A Non-Cloud Serverless Application Pattern Using Git and Docker


Over time I’ve built up a few different small applications which do simple things like track share prices, or track whether a particular file has changed on GitHub. Little apps that only I use.

While building these I’ve come to use an unorthodox application patterns that allow me to run them ‘serverless’ and without the context of a specific cloud provider.

Since they contain the code and accompanying data, they can be run anywhere that Docker and an internet connection is available.

The key ideas are:

  • use Git to store the entire context of the application (including data)
  • use Git’s distributed nature to remove a central server requirement
  • use Docker to ensure the run context is reproducible

This serverless pattern might be useful as thought-provokers for others who write little one-off apps.


Using Git as a Database

What, essentially, is a database? It is a persistent store of data that you can look up data in.

It doesn’t need to support SQL, be multi-threaded, even have a process running continuously.

For many applications, I use Git as a database. Git has a few useful features that many databases share:

  • a logical log (using ‘git log’)
  • a backup/restore mechanism (git push/pull)
  • ability to do transactions ‘commit’

And a few features that many don’t:

  • ability to restore older versions
  • ability to arbitrate between forks of data


However, git can’t easily be made to do the following things as a database:

  • query data using SQL
  • store huge amounts of data
  • concurrency (without a lot of pain!)
  • performance

For my purposes, this is sufficient for a large proportion of my mini-app use cases.


A trivial example of this is available here, using a trivial app that stores the date last run.

A limited form of querying the history is available using ‘git diff’:

$ git diff 'gitdb@{1 minute ago}'
diff --git a/date.txt b/date.txt
index 65627c8..d02c5d8 100644
--- a/date.txt
+++ b/date.txt
@@ -1 +1 @@
-Sat 5 Aug 2017 10:37:33 CEST
+Sat 5 Aug 2017 10:39:47 CEST

Or you can use ‘git log –patch’ to see the history of changes.

$ git log --patch
commit ab32781c28e02799e2a8130e251ac1c990389b65
Author: Ian Miell <ian.miell@gmail.com>
Date: Sat Aug 5 10:39:47 2017 +0200

Update from app_script.sh

diff --git a/date.txt b/date.txt
index 65627c8..d02c5d8 100644
--- a/date.txt
+++ b/date.txt
@@ -1 +1 @@
-Sat 5 Aug 2017 10:37:33 CEST
+Sat 5 Aug 2017 10:39:47 CEST

commit a491c5ea1c9c1660cc64f88921f43fe0040e3832
Author: Ian Miell <ian.miell@gmail.com>
Date: Sat Aug 5 10:37:33 2017 +0200

Update from app_script.sh

diff --git a/date.txt b/date.txt
index a87844b..65627c8 100644
--- a/date.txt
+++ b/date.txt
@@ -1 +1 @@
-Sat 5 Aug 2017 10:37:23 CEST
+Sat 5 Aug 2017 10:37:33 CEST


Note that the application is stateless – the entire state of the system (including data and code) is stored within the git repo. The application can be stood up anywhere that git and bash are available without ‘interruption’.

This statelessness is a useful property for small applications, and one we will try to maintain as we go on.


When Git Alone Won’t Do

The first limit I hit with this approach is generally the limited querying that I can do on the data that’s stored.

That’s when I need SQL to come to my aid. For this I generally use sqlite, for a few reasons:

  • It’s trivial to set up (database is stored in a single file)
  • It’s trivial to back up/restore to a text-based .sql file
  • It’s a good implementation of SQL for the purposes of basic querying

Again, it has limitations compared to ‘non-lite’ sql databases:

  • Does not scale in size (because the db is a single file)
  • Data types are simple/limited
  • No user manager
  • Limited tuning capability


Using sqlite’s backup and restore functionality along with git means we can retain the stateless nature of the application while getting the benefits of SQL querying to mine the data.

GitAsDb2 (1).jpg


As an example I use this pattern to keep track of my share holdings. I can use this to calculate my position/total profit on a daily basis (including dividends) and look at whether I’m genuinely up or down.

I can query the data using sql and persist these queries to ‘report’ files, which are stored in git also, meaning I can run time-based ‘queries’ like this:

 $ git diff 'master@{1 weeks ago}' reports/profits.txt | grep -A10 Overall.profit
 Overall profit
 current_value profit 
 ------------- ----------

Which tells me how my overall profit looks compared to 1 week ago.

A trivial example of such an app which (continuing from the above example) stores the dates the application was run in a sqlite db, is available here.

