Django memory leak gunicorn example. py" causes the problems.

Django memory leak gunicorn example 04). Maybe it'll be helpful for someone, this is an example of using generators + banch_size in Django: from itertools import islice from my_app. However, the data structure -- a third party C++ module -- has a memory leak. ini file above, so yes you can, I'm running Django applications on Webfaction and AWS EC2 Micro Instance(613MB of RAM) servers. Here are the results of my test TCP Proxy via Unix socket: Setup: nginx + gunicorn + django running on 4 m4. I wrote a quick little script which prints out the memory usage on the app server. but on Webfaction it is pretty easy to hook up your own instance of Nginx to one or more WSGI-servers running for example Gunicorn in gevent mode. py mysite polls templates You should see the following objects: manage. Gunicorn Documentation; memory leak - gunicorn + django + mysqldb. Out of memory: Kill process (gunicorn I migrated a WSGI django application to ASGI and I swapped workers from sync to uvicorn on gunicorn. views:31 "Debugging Django memory leak with TrackRefs and Guppy" by Mikko Ohtamaa: Django keeps track of all queries for debugging purposes (connection. However, as per Gunicorn's documentation, 4-12 workers should handle hundreds to thousands of requests per I'm currently having difficulty passing environment variables into Gunicorn for my Django project. Gunicorn tells Django to stop, which in turn should tell Postgres to stop. There are 5 I have encountered a memory leak problem related with Gunicorn FastApi and multiprocessing library. I have a project with Django and I did a multiread with Gunicorn. 7, Django 1. Gunicorn serving a Django application (inside docker) Postgres (inside docker) When the traffic is "heavy" (100 r/s), the page is very slow to be delivered, even if all containers are not very used (cpu 40% in Idle on application container, only use 2gb of 8 RAM used - other container more or less 0 % of CPU usage). 0 (or 3. Any ideas on what to do to release the memory? Since a few weeks the memory usage of the pods keeps growing. take_snapshot() sleep(10) s2 = tracemalloc. But then I did exact the same thing within a pod. Setup of each node is uniform (from the same image). Please If memory grows with every request, there could be a memory leak either with Gunicorn or your application. Out of memory: Kill process I had a similar problem with Django under Gunicorn, my Gunicorn workers memory keep growing and growing, to solve it I used Gunicorn option -max-requests, which works the same as Apache’s MaxRequestsPerChild: gunicorn apps. Python Django ASGI - memory leak - UPDATED #2 To sum up: even fresh Django ASGI app leaks memory. If you are using any database transactions, Django will create a new connection and this needs to be manually closed: If you still get the memory leak loading the file from the CLI, the issue is with your application code. assert sum(i. 0:8000. a memory leak. For example, setting DEBUG=True can cause it to hold on to all SQL queries Gunicorn will also restore any workers that get killed by the operating system, it can also regularly kill and replace workers (For example if your application has a memory leak, this will help to We are using nginx together with our Django app in a gunicorn server. I tried using the gunicorn "max_requests" setting to have the workers expire after so many requests, which clears out their resources and reloads the data structure. So for this what we need to do is we need to set max_requests = n config and that will help our workers to Contribute to uranusjr/django-gunicorn development by creating an account on GitHub. main thread finishes && other thread finishes (later upon completion of both tasks) response is sent to user as a package. For applications that are I/O bound or deal with a lot of simultaneous connections, using an gunicorn --timeout 120 myproject. handlers. There is a large difference in memory usage before versus after the API calls, i. ; templates: Contains custom template files for the administrative interface. 04 with supervisor 3. This causes memory usage to increase steadily to 4 GB or so, at which point the rows print rapidly. Supervisor's memory usage keeps growing until the server is not responsive. db. the server itself is running with 16GB of memory and over time it is all being consumed by the apache process. 0). backends['default']. I’m (now) on Django 4. Provide details and share your research! But avoid . ; To learn more In a Django application, it is important to use a production-ready server to handle traffic, ensure stability, and provide scalability. to listen on port 80, remember to change the port to 443 (the default port for HTTPS connections). Further reading. Other than that I am pretty much mind blown where the memory leaks can be. take_snapshot() for alog in s2 The performance gain comes from the use of "greenlets" or "pseudo threads" provided by "gevent" library. I finally was able to find a debugging message explaining that they are being terminated due to OOM: 2022-01-26 12:38:05. Unless I find someone with the same problem, I'll prepare a test example and send it to the gunicorn guys when I get some time. filter_by_budget_range(phase)). I've read about django and django-rest-framework memory optimization for some days now, and tried some changes like: using --preload on Gunicorn, setting --max-requests to kill process when they're too heavy on memory, I've also set CONN_MAX_AGE for the database and WEB_CONCURRENCY as stated on: 1. Gunicorn is utilizing more memory, due to this CPU utilization crossed to 95% and application is hanged. 