Previous Post
Supercharging dbt with Splitgraph: versioning, sharing, cross-DB joins
Jul 28, 2020 · By Artjoms Iškovs

Splitgraph infrastructure, part 3: Using Docker Compose in production

We finish our overview of Splitgraph's infrastructure by talking about why and how we use Docker Compose to run the Splitgraph registry in production.

At the time of writing, the Splitgraph registry runs on a single 30 EUR/month Scaleway instance. Despite that, we index and let you query over 40,000 public datasets with simple SQL.

This series of articles discusses our infrastructure and how we stay lean while still providing a lot of value to data scientists and engineers.

To run Splitgraph in production, we use Docker Compose. We found it to be a surprisingly effective solution. This article is a collection of tips and tricks for using Docker Compose that we discovered. We'll talk about:

  • structuring a Compose dev-prod configuration using override mixins
  • an automated deploy workflow
  • configuration generation, injection and live reloading
  • how we monitor our services in production

Why Compose?

We are eventually interested in moving to Kubernetes or Nomad (we're even writing a script that automatically converts Compose files to Nomad jobs!). But, these solutions felt like overkill for our use case.

Besides, we relied on Docker Compose a lot when building Splitgraph. Currently, we run integration tests in CI using it. We wanted to match the dev, CI and production workflow as closely as possible.

Compose configs and mixins

Docker Compose allows you to combine multiple Compose configurations into one at runtime. This feature is called "overrides".

Here's a sample Compose definition for a "prod" service:

version: "3.4"
    image: ${DOCKER_REPO}/registry-db:${DOCKER_TAG}
      - 5431
      - ${CONFIG_HOME}/config/registry-db.env
    restart: on-failure
    user: "postgres:pgsockets"
      - registry_dbdata:/var/lib/postgresql/data/pgdata
      - pg_pgb_sockets:/var/pgsockets
      - ${CONFIG_HOME}/config/registry-db.sgadmin.password:/registry-db.sgadmin.password:cached

Our production configuration normally includes:

  • The actual Docker image
  • A policy to automatically restart containers that exited with an error
  • Bind mounts of configuration files
  • Environment files
  • Mounts of data volumes
  • Docker healthcheck definitions

The development Compose file is a mixin on top of the production configuration. For this service, it's:

version: "3.4"
      - ""
      - ./src/py/splitgraph/splitgraph:/splitgraph/splitgraph
      - ./src/luamods/main:/home/postgres/luamods
      - ./src/py/sgr_admin/sgr_admin/embedded/dbs:/home/postgres/pymods
      - ./components/registry/registry-db/etc/postgresql:/etc/postgresql

This can include:

  • Bind mounts for source code for scripted languages
  • Flags for verbose logging
  • Using a more heavyweight Docker image with dev tooling or hot reloads
  • Publishing ports on the host to aid debugging

This lets us avoid repetition in our configuration. We also use this idea to split up different aspects of the stack into separate Compose files. We call these "service groups".

We have runscripts that wrap Compose overrides to give the developer a single script. They can use it to run Compose commands on a service group. The script can inject the "dev" configuration if needed:

#!/usr/bin/env bash -e

if [[ "$1" == "--dev" ]] ; then


exec docker-compose -f \\
    $( [[ -n $DEV ]] && echo "-f" ) \\
    -f \\
    -f \\
    $( [[ -n $DEV ]] && echo "-f" ) $@

Deployment workflow

We deploy Splitgraph using GitLab CI. The deploy process runs in several stages.

Building the production bundle

First, we use our Makefile (we wrote more about how we use Makefiles in a previous blog post) to build a "deploy bundle". This bundle is self-contained and includes only the files needed to run Splitgraph in production:

  • Production Docker Compose configs
  • Runscripts that combine those configs into "service groups"
  • Other useful scripts for production: installation, backups, configuration management, helper script to load into the admin shell
  • Configuration templates

We store the actual tag (commit hash) that we're deploying in a .env file in the bundle that every script sources.