Note that we use .gitignore to ignore the actual db file – the sqlite db is a binary object not well stored in git, and the db can be reconstructed from the db_export.sql backup anywhere.


Using Docker for Portability and Statelessness

Sqlite, git, bash… even with our trivial example the dependencies are mounting up.

One way to manage this is to have your application run in a Docker container.

This is where things get a bit more complicated!

The simplified diagram below helps explain what’s going on. The Dockerfile (see below) creates an image which includes the entire git repo as a folder. The application runs within this image as a container, and imports and exports the sqlite database before and after running the application.


An example application continuing the above ‘date’ one is here.

The files it contains are:

  • db_export.sql
  • Dockerfile
  • access_db.sh
  • app_script.sh
  • run.sh


The db_export.sql is the same exported sqlite3 db as in the previous section.


Here is an annotated Dockerfile:

FROM ubuntu:xenial
# Install the needed applications.
RUN apt-get update -y && apt-get install -y sqlite3 git
# Create the folder we will copy our own contents to.
RUN mkdir /gitdb-sqlite-docker
# This folder should be the default working directory, and the
# working directory from here
WORKDIR /gitdb-sqlite-docker
# Add the contents of the git repo to the image.
ADD . /gitdb-sqlite-docker
# Configure git for me.
RUN git config --global user.email "ian.miell@gmail.com"
RUN git config --global user.name "Ian Miell"
# Expect a github password to be passed in 
# Set the origin so that it can pull/push without logging in
RUN git remote set-url origin \
# The image runs the application by default.
CMD /gitdb-sqlite-docker/app_script.sh


This script runs the container. Before doing this, it:

  • Checks we are not in the container
  • Checks the git history is consistent with the remote
  • Gets the github password
  • Rebuilds the image
  • Runs the image
# Exit on error. 
set -e

# Only run outside the container
if [ -e /.dockerenv ]
 echo 'Must be run outside container only'
 exit 1

# Pull to check we are in sync and checked in.
git pull --rebase -s recursive -X ours origin gitdb-sqlite-docker

# Make sure a github password is supplied
if [[ $GITHUB_PASSWORD == '' ]]
 echo 'Input github password: '

# Build the image.
docker build --build-arg GITHUB_PASSWORD=${GITHUB_PASSWORD} -t ${IMAGE_NAME} .
# Run the container.
docker run ${IMAGE_NAME}


This script is run from the running container. It’s the same as the previous example’s

# Exit on error.
set -e

# Only run inside the container
if [ ! -e /.dockerenv ]
 echo 'Must be run in container only'
 exit 1

# Make sure the code is up to date with the origin, preferring any local
# changes we have made. Rebase to preserve a simpler history.
git pull --rebase -s recursive -X ours origin gitdb-sqlite-docker

rm -f ${DBNAME}
# Import db from git
cat ${DBEXPORTFILE} | sqlite3 ${DBNAME}

# The trivial 'application' here simply writes the date to a file.
DATE="$(date '+%Y-%m-%d %H:%M:%S')"
echo $DATE
echo "insert into dates(date) values(\"${DATE}\");" | sqlite3 ${DBNAME}

# Export db from sqlite
echo ".dump" | sqlite3 ${DBNAME} > ${DBEXPORTFILE}
# Commit the change made.
git commit -am 'Update from app_script.sh'
# Push the changes to the origin.
git push -u origin gitdb-sqlite-docker


This script is similar to the others, except that it gives you access to the database should you want to query it directly.


Makefiles for an Application Interface

At this point (especially if your app is getting a little complicated) it can get a little hairy to keep track of all these scripts, especially what should be run in the running container vs the image.

At this point I usually introduce a Makefile, which allows me to consolidate some code and effectively provides me with an application interface.

If I run ‘make’ I get some help by default:

$ make 
make run - run the dates script
make access - access the dates db

Running ‘make run’ will build the docker image and run the container, adding a date to the database. ‘make access’ gives me access to the database directly as before.

Adding this makefile means that I have a standard interface to running the application – if I have an application of this type, just running ‘make’ will tell me what I can and should do.