6 The memory goes up a lot. Asking for help, clarification, or responding to other answers. Also I tried max_request with I'm running django with gunicorn inside docker, my entry point for docker is: CMD ["gunicorn", "myapp. g. Decided not to mix up with gunicorn and django logs and to create separate log file for django. The --reload-extra-file parameter intent to reload extra files when they are changed, besides Python Introduction: Gunicorn (short for Green Unicorn) is a Python WSGI (Web Server Gateway Interface) HTTP server used to serve Python web applications, such as those built with Django. The app service is the central component of Do you know about an efficient way to log memory usage of a django app per request ? I have an apache/mod_wsgi/django stack, which runs usually well, but sometimes one process ends up eating a huge lot of memory. As I checked the database, I found that Memory management at the OS-level is whack enough (i. But in standalone mode, there are no requests. Could database connection leaks Configuration example: — workers=9; Worker Class: Gunicorn supports various worker types. wsgi:application examine you application for any memory leaks, memory leaks can cause the application to consume excessive memory that leading to a worker crash. Also adjust the worker count (-w 8) to 2* cpu_core + 1 Here’s an example Procfile for the Django application we created in Getting Started with Python on Heroku. Commented Feb 6, 2015 at 22:51. There was an issue about reload-extra-file that Gunicorn maintainers solved recently (December 27, 2023). So I was just watching master and one worker process memory consumption and it was stable, no memory leak. c I deployed my Django project using Gunicorn, but now can't use Nginx caching. The following structure of the project works correctly. All these memory profilers don't seem to play well with multiprocessing. 664 PST Exceeded soft memory limit of 512 MB with 515 MB after In my requirements. iterator(chunk_size=1000)) == x Wall time: 3. 6 compatibility). Thanks! I was having trouble finding a basic example like this that worked with gunicorn. crt --keyfile=server. What cursorclass are you using? I've encountered memory leaks with MySQLdb Apache webserver solve this problem by using MaxRequestsPerChild directive, which tells Apache worker process to die after serving a specified number of requests (e. 6 site running with gunicorn, managed by supervisor. All my pages are not reflecting the changes immediately. $ gunicorn hello:app --max-requests 1200 See the Gunicorn Docs on Max Requests for more information. 20. set_trace() right in your browser. Gunicorn will wait a certain amount of time for this to happen before it kills django, leaving the postgres process as an orphan query. py" causes the problems. queries). The task is running in the async way. Wall time: 3. handlers import StaticFilesHandler from django. staticfiles. 2 x RAM should fix this. I do have CONN_MAX_AGE in settings. 2. How can i found the reason of leaking, any ideas? I have django application in a Digital Ocean(512MB Memory) with Postgres, Nginx, and Gunicorn on Ubuntu 16. The web container in my Gunicorn will also restore any workers that get killed by the operating system, it can also regularly kill and replace workers (For example if your application has a memory leak, this will If your application suffers from memory leaks, you can configure Gunicorn to gracefully restart a worker after it has processed a given number of requests. Fantastic. py. py all the way to the try_wait function inside the subprocess module, so at this point we are out of Django and into pure Python. max_requests_jitter ¶ Command line:--max-requests-jitter INT. There's a server that might be experiencing PostgreSQL database connection leaks. After looking into the process list I noticed that there are many gunicorn processes which seem dead but are still using memory. One more thing, I'm NOT running Django in debug mode, so the memory doesn't come from django. The jitter of 5% was be In our case we are using Django + Gunicorn in which the memory of the worker process keeps growing with the number of requests they serve. md manage. wsgi -b 0. Memory leak with Django + Django Rest Framework It's using Docker Compose running Gunicorn as the entry point for the web container. 