I have created a web application using jsp/tiles/struts/mysql/tomcat. I created new project on Openshift 3 console (Openshift online) https://console.preview.openshift.com/console/ then added tomcat/mySql. I was getting 503 error sometimes and other times, same page was working as expected. 503 error came randomly for any page from my project. When I get 503 error, I refresh some no of times and it goes away, and my page is correctly displayed.
Error that I see is:
"503 Service Unavailable
No server is available to handle this request. "
I did some research:
What I understand from this openshift 2 link:
https://blog.openshift.com/how-to-host-your-java-ee-application-with-auto-scaling/
is that to correct 503 error:
SSH into your application gear using rhc ssh --app <app_name>
Change directory to haproxy/conf
change the following in haproxy.cfg option httpchk GET / to option httpchk GET /api/v1/ping
Restart the HAProxy cartridge from your local machine using RHC rhc cartridge-restart --cartridge haproxy
I dont know if it is also applicable to openshift 3. In openshift 3 where is haproxy.log, haproxy.cfg, haproxy/conf or its slightly different in openshift 3. (Nut thanks to Warrens comments, yes he saw 503 error in openshift related to HAProxy)
Now after 1 week after posting this question:
I am getting Quota Reached Error. I am able to build my project but all deployments are failing. I wonder if 503 error that I was getting earlier(either completely or partially) was related to Quota reached. How should I proceed now.
curl -i localhost:8080/GEA
HTTP/1.1 302 Found Server:
Apache-Coyote/1.1
Location: http://localhost:8080/GEA/
Transfer-Encoding: chunked Date: Tue, 11 Apr 2017 18:03:25 GMT
Tomcat logs do not show any application error.
Will Readiness Probe and Liveness Probe help me? I have not set them yet.
Nor do I know how to set them.
Will scaling help me (I dont know how to set it either)
Do I have to set memory/... all at maximum allowed to ensure project runs smooth?
For me I had a similar situation of getting 503's sometimes and sometimes getting my actual page. the reason was because you have haproxy on the frontend handling the requests. Depending on your setup you may even have a few haproxy pods and your request could be funneled between one of the pods. So as in my case one pod was working and the other not.
So basically
oc get pods -n default
NAME READY STATUS RESTARTS AGE
docker-registry-7-i02rh 1/1 Running 0 75d
registry-console-12-wciib 1/1 Running 0 67d
router-1-533cg 1/1 Running 3 76d
router-1-9utld 1/1 Running 1 76d
router-1-uwf64 1/1 Running 1 76d
As you can see in my output default namespace is where my router(haproxy) pods live. If I change to that namespace
oc project default
Then run
oc logs -f router-1-533cg
on each of the pods you will most likely find a sepcific pod that is behaving bad. You can simply delete, and the replication controller will create a new one
I've been developing an application for some weeks, and it's been running in a OpenShift small gear with DIY 0.1 + PostgreSQL cartridges for several days, including ~5 new deployments. Everything was ok and a new deploy stopped and started everything in seconds.
Nevertheless today pushing master as usual stops the cartridge and it won't start. This is the trace:
Counting objects: 2688, done.
Delta compression using up to 4 threads.
Compressing objects: 100% (1930/1930), done.
Writing objects: 100% (2080/2080), 10.76 MiB | 99 KiB/s, done.
Total 2080 (delta 1300), reused 13 (delta 0)
remote: Stopping DIY cartridge
fatal: The remote end hung up unexpectedly
fatal: The remote end hung up unexpectedly
Logging in with ssh and running the start action hook manually fails because database is stopped. Restarting the gear makes everything work again.
The failing deployment has nothing to do with it, since it only adds a few lines of code, nothing about configuration or anything that might break the boot.
Logs (at $OPENSHIFT_LOG_DIR) reveal nothing. Quota usage seems fine:
Cartridges Used Limit
---------------------- ------ -----
diy-0.1 postgresql-9.2 0.6 GB 1 GB
Any suggestions about what could I check?
