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Container scheduling strategies for integration testing 14 different databases in Github Actions

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DataStation lets you query over 20 SQL and non-SQL data systems. 14+ databases can be tested locally (in Docker; even SQL Server and Oracle!).

Integration testing DataStation support for these databases is the most important part of developing DataStation. Thankfully, since DataStation is an open-source project, these integration tests can run freely in Github Actions.

I'm going to focus on the data systems that can be tested locally in Docker.

A list of DataStation database test files

Every database, everywhere at once

When I first started integration testing databases in DataStation I did one of two things:

  1. Ran docker images in the background
  2. Installed databases globally from Ubuntu packages

One of these two steps happened for each database that needed to be tested. The process ran as part of a setup script.

For example, here are the parts of the setup script for Github Actions that set up MySQL and PostgreSQL. (This is an old commit, it's no longer done like this. As you'll see further on.)

# Set up MySQL
sudo service mysql start
sudo mysql -u root -proot --execute="CREATE USER 'test'@'localhost' IDENTIFIED BY 'test'";
sudo mysql -u root -proot --execute="CREATE DATABASE test";
sudo mysql -u root -proot --execute="GRANT ALL PRIVILEGES ON test.* TO 'test'@'localhost'";

# Set up PostgreSQL
sudo apt-get install postgresql postgresql-contrib
echo "
local  test            test                md5
host   test            test   localhost    md5
local  all             all                 peer" | sudo tee /etc/postgresql/12/main/pg_hba.conf
sudo service postgresql restart
sudo -u postgres psql -c "CREATE USER test WITH PASSWORD 'test'"
sudo -u postgres psql -c "CREATE DATABASE test"
sudo -u postgres psql -c "GRANT ALL ON DATABASE test TO test"

And here is later on in the same script where Docker containers for QuestDB, Elasticsearch, and Prometheus are set up to run in the background.

# Start up questdb
docker run -d -p 8812:8812 questdb/questdb

# Start up elasticsearch
docker run -d -p 9200:9200 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:7.16.3

# Start up prometheus
docker run -d -p 9090:9090 -v $(pwd)/testdata/prometheus:/etc/prometheus prom/prometheus

This was the easy, lazy way to get tests working. But there were two big problems. First off, it was horrible to test these databases locally. When working on tests, you'd have to leave the test file and go into this CI setup script and find the lines that set a database up. If you were trying to test outside of Ubuntu and needed to run one of these databases set up using Ubuntu packages you'd have to figure out how to translate those steps to your OS/distro.

The second big problem was that after months of adding new databases and new database containers for new database tests, Github Actions was crawling to a halt. After setting up the 14th running database (MongoDB) last week, this workflow on Github Actions crashed repeatedly for a week.

Per-test scheduling

So I decided to solve both problems at once by writing a small helper function, withDocker, that each test could call and declare the container setup it needed for its tests to work. The goal here was that 1) all of these local database tests would now only require Docker and 2) each database would only run so long as it was needed. No more 14 databases running at the same time in Github Actions.

Here's an example (source code here) of the useDocker helper for running a ScyllaDB container, setting the stage for DataStation tests to run for Scylla integration.

... imports ...

describe('basic cassandra/scylladb tests', () => {
  test(`runs basic cql query`, async () => {
    await withDocker(
        image: 'docker.io/scylladb/scylla:latest',
        port: '9042',
        program: [
        cmds: [
          `cqlsh -u cassandra -p cassandra -e "CREATE KEYSPACE test WITH REPLICATION = {'class': 'SimpleStrategy', 'replication_factor': 1};"`,
          `cqlsh -u cassandra -p cassandra -e "CREATE ROLE test WITH PASSWORD = 'test' AND LOGIN = true AND SUPERUSER = true;"`,
      async () => {
	    ... do actual test stuff ...
  }, 360_000);

One really useful part of withDocker is that when you specify cmds it will re-run the first element in the list until it succeeds. This is an easy shorthand for waiting for the container to become available so long as you know the command should succeed in normal conditions.

Also, all commands in the cmds list get run within the Docker container with by prefixing the command with docker exec $containerId.

More manual waiting

When you need to wait on something outside of the container you can fill out the optional wait callback. Here's an example (source code here) of using the wait callback to make sure that data has been ingested into Elasticsearch before allowing tests to run.

... imports ...

describe('elasticsearch testdata/documents tests', () => {
  const tests = [
    ... some test data ...

  for (const testcase of tests) {
      'query: "' + testcase.query + '"',
      async () => {
        await withDocker(
            port: 9200,
            env: {
              'discovery.type': 'single-node',
            image: 'docker.elastic.co/elasticsearch/elasticsearch:7.16.3',
            wait: async () => {
              console.log('Awaiting container');
              while (true) {
                try {
                  const r = await fetch('http://localhost:9200');
                } catch (e) {
                  await new Promise((r) => setTimeout(r, 2000));

              // Setting up test docs
              const nDocs = 4;
              for (let i = 0; i < nDocs; i++) {
                  `curl --fail -X POST -H 'Content-Type: application/json' -d @testdata/documents/${
                    i + 1
                  }.json http://localhost:9200/test/_doc`,
                  { stdio: 'inherit' }

              // Wait until all docs have been ingested
              while (true) {
                console.log('Waiting for all docs to be ready');
                try {
                  const r = await fetch('http://localhost:9200/test/_search');
                  const j = await r.json();
                  if (j.hits.hits.length === nDocs) {
                } catch (e) {
                  /* pass */

                await new Promise((r) => setTimeout(r, 2000));
          async () => {
            ... do the actual test ...

Using cmds or wait lets me almost fully avoid using sleep() as the sole way for deciding when tests can be run. But out of laziness, there are some exceptions. For example I haven't yet figured out a CLI or curl based way to test for when Oracle is ready so I just await new Promise(r => setTimeout(r, 60_000)); i.e. wait one minute.

withDocker implementation

You can find the full code on a Github Gist here. It's not perfect and I don't want to maintain it for others. But it might be a useful base for others that are interested in similar style testing.

Impact and next steps

Before this, in the last few weeks after we added the 14th running database, tests were hanging in Github Actions after 45-ish minutes. Now tests are finishing reliably in under 30 minutes (10 minutes goes to setup).

One shortcut I took in the withDocker implementation is putting a hack-y lock in JavaScript on a single image running at a time. And I made this behavior stronger by requiring jest to run one test at a time with --runInBand; not scheduling tests onto worker processes. This was to block tests from failing because a port was in use.

This may be slowing down tests but it may also be possible that multiple containers running at once would cause less work to be done in the shared compute environment that is free Github Actions. I'm not sure.

The way I'm thinking about trying without locks is by having the withDocker function pick a free port and sending it to the callback. Then it could schedule all tests at once without port conflicts. And then without jest's --runInBand flag in place jest could share work across a few processes. I'll have to try it out to see if it improves overall test runtime.


With questions, criticism or ideas, email or Tweet me.

Also, check out DataStation and dsq.