Here’s what the Makefile looks like:

  @echo 'make run - run the dates script'
  @echo 'make access - access the dates db'

.PHONY: help run access restore check_host check_container check_nodiff

access: check_host check_nodiff restore
  # Access the db.
  sqlite3 dates.db
  # Remove the db.
  rm -f dates.db

run: check_host check_nodiff
  # run the script
  @$(MAKE) -f Makefile check_nodiff

restore: check_nodiff
  rm -f dates.db
  cat db_export.sql | sqlite3 dates.db

  # only run in a host
  if [ -e /.dockerenv ]; then exit 1; fi

  # only run in a container
  if [ ! -e /.dockerenv ]; then exit 1; fi

  # Pull to check we do not have local changes
  git pull --rebase -s recursive -X ours origin gitdb-sqlite-docker-makefile

and the rest of the code is available here.


As with everything in IT, there is nothing new under the sun.

You could replace git and Docker with a tar file and an extra shell script to manage it all, and you have something that might look similar.

However, I find this a very useful pattern for quickly throwing up a data-based application that I can run and maintain myself without too much hassle or state management. It uses standard tools, has a clean interface and is genuinely portable across providers.


This is based on work in progress from the second edition of Docker in Practice 

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Run Your Own AWS APIs on OpenShift



This article shows you how you can use OpenShift to set up and test against AWS APIs using localstack.

Example code to run through this using ShutIt is available here.

Here’s an asciicast of the process:


In this walkthrough you’re going to set up an OpenShift system using minishift, and then run localstack in a pod on it.

OpenShift is a RedHat-sponsored wrapper around Kubernetes that provides extra functionality more suited to enterprise production deployments of Kubernetes. Many features from OpenShift have swum upstream to be integrated into Kubernetes (eg role-based access control).

The open source version of OpenShift is called Origin.


Localstack is a project that aims to give you as complete as possible a set of AWS APIs to develop against without incurring any cost. This is great for testing or trying code out before running it ‘for real’ against AWS and potentially wasting time and money.

Localstack spins up the following core Cloud APIs on your local machine:

At present it supports running in a Docker container, or natively on a machine.

It is built on moto, which is a mocking framework in turn built on boto, which is a python AWS SDK.

Running within an OpenShift cluster gives you the capability to run very many of these AWS API environments. You can then create distinct endpoints for each set of services, and isolate them from one another. Also, you can worry less about resource usage as the cluster scheduler will take care of that.

However, it doesn’t run out of the box, so this will guide you through what needs to be done to get it to work.

Started Minishift?

If you don’t have an OpenShift cluster to hand, then you can run up minishift, which gives you a standalone VM with a working OpenShift on it.

Installing minishift is documented here. You’ll need to install it first and run ‘minishift start’ successfully.

Once you have started minishift, you will need to set up your shell so that you are able to communicate with the OpenShift server.

$ eval $(minishift oc-env)

Change the default security context constraints

Security Context Constraints (scc) are an OpenShift concept that allows more granular control over Docker containers’ powers.

They control seLinux contexts, can drop capabilities from the running containers, can determine which user the pod can run as, and so on.

To get this running you’re going to change the default ‘restricted’ scc, but you could create a separate scc and apply that to a particular project. To change the ‘restricted’ scc you will need to become a cluster administrator:

$ oc login -u system:admin

Then you need to edit the restricted scc with:

$ oc edit scc restricted

You will see the definition of the restricted

At this point you’re going to have to do two things:

  • Allow containers to run as any user (in this case ‘root’)
  • Prevent the scc from restricting your capabilities to setuid and setgid

1) Allow RunAsAny

The localstack container runs as root by default.

For security reasons, OpenShift does not allow containers to run as root by default. Instead it picks a random UID within a very high range, and runs as that.

To simplify matters, and allow the localstack container to run as root, change the lines:

 type: MustRunAsRange

to read:

 type: RunAsAny

this allows containers to run as any user.

2) Allow SETUID and SETGID Capabilities

When localstack starts up it needs to become another user to start up elasticache. The elasticache service does not start up as the root user.

To get round this, localstack su’s the startup command to the localstack user in the container.

Because the ‘restricted’ scc explicitly disallows actions that change your user or group id, you need to remove these restrictions. Do this by deleting the lines:


Once you have done these two steps, save the file.

Make a note of the host

If you run:

$ minishift console --machine-readable | grep HOST | sed 's/^HOST=\(.*\)/\1/'

you will get the host that the minishift instance is accessible as from your machine. Make a note of this, as you’ll need to substitute it in later.

Deploy the pod

Deploying the localstack is as easy as running:

$ oc new-app localstack/localstack --name="localstack"

This takes the localstack/localstack image and creates an OpenShift application around it for you, setting up internal services (based on the exposed ports in the Dockerfile), running the container in a pod, and various other management tasks.

