Processing 40 TB of code from ~10 million projects with a dedicated server and Go for $100 (13129 words)

The command line tool I created Sloc Cloc and Code (scc) (which is now modified and maintained by many other excellent people) counts lines of code, comments and makes a complexity estimate for files inside a directory. The latter is something you need a good sample size to make good use of. The way it works is that it counts branch statements in code. However what does that actually mean? For example “This file has a complexity of 10” is not very useful without some context. To solve this issue I thought I would try to run scc at all the source code I could get my hands on. This would also allow me to see if there are any edge cases I didn’t consider in the tool itself. A brute force Q/A trial by fire.

However if I am going to run it over all that code, which is going to be computationally expensive I may as well try to get some use out of it. As such I decided to record everything as I went and see if I could get something interesting in the end, hence this post.

In short I downloaded and processed a lot of code using scc. The raw numbers include,

Lets get the elephant out of the room first. It was not 10 million projects as the “click bait” title indicates. I was shy by 15,000 so I rounded up. Please forgive me.

It took about 5 weeks to download and run scc over the collection of repositories saving all of the data. It took just over 49 hours to crunch the 1 TB of JSON and produce the results below.

Also note its likely I messed up some of the calculations. I will keep this up to date with the data set provided as this occurs.

Methodology

Since I run searchcode.com I already have a collection of over 7,000,000 projects across git, mercurial, subversion and such. So why not try processing them? Working with git is usually the easiest solution so I ignored mercurial and subversion this time and exported the full list of git projects. Turns out I actually had 12 million git repositories being tracked, and I should probably update the page to reflect that.

So now I have 12 million or so git repositories which I need to download and process.

When you run scc you can choose to have it output the results in JSON optionally saving this file to disk like so scc --format json --output myfile.json main.go the results of which look like the following (done for a single file),

[
  {
    "Blank": 115,
    "Bytes": 0,
    "Code": 423,
    "Comment": 30,
    "Complexity": 40,
    "Count": 1,
    "Files": [
      {
        "Binary": false,
        "Blank": 115,
        "Bytes": 20396,
        "Callback": null,
        "Code": 423,
        "Comment": 30,
        "Complexity": 40,
        "Content": null,
        "Extension": "go",
        "Filename": "main.go",
        "Hash": null,
        "Language": "Go",
        "Lines": 568,
        "Location": "main.go",
        "PossibleLanguages": [
          "Go"
        ],
        "WeightedComplexity": 0
      }
    ],
    "Lines": 568,
    "Name": "Go",
    "WeightedComplexity": 0
  }
]

As a larger example here are the results as JSON for the redis project, redis.json. All of the results below come from this output without any supporting additional data.

One thing to keep in mind is that scc generally categories languages based on extension (except where extension is shared such as Verilog and Coq). As such if someone puts a HTML file with a java extension it will be counted as a java file. Usually this isn’t a problem, because why would you ever do that? But of course at scale it is. It is something I discovered later where some files were masquerading as another.

A while back I wrote code to create github badges using scc https://boyter.org/posts/sloc-cloc-code-badges/ and since part of that included caching the results, I modified it slightly to cache the results as JSON in AWS S3.

With the badge code working in AWS using lambda, I took the exported list of projects, wrote about 15 lines of python to clean the format so it matched my lambda and make a request to it. I threw in some python multiprocessing to fork 32 processes to call the endpoint reasonably quickly.

This worked brilliantly. However the problem with the above was firstly the cost, and secondly lambda behind API-Gateway/ALB has a 30 second timeout, so it couldn’t process large repositories fast enough. I knew going in that this was not going to be the most cost effective solution but assuming it came close to $100 I would have been willing to live with it. After processing 1 million repositories I checked and the cost was about $60 and since I didn’t want a $700 AWS bill I decided to rethink my solution. Keep in mind that was mostly storage and CPU, or what was needed to collect this information. Assuming I processed or exported the data it was going to increase the cost considerably.

Since I was already in AWS the hip solution would be to dump the url’s as messages into SQS and pull from it using EC2 instances or fargate for processing. Then scale out like crazy. However despite working in AWS in my day job I have always believed in taco bell programming. Besides it was only 12 million repositories so I opted to implement a simpler (cheaper) solution.

Running this computation locally was out due to the abysmal state of the internet in Australia. However I do run searchcode.com fairly lean using dedicated servers from Hetzner. These boxes are quite powerful, i7 Quad Core 32 GB RAM machines often with 2 TB of disk space (usually unused). As such they usually have a lot of spare compute based on how I use them. The front-end varnish box for instance is doing the square root of zero most of the time. So why not run the processing there?

I didn’t quite taco bell program the solution using bash and gnu tools. What I did was write a simple Go program to spin up 32 go-routines which read from a channel, spawned git and scc subprocesses before writing the JSON output into S3. I actually wrote a Python solution at first, but having to install the pip dependencies on my clean varnish box seemed like a bad idea and it keep breaking in odd ways which I didn’t feel like debugging.

Running this on the box produced the following sort of metrics in htop, and the multiple git/scc processes running (scc is not visible in this screen capture) suggested that everything was working as expected, which I confirmed by looking at the results in S3.

scc-data process load

Presenting and Computing Results

Having recently read https://mattwarren.org/2017/10/12/Analysing-C-code-on-GitHub-with-BigQuery/ and https://psuter.net/2019/07/07/z-index I thought I would steal the format of those posts with regards to how I wanted to present the information. My twist on the previous however is to add jQuery DataTables over the large tables of information. This allows you to sort and search/filter results. As such you can click the headers to sort and use the search box to filter. The search box indicates that this is enabled for you. I also added a jump link near these tables so you can skip over them if you like.

The size of the data I needed to process raised another question. How does one process 10 million JSON files taking up just over 1 TB of disk space in an S3 bucket?

The first thought I had was AWS Athena. But since it’s going to cost something like $2.50 USD per query for that dataset I quickly looked for an alternative. That said if you kept the data there and processed it infrequently this might still work out to be the cheapest solution.

I posted the question on the company slack because why should I solve issues alone.