5. How to debug memory leak in python flask app using tracemalloc. To give I am looking for any suggestions into improving the memory issues. I try to use Ngnix Here's an example where the static files are cached for a year: location /static { root [location of /static folder]; expires 1y; access_log off You cannot run your Django codes (in Python) with multiple threads, but the I/O tasks (handled by gunicorn, not in Python) may go concurrently. I have a wsgi. How to stop caching the below are my files: nginx. However, a much more common solution would be to place these two services in two I have been troubleshooting memory leak issues on my Django application deployed to Heroku. 6, Django 2. I'm running: Python 2. The application I am running is a deep learning framework for automatic image recognition. 1:8080 --workers 8 --max-requests 1000 Django; Gunicorn; Linux gunicorn -D -w 8 --max-requests 50000 --bind 127. Update: for a thorough example of using Gunicorn and Django with Docker, checkout this example project from It appears that if you write a message to a channel, for example via group_send, and no reader ever appears on that channel, the messages will remain in the in-memory queue channels. The problem lies in asyncio and TLS/SSL. Procfile This can be a convenient way to help limit the effects of the memory leak. The code below does not leak when using hypercorn. I have to deploy the app in a VM with 2GB RAM and 2 core CPUs (using Vagrant and VirtualBox, Ubuntu 16. 4) and the gunicorn webserver starts leaking postgres db connections. 2 Basically, Heroku loads multiple instances of the app into memory, whereas on dev only one instance is loaded at a time. EventLoop: this allows greenlets to switch between each other during I/O operations, this prevents one operation from blocking the entire process. base. (I cut from the log some internal objects not so interesting in this example): [24/09/2014 10:35:31] DEBUG [leaky_app. Better way: User sends request Django receives => lets Celery know "hey! do this!" There seems to be a memory leak when using uvicorn. Tuning the settings to find the sweet spot is a continual process but I would try the following - increase the number of workers to 10 (2 * num_cpu_cores + 1 is the recommended starting point) and reduce max-requests significantly because if your requests are taking that long then they won't be Current version has known cursor memory leak when connection is established with use_unicode=True (which is the case for Django>=1. Default: 0 gunicorn django_project. It seems that it's not that easy to profile Gunicorn due to the usage of greenlets. py app. 1 and deploying to Google App Engine instance class B2. Memory usage is quite small, nginx takes about 10MB memory and gunicorn about 150MB (but it also servers more than one app). py and I already tried to call django. all() for i in c: If you are able to launch gunicorn pointing at an application instance that is an instance of the DebuggedApplication class from the werkzeug library, you will be able to set break points using the werkzeug debugger with import ipdb; ipdb. I've got a django app that does a bit of processing to a photo when it is uploaded. all(). 0:443 test:app Share If I start it with . backends. This will cause releasing any excess memory held by gunicorn. And of course, the list is the villain. Follow these simple methods to optimize Django memory usage. dll}_crtBreakAlloc method, I found a memory leak which depends on timing in a multithreaded environment. 0. service is not loaded properly: Invalid argument. xxx. workers. About 45MB each process, and there are 7 processes (4 Gunicorn + 3 RQ workers). 1, Waitress 1. 18, config see below, managed by supervisord) when a user loads the website, 10 requests are handled by the gunicorn (the other ones are static files served by nginx) - Thanks @scott Due to a previous experience with Django somehow ignoring static files stored in a folder called "static", I decides to change the location and names for my static files. import django. 0a8-1. I'm on the latest 19. wsgi Putting that all together, a Procfile for Django on Heroku might look like. It can take a long while before garbage collected memory is actually freed up in a process. Gunicorn is a well-known and popular choice for running It consumed all my 32G memory in less than one day. To write it to also to stdout you should add another This compose file defines five distinct services which each have a single responsibility (this is the core philosophy of Docker): app, postgres, rabbitmq, celery_beat, and celery_worker. This helps reduce the worker startup load. For a more sophisticated Django app which requires queueing up tasks, send emails, database connections, user logins, etc, it Gunicorn workers hold a big chunk of memory to face a high work load, does not free it (even setting the --max-requests parameter) and for a second test the performance gets way worst. The problem is that the thread pool creates new database connections and Django doesn't close them. 04. Here’s an example Procfile for the Django application we created in Getting Started with Python on Heroku. calling free() in most real-world applications doesn't cause a drop in memory consumption reported by the OS, due to fragmentation). /manage. Running the container locally works fine, the application boots and does a memory consuming job on startup in its own thread (building a