Azavea Labs

Where software engineering meets GIS.

Creating Ansible Roles from Scratch: Part 2

In part one of this series, we created the outline of an Ansible role to install Packer with ansible-galaxy, and then filled it in. In this post, we’ll apply the role against a virtual machine, and ultimately, install Packer!

A Playbook for Applying the Role

After all of the modifications from the previous post, the directory structure for our role should look like:

├── defaults
│   └── main.yml
├── meta
│   └── main.yml
└── tasks
    └── main.yml

Now, let’s alter the directory structure a bit to make room for a top level playbook and virtual machine definition to test the role. For the virtual machine definition, we’ll use Vagrant.

To accommodate the top level playbook, let’s move the azavea.packer directory into a roles directory. At the same level as roles, let’s also create a site.yml playbook and a Vagrantfile. After those changes are made, the directory structure should look like:

├── Vagrantfile
├── roles
│   └── azavea.packer
│       ├──
│       ├── defaults
│       │   └── main.yml
│       ├── meta
│       │   └── main.yml
│       └── tasks
│           └── main.yml
└── site.yml

The contents of the site.yml should contain something like:

- hosts: all
  sudo: yes
    - { role: "azavea.packer" }

This instructs Ansible to apply the azavea.packer role to all hosts using sudo.

And the contents of the Vagrantfile should look like:

# -*- mode: ruby -*-
# vi: set ft=ruby :


Vagrant.configure(VAGRANTFILE_API_VERSION) do |config| = "ubuntu/trusty64"

  config.vm.provision "ansible" do |ansible|
    ansible.playbook = "site.yml"

Here we’re making use of the ubuntu/trusty64 box on Vagrant Cloud, along with the Ansible provisioner for Vagrant.

Running vagrant up from the same directory that contains the Vagrantfile should bring up a Ubuntu 14.04 virtual machine, and then attempt use ansible-playbook to apply site.yml. Unfortunately, that attempt will fail, and we’ll be met with the follow error:

ERROR: cannot find role in /Users/hector/Projects/blog/roles/azavea.unzip or
/Users/hector/Projects/blog/azavea.unzip or /etc/ansible/roles/azavea.unzip

Ansible failed to complete successfully. Any error output should be
visible above. Please fix these errors and try again.

Where is this reference to azavea.unzip coming from? Oh, that’s right, we had it listed as a dependency in the Packer role metadata…

Role Dependencies

Role dependencies are references to other Ansible roles needed for a role to function properly. In this case, we need unzip installed in order to extract the Packer binaries from

To resolve the dependency, azavea.unzip needs to exist in the same roles directory that currently houses azavea.packer. We could create that role the same way we did azavea.packer, but azavea.unzip already exists within Ansible Galaxy (actually, so does azavea.packer).

In order to install azavea.unzip into the roles directory, we can use the ansible-galaxy command again:

$ ansible-galaxy install azavea.unzip -p roles
 downloading role 'unzip', owned by azavea
 no version specified, installing 0.1.0
 - downloading role from
 - extracting azavea.unzip to roles/azavea.unzip
azavea.unzip was installed successfully

Now, if we try to reprovision the virtual machine, the Ansible run should complete successfully:

$ vagrant provision
==> default: Running provisioner: ansible...

PLAY [all] ********************************************************************

GATHERING FACTS ***************************************************************
ok: [default]

TASK: [azavea.unzip | Install unzip] ******************************************
changed: [default]

TASK: [azavea.packer | Download Packer] ***************************************
changed: [default]

TASK: [azavea.packer | Extract and install Packer] ****************************
changed: [default]

PLAY RECAP ********************************************************************
default                    : ok=4    changed=3    unreachable=0    failed=0

Before we celebrate, let’s connect to the virtual machine and ensure that Packer was installed properly:

$ vagrant ssh
vagrant@vagrant-ubuntu-trusty-64:~$ packer
usage: packer [--version] [--help]  []

Available commands are:
    build       build image(s) from template
    fix         fixes templates from old versions of packer
    inspect     see components of a template
    validate    check that a template is valid

Globally recognized options:
    -machine-readable    Machine-readable output format.

Excellent! The Packer role we created has successfully installed Packer!

Creating Ansible Roles from Scratch: Part 1

Within Ansible there are two techniques for reusing a set of configuration management tasks, includes and roles. Although both techniques function in similar ways, roles appear to be the official way forward. Ansible Galaxy was built as a repository for roles, and as we’ll see in this post, ansible-galaxy exists to aid in installing and creating them.

Creating a New Role

Let’s start off by creating a role for Packer.

Packer is a useful tool for producing different machine image types with the same set of configuration management tasks. For example, Packer can be used to take a set of Ansible instructions, funnel them through itself, and produce both an AMI and Docker image.

Enough about Packer though, let’s get back to creating an Ansible role for installing Packer.

