Automatic Application Monitoring to keep it always sane and high performing.Second Process Fallback for High Application Reliability.It solves major issues when running Node.js applications inside a container like: The goal of pm2-runtime is to wrap your applications into a proper Node.js production environment. If you liked this post, please recommend and share it with your followers.Production ready Node.js Docker image including PM2. You can find me on Twitter, Clarity or my blog and you can also check my books: SaltStack For DevOps, The Jumpstart Up & Painless Docker. If you resonated with this article, please subscribe to DevOpsLinks : An Online Community Of Diverse & Passionate DevOps, SysAdmins & Developers From All Over The World. The result of the above script is: Number of containers is : 5 #Container name /vote_webapp_1 #Container id 1a29e9652822447a440799306f4edb65003bca9cdea4c56e1e0ba349d5112d3e #Memory usage % 0.697797903077 #The number of times that a process of the cgroup triggered a major fault 15 #Same output as ps aux in *nix Connect Deeper I had 5 containers running, but for simplicity reasons I will just get the output of just 1 of them: docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 1e557c8dc5f7 instavote/vote "gunicorn app:app -b " 6 days ago Up 5 hours 80/tcp, 100/tcp vote_webapp_1 This is the example script: from ntainers import Containers from domonit.ids import Ids from domonit.inspect import Inspect from domonit.logs import Logs from domonit.process import Process from domonit.changes import Changes from domonit.stats import Stats import json c = Containers() i = Ids() print ("Number of containers is : %s " % (sum(1 for i in i.ids()))) if _name_ = "_main_": for c_id in i.ids(): ins = Inspect(c_id) sta = Stats(c_id) proc = Process(c_id, ps_args = "aux") # Container name print ("\n#Container name") print ins.name() # Container id print ("\n#Container id") print ins.id() # Memory usage mem_u = sta.usage() # Memory limit mem_l = sta.limit() # Memory usage % print ("\n#Memory usage %") print int(mem_u)*100/int(mem_l) # The number of times that a process of the cgroup triggered a "major fault" print ("\n#The number of times that a process of the cgroup triggered a major fault") print sta.pgmajfault() # Same output as ps aux in *nix print("\n#Same output as ps aux in *nix") print proc.ps()
#Monit docker containers install#
bin/activate git clone cd DoMonit pip install -r requirements.txt python examples.py Usage ExampleĬreate a virtual environment and clone the project virtualenv domonit cd domonit. Stats : This endpoint returns a live stream of a container’s resource usage statistics. This endpoint is not supported on Windows. On Unix systems this is done by running the ps command. Process : List processes running inside the container id. Logs : Get stdout and stderr logs from the container id Inspect : Return low-level information on the container id The wrapper contains these classes: domonit/ ├── changes.py ├── containers.py ├── errors.py ├── ids.py ├── inspect.py ├── logs.py ├── process.py └── stats.py It’s easy to understand, so anyone even starting using Docker can contribute. The purpose is to create python scripts easily in order to monitor all of your Docker containers and collect all of the possible metrics that Docker API offers- few hundreds of metrics and information.Īnd the project is a POC, so there are many things to add. It could be used with Docker API 1.24 and it is compatible with Docker 1.12.x and later, actually works on *nix and other OSs integration will be added soon. et I needed a simple monitoring utility that I can use in scripts for specific cases without calling the Docker API, that’s why I started Domonit: Deadly Simple Docker Monitoring Wrapper For Docker API In my job and during my experimentations with Docker, Docker Swarm, Micro Services running Docker. In many cases, monitoring could be very specific to a use case or an environment, that’s why I use scripting in these special cases - I almost use Python and sometimes Bash. In my experience, the adoption of managed infrastructure and cloud changed the way we use and manage the infrastructure and I would say that even the nature of the problems are changing, the quality of production is more and more critical and monitoring is becoming more proactive -it is not just the collection and the visualization of several metrics in order to be aware of what’s happening. Running Linux systems in production for more than 10 years, I deployed and tested many infrastructure and production monitoring systems. The same thing is applied to running Docker in production: Monitoring is needed especially if you have a lot of critical applications running containers. Running production without monitoring is not recommended at all. Monitoring gives you a visibility about your infrastructure.
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