For testing, I want to be able to run several IPFS nodes on a single machine.
This is the scenario:
I am building small services on top of IPFS core library, following the Making your own IPFS service guide. When I try to put client and server on the same machine (note that each of them will create their own IPFS node), I will get the following:
panic: cannot acquire lock: Lock FcntlFlock of /Users/long/.ipfs/repo.lock failed: resource temporarily unavailable
Usually, when you start with IPFS, you will use ipfs init, which will create a new node. The default data and config stored for that particular node are located at ~/.ipfs. Here is how you can create a new node and config it so it can run besides your default node.
1. Create a new node
For a new node you have to use ipfs init again. Use for instance the following:
IPFS_PATH=~/.ipfs2 ipfs init
This will create a new node at ~/.ipfs2 (not using the default path).
2. Change Address Configs
As both of your nodes now bind to the same ports, you need to change the port configuration, so both nodes can run side by side. For this, open ~/.ipfs2/configand findAddresses`:
"Addresses": {
"API": "/ip4/127.0.0.1/tcp/5001",
"Gateway": "/ip4/127.0.0.1/tcp/8080",
"Swarm": [
"/ip4/0.0.0.0/tcp/4001",
"/ip6/::/tcp/4001"
]
}
To for example the following:
"Addresses": {
"API": "/ip4/127.0.0.1/tcp/5002",
"Gateway": "/ip4/127.0.0.1/tcp/8081",
"Swarm": [
"/ip4/0.0.0.0/tcp/4002",
"/ip6/::/tcp/4002"
]
}
With this, you should be able to run both node .ipfs and .ipfs2 on a single machine.
Notes:
Whenever you use .ipfs2, you need to set the env variable IPFS_PATH=~/.ipfs2
In your example you need to change either your client or server node from ~/.ipfs to ~/.ipfs2
you can also start the daemon on the second node using IPFS_PATH=~/.ipfs2 ipfs daemon &
Hello, I use ipfs2, after running two daemons at the same time, can indeed open localhost:5001 / webui, run the second localhost:5002 / webui has an error, as shown in the attachment
Here are some ways I've used to create multiple nodes/peers ids.
I use windows 10.
1st node go-ipfs (latest version)
2nd node Siderus Orion ifps (connect to Orion node , not local) -- https://orion.siderus.io/
Use VirtualBox to run a minimal ubuntu installation. (You can set up as many as you want)
Repeat the process and you have 4 nodes or as many as you want.
https://discuss.ipfs.io/t/ipfs-manager-download-install-manage-debug-your-ipfs-node/3534 is another gui that installs and lets you manage all ipfs commands without CMD. He just released it a few days ago and it looks well worth lots of reviews.
Disclaimer I am not a coder or computer professional. Just a huge fan of IPFS! I hope we can raise awareness and change the world.
I have an application (Node.JS) deployed on OpenShift (bronze plan) with the Web Load Balancer activated, the minimum gears active are 3 and the max are 16.
Sometimes in the main gear I can see more than one HAProxy instance running, for example now I have:
> ps -ef|grep /usr/sbin/haproxy
3505 37488 1 1 08:46 ? 00:00:01 /usr/sbin/haproxy -f /var/lib/openshift/<APP_ID>/haproxy//conf/haproxy.cfg -sf 37237
3505 149643 1 1 May28 ? 00:09:08 /usr/sbin/haproxy -f /var/lib/openshift/<APP_ID>/haproxy//conf/haproxy.cfg -sf 114873
looking the logs I can't any error. Any explanation about this?
Thanks!
This could be a consequence of executing Haproxy reload script (/etc/init.d/haproxy). This will usually create a new haproxy process to accept new connections. It will also keep the old process alive until there are still open connections to it. Once they are closed, old haproxy process will be terminated.
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 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
So I have the following in my monitrc file:
check process apache with pidfile /usr/local/apache/logs/httpd.pid
group apache
start program = "/etc/init.d/httpd start"
stop program = "/etc/init.d/httpd stop"
if failed host XXX port 80 protocol http
and request "/monit/token" then restart
if cpu is greater than 60% for 2 cycles then alert
if cpu 80% for 5 cycles then restart
if totalmem 500 MB for 5 cycles then restart
if children 250 then restart
if loadavg(5min) greater than 10 for 8 cycles then stop
if 3 restarts within 5 cycles then timeout
but I keep getting the error that:
Error: service name conflict, apache already defined '/usr/local/apache/logs/httpd.pid'
If the hostname of the server is 'apache' then the conflict is with the default rule for monitoring the system load.
Monit seems to have the implicit rule of 'check system hostname', where the hostname is the output of hostname command.
You can overwrite that by adding just a line like:
check system newhostname
For example:
check system localhost
I saw this error when I forgot to comment out the line:
include /etc/monit/conf.d/*
in a custom /etc/monit/conf.d/myprogram.conf file, so it was recursively including that file.
By any chance do you have an entry with a host name apache beneath this entry or in a separate monit config file?
You have the same service defined more than once. Check all your monit config files for that service. This includes your monitrc and all files listed under the "Includes" section (like include /etc/monit/conf.d/*).
If you redefine "Includes" within a file in one of your "Includes" directories, you will run into recursive reference problems.
Very very important thing : you need monit 5.5
For example in ubuntu 12.04 available in repo only 5.3
So you need to download and install from other repo.
Solution for me , for example :
wget http://mirrors.kernel.org/ubuntu/pool/universe/m/monit/monit_5.5.1-1_amd64.deb && sudo dpkg -i monit_5.5.1-1_amd64.deb
For my case, I simply had to restart monit to get rid of the service name error:
sudo service monit restart
Check if you have had any conflicts for Apache defined in any of the monit conf files under /etc/monit.d/ directory, I accidentally did added nginx for my puma.conf and ran into the same error before.