How to query public data sets using BigQuery
BigQuery is a fully managed data warehouse and analytics platform. Public datasets are available for you to analyze using SQL queries. You can access BigQuery public data sets using the web UI, the command-line tool, or by making calls to the BigQuery REST API using a variety of client libraries such as Java, .NET, or Python.
Currently, BigQuery public datasets are stored in the
location. When you query a public dataset, supply
--location=US flag on the command line, choose
US as the
processing location in the BigQuery web UI, or specify the
location property in the
jobReference section of the job resource
when you use the API. Because the public datasets are stored in the US, you cannot write public
data query results to a table in another region, and you cannot join tables in public datasets
with tables in another region.
To get started using a BigQuery public dataset, create or select a project. The first terabyte of data processed per month is free, so you can start querying public datasets without enabling billing. If you intend to go beyond the free tier, you should also enable billing.
Sign in to your Google Account.
If you don't already have one, sign up for a new account.
Select or create a GCP project.
Make sure that billing is enabled for your project.
- BigQuery is automatically enabled in new projects. To activate BigQuery in a pre-existing project, Enable the BigQuery API.
GitHub is how people build software and is home to the largest community of open source developers in the world, with over 12 million people contributing to 31 million projects on GitHub since 2008.
This 3TB+ dataset comprises the largest released source of GitHub activity to date. It contains a full snapshot of the content of 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.
You can start exploring this data in the BigQuery console:
Here are some examples of SQL queries you can run on this data in BigQuery. For more tips, updated resources, and community content, see the updated resource list at https://medium.com/@hoffa/b3576fd2b150
Most commonly used Go packages
SELECT REGEXP_EXTRACT(line, r'"([^"]+)"') AS url, COUNT(*) AS count FROM FLATTEN( ( SELECT SPLIT(SPLIT(REGEXP_EXTRACT(content, r'.*import\s*[(]([^)]*)[)]'), '\n'), ';') AS line, FROM ( SELECT id, content FROM [bigquery-public-data:github_repos.sample_contents] WHERE REGEXP_MATCH(content, r'.*import\s*[(][^)]*[)]')) AS C JOIN ( SELECT id FROM [bigquery-public-data:github_repos.sample_files] WHERE path LIKE '%.go' GROUP BY id) AS F ON C.id = F.id), line) GROUP BY url HAVING url IS NOT NULL ORDER BY count DESC LIMIT 10
Most commonly used Java packages
This query uses one of the smaller sample tables to find the most popular Java packages.
SELECT package, COUNT(*) count FROM ( SELECT REGEXP_EXTRACT(line, r' ([a-z0-9\._]*)\.') package, id FROM ( SELECT SPLIT(content, '\n') line, id FROM [bigquery-public-data:github_repos.sample_contents] WHERE content CONTAINS 'import' AND sample_path LIKE '%.java' HAVING LEFT(line, 6)='import' ) GROUP BY package, id ) GROUP BY 1 ORDER BY count DESC LIMIT 40;
How many times 'This should never happen' appears
This query uses a smaller sample table to find how many times the comment "this should never happen" is present.
SELECT SUM(copies) FROM [bigquery-public-data:github_repos.sample_contents] WHERE NOT binary AND content CONTAINS 'This should never happen'
Hint: if you run this query against the entire dataset the answer is around 1,000,000!
About the dataset
Dataset Source: GitHub
Category: Technology, Social
Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://help.github.com/articles/github-terms-of-service/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
Update Frequency: Weekly
View in BigQuery: Go to GitHub data