One idea raised was to dump the data into a large SQL database. However this means processing the data into the database, then running queries over it multiple times. Plus the structure of the data meant having a few tables which means foreign keys and indexes to ensure some level of performance. This feels wasteful because we could just process the data as we read it from disk in a single pass. I was also worried about building a database this large. With just data it would be over 1 TB in size before adding indexes.

Seeing as I produced the JSON using spare compute, I thought why not process the results the same way? Of course there is one issue with this. Pulling 1 TB of data out of S3 is going to cost a lot. In the event the program crashes that is going to be annoying. To reduce costs I wanted to pull all the files down locally and save them for further processing. Handy tip, you really do not want to store lots of little files on disk in a single directory. It sucks for runtime performance and file-systems don’t like it.

My answer to this was another simple go program to pull the files down from S3 then store them in a tar file. I could then process that file over and over. The process itself is done though very ugly go program to process the tar file so I could re-run my questions without having to trawl S3 over and over. I didn’t bother with go-routines for this code for two reasons. The first was that I didn’t want to max out my server, so this limits it to a single core for the hard CPU work (another to read the tar file was mostly blocked on the processor). The second being I didn’t want to ensure it was thread-safe.

With that done, what I needed was a collection of questions to answer. I used the slack brains trust again and crowd-sourced my work colleagues while I came up with some ideas of my own. The result of this mind meld is included below.

You can find all the code I used to process the JSON including that which pulled it down locally and the ugly python script I used to mangle it into something useful for this post https://github.com/boyter/scc-data Please don’t comment on it, I know the code is ugly and it is something I wrote as a throwaway as I am unlikely to ever look at it again.

If you do want to review code I have written to be read by others have a look at the source of scc.

Cost

I spent about $60 in compute while trialling lambda. I have not looked at the S3 storage cost yet but it should be close to $25 based on the size of the data. However this is not including the transfer costs which I also have not observed. Please note I cleared the bucket when I was finished with it so this is not an ongoing cost for me.

However after time I chose not to use AWS in the end because of cost. So what’s the real cost assuming I wanted to do it again?

Well to start all the software used is free as in freedom and open source. So nothing to worry about there.

In my case the cost would be free as I used “free compute” left over from searchcode. Not everyone has compute lying around however. So lets assume another person wishes to replicate this and as such needs to get a server.

It could be done for €73 using the cheapest new dedicated server from Hetzner https://www.hetzner.com/dedicated-rootserver However that cost includes a new server setup fee. If you are willing to wait and poke around on their auction house https://www.hetzner.com/sb you can find much cheaper servers with no setup fee at all. At time of writing I found the below machine which would be perfect for this project and is €25.21 a month with no setup fee.

hetzner server

Best part though? You can get the VAT removed if you are outside the EU. So give yourself an additional 10% discount on top if you are in this situation.

So were someone to do this from scratch using the same method I eventually went with it would cost under $100 USD to redo the same calculations, and more likely under $50 if you are a little patient or lucky. This also assumes you use the server for less than 2 months which is enough time to download and process. This also includes enough time for you to get a list of 10 million repositories to consider processing as well.

If I were to use a gzipped tar file in my analysis (which isn’t that hard to do really) I could even do 10x the repositories on the same machine as the resulting file would still be small enough to fit on the same hard disk. That would take longer to download though which is going to increase the cost for each additional month and this might take multiple months to do.

Going much larger then 100 million repositories however is going to require some level of sharding. Still it is safe to say that you could redo the entire process I did or larger one on the same hardware without much effort or code changes.

Data Sources

From the three sources, github, bitbucket and gitlab how many projects came from each? Note that this is counted before excluding empty repositories hence the sum is over the number of repositories that actually form the counts below this point.

source count
github 9,680,111
bitbucket 248,217
gitlab 56,722

Sorry to the GitHub/Bitbucket/GitLab teams if you read this. If this caused any issues for you (I doubt it) I will shout you a refreshing beverage of your choice should we ever meet.

How many files in a repository?

On to the real questions. Lets start with a simple one. How many files are in an average repository? Do most projects have a few files in them, or many? By looping over the repositories and counting the number of files we can then drop them in buckets of 1, 2, 10, 12 or however many files it has and plot it out.

scc-data files per project

The X-axis in this case being buckets of the count of files, and Y-axis being the count of projects with that many files. This is limited to projects with less than 1000 files because the plot looks like empty with a thin smear on the left side if you include all the outliers.

As it turns out most repositories have less than 200 files in them.

However what about plotting this by percentile, or more specifically by 95th percentile so its actually worth looking at? Turns out the vast majority 95% of projects have less than 1,000 files in them. While 90% of them have less than 300 files and 85% have less than 200.

scc-data files per project 95th

If you want to plot this yourself and do a better job than I here is a link to the raw data filesPerProject.json.

Whats the project breakdown per language?

This means for each project scanned if a Java file is identified increment the Java count by one and for the second file do nothing. This gives a quick view of what languages are most commonly used. Unsurprisingly the most common languages include markdown, .gitignore and plain text.

Markdown the most commonly used language in any project is included in just over 6 million projects which is about 23 of the entire project set. This makes sense since almost all projects include a README.md which is displayed in HTML for repository pages.

The full list is included below.

