Skip to content
A continuous integration tool for Looker and LookML.
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.circleci
.github
docs
spectacles Add command to print version Jan 22, 2020
tests
.gitignore
CONTRIBUTING.md
LICENSE
README.md
config.example.yml
requirements.txt Remove requirements we no longer use Nov 22, 2019
setup.cfg
setup.py

README.md

spectacles

CircleCI codecov

What is spectacles?

spectacles is a command-line, continuous integration tool for Looker and LookML. spectacles runs validators which perform a range of tests on your Looker instance and your LookML. Each validator interacts with the Looker API to run tests that ensure your Looker instance is running smoothly. You can run spectacles locally during LookML and content development, or you can run it in production as a continuous integration pipeline.

You can run the following validators as subcommands (e.g. spectacles sql):

Installation and documentation

spectacles is distributed on PyPi and is easy to install with pip:

pip install spectacles

You can find detailed documentation on our website spectacles.dev.

Why we built this

Occasionally, when we make changes to LookML or our data warehouse, we break downstream experiences in Looker. For example:

  • We change the name of a database column without changing the corresponding sql field in our Looker view, leaving our users with a database error when using that field.
  • We add an invalid join to an explore that fans out our data, inflating a key metric that drives our business without realising.
  • We make changes to LookML without remembering to check the Content Validator for errors, disrupting Dashboards and Looks that our users rely on
  • We give a new dimension a confusing name, causing other developers in our team to spend extra time trying to figure out how it should be used.

We believe in the power of continuous integration for analytics. We believe that automated testing should catch these errors before they are ever pushed to production. Automated testing allows developers to maintain their speed of development high, without compromising on the quality of their code or the quality data they produce.

We wanted a single tool to perform these checks for us and establish baseline performance expectations for our Looker instances. We built spectacles to enhance the business intelligence layer of analytics CI pipelines.

You can’t perform that action at this time.