The first step in creating a role is creating its directory structure. In order to create the base directory structure, we’re going to use a tool bundled with Ansible (since 1.4.2) called ansible-galaxy:

$ ansible-galaxy init azavea.packer
azavea.packer was created successfully

That command will create an azavea.packer directory with the following structure:

├── defaults
│   └── main.yml
├── files
├── handlers
│   └── main.yml
├── meta
│   └── main.yml
├── tasks
│   └── main.yml
├── templates
└── vars
    └── main.yml

Explaining the Role Directory Structure

A role’s directory structure consists of defaults, vars, files, handlers, meta, tasks, and templates. Let’s take a closer look at each:


Within defaults, there is a main.yml file with the default variables used by a role. For the Packer role, there is only a packer_version default variable. As of this post, the most recent version of Packer is 0.7.1, so we’ll set it to that:

packer_version: "0.7.1"


vars and defaults house variables, but variables in vars have a higher priority, which means that they are more difficult to override. Variables in defaults have the lowest priority of any variables available, which means they’re easy to override. Placing packer_version in defaults instead of vars is desirable because now it is easier to override when you want to install an older or newer version of Packer:

- hosts: all
  sudo: yes
    - { role: "azavea.packer", packer_version: "0.7.0" }

All of that said, we’re set with packer_version in defaults, so the vars directory is not needed either.


files is where you put files that need to be added to the machine being provisioned, without modification. Most of the time, files in files are referenced by copy tasks.

The Packer role has no need for files, so we’ll delete that directory.


handlers usually contain targets for notify directives, and are almost always associated with services. For example, if you were creating a role for NTP, you might have an entry in handlers/main.yml for restarting NTP after a task finishes altering the NTP configuration file.

Packer isn’t a service, so there is no need for the handlers directory.


meta/main.yml houses one of the biggest differences between includes from roles: metadata. The metadata of an Ansible role consists of attributes such as author, supported platforms, and dependencies. Most of this file is commented out by default, so I usually go through and fill in or uncomment relevant attributes, then delete anything else.

For the Packer role, I trimmed things down to:

  author: Hector Castro
  description: An Ansible role for installing Packer.
  company: Azavea Inc.
  license: Apache
  min_ansible_version: 1.2
  - name: Ubuntu
    - trusty
  - cloud
  - system
  - { role: "azavea.unzip" }

Ignore the dependencies bit for right now. We’ll come back to it later.


tasks houses a series of Ansible plays to install, configure, and run software. For Packer, we need to download a specific version, and since it’s packaged as a compiled binary in a ZIP archive, extract it. Accomplishing that with Ansible’s built-in get_url and unarchive modules looks like this:

- name: Download Packer
  get_url: >
   url={{ packer_version }}
   dest=/usr/local/src/packer_{{ packer_version }}

- name: Extract and install Packer
  unarchive: src=/usr/local/src/packer_{{ packer_version }}


templates is similar to files except that templates support modification as they’re added to the machine being provisioned. Modifications are achieved through the Jinja2 templating language. Most software configuration files become templates.

Packer takes most of its configuration parameters via command-line arguments, so the templates directory is not needed.


Congratulations! You now have all of the components necessary for an Ansible role. In part two of this series, we’ll take a look at creating a small playbook to apply the role against a local virtual machine. We’ll also take a closer look at the dependencies listed in the role metadata.

Google Summer of Code – A GeoTiff reader for GeoTrellis

Applying to Google Summer of Code

I first heard of Google Summer of Code (from here on GSOC) when a
former student at my university in Stockholm told our class how he
nailed a job at Google. He said that he performed very well in a
competitive programming tournament and that he also had done GSOC for
the Python Software Foundation. I was already trying out competitive
programming and had zero experience of working with open source.

When the GSOC 2014 season started and the accepted organizations were
announced I decided to do something that few people would apply to,
i.e. not submitting to the Twitter Open Source Organization, to
increase my own chances to join the program. I submitted 3 proposals,
one was writing a GUI test library for OWASP ZAP, a desktop program
written in Java for testing attacks on a web server, one was a graph
format exporter for Bio4j and the last one was the GeoTiff reader for

The first one, the OWASP ZAP GUI test library, seemed the most boring
one and was in Java, but the guys maintaining it was very
friendly. The second one was supposed to be in Scala but was changed
to Java. I really wanted to learn more about Scala and Functional
Programming in General and when I got accepted to all 3 proposals I
talked to Rob Emanuele, who later has been my mentor during GSOC,
which instantly told me that if I want to do Scala this summer,
GeoTrellis was the way to go!


It was both exciting and a little bit frightening to work with Scala
when I haven’t written anything in Scala before, and truth be told, it
isn’t as easy as Java at all. Rob recommended the Coursera course for
Scala and I did the whole thing, it was great. I had a lot of stuff in
school so I actually didn’t prepare too much for the actual project,
except exploring the GeoTrellis source code for a bit. I also found
some specifications for Tiff and GeoTiff and tried to read those, but
I didn’t understand too much. I also got a book from Azavea, which was
about Rasters and Map Algebra, which was a very good read for this

Start to Midterms

GeoTiffs are essentially Tiff files with a few add-ons; GeoTiffs are a
superset of Tiffs. I started reading the Tiff 6.0 specification, and
since that was written in 1992 it felt a bit outdated and hard to
interpret. But I worked hard and tried to read in all the tags (Tiff =
Tagged Image File Format) and all the extra stuff that GeoTiff brought
in to the picture. It went pretty slow because I was both learning to
use Scala and getting familiar with working in a larger group of
developers, with a rather big codebase. I got a lot of help by my
mentor Rob and he read and commented my code on Github, making stuff a
million times easier.