Oh, dumb mistake. My last working deployment involved a change in the binary name, which now matches the gear name. stop script, with ps grep and so on was wrong, not killing only the application but also the connection. Changing it fixed the issue.
Solution inspired by this blogpost.
I found there are still failed request when the traffic is high using command like this
haproxy -f /etc/haproxy.cfg -p /var/run/haproxy.pid -sf $(cat /var/run/haproxy.pid)
to hot reload the updated config file.
Here below is the presure testing result using webbench :
/usr/local/bin/webbench -c 10 -t 30 targetHProxyIP:1080
Webbench – Simple Web Benchmark 1.5
Copyright (c) Radim Kolar 1997-2004, GPL Open Source Software.
Benchmarking: GET targetHProxyIP:1080
10 clients, running 30 sec.
Speed=70586 pages/min, 13372974 bytes/sec.
**Requests: 35289 susceed, 4 failed.**
I run command
haproxy -f /etc/haproxy.cfg -p /var/run/haproxy.pid -sf $(cat /var/run/haproxy.pid)
several times during the pressure testing.
In the haproxy documentation, it mentioned
They will receive the SIGTTOU
611 signal to ask them to temporarily stop listening to the ports so that the new
612 process can grab them
so there is a time period that the old process is not listening on the PORT(say 80) and the new process haven’t start to listen to the PORT (say 80), and during this specific time period, it will cause the NEW connections failed, make sense?
So is there any approach that makes the configuration reload of haproxy that will not impact both existing connections and new connections?
On recent kernels where SO_REUSEPORT is finally implemented (3.9+), this dead period does not exist anymore. While a patch has been available for older kernels for something like 10 years, it's obvious that many users cannot patch their kernels. If your system is more recent, then the new process will succeed its attempt to bind() before asking the previous one to release the port, then there's a period where both processes are bound to the port instead of no process.
There is still a very tiny possibility that a connection arrived in the leaving process' queue at the moment it closes it. There is no reliable way to stop this from happening though.
I have 2 rails apps hosted on the same server and each one of them have its own configuration for thinking_sphinx /searchd with different ports configured. I managed to get this set up working and I have 2 instances of searchd running.
My problem is getting Monit to monitor these 2 instances. Even though these 2 instances of searchd have its own PID in separate directories, I was not able to define the configuration in the monitrc because the process names in this case are the same, namely searchd.
In my monitrc, i have 2 separate commands as follows:
check process searchd with pidfile /var/www/app1/shared/pids/production.sphinx.pid
start program=....
stop program=....
check process searchd with pidfile /var/www/app2/shared/pids/production.sphinx.pid
start program=...
stop program=...
Monit requires a unique process name. Is it possible to start up my second instance of searchd using a different process name?
Thanks for the help.
You can call the process whatever you wish in monit configuration files - it doesn't need to match the executable. So:
check process searchd_app1 with pidfile /var/www/app1/shared/pids/production.sphinx.pid
start program=....
stop program=....
check process searchd_app2 with pidfile /var/www/app2/shared/pids/production.sphinx.pid
start program=...
stop program=...
I have setup gunicorn with 3 workers, 30 worker connections and using eventlet worker class. It is set up behind Nginx. After every few requests, I see this in the logs.
[ERROR] gunicorn.error: WORKER TIMEOUT (pid:23475)
None
[INFO] gunicorn.error: Booting worker with pid: 23514
Why is this happening? How can I figure out what's going wrong?
We had the same problem using Django+nginx+gunicorn. From Gunicorn documentation we have configured the graceful-timeout that made almost no difference.
After some testings, we found the solution, the parameter to configure is: timeout (And not graceful timeout). It works like a clock..