skip table to next section

language project count
Markdown 6,041,849
gitignore 5,471,254
Plain Text 3,553,325
JavaScript 3,408,921
HTML 3,397,596
CSS 3,037,754
License 2,597,330
XML 2,218,846
JSON 1,903,569
YAML 1,860,523
Python 1,424,505
Shell 1,395,199
Ruby 1,386,599
Java 1,319,091
C Header 1,259,519
Makefile 1,215,586
Rakefile 1,006,022
PHP 992,617
Properties File 909,631
SVG 804,946
C 791,773
C++ 715,269
Batch 645,442
Sass 535,341
Autoconf 505,347
Objective C 503,932
CoffeeScript 435,133
SQL 413,739
Perl 390,775
C# 380,841
ReStructuredText 356,922
MSBuild 354,212
LESS 281,286
CSV 275,143
C++ Header 199,245
CMake 173,482
Patch 169,078
Assembly 165,587
XML Schema 148,511
m4 147,204
JavaServer Pages 142,605
Vim Script 134,156
Scala 132,454
Objective C++ 127,797
Gradle 126,899
Module-Definition 120,181
Bazel 114,842
R 113,770
ASP.NET 111,431
Go Template 111,263
Document Type Definition 109,710
Gherkin Specification 107,187
Smarty Template 106,668
Jade 105,903
Happy 105,631
Emacs Lisp 105,620
Prolog 102,792
Go 99,093
Lua 98,232
BASH 95,931
D 94,400
ActionScript 93,066
TeX 84,841
Powershell 80,347
AWK 79,870
Groovy 75,796
LEX 75,335
nuspec 72,478
sed 70,454
Puppet 67,732
Org 67,703
Clojure 67,145
XAML 65,135
TypeScript 62,556
Systemd 58,197
Haskell 58,162
XCode Config 57,173
Boo 55,318
LaTeX 55,093
Zsh 55,044
Stylus 54,412
Razor 54,102
Handlebars 51,893
Erlang 49,475
HEX 46,442
Protocol Buffers 45,254
Mustache 44,633
ASP 43,114
Extensible Stylesheet Language Transformations 42,664
Twig Template 42,273
Processing 41,277
Dockerfile 39,664
Swig 37,539
LD Script 36,307
FORTRAN Legacy 35,889
Scons 35,373
Scheme 34,982
Alex 34,221
TCL 33,766
Android Interface Definition Language 33,000
Ruby HTML 32,645
Device Tree 31,918
Expect 30,249
Cabal 30,109
Unreal Script 29,113
Pascal 28,439
GLSL 28,417
Intel HEX 27,504
Alloy 27,142
Freemarker Template 26,456
IDL 26,079
Visual Basic for Applications 26,061
Macromedia eXtensible Markup Language 24,949
F# 24,373
Cython 23,858
Jupyter 23,577
Forth 22,108
Visual Basic 21,909
Lisp 21,242
OCaml 20,216
Rust 19,286
Fish 18,079
Monkey C 17,753
Ada 17,253
SAS 17,031
Dart 16,447
TypeScript Typings 16,263
SystemVerilog 15,541
Thrift 15,390
C Shell 14,904
Fragment Shader File 14,572
Vertex Shader File 14,312
QML 13,709
ColdFusion 13,441
Elixir 12,716
Haxe 12,404
Jinja 12,274
JSX 12,194
Specman e 12,071
FORTRAN Modern 11,460
PKGBUILD 11,398
ignore 11,287
Mako 10,846
TOML 10,444
SKILL 10,048
AsciiDoc 9,868
Swift 9,679
BuildStream 9,198
ColdFusion CFScript 8,614
Stata 8,296
Creole 8,030
Basic 7,751
V 7,560
VHDL 7,368
Julia 7,070
ClojureScript 7,018
Closure Template 6,269
AutoHotKey 5,938
Wolfram 5,764
Docker ignore 5,555
Korn Shell 5,541
Arvo 5,364
Coq 5,068
SRecode Template 5,019
Game Maker Language 4,557
Nix 4,216
Vala 4,110
COBOL 3,946
Varnish Configuration 3,882
Kotlin 3,683
Bitbake 3,645
GDScript 3,189
Standard ML (SML) 3,143
Jenkins Buildfile 2,822
Xtend 2,791
ABAP 2,381
Modula3 2,376
Nim 2,273
Verilog 2,013
Elm 1,849
Brainfuck 1,794
Ur/Web 1,741
Opalang 1,367
GN 1,342
TaskPaper 1,330
Ceylon 1,265
Crystal 1,259
Agda 1,182
Vue 1,139
LOLCODE 1,101
Hamlet 1,071
Robot Framework 1,062
MUMPS 940
Emacs Dev Env 937
Cargo Lock 905
Flow9 839
Idris 804
Julius 765
Oz 764
Q# 695
Lucius 627
Meson 617
F* 614
ATS 492
PSL Assertion 483
Bitbucket Pipeline 418
PureScript 370
Report Definition Language 313
Isabelle 296
JAI 286
MQL4 271
Ur/Web Project 261
Alchemist 250
Cassius 213
Softbridge Basic 207
MQL Header 167
JSONL 146
Lean 104
Spice Netlist 100
Madlang 97
Luna 91
Pony 86
MQL5 46
Wren 33
Just 30
QCL 27
Zig 21
SPDX 20
Futhark 16
Dhall 15
FIDL 14
Bosque 14
Janet 13
Game Maker Project 6
Polly 6
Verilog Args File 2

How many files in a repository per language?

An extension of the above, but averaged over however many files are in each language per repository. So for projects that contain java, how many java files exist in that project, and on average for all projects how many files is that?

You can use this to see if a project is larger or smaller than usual for your language of choice.

