GitHub data is available for public analysis using Google BigQuery, and we’d like to help you take it for a spin.
If you’d like to find out more about what data is available and how it’s been used so far, watch this conversation between GitHub Data Analyst Alyson La and Google Developer Advocate Felipe Hoffa. You’ll learn the story behind the datasets and what types of analysis they make possible. You’ll also see how we’ve visualized data with Tableau and Looker.
There’s a lot of data out there, but it’s all available through BigQuery in two large data sets. The original, community-led GitHub Archive project launched in 2012 and captures almost 30 million events monthly, including issues, commits, and pushes. Last year, we worked with Google to release The GitHub Public Data Set, separate tables with information on all projects that have open source licenses, including commits, file contents, and file paths.
You can also use the GH torrent project to complement the existing datasets with additional metadata.
Data gives us insight into how people build software, and the activities of open source communities on GitHub represent one of the richest datasets ever created of people working together at scale.
In 2012, the community led project, GitHub Archive was launched, providing a glimpse into the ways people build software on GitHub. Today, we’re delighted to announce that, in collaboration with Google, we are releasing a collection of additional BigQuery tables to expand on the data from that project1.
This 3TB+ dataset comprises the largest released source of GitHub activity to date. It contains activity data for more than 2.8 million open source GitHub repositories including more than 145 million unique commits, over 2 billion different file paths and the contents of the latest revision for 163 million files, all of which are searchable with regular expressions.
With this new dataset, it’s a simple query to find out which are the most commonly used Go packages, which US-schools have the most open source contributors and find all of the things that should never happen.
Just as books capture thoughts and ideas, software encodes human knowledge in a machine-readable form. This dataset is a great start toward the pursuit of documenting the open source community’s vast repository of knowledge—but there’s more to be done. Over the coming months, you can expect to hear from us on how we hope to make open source data even more available, portable, and useful.
Whether you’re a researcher studying open source communities, an organization looking to monitor the health of your open source projects, or curious about the latest trends in software development, go check out the new dataset hosted on Google Cloud to analyze one of the largest datasets of people collaborating on the planet.
1. If you’d like to hear more about the data release then check out this episode of The Changelog.
Recently we took a look at the popularity of programming languages used on GitHub.com.
Below is a graph that shows the change in rank of languages since GitHub launched in 2008.
The rank represents languages used in public & private repositories, excluding forks, as detected by Linguist.
It should be noted that this graph represents each language’s relative popularity on GitHub. For example, Ruby on Rails has been on GitHub since 2008, which may explain Ruby’s early popularity.
Between 2008 and 2015 GitHub gained the most traction in the Java community, which changed in rank from 7th to 2nd. Possible contributing factors to this growth could be the growing popularity of Android and the increasing demand for version control platforms at businesses and enterprises.