Create the routes

If you want to access the services from outside, you need to create OpenShift routes, which create an external address to access services within the OpenShift network.

For example, to create a route for the sqs service, create a file like this:

apiVersion: v1 
- apiVersion: v1 
  kind: Route 
      openshift.io/host.generated: "true" 
    name: sqs 
    selfLink: /oapi/v1/namespaces/test/routes/sqs 
    host: sqs-test.HOST.nip.io 
      targetPort: 4576-tcp 
      kind: Service 
      name: localstack 
      weight: 100 
    wildcardPolicy: None 
    - conditions: 
      - lastTransitionTime: 2017-07-28T17:49:18Z 
        status: "True" 
        type: Admitted 
      host: sqs-test.HOST.nip.io 
      routerName: router 
      wildcardPolicy: None 
kind: List 
metadata: {} 
resourceVersion: "" 
selfLink: ""

then create the route with:

$ oc create -f 

See above for the list of services and their ports.

If you have multiple localstacks running on your OpenShift cluster, you might want to prepend the host name with a unique name for the instance, eg

host: localstackenv1-sqs-test.HOST.nip.io

.Look upon your work

Run an ‘oc get all’ to see what you have created within your OpenShift project:

$ oc get all
is/localstack latest 15 hours ago

dc/localstack 1 1 1 config,image(localstack:latest)

rc/localstack-1 1 1 1 15h

routes/apigateway apigateway-test. localstack 4567-tcp None
routes/cloudformation cloudformation-test. localstack 4581-tcp None
routes/cloudwatch cloudwatch-test. localstack 4582-tcp None
routes/dynamodb dynamodb-test. localstack 4569-tcp None
routes/dynamodbstreams dynamodbstreams-test. localstack 4570-tcp None
routes/es es-test. localstack 4578-tcp None
routes/firehose firehose-test. localstack 4573-tcp None
routes/kinesis kinesis-test. localstack 4568-tcp None
routes/lambda lambda-test. localstack 4574-tcp None
routes/redshift redshift-test. localstack 4577-tcp None
routes/route53 route53-test. localstack 4580-tcp None
routes/s3 s3-test. localstack 4572-tcp None
routes/ses ses-test. localstack 4579-tcp None
routes/sns sns-test. localstack 4575-tcp None
routes/sqs sqs-test. localstack 4576-tcp None
routes/web web-test. localstack 8080-tcp None

svc/localstack  4567/TCP,4568/TCP,4569/TCP,4570/TCP,4571/TCP,4572/TCP,4573/TCP,4574/TCP,4575/TCP,4576/TCP,4577/TCP,4578/TCP,4579/TCP,4580/TCP,4581/TCP,4582/TCP,8080/TCP 15h

po/localstack-1-hnvpw 1/1 Running 0 15h

Each route created is now accessible as an AWS service ready to test your code.

Access the services

Can now hit the services from your host, like this:

$ aws --endpoint-url=http://kinesis-test. kinesis list-streams
 "StreamNames": []

For example, to create a kinesis stream:

$ aws --endpoint-url=http://kinesis-test. kinesis create-stream --stream-name teststream --shard-count 2
$ aws --endpoint-url=http://kinesis-test. kinesis list-streams
 "StreamNames": [

This is an extract from my book

This is a work in progress from the second edition of Docker in Practice 

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Dockerized Headless Chrome Example

For those of us obsessed with automation, the PhantomJS library was manna from heaven, allowing you to programmatically automate web interactions against a ‘real’ web browser without need a screen to interact with.

Earlier this year, the principal maintainer announced that he was stepping down from the project in favour of ‘Headless Chrome’.

Headless Chrome is still new, and there isn’t much material to chew on yet, but I came across this blog post, which shows how to set up an Ubuntu Trusty server with a simple screen grabber script.

I thought I’d transfer this to a Docker container, as lightweight spinning up of these processes will be a boon for testing.

Here’s a video of the script getting screenshots of two randomly-chosen websites:


The repository is here.

My book on Docker:

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Convert a Server to a Docker Container (Update II)

How and Why?

Let’s say you have a server that has been lovingly hand-crafted that you want to containerize.

Figuring out exactly what software is required on there and what config files need adjustment would be quite a task, but fortunately blueprint exists as a solution to that.

What I’ve done here is automate that process down to a few simple steps. Here’s how it works:


You kick off a ShutIt script (as root) that automates the bash interactions required to get a blueprint copy of your server, then this in turn kicks off another ShutIt script which creates a Docker container that provisions the container with the right stuff, then commits it. Got it? Don’t worry, it’s automated and only a few lines of bash.