skip table to next section

language average file count
ABAP 1.0008927583699165
ASP 1.6565139917314107
ASP.NET 346.88867258489296
ATS 7.888545610390882
AWK 5.098807478952136
ActionScript 15.682562363539644
Ada 7.265376817272021
Agda 1.2669381110755398
Alchemist 7.437307493090622
Alex 20.152479318023637
Alloy 1.0000000894069672
Android Interface Definition Language 3.1133707938643074
Arvo 9.872687772928423
AsciiDoc 14.645389421879814
Assembly 1049.6270518312476
AutoHotKey 1.5361384288472488
Autoconf 33.99728695464163
BASH 3.7384110335355545
Basic 5.103623499110781
Batch 3.943513588378872
Bazel 1.0013122734382187
Bitbake 1.0878349272366024
Bitbucket Pipeline 1
Boo 5.321822367969364
Bosque 1.28173828125
Brainfuck 1.3141119785974242
BuildStream 1.4704635441667189
C 15610.17972307699
C Header 14103.33936083782
C Shell 3.1231084093649315
C# 45.804460355773394
C++ 30.416980313492328
C++ Header 8.313450764990089
CMake 37.2566873554469
COBOL 3.129408853490878
CSS 5.332398714337156
CSV 8.370432089241898
Cabal 1.0078125149013983
Cargo Lock 1.0026407549221519
Cassius 4.657169356495984
Ceylon 7.397692655679642
Clojure 8.702303821528872
ClojureScript 5.384518778099244
Closure Template 1.0210028022356945
CoffeeScript 45.40906609668401
ColdFusion 13.611857060674573
ColdFusion CFScript 40.42554202020521
Coq 10.903652047164622
Creole 1.000122070313864
Crystal 3.8729367926098117
Cython 1.9811811237515262
D 529.2562750397005
Dart 1.5259554297822313
Device Tree 586.4119588123021
Dhall 5.072265625
Docker ignore 1.0058596283197403
Dockerfile 1.7570825852789156
Document Type Definition 2.2977520758534693
Elixir 8.916658446524252
Elm 1.6702759813968946
Emacs Dev Env 15.720268315288969
Emacs Lisp 11.378847912292201
Erlang 3.4764894379621607
Expect 2.8863991651091614
Extensible Stylesheet Language Transformations 1.2042068607534995
F# 1.2856606249320954
F* 32.784058919015
FIDL 1.8441162109375
FORTRAN Legacy 11.37801716560221
FORTRAN Modern 27.408192558594685
Fish 1.1282354207617833
Flow9 5.218046229973186
Forth 10.64736177807574
Fragment Shader File 3.648087980622546
Freemarker Template 8.397226930409037
Futhark 4.671875
GDScript 3.6984173692608313
GLSL 1.6749061330076334
GN 1.0193083210608163
Game Maker Language 3.6370866431049604
Game Maker Project 1.625
Gherkin Specification 60.430588516231666
Go 115.23482489228113
Go Template 28.011342078505013
Gradle 5.628880473160033
Groovy 6.697367294187844
HEX 22.477003537989486
HTML 4.822243456786672
Hamlet 50.297887645777536
Handlebars 36.60120978679127
Happy 5.820573911044464
Haskell 8.730027121836951
Haxe 20.00590981880653
IDL 79.38510300176867
Idris 1.524684997890027
Intel HEX 113.25178379632708
Isabelle 1.8903018088753136
JAI 1.4865150753259275
JSON 6.507823973898348
JSONL 1.003931049286678
JSX 4.6359645801363465
Jade 5.353279289700571
Janet 1.0390625
Java 118.86142228014006
JavaScript 140.56079100796154
JavaServer Pages 2.390251418283771
Jenkins Buildfile 1.0000000000582077
Jinja 4.574843152310338
Julia 6.672268339671913
Julius 2.2510109380818903
Jupyter 13.480476117239338
Just 1.736882857978344
Korn Shell 1.5100887455636172
Kotlin 3.9004723322169363
LD Script 16.59996086864524
LESS 39.6484785300563
LEX 5.892075421476933
LOLCODE 1.0381496530137617
LaTeX 5.336103768010524
Lean 1.6653789470747329
License 5.593879701111845
Lisp 33.15947937896521
Lua 24.796117625764612
Lucius 6.5742989471450155
Luna 4.437807061133055
MQL Header 13.515527575704464
MQL4 6.400151428436254
MQL5 46.489316522221515
MSBuild 4.8321384193507875
MUMPS 8.187699062741014
Macromedia eXtensible Markup Language 2.1945287114300807
Madlang 3.7857666909751373
Makefile 1518.1769808494607
Mako 3.410234685769436
Markdown 45.687500000234245
Meson 32.45071679724949
Modula3 1.1610784588847318
Module-Definition 4.9327688042002595
Monkey C 3.035163164383345
Mustache 19.052714578803542
Nim 1.202213335585401
Nix 2.7291879559930488
OCaml 3.7135029841909697
Objective C 4.9795510788040005
Objective C++ 2.2285232767506264
Opalang 1.9975597794742732
Org 5.258117805392903
Oz 22.250069644336204
PHP 199.17870638869982
PKGBUILD 7.50632295051949
PSL Assertion 3.0736406530442473
Pascal 90.55238627885495
Patch 25.331829692384225
Perl 27.46770444081142
Plain Text 1119.2375825397799
Polly 1
Pony 3.173291031071342
Powershell 6.629884642978543
Processing 9.139907354078636
Prolog 1.816763080890156
Properties File 2.1801967863634255
Protocol Buffers 2.0456253005879304
Puppet 43.424491631161054
PureScript 4.063801504037935
Python 22.473917606983292
Q# 5.712939431518483
QCL 7.590678825974464
QML 1.255201818986247
R 2.3781868952970115
Rakefile 14.856192677576413
Razor 62.79058974450959
ReStructuredText 11.63852408056825
Report Definition Language 23.065085061465403
Robot Framework 2.6260137148703535
Ruby 554.0134362337432
Ruby HTML 24.091116656979562
Rust 2.3002003813895207
SAS 1.0032075758254648
SKILL 1.9229039972029645
SPDX 2.457843780517578
SQL 2.293643752864969
SRecode Template 20.688193360975845
SVG 4.616972531365432
Sass 42.92418345584642
Scala 1.5957851387966393
Scheme 10.480490204644848
Scons 2.1977062552968114
Shell 41.88552208947577
Smarty Template 6.90615223295527
Softbridge Basic 22.218602385698304
Specman e 2.719783829645327
Spice Netlist 2.454830619852739
Standard ML (SML) 3.7598713626650295
Stata 2.832579915520368
Stylus 7.903926412469745
Swift 54.175594149331914
Swig 2.3953681161240747
SystemVerilog 7.120705494624247
Systemd 80.83254275520476
TCL 46.9378307136513
TOML 1.0316491217260413
TaskPaper 1.0005036006351133
TeX 8.690789447558961
Thrift 1.620168483240211
Twig Template 18.33051814392764
TypeScript 1.2610517452930048
TypeScript Typings 2.3638072576034137
Unreal Script 2.9971615019965148
Ur/Web 3.420488425604595
Ur/Web Project 1.8752869585517504
V 1.8780624768245784
VHDL 5.764059992075602
Vala 42.22072166146626
Varnish Configuration 1.9899734258599446
Verilog 1.443359777332832
Verilog Args File 25.5
Vertex Shader File 2.4700152399875077
Vim Script 3.2196359799822662
Visual Basic 119.8397831247842
Visual Basic for Applications 2.5806381264096503
Vue 249.0557418123258
Wolfram 1.462178856761796
Wren 227.4526259500999
XAML 2.6149608174399264
XCode Config 6.979387911493798
XML 146.10128153519918
XML Schema 6.832042266604565
Xtend 2.87054940757827
YAML 6.170148717655746
Zig 1.071681022644043
Zsh 2.6295064863912088
gitignore 6.878908416722053
ignore 1.0210649380633772
m4 57.5969985568356
nuspec 3.245791111381787
sed 1.3985770380241234

How many lines of code are in a typical file per language?