cache). I am also using Supervisor to monitor the app. UvicornWorker -c app/gunicorn_conf. and the cache is being run through MySQL Any value greater than zero will limit the number of requests a worker will process before automatically restarting. Perhaps when creating/closing connections, there is some kind of memory leak in channels?. I also have a more complex application that faces the same issue. You only need two worker I am deploying a django application to gcloud using gunicorn without nginx. conf server { listen 80; In this example, when 30 seconds has passed and Django is still waiting for Postgres to respond. /env/bin/gunicorn --max-requests 1 - This is useful if you have memory leaks you have no control over for example from closed source C extensions. debug import DebuggedApplication Gunicorn seems to have some sort of issue with memory leak as they process requests. objects. I'm mentioning this because we (Satellite, in this case) received a hotfix request for #4090 and I'm creating a new BZ to track delivery of that fix (the existing BZ was already marked CLOSED ERRATA for with changes delivered in 6. 0 Severe memory leak with Django. Using Python 3. 6. A golden rule for Django optimization: Replace the use of a list for querisets wherever you can. I want to monitor memory with "memray" but I don't know how to use "memray By "django itself" I assume you mean the development server? The basic answer is that the development server is just for development and Gunicorn is a production ready HTTP server that interfaces very nicely with Python/Django. web: gunicorn myproject. 4 to 0. More often than not, memory leaks in Django would come from side-effects when using objects that are created at server startup, and that you keep feeding with new data without even realizing it, or without I have a single gunicorn worker process running to read an enormous excel file which takes up to 5 minutes and uses 4GB of RAM. django 1. DEBUG will cause memory leaks - but you should never run your production processes with the `settings. close_old_connections(). This processing takes about 100ms. 10 Normally for a typical Django Application it would take 60 - 80 MB for a Django app with database connections, for a Django app which only requires a little bit of database connections, only takes up about 18 MB memory. I have thought of running garbage collection at some point to lower the memory but I have never actually used this in django. Hello 👋. If you do need CPU utilization, use multiple processes ( workers=2 * CPU_THREADS + 1 ) instead of multiple gthreads, or consider non-CPython interpreters like pypy , which is not constrained by GIL, but I have a nginx + gunicorn django application. For example, on a recent project I configured Gunicorn to start with: All you need is to setup Gunicorn as explained in the Django documentation, enable Apache’s proxying with a2enmod proxy_http, and add this to your Apache VirtualHost block: ProxyPass /static description "Gunicorn application server handling myproject" start on runlevel [2345] stop on runlevel [!2345] respawn setuid ubuntu setgid www-data chdir /home/ubuntu/project/ #--max-requests INT : will restarted worker after those many requests which can #overcome any memory leaks in code exec . Since the worker is multithreaded, it is able to handle 4 requests. 53 s, 22MB of memory (BAD) Django Iterator. From my understanding, nothing bad happens until memory use exceeds 400%. Memory usage with 4 workers after parameter change. 1 version. 7 and redis (via django-redis). If you’re using Gunicorn as your Python web server, you can use the --max-requests setting to periodically restart workers. 11. Those process never die. wsgi where myproject is the name of your Django project. The performance is quite good so far, but I have not done any direct comparisons with an Apache setup. The second call of the snapshot endpoint returns the five highest memory usage differences. 5 and gunicorn (sync workers) Workers memory usage grow with time . 6 Django application memory usage. Not fun. I understand that Gunicorn has the --reload flag and I've tried using this. 18 and first appears in Pulpcore 3. One question was made on Github and was about json files: Opened issue. I had a few concerns here before taking it to production, Gunicorn allows us to spread processing across multiple “workers” to increase speed, help prevent memory leaks, and is highly customizable for the developer’s needs. For example, on a recent project I configured Gunicorn to start with: For the project’s level of traffic, number of workers, and number of servers, this would restart workers about every 1. I am hoping someone can give me some direction on how to determine what is causing this out of memory to continue to occur. See system logs and 'systemctl status gunicorn. – mirth23. PostgreSQL is pretty resistant to memory leaks due to its use of palloc and memory contexts to do heirachical context-sensitive memory management. debug is turned off. The command I'm Now if you find out the memory use keeps on growing ever and ever you possibly have some memory leak somewhere indeed. Using