There are therefore 3 main steps to getting into your container:

– Install ShutIt on the server

– Run the ‘copyserver’ ShutIt script

– Run your copyserver Docker image as a container

Step 1

Install ShutIt as root:

sudo su -
pip install shutit

The pre-requisites are python-pip, git and docker. The exact names of these in your package manager may vary slightly (eg docker-io or docker.io) depending on your distro.

You may need to make sure the docker server is running too, eg with ‘systemctl start docker’ or ‘service docker start’.

Step 2

Check out the copyserver script:

git clone https://github.com/ianmiell/shutit_copyserver.git

Step 3

Run the copy_server script:

cd shutit_copyserver/bin

There is a prompt to ask what docker base image you want to use.

Make sure you use one as close to the original server as possible, eg ubuntu/trusty or ubuntu:14.04 rather than just ‘ubuntu’.

Step 4

Run the built image:

docker run -ti copyserver /bin/bash

You are now in a practical facsimile of your server within a docker container!

Gotchas Checklist

If it doesn’t work, here’s a checklist of things that might have gone wrong:

  • Python 2.7+ is required
  • Using the wrong image – make sure it’s as close to the original as possible
  • Not having Docker installed on the host
  • The server is not apt or yum based
  • The server may run out of memory (at least 1G recommended)

If none of the above work, send the output of:

./copy_server.sh -l debug --echo
cd /tmp/shutit_copyserver && shutit build --echo -d docker -s repository tag yes -s repository_name copyserver -l DEBUG

to an issue on github.


My book on Docker:

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Automating Dockerized Jenkins Upgrades


If you’ve used Jenkins for a while in production, then you will be aware that Jenkins frequently publishes updates to its server for security and functionality changes.

On a dedicated, non-dockerized host, this is generally managed for you through package management. With Docker it can get slightly more complicated to reason about upgrades, as you’ve likely separated out the context of the server from its data.


You want to reliably upgrade your Jenkins server.


This technique is delivered as a Docker image composed of a number of parts. First we will outline the Dockerfile that builds the image. This Dockerfile draws from the library docker image (which contains a docker client) and adds a script that manages the upgrade.

The image is run in a docker command that mounts the docker items on the host, giving it the ability to manage any required Jenkins upgrade.


We start with the Dockerfile:

FROM docker                                                    <1>
ADD jenkins_updater.sh /jenkins_updater.sh                     <2>
RUN chmod +x /jenkins_updater.sh                               <3>
ENTRYPOINT /jenkins_updater.sh                                 <4>

<1> – Use the ‘docker’ standard library image

<2> – Add in the ‘jenkins_updater.sh’ script (see below)

<3> – Ensure that the ‘jenkins_updater.sh’ script is runnable

<4> – Set the default entrypoint for the image to be the ‘jenkins_updater.sh’ script

The above Dockerfile encapsulates the requirements to back up Jenkins in a runnable Docker image. It uses the ‘docker’ standard library image. We use this to get a Docker client to run within a container. This container will run the script in the next listing to manage any required upgrade of Jenkins on the host.

NOTE: If your docker daemon version differs from the version in the ‘docker’ Docker image, then you may run into problems. Try to use the same version.


This is the shell script that manages the upgrade within the container:

#!/bin/sh                                                        <1>
set -e                                                           <2>
set -x                                                           <3>
if ! docker pull jenkins | grep up.to.date                       <4>
 docker stop jenkins                                             <5>
 docker rename jenkins jenkins.bak.$(date +%Y%m%d%H%M)           <6>
 cp -r /var/docker/mounts/jenkins_home \                         <7>
       /var/docker/mounts/jenkins_home.bak.$(date +%Y%m%d%H%M)   <7>
 docker run -d \                                                 <8>
     --restart always \                                          <9>
     -v /var/docker/mounts/jenkins_home:/var/jenkins_home \      <10>
     --name jenkins \                                            <11>
        -p 8080:8080 \                                           <12>
     jenkins                                                     <13>

<1> – This script uses the ‘sh’ shell (not the ‘/bin/bash’ shell) because only ‘sh’ is available on the ‘docker’ Docker image

<2> – This ‘set’ command ensures the script will fail if any of the commands within it fail

<3> – This ‘set’ command logs all the commands run in the script to standard output

<4> – The ‘if’ block only fires if ‘docker pull jenkins’ does not output ‘up to date’