I suppose you could also look at this as what languages on average have the largest files? Using the average/mean for this pushes the results out to stupidly high numbers. This is because projects such as sqlite.c which is included in many projects is joined from many files into one, but nobody ever works on that single large file (I hope!).

So I calculated this using the median value. Even so there are still some definitions with stupidly high numbers such as Bosque and JavaScript.

So I figured why not have both? I did one small change based on the suggestion of Darrell (Kablamo’s resident and most excellent data scientist) and modified the average value to ignore files over 5000 lines to remove the outliers.

skip table to next section

language mean < 5000 median
ABAP 139 36
ASP 513 170
ASP.NET 315 148
ATS 945 1,411
AWK 431 774
ActionScript 950 2,676
Ada 1,179 13
Agda 466 89
Alchemist 1,040 1,463
Alex 479 204
Alloy 72 66
Android Interface Definition Language 119 190
Arvo 257 1,508
AsciiDoc 519 1,724
Assembly 993 225
AutoHotKey 360 23
Autoconf 495 144
BASH 425 26
Basic 476 847
Batch 178 208
Bazel 226 20
Bitbake 436 10
Bitbucket Pipeline 19 13
Boo 898 924
Bosque 58 199,238
Brainfuck 141 177
BuildStream 1,955 2,384
C 1,052 5,774
C Header 869 126,460
C Shell 128 77
C# 1,215 1,138
C++ 1,166 232
C++ Header 838 125
CMake 750 15
COBOL 422 24
CSS 729 103
CSV 411 12
Cabal 116 13
Cargo Lock 814 686
Cassius 124 634
Ceylon 207 15
Clojure 521 19
ClojureScript 504 195
Closure Template 343 75
CoffeeScript 342 168
ColdFusion 686 5
ColdFusion CFScript 1,231 1,829
Coq 560 29,250
Creole 85 20
Crystal 973 119
Cython 853 1,738
D 397 10
Dart 583 500
Device Tree 739 44,002
Dhall 124 99
Docker ignore 10 2
Dockerfile 76 17
Document Type Definition 522 1,202
Elixir 402 192
Elm 438 121
Emacs Dev Env 646 755
Emacs Lisp 653 15
Erlang 930 203
Expect 419 195
Extensible Stylesheet Language Transformations 442 600
F# 384 64
F* 335 65
FIDL 655 1,502
FORTRAN Legacy 277 1,925
FORTRAN Modern 636 244
Fish 168 74
Flow9 368 32
Forth 256 62
Fragment Shader File 309 11
Freemarker Template 522 20
Futhark 175 257
GDScript 401 1
GLSL 380 29
GN 950 8,866
Game Maker Language 710 516
Game Maker Project 1,290 374
Gherkin Specification 516 2,386
Go 780 558
Go Template 411 25,342
Gradle 228 22
Groovy 734 13
HEX 1,002 17,208
HTML 556 1,814
Hamlet 220 70
Handlebars 506 3,162
Happy 1,617 0
Haskell 656 17
Haxe 865 9,607
IDL 386 210
Idris 285 42
Intel HEX 1,256 106,650
Isabelle 792 1,736
JAI 268 41
JSON 289 39
JSONL 43 2
JSX 393 24
Jade 299 192
Janet 508 32
Java 1,165 697
JavaScript 894 73,979
JavaServer Pages 644 924
Jenkins Buildfile 79 6
Jinja 465 3,914
Julia 539 1,031
Julius 113 12
Jupyter 1,361 688
Just 62 72
Korn Shell 427 776
Kotlin 554 169
LD Script 521 439
LESS 1,086 17
LEX 1,014 214
LOLCODE 129 4
LaTeX 895 7,482
Lean 181 9
License 266 20
Lisp 746 1,201
Lua 820 559
Lucius 284 445
Luna 85 48
MQL Header 793 10,337
MQL4 799 3,168
MQL5 384 631
MSBuild 558 160
MUMPS 924 98,191
Macromedia eXtensible Markup Language 500 20
Madlang 368 340
Makefile 309 20
Mako 269 243
Markdown 206 10
Meson 546 205
Modula3 162 17
Module-Definition 489 7
Monkey C 140 28
Mustache 298 8,083
Nim 352 3
Nix 240 78
OCaml 718 68
Objective C 1,111 17,103
Objective C++ 903 244
Opalang 151 29
Org 523 24
Oz 360 7,132
PHP 964 14,660
PKGBUILD 131 19
PSL Assertion 149 108
Pascal 1,044 497
Patch 676 12
Perl 762 11
Plain Text 352 841
Polly 12 26
Pony 338 42,488
Powershell 652 199
Processing 800 903
Prolog 282 6
Properties File 184 18
Protocol Buffers 576 8,080
Puppet 499 660
PureScript 598 363
Python 879 258
Q# 475 5,417
QCL 548 3
QML 815 6,067
R 566 20
Rakefile 122 7
Razor 713 1,842
ReStructuredText 735 5,049
Report Definition Language 1,389 34,337
Robot Framework 292 115
Ruby 739 4,942
Ruby HTML 326 192
Rust 1,007 4
SAS 233 65
SKILL 526 123
SPDX 1,242 379
SQL 466 143
SRecode Template 796 534
SVG 796 1,538
Sass 682 14,653
Scala 612 661
Scheme 566 6
Scons 545 6,042
Shell 304 4
Smarty Template 392 15
Softbridge Basic 2,067 3
Specman e 127 0
Spice Netlist 906 1,465
Standard ML (SML) 478 75
Stata 200 12
Stylus 505 214
Swift 683 663
Swig 1,031 4,540
SystemVerilog 563 830
Systemd 127 26
TCL 774 42,396
TOML 100 17
TaskPaper 37 7
TeX 804 905
Thrift 545 329
Twig Template 713 9,907
TypeScript 461 10
TypeScript Typings 1,465 236,866
Unreal Script 795 927
Ur/Web 429 848
Ur/Web Project 33 26
V 704 5,711
VHDL 952 1,452
Vala 603 2
Varnish Configuration 203 77
Verilog 198 2
Verilog Args File 456 481
Vertex Shader File 168 74
Vim Script 555 25
Visual Basic 738 1,050
Visual Basic for Applications 979 936
Vue 732 242
Wolfram 940 973
Wren 358 279,258
XAML 703 24
XCode Config 200 11
XML 605 1,033
XML Schema 1,008 248
Xtend 710 120
YAML 165 47,327
Zig 188 724
Zsh 300 9
gitignore 33 3
ignore 6 2
m4 959 807
nuspec 187 193
sed 82 33

