400,000 GitHub repositories, 1 billion files, 14 terabytes of code: Spaces or Tabs?

Tabs or spaces. We are going to parse a billion files among 14 programming languages to decide which one is on top.

Felipe Hoffa
3 min readAug 30, 2016

The rules:

  • Data source: GitHub files stored in BigQuery.
  • Stars matter: We’ll only consider the top 400,000 repositories — by number of stars they got on GitHub during the period Jan-May 2016.
  • No small files: Files need to have at least 10 lines that start with a space or a tab.
  • No duplicates: Duplicate files only have one vote, regardless of how many repos they live in.
  • One vote per file: Some files use a mix of spaces or tabs. We’ll count on which side depending on which method they use more.
  • Top languages: We’ll look into files with the extensions (.java, .h, .js, .c, .php, .html, .cs, .json, .py, .cpp, .xml, .rb, .cc, .go).



I used the already existing [bigquery-public-data:github_repos.sample_files] table, that lists the files of the top 400,000 repositories. From there I extracted all the contents for the files with the top languages extensions:

SELECT a.id id, size, content, binary, copies, sample_repo_name , sample_path
SELECT id, FIRST(path) sample_path, FIRST(repo_name) sample_repo_name
FROM [bigquery-public-data:github_repos.sample_files]
WHERE REGEXP_EXTRACT(path, r'\.([^\.]*)$') IN ('java','h','js','c','php','html','cs','json','py','cpp','xml','rb','cc','go')
) a
JOIN [bigquery-public-data:github_repos.contents] b
ON a.id=b.id
864.6s elapsed, 1.60 TB processed

That query took a relative long time since it involved joining a 190 million rows table with a 70 million rows one, and over 1.6 terabytes of contents. But don’t worry about having to run it, since I left the result publicly available at [fh-bigquery:github_extracts.contents_top_repos_top_langs].

In the [contents] table we have each unique file represented only once. To see the total number of files and size represented:

SELECT SUM(copies) total_files, SUM(copies*size) total_size
FROM [fh-bigquery:github_extracts.contents_top_repos_top_langs]
1 billion files, 14 terabytes of code

Then it was time to run the ranking according to the previously established rules:

SELECT ext, tabs, spaces, countext, LOG((spaces+1)/(tabs+1)) lratio
SELECT REGEXP_EXTRACT(sample_path, r'\.([^\.]*)$') ext,
SUM(best='tab') tabs, SUM(best='space') spaces,
COUNT(*) countext
SELECT sample_path, sample_repo_name, IF(SUM(line=' ')>SUM(line='\t'), 'space', 'tab') WITHIN RECORD best,
SELECT LEFT(SPLIT(content, '\n'), 1) line, sample_path, sample_repo_name
FROM [fh-bigquery:github_extracts.contents_top_repos_top_langs]
HAVING REGEXP_MATCH(line, r'[ \t]')
HAVING c>10 # at least 10 lines that start with space or tab
ORDER BY countext DESC
16.0s elapsed, 133 GB processed

Analyzing each line of 133 GBs of code in 16 seconds? That’s why I love BigQuery.

Update: Press and social media

Reddit, Hacker News, Slashdot

Gizmodo, Business Insider, ADTMag, i-programmer

Gizmodo .es, Business Insider .pl, Le blog du Modérateur, Tecnoblog (pt), Biglobe (jp), My Drivers (cn), Genk .vn.


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Felipe Hoffa

Data Cloud Advocate at Snowflake ❄️. Originally from Chile, now in San Francisco and around the world. Previously at Google. Let’s talk data.