Midterms to End

After the midterms I had tried to do some decompressions and I also
did a pull request for fixing a locale bug (the dreaded comma vs dot)
in the whole of GeoTrellis. From here on stuff got more easy and with
the help of Rob I really started to get things done. Today the reader
supports all of the Tiff 6.0 specification decompressions except JPEG
and also works fine with ZLib. The reader is now used in other parts
of GeoTrellis and it is really nice to see that something I have
created is used by others.


I will continue after the GSOC 2014 season is over to work with
GeoTrellis and further improve the reader and also create a GeoTiff
writer. I look very much forward to doing this and I’m very grateful
for both the program, the people at Azavea and my mentor throughout
the program.

Batch District Matching Using the Cicero API with OpenRefine

OpenRefine (formerly Google Refine) is an awesome open source tool for working with data. If you haven’t heard of it before, in the words of Christopher Groskopf, “”Once you’ve clustered and reconciled your crufty public dataset into a glistening gem of normality you won’t know how you lived without it.”

Even if you have a dataset that’s useable already though, you might want to add more data to it. This is often why clients come to us for Cicero batch processing and district stamping. Clients can give us a spreadsheet of data with street addresses, often a list of supporters or members exported from their CRM system. Then, we can use the expansive database of elected officials and political districts that underpins our Cicero API to process these large batch processing jobs, geocoding and providing official and district information for each record.

However, one of the cool things about OpenRefine is that you can use it yourself to perform similar batch processing tasks with external APIs, like Cicero! In this blog post, we’ll use OpenRefine to add Philadelphia city council district information to an open government dataset of all Charter School locations in the city. Why charter school data? Whether you’re for or against them, there’s no question that charter schools are a tough local political issue being debated by communities across the country. Using OpenRefine and Cicero to determine the council districts of each charter school in Philadelphia would enable us to determine how many charter schools are in each councilmember’s district. That would be useful information to make councilmembers aware of if we were conducting local advocacy work on the merits or drawbacks of this educational approach. With 84 charters in the city, too, this would be a laborious task without OpenRefine!

We’ll start by downloading the zipped CSV file from the School District of Philadelphia’s Open Data Initiative site, which can be found through OpenDataPhilly. We see that the file has a few key fields we’ll be using to interact with Cicero – address, zip code, city and state.

Mmmmm, tabular data.

Mmmmm, tabular data.


GeoTrellis Transit on iOS with WhirlyViz

I was recently introduced to Steve Gifford at Mousebird Consulting, a software firm based in San Francisco that builds mapping tools for the iOS platform.  Steve and his colleagues are the developers of the open source iOS mapping framework, WhirlyGlobe Maply.  The framework enables them to build both 2D and 3D mapping applications for iPhones and iPads.  It’s slick, impressive technology that is sort of a combination of the Google Earth globe and a conventional, web-based mapping application.


Mousebird Consulting joined the LocationTech working group at the Eclipse Foundation in March.  LocationTech is a young organization and while there are now several projects moving through the incubation process (GeoTrellis is one of them), there is not yet a lot of coordination or integration between projects.  So I was really excited to see Steve take the initiative to integrate one of our GeoTrellis examples, the GeoTrellis Transit API demo, into Mousebird’s WhirlyViz application. GeoTrellis Transit is an extension of the core GeoTrellis framework.


While the core GeoTrellis is primarily focused on fast, distributed raster data processing, the GT Transit project adds support for fast network routing and incorporates both GTFS and OpenStreetMap parsing, a high performance network data structure and support for routing and calculation of time-dependent “travelsheds”, the area a traveler can reach within X minutes.  By “time-dependent”, I mean that GT Transit can calculate transit access areas for a specific time of day and days of week using the schedule information encoded in a GTFS data set.  All of this is wrapped by an API.  When we launched GeoTrellis Transit, we also set up a couple of demos using data for Philadelphia – a travelshed calculator and a “scenic route” demo that shows where you can wander between a starting and ending point and still arrive on time. The WhirlyViz app has some nice design features.  It’s a native iOS app, but it uses JSON and Javascript for configuration, and Steve was able to add a new configuration without having to roll out a new application.  Steve picked up the Travelshed API and turned it into a new configuration of the WhirlyViz app.  It’s pretty cool.  In addition to showing the travelsheds, you can set the day-of-week, time-of-day and transit modes.  He wrote up some details in a blog post he published last week.  Here are a few screenshots.

GeoTrellis Transit in WhirlyViz

GeoTrellis Transit uses OpenStreetMap and a GTFS file to enable generation of “travel-sheds”. This one shows walking distance are around downtown.

GeoTrellis Transit in WhirlyViz

The accessible area changes a great deal when we add access to regional rail.