Average complexity for file in each language?

What’s the average complexity per file for each language?

The complexity estimate isn’t really directly comparable between languages. Pulling from the README of scc itself

The complexity estimate is really just a number that is only comparable to files in the same language. It should not be used to compare languages directly without weighting them. The reason for this is that its calculated by looking for branch and loop statements in the code and incrementing a counter for that file.

So comparing languages to each other is not the idea here, although it may be comparable between similar languages such as Java and C for example. Your mileage may vary.

This is more useful when you think about it applying to single files in the same language. So you could answer the question “On average is the file I am working with more or less complex than average?”.

I should mention that I am always looking to improve this calculation and looking for submissions to scc to assist with this goal. Usually it is a case of just adding some keywords to the languages.json file so any programmer of any skill level should be able to assist with this.

skip table to next section

language complexity
ABAP 11.180740488380376
ASP 11.536947250366211
ASP.NET 2.149275320643484
ATS 0.7621728432717677
AWK 0
ActionScript 22.088579905848178
Ada 13.69141626294931
Agda 0.19536590785719454
Alchemist 0.3423442907696928
Alex 0
Alloy 6.9999997997656465
Android Interface Definition Language 0
Arvo 0
AsciiDoc 0
Assembly 1.5605608227976997
AutoHotKey 423.87785756399626
Autoconf 1.5524294972419739
BASH 7.500000094871363
Basic 1.0001350622574257
Batch 1.4136352496767306
Bazel 6.523681727119303
Bitbake 0.00391388021490391
Bitbucket Pipeline 0
Boo 65.67764583729533
Bosque 236.79837036132812
Brainfuck 27.5516445041791
BuildStream 0
C 220.17236548200242
C Header 0.027589923237434522
C Shell 1.4911166269191476
C# 1.0994400597744005
C++ 215.23628287682845
C++ Header 2.2893104921677154
CMake 0.887660006199008
COBOL 0.018726348891789816
CSS 6.317460331175305E-176
CSV 0
Cabal 3.6547924155738194
Cargo Lock 0
Cassius 0
Ceylon 21.664400369259404
Clojure 0.00009155273437716484
ClojureScript 0.5347588658332859
Closure Template 0.503426091716392
CoffeeScript 0.02021490140137264
ColdFusion 6.851776515250336
ColdFusion CFScript 22.287403080299764
Coq 3.3282556015266307
Creole 0
Crystal 1.6065794006138856
Cython 42.87412906489837
D 0
Dart 2.1264450684815657
Device Tree 0
Dhall 0
Docker ignore 0
Dockerfile 6.158891172385556
Document Type Definition 0
Elixir 0.5000612735793482
Elm 5.237952479502043
Emacs Dev Env 1.2701271416728307E-61
Emacs Lisp 0.19531250990197657
Erlang 0.08028322620528387
Expect 0.329944610851471
Extensible Stylesheet Language Transformations 0
F# 0.32300702900710193
F* 9.403954876643223E-38
FIDL 0.12695312593132269
FORTRAN Legacy 0.8337643985574195
FORTRAN Modern 7.5833590276411185
Fish 1.3386242155247368
Flow9 34.5
Forth 2.4664166555765066
Fragment Shader File 0.0003388836600090293
Freemarker Template 10.511094652522283
Futhark 0.8057891242233386
GDScript 10.750000000022537
GLSL 0.6383056697891334
GN 22.400601854287807
Game Maker Language 4.709514207365569
Game Maker Project 0
Gherkin Specification 0.4085178437480328
Go 50.06279203974034
Go Template 2.3866690339840662E-153
Gradle 0
Groovy 3.2506868488244898
HEX 0
HTML 0
Hamlet 0.25053861103978114
Handlebars 1.6943764911351036E-21
Happy 0
Haskell 28.470107150053625
Haxe 66.52873523714804
IDL 7.450580598712868E-9
Idris 17.77642903881352
Intel HEX 0
Isabelle 0.0014658546850726184
JAI 7.749968137734008
JSON 0
JSONL 0
JSX 0.3910405338329044
Jade 0.6881713929215119
Janet 0
Java 297.22908150612085
JavaScript 1.861130583340945
JavaServer Pages 7.24235416213196
Jenkins Buildfile 0
Jinja 0.6118526458846931
Julia 5.779676990326951
Julius 3.7432448068125277
Jupyter 0
Just 1.625490248219907
Korn Shell 11.085027896435056
Kotlin 5.467347841779503
LD Script 6.538079182471746E-26
LESS 0
LEX 0
LOLCODE 5.980839657708373
LaTeX 0
Lean 0.0019872561135834133
License 0
Lisp 4.033602018074421
Lua 44.70686769972825
Lucius 0
Luna 0
MQL Header 82.8036524637758
MQL4 2.9989408299408566
MQL5 32.84198718928553
MSBuild 2.9802322387695312E-8
MUMPS 5.767955578948634E-17
Macromedia eXtensible Markup Language 0
Madlang 8.25
Makefile 3.9272747722381812E-90
Mako 0.007624773579836673
Markdown 0
Meson 0.3975182396400463
Modula3 0.7517121883916386
Module-Definition 0.25000000023283153
Monkey C 9.838715311259486
Mustache 0.00004191328599945435
Nim 0.04812580073302998
Nix 25.500204694250044
OCaml 16.92218069843716
Objective C 65.08967337175548
Objective C++ 10.886891531550603
Opalang 1.3724696160763994E-8
Org 28.947825231747235
Oz 6.260657086070324
PHP 2.8314653639690874
PKGBUILD 0
PSL Assertion 0.5009768009185791
Pascal 4
Patch 0
Perl 48.16959255514553
Plain Text 0
Polly 0
Pony 4.91082763671875
Powershell 0.43151378893449877
Processing 9.691001653621564
Prolog 0.5029296875147224
Properties File 0
Protocol Buffers 0.07128906529847256
Puppet 0.16606500436341776
PureScript 1.3008141816356456
Python 11.510142201304832
Q# 5.222080192729404
QCL 13.195626304795667
QML 0.3208023407643109
R 0.40128818821921775
Rakefile 2.75786388297917
Razor 0.5298294073055322
ReStructuredText 0
Report Definition Language 0
Robot Framework 0
Ruby 7.8611656283491795
Ruby HTML 1.3175727506823756
Rust 8.62646485221385
SAS 0.5223999023437882
SKILL 0.4404907226562501
SPDX 0
SQL 0.00001537799835205078
SRecode Template 0.18119949102401853
SVG 1.7686873200833423E-74
Sass 7.002974651049148E-113
Scala 17.522343645163424
Scheme 0.00003147125255509322
Scons 25.56868253610655
Shell 6.409446969197895
Smarty Template 53.06143077491294
Softbridge Basic 7.5
Specman e 0.0639350358484781
Spice Netlist 1.3684555315672042E-48
Standard ML (SML) 24.686901116754818
Stata 1.5115316917094068
Stylus 0.3750006556512421
Swift 0.5793484510104517
Swig 0
SystemVerilog 0.250593163372906
Systemd 0
TCL 96.5072605676113
TOML 0.0048828125000002776
TaskPaper 0
TeX 54.0588040258797
Thrift 0
Twig Template 2.668124511961211
TypeScript 9.191392608918255
TypeScript Typings 6.1642456222327375
Unreal Script 2.7333421227943004
Ur/Web 16.51621568240534
Ur/Web Project 0
V 22.50230618938804
VHDL 18.05495198571289
Vala 147.2761703068509
Varnish Configuration 0
Verilog 5.582400367711671
Verilog Args File 0
Vertex Shader File 0.0010757446297590262
Vim Script 2.4234658314493798
Visual Basic 0.0004882812500167852
Visual Basic for Applications 4.761343429454877
Vue 0.7529517744621779
Wolfram 0.0059204399585724215
Wren 0.08593750013097715
XAML 6.984919309616089E-10
XCode Config 0
XML 0
XML Schema 0
Xtend 2.8245844719990547
YAML 0
Zig 1.0158334437942358
Zsh 1.81697392626756
gitignore 0
ignore 0
m4 0
nuspec 0
sed 22.91158285739948

