requests https://github.com/lukaszkujawa/node-web-crawler/blob/master/webcrawler/agent.js from multiple "threads" (with async.queue).
If you would like to run the code it requires CouchDB and after npm install do:
$ node crawler.js -c conf.example.json
I know that Node doesn't go crazy with garbage collection but after 10min of heavy crawling used memory can go easily over 1GB.
(tested with v0.10.21 and v0.10.22)
For what it's worth, Node's memory usage will grow and grow even if your actual used memory isn't very large. This is for optimization on behalf of the V8 engine. To see your real memory usage (to determine if there is actually a memory leak) consider dropping this code (or something like it) into your application:
setInterval(function () {
if (typeof gc === 'function') {
gc();
}
applog.debug('Memory Usage', process.memoryUsage());
}, 60000);
Run node --expose-gc yourApp.js. Every minute there will be a log line indicating real memory usage immediately after a forced garbage collection. I've found that watching the output of this over time is a good way to determine if there is a leak.
If you do find a leak, the best way I've found to debug it is to eliminate large sections of your code at a time. If the leak goes away, put it back and eliminate a smaller section of it. Use this method to narrow it down to where the problem is occurring. Closures are a common source, but also check for anywhere else references may not be cleaned up. Many network applications will attach handlers for sockets that aren't immediately destroyed.
So, after reading a little about non-blocking code, does...
response.write(thisWillTakeALongTime());
...block the process? If so, do we need to pass the response into pretty much every slow function call we make, and have that function handle the response?
Thanks for helping to clarify!
Yes, it will block the event loop. And passing the response object into the slow function won't help, no matter where you call the slow function you will be blocking the event loop.
As to how to fix it, we will need more information.
What is making your slow function slow? Are you performing large calculations?
Are you doing sync versions of file/database calls?
It depends on what you mean by process. The web server has already finished serving the page at this point you js will execute however the request is synchronous so the javascript will continue to devote its in your function until it returns regardless even if take years. (hopefully by this point the browser will detect your script is taking too long and give you the opportunity to kill it). Even still you suffer the embarrassment of the user having to kill your javascript functionality and them not being able to use the page.
So how do you solve the problem. The time when this gets particular important is when your js is making the problem because at the point a whole host of things of things can go wrong. Imagine that your user is on the other side of the earth. the network latency could make your js painfully slow. when using ajax its preferable to use Asynchronous requests which get around this. I personally recommend using jquery as it makes async ajax calls really easy and the documentation on the side is quite straight forward. The other thing I recommend is making the return output small. It made be better to return json output and build the needed html from that.