<5> – When upgrading, begin by stopping the jenkins container

<6> – Once stopped, rename the jenkins container to ‘jenkins.bak.’ followed by the time to the minute

<7> – Copy the Jenkins container image state folder to a backup

<8> – Run the docker command to start up Jenkins, and run it as a daemon

<9> – Set the jenkins container to always restart

<10> – Mount the jenkins state volume to a host folder

<11> – Give the container the name ‘jenkins’ to prevent multiple of these containers running simultaneously by accident

<12> – Publish the 8080 port in the container to the 8080 port on the host

<13> – Finally, the jenkins image name to run is given to the docker command

The above script tries to pull jenkins from the docker hub with the ‘docker pull’ command. If the output contains the phrase ‘up to date’, then the ‘docker pull | grep …’ command returns true. However, we only want to upgrade when we did _not_ see ‘up to date’ in the output. This is why the ‘if’ statement is negated with a ‘!’ sign after the ‘if’.

The result is that the code in the ‘if’ block is only fired if we downloaded a new version of the ‘latest’ Jenkins image. Within this block, the running Jenkins container is stopped and renamed. We rename it rather than delete it in case the upgrade did not work and we need to reinstate the previous version.

Further to this rollback strategy, the mount folder on the host containing Jenkins’ state is backed up also.

Finally, the latest-downloaded Jenkins image is started up using the docker run command.

NOTE: You may want to change the host mount folder and/or the name of the running Jenkins container based on personal preference.

The attentive reader might be wondering how this Jenkins image is connected to the host’s Docker daemon. To achieve this, the image is run using a commonly-used method in the book:

The jenkins-updater image invocation

docker run                                                 <1>
    --rm \                                                 <2>
    -d \                                                   <3>
 -v /var/lib/docker:/var/lib/docker \                      <4>
 -v /var/run/docker.sock:/var/run/docker.sock \            <5>
 -v /var/docker/mounts:/var/docker/mounts                  <6>
 dockerinpractice/jenkins-updater                          <7>

<1> – The docker run command

<2> – You want the container to be removed when it has completed its job

<3> – Run the container in the background

<4> – Mount the host’s docker daemon folder to the container

<5> – Mount the host’s docker socket to the container so the docker command will work within the container

<6> – Mount the host’s docker mount folder where the Jenkins data is stored, so that the jenkins_updater.sh script can copy the files

<7> – The dockerinpractice/jenkins-updater image is the image to be run

Automating the upgrade

This one-liner makes it easy to run within a crontab. We run this on our home

servers. The crontab line looks like this:

0 * * * * docker run --rm -d -v /var/lib/docker:/var/lib/docker -v /var/run/docker.sock:/var/run/docker.sock -v /var/docker/mounts:/var/docker/mounts dockerinpractice/jenkins-updater 

NOTE: The above is all on one line because crontab does not ignore newlines if there is a backslash in front in the way that shellscripts do.

The end result is that a single crontab entry can safely manage the upgrade of your Jenkins instance without you having to worry about it. The task of automating the cleanup of old backed up containers and volume mounts is left as an exercise for the reader.


This technique exemplifies a few things which we come across throughout the book which can be applied in similar contexts to situations other than Jenkins.

First, it uses the core docker image to communicate with the Docker daemon on the host. Other portable scripts might be written to manage Docker daemons in other ways. For example, you might want to write scripts to remove old volumes, or report on the activity on your daemon.

More specifically, the ‘if’ block pattern could be used to update and restart other images when a new one is available. It is not uncommon for images to be updated for security reasons, or to make minor upgrades.

If you are concerned with difficulties in upgrading versions, it’s also worth pointing out that you need not take the ‘latest’ image tag (which this technique does). Many images have different tags that track different version numbers.

For example, your image ‘exampleimage’ might have a exampleimage:latest tag, as well as an exampleimage:v1.1 tag, and a exampleimage:v1. Any of these might be updated at any time, but the :v1.1 tag is less likely to move to a new version than the :latest one. The :latest one could move to the same version as a new  :v1.2 one (which might require steps to upgrade) or even a :v2.1 one, where the new major version ‘2’ indicates a change more likely to be disruptive to any upgrade process.

This technique also outlines a rollback strategy for docker upgrades. The separation of container and data (using volume mounts) can create tension about the stability of any upgrade. By retaining the old container and a copy of the old data at the point where the service was working, it is easier to recover  from failure.

Database Upgrades and Docker

Database upgrades are a particular context in which these stability concerns are germane.

