I am making a web-app currently and I'm using WebStorm for my front and back end. My stack is as follows: Vue3(with axios), Node.js (with Express and Coors as well as mysql and mysql2), and of course, MySQL which I am using a server from AWS.
Below is my code for allowing more than one localhost to connect to the backend Node.js.
const corsOptions = {
origin:["http://localhost:3000", "http://192.168.56.1:3000", "http://10.3.14.231:3000/"]
}
The last 2 in the array are the "Network" links I get when I npm run dev -- --host. I then get this in the terminal:
> Network: http://192.168.56.1:3000/
> Network: http://10.3.14.231:3000/
> Local: http://localhost:3000/
So far, when I type any of the links in network to my phone, it doesn't work for the SQL. The Display and front end pop up just fine, but when I make an account, Firebase will work, but nothing is sent to MySQL.
If there's any questions please ask for clarification. I don't know if this will be an issue when I actually launch it, but I can't find anything else on this problem.
I have asp.net core application hosted on GCP App Engine. When I try to deploy the application it fails on last step:
Updating service [name] (this may take several minutes)... ...failed
ERROR: (gcloud.app.deploy) Error Response: [9] An internal error occurred while processing task /app-engine-flex/flex_await_healthy/flex_await_healthy>blablabla.wm.1
The exception stack trace show that service running in background couldn't find MySQL table (that table obviously exists).
my app.yaml file:
service: XXX
runtime: custom
env: flex
automatic_scaling:
max_concurrent_requests: 80
min_num_instances: 1
max_num_instances: 1
resources:
cpu: XXX
memory_gb: XXX
beta_settings:
cloud_sql_instances: "XXX:XXXX:XXXX=tcp:3306"
It looks like the application is deployed properly despite the error. This is the only error and backgroud service desn't throw any exceptions at later point. In fact it works properly and can connect to the database.
My guess was that maybe GCP is checking health while the application is not connected do database. So I tried to add liveness_check and readiness_check to app.yaml and configured dedicated /healthcheck endpoint in my application but it didn't make any change.
Any ideas how to fix it and what might be a cause?
Deploying app with new version fixed the issue
We are using the AZ CLI GitHub Action azure/CLI (https://github.com/marketplace/actions/azure-cli-action)
The script that this workflow calls makes an HTTP request to an external API. This cURL call fails with the following:
curl: (60) SSL certificate problem: certificate has expired
More details here: curl.haxx.se/docs/sslcerts.html
curl failed to verify the legitimacy of the server and therefore could not
establish a secure connection to it. To learn more about this situation and
how to fix it, please visit the web page mentioned above.
However I can confirm that the same request works locally.
The problem workflow step looks like this:
- name: Run script
uses: azure/CLI#1.0.4
with:
azcliversion: 2.0.72
inlineScript: |
$GITHUB_WORKSPACE/github/scripts/script.sh
Why does cURL think that the SSL cert for the external API domain is expired, when I can make the same call to the same API domain successfully on my own machine?
It seems the problem was that the azcliversion points to a version of the AZ CLI that has outdated certificates.
The problem was solved by removing the azcliversion field altogether, as the default version is latest, as specified in the docs for the action:
azcliversion – Optional Example: 2.0.72, Default: latest
So the step now looks like this:
- name: Run script
uses: azure/CLI#1.0.4
with:
inlineScript: |
$GITHUB_WORKSPACE/github/scripts/script.sh
Probably related to this: https://twitter.com/letsencrypt/status/1443621997288767491
Our cross-signed DST Root CA X3 expired today. If you are hitting an error, check out fixes in our community forum. We're seeing higher than normal renewals, so you may experience a slowdown in getting your certificates.
I keep getting a 503 but no errors in the log when trying to host my keystone.js app on openshift, has anyone successfully hosted a keystone app with them? Everything works fine on localhost.
I am using a fresh install of keystone.js with no blog or cloudinary.
Your providing very little information to give you a definitive answer. What options are you passing to keystone.init()? Are you using dotenv? If so, what are you setting there? Did you set any environment variables using rhc set-env?
I ask because a common (though not by far the only) culprit of 503 errors in Node.js applications on OpenShift is a port number overriding OpenShift's. Keystone looks at process.env.PORT before it looks at process.env.OPENSHIFT_INTERNAL_PORT. So, if you have PORT set on your .env or with rhc set-env it will take precedence over OPENSHIFT_INTERNAL_PORT.
I came across a similar question on the KeystoneJS Google Group. In that other case the developer had added a MONGODB cartridge to his app, but had not set the connection string for the cartridge in Keystone.
If this is your case as well you need to set the Keystone mongo option in Keystone.init() or using Keystone.set('mongo', 'connection_sring'). When you created the cartridge you got a url and some credentials. OpenShit passes these to your application in environment variables. You can build the mongo connection string as follows:
var connectionString = process.env.OPENSHIFT_MONGODB_DB_USERNAME + ":" + process.env.OPENSHIFT_MONGODB_DB_PASSWORD + "#" + process.env.OPENSHIFT_MONGODB_DB_HOST + '/' + process.env.OPENSHIFT_APP_NAME;
keystone.set('mongo', connectionString);
or
keystone.init({
...
mongo: connectionString,
...
});
Or you can use rhc set-env to set the MONGO environment variable as follows:
rhc set-env MONGO=http://{username}:{password}#{connection url}/{dbname} -a your_app_name
The connection url above is the one you got from OpenShift when you created the cartridge. If looks like a standard MONGODB url (e.g. mongodb://127.6.85.129:27017/).
These are just my best guesses, given that your question is a bit thin on details. You may want to post some more specifics so we can more accurately assess your problem.
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