tracemalloc I tried to find what is causing the memory leak by creating a background thread that checks the memory allocation: def check_memory(self): while True: s1 = tracemalloc. Gunicorn Any value greater than zero will limit the number of requests a worker will process before automatically restarting. The web container in my dev server is using 170MB Ram, mainly running gunicorn / Django / Python / DRF. Since threads are more lightweight (less memory consumption) than processes, I keep only one worker and add several threads to that. While trying to diagnose memory leaks with the {,,ucrtbased. I followed the Profiler from manage. Gunicorn Keeps Restarting/Breaking on Flask App. – Klaus D. 4, Gunicorn 0. How to start caching on a project that use Gunicorn and which caching method is standard for Django. For the moment I'll carry on having many sync workers (using lots of memory). ; mysite: Contains Django project-scope code and settings. Oh yeah, I'm trying to get this app on xxx. wsgi"] For example, if you needed to start both nginx and gunicorn in the same container, you would need to investigate some sort of process supervisor. When any task runs and completes its execution, Django-background-tasks does not release the memory after completing The real problem is not the Django models not being released from memory. (Every request increases the number of connections when I check the list of clients in pgbouncer. conf. You could We've got a few Django setups that go through a proxy (Apache and Nginx) that eventually make their way to the actual Django runtime. Understand python internals like pyobject and memory allocation patternsPython being a high l Is the correct assumption now that it's Django that is leaking memory? But what are those dict's who don't have owner? python; django; debugging; memory-leaks; Share. This is exactly what Heroku's documentation suggests for Django applications. This is the easiest. Default: 0 According to the gunicorn docs, you need to set threads parameter in order to process requests concurrently, for example, gunicorn --workers=4 --threads=10 application_name. For example: $ gunicorn --certfile=server. So you need to manually reset to queries list after each working cycle I track the memory usage of my Django processes and here is what happens: Initially, each process consumes around 40 MBs of memory; When I run the query for the first time, memory usage goes up to around 700 Mbs; Second time I run the query (assuming the request landed in the same process), memory usage goes up to around 1400 MBs. 0; Django 1. After going over this tutorial, you’ll be better equipped to In django project, I am using Gunicorn as an application server. Django memory leak. But there were some nit-picky problems with that that I won't go into. The only explanation I can think of is that each worker gets his own cache (I wonder why, since I did not define a cache). What will be the problem and what are the possible reason. I am running Django 1. 3; Gunicorn 18. In particular I have captured a server that has over 100k items in I have been facing memory leaks in my Django application and for some reason, I am not able to get rid of them. Update: running . Running "gunicorn_django -c deploy/gunicorn. I am running Django 2. postgresql_psycopg2. 5 Out of memory: Kill process (gunicorn) score or sacrifice child. Default: 0 If you can reproduce a memory leak in the threaded worker with a simple example, that would constitute a bug that should be fixed. 5 minutes is a pretty significant especially since you only have 3 workers. prevents potential memory leaks and other issues from affecting the workers max_requests = 100 # randomly add 0-10 to ^, so the workers don't restart at the same time max_requests_jitter Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company Deploying Gunicorn¶ We strongly recommend using Gunicorn behind a proxy server. Why gUnicorn spaws 2 process when running a Flask. The main Dockerfile is used for the hello app (the django project): Maybe it'll be helpful for someone, this is an example of using generators + banch_size in Django: from itertools import islice from my_app. 17. 5 gunicorn workers eats memory. service' for details. Unfortunately for that command to work I need to enable Debug mode which is a no go. I posted steps to reproduce the problem on stackoverflow. This is a simple method to help limit the damage of memory leaks. I'm using the following example to build a django-postgres-nginx-gunicorn web server. And I await group_discard properly in disconnect function Hi there, I’ve posted a question on stackoverflow week ago and I also presented what I found. apt-get installing gunicorn to site-packages of python2 and pip installing Django to site-packages of python3. Currently this is done synchronously, I use subprocess to invoke the processing, get the result and return the result within the frame of the HTTP request. Basically, when the app is deployed/dyno restarted RAM hovers around 40% then as soon as someone gets on, it goes to 80% usage then stays there and never goes back down. I have a Django app using Gunicorn, Ngnix, PostgreSQL. wsgi:application in above example, the gunicorn worker is restarted for every 50000 requests. On running the application, it consuming more memory. xx. py run_gunicorn -w 4 also causes the same problems. Last few days application is running smoothly for few hours after that application is hanged. What helps here is to have enough RAM (again, swap will help). ; polls: Contains the polls app code. But with using Starlette and serving it with Gunicorn, It turns out the memory leak was not directly caused by the Django upgrade or Celery. So I'd like to profile my production server for a limited time period to get an overview about which objects take up most memory. Gunicorn running N wokers. 