If you want to upgrade your database to a new version, you have to consider whether the upgrade requires a change to the data structures and storage of the database’s data. It’s not enough simply to run the new version’s image as a container and expect it to work.

It gets a bit more complicated if the database is ‘smart’ enough to know which version of the data it is ‘seeing’, and can perform the upgrade itself accordingly. In these cases, you might be more comfortable upgrading.

Many factors feed into your upgrade strategy. Your app might tolerate an ‘optimistic’ approach (as we see here in the Jenkins example) which assumes everything will be OK, and prepares for failure when (not if) it occurs. On the other hand, you might demand 100% uptime, and not tolerate failure of any kind at all. In such cases, a fully-tested upgrade plan and a deeper knowledge of the platform than running ‘docker pull’ is generally desired (with or without the involvement of Docker).

Although Docker does not remove the upgrade problem, the immutability of the versioned images can make it simpler to reason about them. Docker can also help you prepare for failure in two ways: backing up state in host volumes, and making testing predictable state more easy. The hit you take in managing and  understanding what Docker is doing can give you more control and certainty  about the upgrade process.



This technique is taken from the upcoming second edition of my book Docker in Practice:

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Things I Wish I Knew Before Using Jenkins Pipelines

I started playing with Pipelines using the web interface, then hit a block as I didn’t really know the ropes.

Here’s some things I wish I’d known first:

Wrap Steps in a Node

All code that does steps in a pipeline should be wrapped in a node block:

node() {

If the node is not specified, eg:

def myvariable='blah'

then by default it will run on the master.

You can specify the node by supplying an argument:

node('mynode') {

If the pipeline code is not in a node block, then it’s run on the master in some kind of lightweight node/thread.

Checkout scm applies only when called from source

This was a gotcha for me. ‘checkout scm’ is a great single line that checks out the source the Jenkinsfile is taken from

But when updating the script in the browser this won’t work!

When storing your source in source control, you can then switch to using ‘checkout scm’. Otherwise use the ‘git’ function.

Wrap in try / catch

Your code can be wrapped in a try/catch block.

I use this along with timeout() to see whether a node is available before using it:

def nodetest() { 
  sh('echo alive on $(hostname)') 
// By default we use the 'welles' node, which could be offline.
try { 
  // Give it 5 seconds to run the nodetest function
  timeout(time: 5, unit: 'SECONDS') { 
    node(usenode) { 
} catch(err) { 
  // Uh-oh. welles not available, so use 'cage'.
// We know the node we want to use now.
node(usenode) {

Oh yeah, and functions are available to you too.

Handy, eg for seeing whether a node is available.

Wrap things in stages

Want those neat stages to show up in the Jenkins job homepage?

Then wrap your stuff in stages, eg:

stage('setupenv') {
  node(nodename) {
    sh 'mkdir -p ' + builddir
    dir(builddir) {
      checkout([$class: 'GitSCM', branches: [[name: '*/master']], doGenerateSubmoduleConfigurations: false, extensions: [[$class: 'SubmoduleOption', disableSubmodules: false, parentCredentials: false, recursiveSubmodules: true, reference: '', trackingSubmodules: false]], submoduleCfg: [], userRemoteConfigs: [[url: 'https://github.com/ianmiell/shutit']]])
stage('shutit_tests') {
  node(nodename) {
    dir(builddir + '/shutit-test') {
      sh('PATH=$(pwd)/..:${PATH} ./run.sh -s tk.shutit.shutit_test shutit_branch master -l info 2>&1')

It’s a good idea to keep stages discrete, as they can be isolated from one another – for example, you could switch one stage to another node if you want (but then you might want to look into stash()ing files…).

WTF the Deal is with Pipeline Syntax vs Groovy?

Pipeline syntax may be preferable to groovy, but is newer. See here:


The docs confusingly assume a familiarity with both.

It’s Still a Bit Buggy

I tried to change the branch the Jenkinsfile was pulled from, but the old branch persisted, and it wouldn’t pick up changes from the new one. I ended up having to create a new job and delete the old one


Want to force user input before continuing?


input('OK to continue?')

But – seemed to work better for me when I had defined stages first!


There are Jenkinsfile examples here but they look a bit unloved.

Some gists were more useful to me.

This intro was pretty good too.

And the canonical reference is here.

Docker in Practice

This is a work in progress from the second edition of Docker in Practice 

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Five Books I Advise Every DevOps Engineer to Read

Here is a list of books that have helped and changed not only my career, but also my life.

