When integrating a PHP application connecting to a PostgreSQL database, both services running as Docker containers using the official PHP and Postgre images, you might encounter (as I have) an error that looks something like this:
Uncaught Error: Call to undefined function pg_connect() in ...
It’s actually a simple error, which means that there’s something wrong with the connection between the app and the PostgreSQL database, but when I first stumbled on it I had a hard time finding out what I needed to do to fix it. There was definitely something missing from the Docker setup, but I did not know what it was until I sought help from a teammate.
Apparently the official PHP docker image does not contain the PDO and PGSQL drivers necessary for the successful connection. Silly me for assuming it does.
The fix is simple. We have to create a Dockerfile that updates our PHP image with the required drivers, which contains the following code:
RUN apt-get update
# Install PDO and PGSQL Drivers
RUN apt-get install -y libpq-dev \
&& docker-php-ext-configure pgsql -with-pgsql=/usr/local/pgsql \
&& docker-php-ext-install pdo pdo_pgsql pgsql
Easy peasy. And to run this Dockerfile from a docker-compose.yml file, we’ll need the build, context, and dockerfile commands, replacing the single image command that does not use a Dockerfile:
# All your other settings follow ...
And that should be all that you need to do! 🙂
I spent the recent weeks of January tinkering with Docker in both Windows 7 and Mac OS. I played with it a lot because I thought it’s something that’s useful for a grand new project we have at work, and I thought that integrating our legacy application code to it would help me learn about it more. And the exercise did help me understand the tool better, including some nuances with application performance and database connections. I was able to dockerize our legacy apps too! 🙂
Some notes to remember related to the exercise:
- Windows 7 Docker Toolbox and Docker for Mac has a performance issue with volume mounts through docker-compose. Legacy apps composed of a large number of files (especially with dependency directories) will run, but they will be painfully slow out-of-the-box. Fortunately for Docker for Mac users, docker-sync has an effective workaround for this problem. It involves running an in-sync container for the application code, separate from the docker-compose file. Unfortunately, I have not found any workarounds for said performance issue for Windows 7 (and perhaps Windows 8) Docker Toolbox users.
- Often we have to update the host file of our server machine so that we can run applications locally using a distinct, easy-to-remember URL through a browser. This means we need to add extra hosts to necessary docker containers too if we dockerize our apps. We can do this by using the extra_hosts command in docker-compose.
- The official Postgresql docker container does not include the pdo, pdo_pgsql, and pgsql drivers, which handles the connection between the application and the database. To install those drivers inside the official container, we’ll need to use a Dockerfile and build it from the docker-compose file with the build and context commands.
- Sometimes we have a need to copy the Postgresql DB files from a running container to set up a proper volume mount of database data from host to container. We can copy that data by using the convenient docker cp <source> <destination> command. I had this work in Docker for Mac. However, for Windows 7 Docker Toolbox users, a docker container is unable to use such copied data, perhaps because of the difference in OS between host and container, so I had to resort to restoring and backing up data every time I start and stop my application containers.
- As a tester, what Docker provides me is a convenient tool to test all sorts of interesting application configurations as much as I want to on a single machine, see if the apps break if I changed some service config, and find out which configurations work or not. I can add or remove a new service, or even update an existing service to a new version, like updating PHP from 5.6 to 7.1, and immediately see what impact it has on the apps themselves. These kinds of tests are often left to operations engineers, but I’m glad there is a now a way to do such tests on my own machine, before application changes even reach a dedicated testing server.
- Even if Docker makes it easy to setup an application development environment from scratch with docker-compose and Dockerfiles, it is still important to maintain a wiki of the necessary machine configurations a programmer needs to perform in order to reset or build the apps with only a single command, or two. Subtle things like custom docker-compose files, .env and php.ini files, host files, Nginx configs, or turning long docker commands into shortcuts with shell scripts or make commands.
- Dockerizing our legacy apps pushed me on a discussion with programmers about the ways they run said applications on their machines. Most of them actually just test code changes directly on Staging or another available development server. That speaks about one habit we have as a development team, and likely the reason why our apps are a pain to setup locally.
When I first tried Docker a couple of years back, I did not find it much different from using a virtual machine. Perhaps because I was experimenting with it on Windows, or perhaps it was still a relatively new app back then. I remember not having a pleasant experience installing and running it on my machine, and at the time it was just easier to run and debug Selenium tests on a VM.
I tried again recently.
Building a Docker image containing test code with dependencies automatically installed, with Docker Toolbox on Windows 7
And I was both surprised and delighted to be able to build a Docker image with an existing test code and its dependencies automatically installed, right out of the box. This is very promising; I can now build development environments or tools which can run on any machine I own or for teams. To use them we just need install Docker and download the shared image. No more setup problems! Of course, there’s still a lot to test – we’ll probably want to have an image be slim in size, automatically update test code from a remote repository, among other cool things. I’ll try those next.
Here’s what the Dockerfile looks like:
RUN mkdir /usr/src/app
ADD . /usr/src/app/
RUN gem install bundler
RUN bundle install
Short and easy to follow. Then we build the image by running the following command on the terminal (on the root project directory):
docker build -t [desired_image_name] .
To run and access the image as a container:
docker run -i -t [image_name]:[tag_name] /bin/bash
And from there we can run our cucumber tests inside the container the same way as we do on our local machine.