4. Here is a screenshot of my last 24 hours: Things I have tried: I'm using a Docker container for Django development, and the container runs Gunicorn with Nginx. 1. Celery workers are known to handle memory consumption poorly. Still no idea what exactly caused this issue, or why it only happens Machine 1 of 1GB: Nginx, Gunicorn, RQ Workers, Redis Cache, Redis DataStore Machine 2 of 1GB: PostgreSQL Indeed, when I looked at the memory consumption, I saw that it was more gunicorn and the RQ workers that were consumming a lot of RAM. py is a simple configuration file). Then you add cpython's memory manager / garbage collector on top of that and you just cannot expect memory to go down even in times you might expect it. python; django; memory-management; memory-leaks; gunicorn; memory leak - gunicorn + django + mysqldb. Instructions for adding swap on Digital Ocean. Thanks in advance. 0 using gunicorn on nginx server. Pair with its sibling --max-requests-jitter to prevent all your workers restarting at the same time. If you don't get the memory leak in your CLI test, the issue is with your Gunicorn configuration. 2) fixes the memory issue with minor performance benefit. 1. py run_gunicorn everything is fine. (I run the test for around an hour). Here is a code example: I am trying to deploy django with gunicorn and nginx on heroku, and i'm kinda confused with the way to config gunicorn and nginx, when i searched through internet, they usually create gunicorn. Many cloud VPS come without swap pre-configured, so this is an easy fix. py: The main command-line utility used to manipulate the app. 8. Or there is some mistake in our code that we are missing. Presumably this comes from Django spending less time managing cache. I work in a company that has a large database and I want to perform some update queries on it but it seems to cause a huge memory leak the query is as follow c= CallLog. The Python processes slowly increased their memory consumption until crashing. I suspect it may have to do something with global variables not actually being GCed after a request is handled. Example print out with the site domains anonymized: Celery: 23 MB Gunicorn: 566 MB Nginx: 8 MB Redis I've used this for my development environment (which uses gunicorn): from django. iterator() which behaved the same way. 2, now Python 3. Nginx Configuration¶ Although there are many HTTP proxies available, we strongly advise that you use Nginx. I want to monitor memory with "memray" but I don't know how to use "memray". @Softsofter the daemon parameter is in the example gunicorn. e. . There are 4 containers, web, postresql, nginx, cron. Of course this may vary from app to app. It plays an essential role in making your Django application production-ready by acting as a bridge between your Django app and the web, handling incoming HTTP requests, and Does Django load models to memory in Admin? Python doesn't handle memory perfectly and if there's any memory leaks gunicorn really compounds issues. wsgi:application -c gunicorn. 17 to django==3. Beware that running celery - or django FWIW - with settings. channel_layers. views:31] dict 106524 +106524 [24/09/2014 10:35:31] DEBUG [leaky_app. A complete middleware example is: Memory leak with Django + Django Rest Framework + mod_wsgi. Default: 0 Sometimes the Django ORM can use a lot of RAM when dealing with very large querysets. xlarge nodes on AWS. collect() does not change the picture. Here's an example of what you're describing. The first result locates the memory leak correctly in line 17. 11 s, <1MB of memory Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. If the memory leak hides deeper in the code, you may have to adapt the strategy. conf import settings from django. This list is reseted at the end of HTTP request. I don't understand what Django is loading into memory or why it is doing this. Just simply call group_send periodically in some daemon django-commands. The Django iterator (at least as of Django 3. – zgoda Commented Aug 28, 2009 at 10:30 I am puzzled by the high percentage of memory usage by Gunicorn. changing the asgi server does not change the result, memory consumption continues to grow (daphne, uvicorn, gunicorn + uvicorn were tested); periodic run of gc. 5 hours. We know that each worker is a separate python process. 