Development with Docker

I have not seen a lot of great examples of how to use Docker as a developer. There are tons of examples of how to build images; how to use existing images; etc. Writing code that will end up running inside of a container and more so writing code that gets compiled, debugged, and developed in a container is a bit tricker. This post dives into my personal usage of containers for development. I don’t know if this is normal or even good, but I can definitely vouch that it works.

First off, I am developing with an interpreted language most of the time. I still think these issues apply with compiled languages but they are easier to ignore and sweep under the rug. In this post I’ll show I create layered images for developing a simple web service in Perl. It could be Ruby or Python of course, I just know Perl the best so I’m using it for the examples.

Here is a simple Makefile to build the images:

api-image: docker build -f ./Dockerfile.api -t pw/api . db-image: exit 1 perl-base-image: docker build -f ./Dockerfile.perl-base -t pw/perl-base .

I can build three images, one of which (db) is not-yet-defined but planned.

base

Here is Dockerfile.perl-base

FROM alpine:3.4 ADD cpanfile /root/cpanfile RUN \ apk add --update build-base wget perl perl-dev && \ cpan App::cpm && \ cd /root && \ cpm -n --installdeps .

I use Alpine as the underlying image for my containers if possible, because it is almost as lightweight as it gets. Beware though, if you use it you may run into problems because it uses musl instead of glibc. I have only run into issues twice though, and one was a bug in the host kernel.

Next I add the cpanfile to the image. I could probably do something weird like build the Dockerfile and directly add the lines from the cpanfile to the Dockerfile, but that doesn’t seem worth the effort to me.

Finally I, in a single layer (hence the && \ ’s:)

Install Perl (which is a very recent 5.22)

Install cpm

Install the dependencies of the application

Basically what the above gives you is a cache layer where most of your dependencies are installed. This can hugely speed development while you are adding dependencies to the next layer. This methodology is also useful at deployment time, because new builds of the codebase need not rebuild the entire base image, but instead just one or more layers on top. The base image in this example is over 400 megs, and that’s with Alpine; if it were Ubuntu it would likely be over 700. The point is you don’t want to have to push that whole base layer to production for a spelling fix.

api

Here is Dockerfile.api

FROM pw/perl-base ADD . /opt/api WORKDIR /opt/api RUN cpm -n --installdeps .

Sometimes I’ll add extra bits to the RUN directive. Like currently in the project I’m working on it’s:

RUN apk add perl-posix-strftime-compiler && cpanm --installdeps .

Because I needed Alpine’s patched POSIX::Strftime::Compiler. That will at some point be baked into the lower layer.

If your project is sufficiently large, it is also likely worth it to break api into two layers. One called, for example, staging , which is almost exactly the same as the base layer, but it’s FROM is your base . api then becomes just the ADD and WORKDIR directives.

Another pretty cool refinement is to use docker run to build images. If you have special build requirements this is super handy. A couple reasons why one might need this would include needing to run multiple programs at once during the build, or needing to mount code that will not be added directly to an image. Here’s how it’s done:

FROM=pw/stage2 TMP_DIR=$(mktemp -td tmp.$1.XXXXXXXX) # start container docker run -d \ --name $TMPNAME \ --volume $TMP_DIR:/tmp \ $FROM /sbin/init # build docker exec $TMPNAME build --my /code # save to pw/api docker commit -m "build --my /code" $TMPNAME pw/api docker rm -f $TMPNAME sudo rm -rf $TMP_DIR

Both of these refinements are arguably gross, but they really help speed development and solve problems, so until there are better ways, I’m happy with them.

Running

The above is a useful workflow for building your images, but that does not answer how the containers are used during development. There are a couple pieces to the answer there. First is this little script, which I placed in maint/dev-api :

#!/bin/dash exec docker run --rm \ --link some-postgres:db \ --publish 5000:5000 \ --user "$(id -u)" \ --volume "$(pwd):/opt/api" \ pw/api "[email protected]"

The --link and --publish directives are sorta ghetto. At some point I’ll make the script dispatch based on the arguments and only link or publish if needed.

If possible I always use a non-root user, hence the --user directive. It is probably silly, but you almost never need root anyway, so you might as well not give it to the container. This has the nice side effect of ensuring that any files created from the container in a volume have the right owner.

The --volume should be clear: it replaces the code you built into the image with the code that’s on your laptop, without requiring a rebuilt image.

The other part to make this all work are a few more directives in the Makefile:

prepare-migrations: maint/dev-api perl -Ilib bin/update-database run-migrations: docker run --rm --link some-postgres:db pw/api perl -Ilib bin/update-database 1 run-db: docker run --name some-postgres -d postgres rm-db: docker rm -f some-postgres

I haven’t gotten around to creating a database container; I’m just using the official docker one for now. I will eventually replicate it for my application in a more lightweight fashion, but this helps me get up and get going. I wouldn’t have made the rm-db directive except the docker tab completion seems to be pretty terrible, but the make tab completion is perfect.

run-migrations is a little weird. It requires a complete rebuild just to update some DDL; but I believe it will be worth it in the long term. I suspect that I’ll be able to push the api container to some host, run-migrations , and it be done, instead of needing a checkout of the code on the host.

Linking

One of the details above that I haven’t gone into is the --link directive. This sets up the container so that it has access to the other container, with some environment variables set for the exposed ports in the linked container. On the face of it, this is just a way to connect two containers. Here is how I’m connecting from a script that deploys database code:

#!/usr/bin/env perl use 5.22.0; use warnings; use DBIx::RetryConnect 'Pg'; use PW::Schema; my $s = PW::Schema->connect( "dbi:Pg:dbname=postgres;host=$ENV{DB_PORT_5432_TCP_ADDR}", 'postgres', $ENV{DB_ENV_POSTGRES_PASSWORD}, );

Notice that I simply use some environment variables that follow a fairly obvious pattern (though it can be referenced by linking a container running env more easily than the docs.)

One other subtle detail is the use of DBIx::RetryConnect. With containers it is much more common to start all of your containers concurrently, versus with typical init systems or even virtual machines. This means baking retries into your applications, as it stands today, is a requirement. Either that or you add stupid sleep statements and hope nothing ever gets run on an overloaded machine.

Linking is pretty cool. For those who haven’t investigated this space much, linking seems like some cool magic “thing.” Linking is actually a builtin service discovery method for allowing containers to know about each other. But linking has a major drawback: to link containers in docker you have to start the containers serially. This is because links are resolved at container creation time. Worse yet you can’t change the environment variables of a running program, so links cannot be updated. This is at the very least a hassle because it introduces a synthetic, implied ordering to the starting of containers.

You can resolve the ordering problem with docker network :

# run API container docker run -d \ --name $NAME \ pw/api www # add to network docker network create pw docker network connect pw $NAME # run db container docker run --name db -d postgres docker network connect pw db

Order no longer matters and you have much more flexibility with how you do discovery. But now you need to make a decision about discovery, as the environment variables will no longer be magically set for you. I strongly believe that this is where anyone doing anything moderately serious will end up anyway. The serialization of startup is just too finicky to be seriously considered.

I haven’t done enough with service discovery myself to recommend any path forward, but knowing the name to search for should give you plenty of rope.

I hope the ideas and examples above help anyone who is grappling with how to use Docker. Any criticisms or other ideas are welcome.

Posted Mon, Jul 18, 2016

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