If there’s a theme to them, it’s that they are less about IT than how people interact with technology, and how an understanding of that can make you and your organisation more efficient.

The Goal 

Published in 1984, before Windows 1.0 was released, The Goal is still read by many and recommended by the likes of Jeff Bezos.


Unusually for a business book, it’s a novel. A manager of a factory that is threatened with closure has three months to turn around its dysfunctional organisation. After going on a bender and having a row with his wife, he sobers up and bumps into an old friend that guides him on how to debug his business. He makes it up with his wife and figures out how to turn things around at work.

Many readers will already be aware of The Phoenix Project, a book popular among the DevOps community. TPP is based on The Goal, and has a similar plot. Personally, I prefer The Goal over The Phoenix Project for a couple of reasons.

First, it’s really well-written. It’s a good enough novel that my wife (a mental health nurse with zero interest in IT) read it and enjoyed it in a couple of sittings.

Second, the fact that it’s not about 21st-century software encourages you to think about your work in terms of systems rather than our specific local terms. Continuous improvement, problems of delivery flow, disaffected staff, and angry spouses have always been with us, and the solutions can be surprisingly similar.

For me, this book emphasised and backed up my instinct that in an imperfect world, the importance of focussing on the biggest problems first and the human factor are vital in improving any delivery environment.

The Checklist Manifesto


How have different industries dealt with failure?

Atul Gawande is a surgeon and public health researcher who here looks at three fields: medicine, construction, and aviation. All these fields have little tolerance for failure (buildings that fall down, planes that fall out of the sky, and doctors that kill tend to attract headlines and unwanted attention).

What becomes clear is that there is a maturity model for dealing with failure – first a ‘hero’ model is espoused (think of the aviation heroes of early 20th century adventure and wars, or 18th century doctors), and then complexity ensues, reducing the pool of ‘heroes’ to zero. Following that there’s a crisis, and the implementation of simple processes helps manage the chaos. Aviation went from having the ‘fighter ace’ to ‘too much plane to fly’ and moved to a training model using checklists and human-friendly processes. In medicine, simple checklists help reduce error (and the cost of lawsuits). Standard processes along with creativity – applied reliably and when required – helps buildings stay up.

This book stiffened my resolve to improve documentation and process in a growing business, which I wrote about at more length here.

The Practice of Management


Another oldie, this time from 1954, it discusses businesses of the day and their challenges in a timeless way (aside from the complete absence of the feminine pronoun).

A glance through it will reveal the same concerns we have always had – the section on ‘Automation’ is itself a fascinating historical document, and applies to today just as much as it did 60 years ago. There is a section on the importance of ‘innovation’ that reads like a contemporary call to arms. If you thought Google was the first organisation to try and do without middle management, there’s a chapter headed ‘Ford’s Attempt To Do Without Managers’.

If last century is so last century, then he looks to the Roman army and the Jesuits (‘the oldest elite corps) for how management training has been shown to work.

This book is a great mind-expander for those who need to start thinking about human organisations and their challenges in delivering what we now call ‘value’.

The Art of Business Value


This is more a work of practical philosophy than about business. Mark Schwartz is a CIO working in the field, who here deconstructs some of the lazy assumptions and rhetoric surrounding what has come to be known as capital a ‘Agile’.

Practical and down-to-earth, he first breaks down what ‘business value’ might mean, and shows that there is little clarity about what this often taken-for-granted concept stands for. Other terms get similar treatment: who is the ‘customer’ in agile; is profit and business success the same thing; how granular can the organisation be?

This book gives you the courage to ask simple questions and not take for granted that the messages you’re getting about how to work are based on solid foundations.

I talked about trusting your local knowledge a while back here and wrote the talk up here.

It’s not often you get a book that’s lucid, useful, and quotes French philosophy while making it relevant.

Getting Things Done


I picked up this book almost by accident in a bookstore while on holiday. I’d read so many stories and references to it on HackerNews I was ready to mock its easy slogans and trite advice.

Damn, was I wrong! This book didn’t so much change my life as turn it upside down. I was a stressed out, time-poor SRE who couldn’t possibly fulfil all his obligations, and using the advice and guidance here I have since transformed my career by writing a book, developing this blog, and moving jobs.

Again, this is less about technology than the human element to improving efficiency.

Its advice was so pragmatic and sensible that I rue the fact that I didn’t read it decades before.

Did I Miss One?

I’m always on the lookout for good and classic works to read and make me think. If I missed any, let me know.


My book Docker in Practice:

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