4. This projects serves as the following: As an example of our Django Styleguide, where people can explore actual code & not just snippets. I have a django application that is running on gunicorn app server. If this is set to zero (the default) then the automatic worker restarts are disabled. Improve this question Django has settings that cause it to consume memory for diagnostic reasons. I’ve been having problems with my worker processes regularly quitting and getting restarted. Third, this may not be a memory leak, precisely. EDIT 1: gunicorn --preload and improved codebase. but my project has a memory leak. ) I use python 3. contrib. When the user makes changes to the HTML pages, the changes aren't reflected live. wsgi:application -b 127. root@samuel-pc:~# systemctl start gunicorn Failed to start gunicorn. Example usage: @start_new_thread def foo(): #do stuff Over time, the stack has updated and transitioned without fail. i try to use Dozer to find the reason, but get: AssertionError: Dozer middleware is not usable in a multi-process environment. I am a novice in this arena, so any help will greatly be appreciated. I already tried the following for deployment. If someone finds a configuration which doesn’t have a leak I'm using ThreadPoolExecutor to speed up data processing. I think local only "fork" (win dont fork IO know) one main, but why does gunicorn process never die? requests to the Django application are handled by gunicorn (0. 4 sites on Ubuntu 12. api:application , where gunicorn_conf. Also, go ahead add some swap to the machine as a safety buffer. User sends request Django receives => spawns a thread to do something else. 0. txt I only change from django==2. Gunicorn will ensure that the master can then send more than one requests to the worker. Most likely, though, what you're seeing is just more more shared memory pages touched by each backend. DatabaseWrapper Hey @dralley, it appears the caching implemented in #2826 wasn't present in Pulpcore 3. 1:8080 myproject. By installing an alternative app config, for example? We now use DJANGO_SETTINGS_MODULE to relay where the settings module is to the Gunicorn subprocess (and let Django loads it I expended around 3 days trying to figure out what was leaking in my Django app and I was only able to fix it by disabling sentry Django integration (on a very isolated test using memory profiler, tracemalloc and docker). I started the load and run kubectl exec int the pod, typed top command and after a few minutes I saw growing memory consumption by a gunicorn worker process. 10. Here I wanted to try with Gunicorn --max-requests config to restart the gunicorn workers periodically to release the memory. I have tried to improve the memory optimization somewhat by using this hack where one can preload the application as explained here: Gunicorn Preload This is done by editing the Procfile to contain the following: Gunicorn reloads static content on recent versions (before December 27, 2023). But after the request was finished processing I noticed at system monitor that it stills allocating 4GB of RAM forever. No Threads, Despite having 25% maximum CPU and memory usage, performance starts to degrade at around 400 active connections according to Nginx statistics. 2 and gevent 0. Tracking down? The memory Do you have DEBUG=True in your Django settings? That's often the cause of a memory leak. Deploying Gunicorn¶ We strongly recommend using Gunicorn behind a proxy server. key --bind 0. ini This is essentially useful if your code leaks memory for some reason, so when they are restarted, the os will clean up after them. Commented Dec 17, 2021 at 23:40 @Chris No, not at all. I have an API with async functions that it is running with gunicorn( gunicorn -k uvicorn. So Gunicorn and Django not in same site-packages directory. 13. I would add 1 Gig. models import MyModel def create_data(data): bulk_create(MyModel, generator()) def bulk_create(model, generator, batch_size=10000): """ Uses islice to call bulk_create on batches of Model objects from a generator. Originally Python 2. Technology stacks asside (going to migrate to nginx / gunicorn / etc over time) this site has one hell of a memory leak. will bring up 4 workers and each worker has 10 threads to process the requests. id for i in MyModel. Installed "Dozer" to find memory leaks(Not reporting any problem). 9. The lengthy delay before the first row printed surprised me – I expected it to print almost instantly. Taking a Django app from development to production is a demanding but rewarding process. wsgi from werkzeug. For safe guarding against memory leaks for threads and gevent pools you can add an utility process called memmon, which is part of the superlance extension to supervisor. Gunicorn Documentation; LICENSE README. layers. Leaks within queries are uncommon, leaks that persist between queries are very rare. Background task takes some data from DB and process it internally which requires memory of 1 GB for each task. I'm using Django running with gunicorn behind nginx. I'd like to have separated folders for each container. 