So, Do:
1) open the gunicorn configuration file
2) set the TIMEOUT to what ever you need - the value is in seconds
NUM_WORKERS=3
TIMEOUT=120
exec gunicorn ${DJANGO_WSGI_MODULE}:application \
--name $NAME \
--workers $NUM_WORKERS \
--timeout $TIMEOUT \
--log-level=debug \
--bind=127.0.0.1:9000 \
--pid=$PIDFILE
On Google Cloud
Just add --timeout 90 to entrypoint in app.yaml
entrypoint: gunicorn -b :$PORT main:app --timeout 90
Run Gunicorn with --log-level debug.
It should give you an app stack trace.
Is this endpoint taking too many time?
Maybe you are using flask without asynchronous support, so every request will block the call. To create async support without make difficult, add the gevent worker.
With gevent, a new call will spawn a new thread, and you app will be able to receive more requests
pip install gevent
gunicon .... --worker-class gevent
The Microsoft Azure official documentation for running Flask Apps on Azure App Services (Linux App) states the use of timeout as 600
gunicorn --bind=0.0.0.0 --timeout 600 application:app
https://learn.microsoft.com/en-us/azure/app-service/configure-language-python#flask-app
WORKER TIMEOUT means your application cannot response to the request in a defined amount of time. You can set this using gunicorn timeout settings. Some application need more time to response than another.
Another thing that may affect this is choosing the worker type
The default synchronous workers assume that your application is resource-bound in terms of CPU and network bandwidth. Generally this means that your application shouldn’t do anything that takes an undefined amount of time. An example of something that takes an undefined amount of time is a request to the internet. At some point the external network will fail in such a way that clients will pile up on your servers. So, in this sense, any web application which makes outgoing requests to APIs will benefit from an asynchronous worker.
When I got the same problem as yours (I was trying to deploy my application using Docker Swarm), I've tried to increase the timeout and using another type of worker class. But all failed.
And then I suddenly realised I was limitting my resource too low for the service inside my compose file. This is the thing slowed down the application in my case
deploy:
replicas: 5
resources:
limits:
cpus: "0.1"
memory: 50M
restart_policy:
condition: on-failure
So I suggest you to check what thing slowing down your application in the first place
Could it be this?
http://docs.gunicorn.org/en/latest/settings.html#timeout
Other possibilities could be your response is taking too long or is stuck waiting.
This worked for me:
gunicorn app:app -b :8080 --timeout 120 --workers=3 --threads=3 --worker-connections=1000
If you have eventlet add:
--worker-class=eventlet
If you have gevent add:
--worker-class=gevent
I've got the same problem in Docker.
In Docker I keep trained LightGBM model + Flask serving requests. As HTTP server I used gunicorn 19.9.0. When I run my code locally on my Mac laptop everything worked just perfect, but when I ran the app in Docker my POST JSON requests were freezing for some time, then gunicorn worker had been failing with [CRITICAL] WORKER TIMEOUT exception.
I tried tons of different approaches, but the only one solved my issue was adding worker_class=gthread.
Here is my complete config:
import multiprocessing
workers = multiprocessing.cpu_count() * 2 + 1
accesslog = "-" # STDOUT
access_log_format = '%(h)s %(l)s %(u)s %(t)s "%(r)s" %(s)s %(b)s "%(q)s" "%(D)s"'
bind = "0.0.0.0:5000"
keepalive = 120
timeout = 120
worker_class = "gthread"
threads = 3
I had very similar problem, I also tried using "runserver" to see if I could find anything but all I had was a message Killed
So I thought it could be resource problem, and I went ahead to give more RAM to the instance, and it worked.
You need to used an other worker type class an async one like gevent or tornado see this for more explanation :
First explantion :
You may also want to install Eventlet or Gevent if you expect that your application code may need to pause for extended periods of time during request processing
Second one :
The default synchronous workers assume that your application is resource bound in terms of CPU and network bandwidth. Generally this means that your application shouldn’t do anything that takes an undefined amount of time. For instance, a request to the internet meets this criteria. At some point the external network will fail in such a way that clients will pile up on your servers.
If you are using GCP then you have to set workers per instance type.
