Analyzing GitHub Data with BigQuery Using GH Archive

GH Archive is a record of the public GitHub timeline, made available as a BigQuery public dataset and through downloadable archives.

All public GitHub Issues, Releases, Stars, Pull Requests, Commits, and more included on the public GitHub timeline. GH Archive makes this metadata available for analysis, including over 15 event types. You can easily analyze GH Archive data by using the Google Cloud Console to query the dataset.

This repository shares examples for how you can use BigQuery and the GH Archive dataset to analyze public GitHub activity for your next project.

Outline

• Getting Started
• Querying Basic Community Metrics
• Queries for Inspiration
• Additional Resources

Getting Started

What can GH Archive data be used for?

GH Archive has a list of research, visualizations, and talks based on their dataset which may spur some ideas! A sampling of some of those projects include:

• GitLive: a visualization of what's happening on GitHub in real-time
• Changelog Nightly: an email newsletter featuring the top new and top starred projects on GitHub
• GitHut: a visualization of programming languages used on GitHub

Whether you're an individual developer, a community, or an Open Source Program Office (OSPO) managing multiple projects, the GH Archive dataset may be useful for you. Once your queries are written, you can apply them to new repositories and GitHub organizations, as well as adjust the time periods you're analyzing.

Tips for using BigQuery as you begin

• If you're new to BigQuery, check out A Beginner's Guide to BigQuery as well as Google Cloud's guide: "Explore the Google Cloud Console" to become familiar with the console which we'll use in these examples.
• At the time of writing, the first 1 TiB per month of queries to BigQuery is free. Up-to-date details on pricing is available on the BigQuery pricing page.
• Within the Google Cloud Console, after you type your query and before it is run you'll be told how much data will be queried.
• When starting with a query, start by querying a smaller amount of data (for example, one day) to reduce costs and imrpove your queries. After confirming that it works as intended, expand your query to more data (for example, a month or year).

Querying Basic Community Metrics

Each of the following example queries build upon one another, looking at GitHub activity for projects released by the Apache Software Foundation. We'll run these queries by entering them into the Google Cloud Web Console.

Note that while these queries look similar, there are noticable differences that impact what data is being queried as well as the results. Do you notice the differences?

Project Contributors on a Single Day

• Metric: unique contributors to a single project on a single day
• Example project: Apache Cassandra on February 1, 2024

SELECT
  COUNT(DISTINCT events.actor.id)
FROM `githubarchive.day.20240201` AS events
  WHERE
    events.repo.name = 'apache/cassandra'

• ✅ Result: 5
• 💾 Query processed: 157.69 MB

Project Contributors in a Month

• Metric: unique contributors to a single project for an entire month
• Example project: Apache Cassandra in February, 2024

SELECT
  COUNT(DISTINCT events.actor.id)
FROM `githubarchive.month.202402` AS events
  WHERE
    events.repo.name = 'apache/cassandra'

• ✅ Result: 95
• 💾 Query processed: 5.08 GB

Organizational metrics

• Metric: unique monthly contributors last month to all projects in a GitHub org
• Example project: the Apache Software Foundation

SELECT
  COUNT(DISTINCT events.actor.id)
FROM `githubarchive.month.202402` AS events
  WHERE
    events.org.login = 'apache'

• ✅ Result: 13,452
• 💾 Query processed: 2 GB

Comparing these examples

Each examples retrieves a single value as their output using COUNT() in the query. The examples are also all counting unique contributors (note the use of DISTINCT in the query to ensure that contributors are not double-counted).

What's different? A few things!

1. The data being queried. Notice the difference between these examples:

• Day: githubarchive.day.20240201
• Month: githubarchive.month.202402
• Year: githubarchive.year.2023

2. The scope of the query's WHERE statements:

• Specific Repository: events.repo.name = 'apache/cassandra'
• GitHub Organization: events.org.login = 'apache'

Queries for Inspiration

Here are some additional queries that may spur ideas for how you can use the GH Archive dataset:

OSS Releases by Organization

• Intended Result: open source releases for a specific org on a specific day
• Example Query: Apache Software Foundation on April 24, 2024

SELECT
  repo.name, created_at, id
FROM `githubarchive.day.20240424` AS events
  WHERE
    events.org.login IN ('apache') and type IN ('ReleaseEvent')

• ✅ Result:

row	name	created_at	id
1	apache/apisix	2024-04-24 07:56:23 UTC	37761915325
2	apache/pulsar-dotpulsar	2024-04-24 17:26:50 UTC	37783197893

• 💾 Query processed: 341.22 MB

This query returns a list of results, in contrast to the prior example where a single metric was returned using COUNT. Results can easily be saved and exported from BigQuery and used by other tools.

Additional Resources

"Exploring GitHub with BigQuery at GitHub" (video)(2017) introduces you to the BigQuery UI, writing queries to access GH Archive, and visualizing data with tools like Tableau and Looker.

GH Archive data is also used by multiple services which analyze GitHub activity and provide higher-level interfaces, APIs, and open source tools:

• Ecosyste.ms Timeline: WebUI and open API service of over 8 billion events for every public repo on GitHub dating back to 2015
• DevStats: CNCF-created tool for that visualizes GH Archive data using Grafana dashboards