Average comments for file in each language?

What’s the average amount of comments used per file in each language?

You could probably rephrase this to asking what developers write the most comments assuming you squint enough.

skip table to next section

language comments
ABAP 56.3020026683825
ASP 24.67145299911499
ASP.NET 9.140447860406259E-11
ATS 41.89465025163305
AWK 11.290069486393975
ActionScript 31.3568633027012
Ada 61.269572412982384
Agda 2.4337660860304755
Alchemist 2.232399710231226E-103
Alex 0
Alloy 0.000002207234501959681
Android Interface Definition Language 26.984662160277367
Arvo 0
AsciiDoc 0
Assembly 2.263919769706678E-72
AutoHotKey 15.833985920534857
Autoconf 0.47779749499136687
BASH 34.15625059662068
Basic 1.4219117348874069
Batch 1.0430908205926455
Bazel 71.21859817579139
Bitbake 0.002480246487177871
Bitbucket Pipeline 0.567799577547725
Boo 5.03128187009327
Bosque 0.125244140625
Brainfuck 0
BuildStream 12.84734197699206
C 256.2839210573451
C Header 184.88885430308878
C Shell 5.8409870392823375
C# 30.96563720101839
C++ 44.61584829131642
C++ Header 27.578790410119197
CMake 1.7564333047949374
COBOL 0.7503204345703562
CSS 4.998773531463529
CSV 0
Cabal 4.899812531420634
Cargo Lock 0.0703125
Cassius 0.07177734654413487
Ceylon 3.6406326349824667
Clojure 0.0987220821845421
ClojureScript 0.6025725119252456
Closure Template 17.078124673988057
CoffeeScript 1.6345682790069884
ColdFusion 33.745563628665096
ColdFusion CFScript 13.566947396771592
Coq 20.3222774725393
Creole 0
Crystal 6.0308081267588145
Cython 21.0593019957583
D 0
Dart 4.634361584097128
Device Tree 33.64898256434121
Dhall 1.0053101042303751
Docker ignore 8.003553375601768E-11
Dockerfile 4.526245545632278
Document Type Definition 0
Elixir 8.0581139370409
Elm 24.73191350743249
Emacs Dev Env 2.74822998046875
Emacs Lisp 12.168370702306452
Erlang 16.670030919109056
Expect 3.606161126133445
Extensible Stylesheet Language Transformations 0
F# 0.5029605040200058
F* 5.33528354690743E-27
FIDL 343.0418392068642
FORTRAN Legacy 8.121405267242158
FORTRAN Modern 171.32042583820953
Fish 7.979248739519377
Flow9 0.5049991616979244
Forth 0.7578125
Fragment Shader File 0.2373057885016209
Freemarker Template 62.250244379050855
Futhark 0.014113984877253714
GDScript 31.14457228694065
GLSL 0.2182627061047912
GN 17.443267241931284
Game Maker Language 3.9815753922640824
Game Maker Project 0
Gherkin Specification 0.0032959059321794604
Go 6.464829990599041
Go Template 4.460169822267483E-251
Gradle 0.5374194774415457
Groovy 32.32068506016523
HEX 0
HTML 0.16671794164614084
Hamlet 4.203293477836184E-24
Handlebars 0.9389737429747177
Happy 0
Haskell 20.323476462551376
Haxe 9.023509566990532
IDL 1.01534495399968
Idris 0.36279318680267497
Intel HEX 0
Isabelle 4.389802167076498
JAI 2.220446049250313E-16
JSON 0
JSONL 0
JSX 0.9860839844113964
Jade 0.25000000000034117
Janet 9.719207406044006
Java 330.66188089718935
JavaScript 22.102491285372537
JavaServer Pages 4.31250095370342
Jenkins Buildfile 0
Jinja 2.5412145720173454E-50
Julia 12.542627036271085
Julius 0.24612165248208867
Jupyter 0
Just 0.3186038732601446
Korn Shell 40.89005232702741
Kotlin 0.3259347784770708
LD Script 3.7613336386434204
LESS 15.495439701029127
LEX 55.277186392539086
LOLCODE 13.578125958700468
LaTeX 3.316717967334341
Lean 21.194565176965895
License 0
Lisp 88.10676444837796
Lua 76.67247973843406
Lucius 0.3894241626790286
Luna 16.844066019174637
MQL Header 82.22436339969337
MQL4 1.957314499740677
MQL5 27.463183855085845
MSBuild 0.19561428198176206
MUMPS 5.960464477541773E-8
Macromedia eXtensible Markup Language 0
Madlang 6.75
Makefile 1.2287070602578574
Mako 1.3997604187154047E-8
Markdown 0
Meson 4.594536366188615
Modula3 3.4375390004645627
Module-Definition 7.754887182446689
Monkey C 0.02734480644075532
