I’ve written about Docker before, as I am a big fan of it. And for this post, I’m going to talk about some practical situations in which I’ve used Docker in real life, both for testing and software development!
But first, let’s recap what Docker IS and IS NOT:
Docker containers spin up quickly (1-2 seconds or less)
Docker containers DO have separated process tress and filesystems
Docker containers ARE NOT virtual machines
Docker containers ARE intended to be ephemeral. (short-lived)
You CAN, however, mount filesystems from the host machine into Docker, so those files can live on after the container shuts down (or is killed).
You SHOULD only run one service per Docker container.
Everybody got that? Good. Now, let’s get into some real life things I’ve used Docker for.
Experimenting in Linux
Want to test out some commands or maybe a shell script that you’re worried might be destructive? No worries, try it in a Docker container, and if you nuke the filesystem, there will be no long-term consequences.
# Start a container with Alpine Linux
$ docker run -it alpine
# Let's do something dumb
$ rm /bin/ls
$ ls -l
/bin/sh: ls: not found
# Just exit the container, restart it, and our filesystem is back!
/ # exit
[unifi:~/tmp ] $ docker run -it alpine
/ # ls
bin dev etc home lib media mnt proc root run sbin srv sys tmp usr var
And all of the above takes just a couple of seconds! This works with other Linux distros as well, such as CentOS and Unbuntu–just change your Docker command accordingly:
docker run -it centos
docker run -it ubuntu
Yes, that means you could run CentOS in a container under Ubuntu or vice-versa. Docker doesn’t care. 🙂
In my case, over my Christmas vacation, I checked into a Mom and Pop hotel, or rather a motel! It was about 24 rooms all in a row, occupying a single floor. Since they were on a budget, their Internet offering consisted of what appeared to be 5 or 6 Linksys routers set up every few rooms. You’d simply connect to the closest access point and have Internet.
But there was a problem: determining which access point was closest to me! The signal strength indicator on my computer showed several of them were 3/3 bars so that wasn’t much help. I tried connecting to the first one, but had virtually no Internet connectivity.
Running that command will print up a confirmation screen so that you can back out and change any options (such as hosts to ping), and when you’re ready, just hit <ENTER> to start the container.
In the above example, I added in the TARGETS environment variable, and was sure to include 192.168.1.1, which was the IP for each router (they were all the same). Then I set Splunk “real-time mode” and periodically checked that tab as I was working. This is what I saw:
In a previous post, I wrote about using Splunk to monitor network health and connectivity. While building that project, I thought it would be nice if I could build a more generic application which could be used to perform ad hoc data analysis on pre-existing data without having to go through a complicated process each time I wanted to do some analytics.
So I built Splunk Lab! It is a Dockerized version of Splunk which, when started, will automatically ingest entire directories of logs. Furthermore, if started with the proper configuration, any dashboards or field extractions which are created will persist after the container is terminated, which means they can be used again in the future.
A typical use case for me has been to run this on my webserver to go through my logs on a particularly busy day and see what hosts or pages are generating the most traffic. I’ve also used this when a spambot starts hitting my website for invalid URLs.
This will print a confirmation screen where you can back out to modify options. By default, logs are read from logs/, config files and dashboards are stored in app/, and data that Splunk ingests is written to data/.
Once the container is running, you will be able to access it at https://localhost:8000/ with the username “admin” and the password that you specified at startup.
First things first, let’s verify our data was loaded and do some field extractions!
I’ve been using Splunk professionally over the last several years, and I’ve become a big fan of using it for my data processing needs. Splunk is very very good about ingesting just about any kind of event data, optionally extracting fields at search time, and providing tools to graph that data, find trends, and see what is really happening on your platform. This is important when your platform consists of thousands of servers, as it does at my day job!
While Splunk can handle events in timestamp key=value key2=value2 format, it also has support for dozens of standardized formats such as syslog, Apache logs, etc. If your data is in a customized format, no problem! Splunk can extract that data at either index or search time! Finally, there’s the Search Processing Language, which is like SQL but for your event data. With SPL, you can run queries, generate graphs, and combine them all programatically.
So yeah, I’m a huge fan of Splunk. One thing I use it for out of the of office is to graph the health of my Internet connection. This is useful both for when I’m at home and when I am traveling–I just feed the output of ping into Splunk and then I can get graphs of packet loss and network latency.
Let’s just jump into an example screen–here’s what I saw when I was a friend’s place and I uploaded a video to YouTube:
TL;DR If you are comfortable with Docker and Docker Compose, you can go straight to the GitHub repo and get started. For the everyone else, read on…
When I stood up this website, I wanted to do so in Docker, but I ran into an issue: the official WordPress Docker image runs Apache. Apache is a nice webserver for small amounts of traffic, but it does not scale well. As more concurrent connections come into a server running Apache, more copies of the httpd process are forked, which causes RAM usage to go up. Having RAM usage regularly go up and down is not ideal.
Fortunately, there is a better way. The Nginx webserver, combined with PHP running in FPM mode scales much better as the memory usage is more constant, which means that peak loads on the server won’t cause you to thrash the swapfile. Encryption would also be nice, so I wanted to have some SSL going as well.
I couldn’t find any existing solutions, so I built one! In this post, I’m going to walk through each piece of the puzzle.
I’m a big fan of the Discord Musicbot, and run it on some Discord servers that I admin. Wanting to run it on a server, I first created an Ansible playbook and launched a server on Digital Ocean. But after a few months, I noticed that the server was sitting over 90% idle. Surely there had to be a better way.
So I next tried Docker, and created a Dockerized version of the Musicbot. I was quite happy with how much easier it was to spin up the bot, but still didn’t want to run it on a dedicated server on Digital Ocean. Aside from having unused capacity, if that machine were to go down, I’d have to do something manually.
I thought about running it in some sort of hosted Docker cluster, and came across Amazon’s container service. So this post is about creating your own cluster in ECS and hosting a Docker container in it. I found the process slightly confusing when I did it the first time, and wanted to share my experience here.