Deploy home directory

On the server, we have a "deploy home" directory that contains service configuration files. It also contains a config.json file that includes:

  • configuration flags for the current deploy
  • secrets
  • third-party API keys

The CI script copies the bundle into this directory and extracts it. We keep copies of previous deploy bundles too, each one in a timestamped directory. This means we can roll back to an earlier version in case of a failed deploy by running that bundle's installation script.

Pulling and tagging the containers

The installation script first pulls all required containers from the GitLab CI registry. At this point, it uses the deploy's commit hash as the tag.

The script then retags these containers with a :production tag. This is to avoid Compose reloading all services on every deploy. If an image's tag (not only the SHA hash) has changed, Compose will treat the service's configuration as changed. Keeping the tag fixed prevents that.

Configuration generation

The script then runs the "configurator". It's a container with a Python application that uses Jinja. It uses the bundle's config templates and the config.json file to regenerate configuration for all services that we run.

We wrote more about the configurator in a previous blog post.

We bind mount configuration into containers. We prefer this to baking it into the image. This lets us make emergency hotfixes without having to go through the CI pipeline.

Database migrations

Next, the installation script runs schema migrations. We check all our migrations into version control and run integration tests on them in CI.

These migrations form part of the "admin" container. We also use this container to run other Splitgraph instance management tasks.

Finally, we implement a lot of Splitgraph functionality as PostgreSQL functions in languages like PL/Python or PL/Lua. We package those up as PostgreSQL extensions which we also install at migration time.

Reloading services

We have several service groups that we deploy one after another. We use ./ up -d to upgrade most of them. This means that Docker Compose only recreates containers for services with changed specifications. You can read more about this behavior in the Compose documentation.

This is not a zero downtime deploy, but we try to minimize excess container restarts by pinning third-party container hashes. For example, instead of haproxy:latest, we use haproxy@sha256:e6f9faf0c2a0cf2d2d5a53307351fa896d....

In some cases, we run many copies of the same service group for high availability. This is the case for our REST API. There, the script waits until the replica is healthy before upgrading the next one:

#!/bin/bash -e

# Wait for a container to become healthy.


while true; do
  status=$(docker inspect -f '{{.State.Health.Status}}' "$CONTAINER")
  if [ "$status" != "starting" ]; then
  if (( timeout < 0 )); then
    echo "Timed out waiting for $CONTAINER to become healthy"
    exit 1
  sleep 2
  timeout=$(( timeout - 2))
  echo "Waiting for $CONTAINER to become healthy, $timeout s.."

if [ "$status" == "unhealthy" ]; then
  echo "$CONTAINER is unhealthy!"
  exit 1

echo "$CONTAINER is healthy."

Reloading configuration

Most containers don't get recreated or restarted on deploy, which helps us minimize downtime. But what do we do if we need to reload a service's configuration without restarting it?

The answer is simple. Lots of services like HAProxy, Prometheus, PgBouncer or NGINX support configuration reloads via a SIGHUP signal. You can send a UNIX signal to a container with Docker:

$ docker kill --signal SIGHUP container_name

All we need to do to apply new configuration is send SIGHUP to relevant containers after a deploy. If the configuration has errors, these services will silently switch back to the old one.

There's a caveat here with bind mounts. You have to make sure you're bind mounting the directory with the configuration file rather than the actual file. This is because of how Docker bind mounts interact with Linux inodes. More on Docker GitHub.

Healthchecks and monitoring

As the final step, the deploy runs docker-compose ps to make sure all containers that have healthchecks pass them. If there are unhealthy containers, the deploy script raises an alert in our team chat.

We also use Prometheus and Grafana to scrape various container statistics, plot them and alert on them. In particular, we have alerting rules on:

  • HAProxy backend availability
  • High HTTP response latencies
  • High node resource usage
  • High rate of non-2xx responses
  • Failed Airflow jobs

We check our Prometheus configs into version control. We do the same with Grafana dashboards. We load those from configuration files using Grafana's provisioning feature.


In this article, we discussed how we use Docker Compose to run Splitgraph in production, as well as how to use it for automated deploys.

This is the final article in the series that talks about our build, test and deploy infrastructure. If you're interested in learning more about Splitgraph, you can check our frequently asked questions section, follow our quick start guide or visit our website.

Port 5432 is open: introducing the Splitgraph Data Delivery Network