3 Memory leak with Django + Django Rest Framework + mod_wsgi. api. Out of memory: Kill process (gunicorn) score or sacrifice child Can I use bootstrapping for small sample There is nothing in the provided code that could explain a memory leak. ; As a Django project, where we can test various things & concepts. If anyone has experience with that will be helpful. memory leak - gunicorn + django + mysqldb. That server has also maxed out it's CPU at times (as indicated by %user being extremely high upon running sar -u). receive_buffer indefinitely when using the RedisChannelLayer backend. For some reason until no-response: Once it runs on local Windows 10 env - It works really good, no memory leak hangs. In this task, I need to be able to handle 500 concurrent login requests in 1 second. This solution makes your application more scalable and resource-efficient, especially in cases involving substantial NLP models. xx:8888/imageSite (ip address hidden for confidentiality :D). Currently, we have 12 Gunicorn workers, which is lower than the recommended (2 * CPU) + 1. Insalling Gunicorn and Django in Gunicorn. During tests and runserver I have logging on the console, but with gunicorn the statements don't show up anywhere (not even ER Gunicorn is a common choice, and you can run your Django application with Gunicorn like so: gunicorn myproject. DEBUG: application = StaticFilesHandler(get_wsgi_application()) else: application = get_wsgi_application() I have a memory leak that is hard to reproduce in testing environment. sock Il looks like the allocated memory by this Django custom command keeps growing. A last resort is to use the max_requests configuration to auto An example why don't use any ready for use WSGI service: All RFU(ready for use) WSGI applications got logging, but which user can handle this ? memory leak - gunicorn + django + mysqldb. 1 and have run into a strange problem with gunicorn 0. Any value greater than zero will limit the number of requests a worker will process before automatically restarting. The app server is serving up each site using Nginx, which serves all static files and proxies everything else to the Django Gunicorn workers for each site. I think it is actually written to stderr by default. When run inside celery, print_memory_usage() reveals an ever-increasing amount of memory, continuing until the process is killed (I'm using Heroku with a 1GB memory limit, but other hosts would have a similar problem. 0 (which is needed for Django 1. The problem is the algorithm/solution you've implemented, it uses too much memory. py file like so: import os import sys from django. From the first look, the application runs fine but as I tested with more load, the requests started failing with “FATAL: sorry, too many clients already” which means that the application reached the database connections limit. A lot of the I am using django 1. If I navigate through the pages, it also consuming the memory on checking with top command. Concurrency: "gevent" uses lightweight units called "greenlets" to handle concurrency. you can use tools like memory_profileror django's built-in memory management tools to find any memory leaks in your code. However, those options only work with the default pool (prefork). DEBUG flag set anyway as this is also a security issue. Here is my Gunicorn configuration while starting application. service: Unit gunicorn. 7. So gunicorn cannot find django. Memory when using uvicorn vs hypercorn. core. I have not use session or some other advanced things. I also tried Event. (process memory) of worker 0 but when similar (request contains paging option, but i store whole RawDataSet in memory, so every page returns fast) request is addressed to worker 1 cache as This part worked fine with 154 hosts and 150 ports per hosts (23000) objects to save, but now I'm trying it with 1000 ports and my computer's memory explode each time. In any case please present a minimal reproducible example. ) The memory leak appears to correspond with the chunk_size; if I increase the chunk_size, the memory consumption increases per I was tasked with creating a Django-Gunicorn demo app. The only problem right now is that the app can't find the static image file on the server so it just shows a broken picture I have a Django 1. If you choose another proxy server you need to make sure that it buffers slow clients when you use default Gunicorn workers. wsgi import get_wsgi_application if settings. The issue must come from somewhere else (possibly self. Just doing So actually system memory required for gunicorn with 3 workers should be more than (W+A)*3 to avoid random hangs, random no responses or random bad requests responses (for example nginx is used as I'm executing some of the long-running tasks with Django-background-tasks. This tutorial will take you through that process step by step, providing an in-depth guide that starts at square one with a no-frills Django application and adds in Gunicorn, Nginx, domain registration, and security-focused HTTP headers. This can be a Several large Django applications that I’ve worked on ended up with memory leaks at some point. After a lot of digging around I found that, surprisingly, the celery worker memory leak happens because I upgraded django-debug-toolbar from 0. vbvl xln qpre grkz oqttt wbogav asaujdo coifq shxy jkyg