Link to GCP best practices https://cloud.google.com/appengine/docs/standard/python3/runtime
timeout is a key parameter to this problem.
however it's not suit for me.
i found there is not gunicorn timeout error when i set workers=1.
when i look though my code, i found some socket connect (socket.send & socket.recv) in server init.
socket.recv will block my code and that's why it always timeout when workers>1
hope to give some ideas to the people who have some problem with me
For me, the solution was to add --timeout 90 to my entrypoint, but it wasn't working because I had TWO entrypoints defined, one in app.yaml, and another in my Dockerfile. I deleted the unused entrypoint and added --timeout 90 in the other.
For me, it was because I forgot to setup firewall rule on database server for my Django.
Frank's answer pointed me in the right direction. I have a Digital Ocean droplet accessing a managed Digital Ocean Postgresql database. All I needed to do was add my droplet to the database's "Trusted Sources".
(click on database in DO console, then click on settings. Edit Trusted Sources and select droplet name (click in editable area and it will be suggested to you)).
Check that your workers are not killed by a health check. A long request may block the health check request, and the worker gets killed by your platform because the platform thinks that the worker is unresponsive.
E.g. if you have a 25-second-long request, and a liveness check is configured to hit a different endpoint in the same service every 10 seconds, time out in 1 second, and retry 3 times, this gives 10+1*3 ~ 13 seconds, and you can see that it would trigger some times but not always.
The solution, if this is your case, is to reconfigure your liveness check (or whatever health check mechanism your platform uses) so it can wait until your typical request finishes. Or allow for more threads - something that makes sure that the health check is not blocked for long enough to trigger worker kill.
You can see that adding more workers may help with (or hide) the problem.
The easiest way that worked for me is to create a new config.py file in the same folder where your app.py exists and to put inside it the timeout and all your desired special configuration:
timeout = 999
Then just run the server while pointing to this configuration file
gunicorn -c config.py --bind 0.0.0.0:5000 wsgi:app
note that for this statement to work you need wsgi.py also in the same directory having the following
from myproject import app
if __name__ == "__main__":
app.run()
Cheers!
Apart from the gunicorn timeout settings which are already suggested, since you are using nginx in front, you can check if these 2 parameters works, proxy_connect_timeout and proxy_read_timeout which are by default 60 seconds. Can set them like this in your nginx configuration file as,
proxy_connect_timeout 120s;
proxy_read_timeout 120s;
In my case I came across this issue when sending larger(10MB) files to my server. My development server(app.run()) received them no problem but gunicorn could not handle them.
for people who come to the same problem I did. My solution was to send it in chunks like this:
ref / html example, separate large files ref
def upload_to_server():
upload_file_path = location
def read_in_chunks(file_object, chunk_size=524288):
"""Lazy function (generator) to read a file piece by piece.
Default chunk size: 1k."""
while True:
data = file_object.read(chunk_size)
if not data:
break
yield data
with open(upload_file_path, 'rb') as f:
for piece in read_in_chunks(f):
r = requests.post(
url + '/api/set-doc/stream' + '/' + server_file_name,
files={name: piece},
headers={'key': key, 'allow_all': 'true'})
my flask server:
#app.route('/api/set-doc/stream/<name>', methods=['GET', 'POST'])
def api_set_file_streamed(name):
folder = escape(name) # secure_filename(escape(name))
if 'key' in request.headers:
if request.headers['key'] != key:
return 404
else:
return 404
for fn in request.files:
file = request.files[fn]
if fn == '':
print('no file name')
flash('No selected file')
return 'fail'
if file and allowed_file(file.filename):
file_dir_path = os.path.join(app.config['UPLOAD_FOLDER'], folder)
if not os.path.exists(file_dir_path):
os.makedirs(file_dir_path)
file_path = os.path.join(file_dir_path, secure_filename(file.filename))
with open(file_path, 'ab') as f:
f.write(file.read())
return 'sucess'
return 404
in case you have changed the name of the django project you should also go to
cd /etc/systemd/system/
then
sudo nano gunicorn.service
then verify that at the end of the bind line the application name has been changed to the new application name