Mustache 0.0000038370490074157715
Nim 0.8432132130061808
Nix 165.09375
OCaml 27.238212826702338
Objective C 32.250000004480256
Objective C++ 4.688333711547599
Opalang 3.2498599900436704
Org 2.4032862186444435
Oz 11.531631554476924
PHP 0.37573912739754056
PKGBUILD 0
PSL Assertion 4.470348358154297E-7
Pascal 274.7797153576955
Patch 0
Perl 42.73014043490598
Plain Text 0
Polly 0
Pony 0.2718505859375
Powershell 2.0956492198317282
Processing 11.358358417519032
Prolog 6.93889390390723E-17
Properties File 4.297774864451927
Protocol Buffers 5.013992889700926
Puppet 1.9962931947466012
PureScript 6.608705271035433
Python 15.208443286809963
Q# 0.4281108849922295
QCL 13.880147817629737
QML 16.17036877582475
R 5.355639399818855
Rakefile 0.4253943361101697
Razor 0.2500305203720927
ReStructuredText 0
Report Definition Language 1.8589575837924928E-119
Robot Framework 0
Ruby 8.696056880656087
Ruby HTML 0.031281024218515086
Rust 22.359375028118006
SAS 0.7712382248290134
SKILL 0.002197265625
SPDX 0
SQL 0.4963180149979617
SRecode Template 17.64534428715706
SVG 0.780306812508952
Sass 1.6041624981030795
Scala 2.7290137764062656
Scheme 18.68675828842983
Scons 9.985132321266597
Shell 19.757167057040007
Smarty Template 0.0009841919236350805
Softbridge Basic 4.76177694441164E-25
Specman e 0.1925095270881778
Spice Netlist 5.29710110812646
Standard ML (SML) 0.20708566564292288
Stata 0.04904100534194722
Stylus 4.534405773074049
Swift 1.8627019961192913E-9
Swig 11.786422730001505
SystemVerilog 0.00009708851624323821
Systemd 0
TCL 382.839838598133
TOML 0.37500173695180483
TaskPaper 0
TeX 8.266233975096164
Thrift 50.53134153016524
Twig Template 0
TypeScript 8.250029131770134
TypeScript Typings 37.89904005334354
Unreal Script 46.13322029508541
Ur/Web 0.04756343913582129
Ur/Web Project 6.776263578034403E-21
V 28.75797889154211
VHDL 37.47892257625405
Vala 74.26528331441615
Varnish Configuration 19.45791923156868
Verilog 4.165537942430622
Verilog Args File 0
Vertex Shader File 1.7979557178975683
Vim Script 0
Visual Basic 0.26300267116040704
Visual Basic for Applications 0.3985138943535276
Vue 5.039982162930666E-52
Wolfram 70.01674025323683
Wren 30694.003311276458
XAML 0.5000169009533838
XCode Config 13.653495818959595
XML 3.533205032457776
XML Schema 0
Xtend 19.279739396268607
YAML 1.1074293861154887
Zig 0.507775428428431
Zsh 6.769231127673729
gitignore 1.3347179947709417E-20
ignore 0.0356445312500015
m4 5.4183238737327075
nuspec 3.640625
sed 6.423678000929861

What are the most common filenames?

What filenames are most common across all code-bases ignoring extension and case?

Had you asked me before I started this I would have said, README, main, index, license. Thankfully the results reflect my thoughts pretty well. Although there are a lot of interesting ones in there. I have no idea why so many projects contain a file called 15 or s15.

The makefile being the most common surprised me a little, but then I remembered it is used in many new JavaScript projects. Another interesting thing to note is that it appears jQuery is still king and reports of its death are greatly exaggerated, with it appearing as #4 on the list.

file-name count
makefile 59,141,098
index 33,962,093
readme 22,964,539
jquery 20,015,171
main 12,308,009
package 10,975,828
license 10,441,647
__init__ 10,193,245
strings 8,414,494
android 7,915,225
config 7,391,812
default 5,563,255
build 5,510,598
setup 5,291,751
test 5,282,106
irq 4,914,052
15 4,295,032
country 4,274,451
pom 4,054,543
io 3,642,747
system 3,629,821
common 3,629,698
gpio 3,622,587
core 3,571,098
module 3,549,789
init 3,378,919
dma 3,301,536
bootstrap 3,162,859
application 3,000,210
time 2,928,715
cmakelists 2,907,539
plugin 2,881,206
base 2,805,340
s15 2,733,747
androidmanifest 2,727,041
cache 2,695,345
debug 2,687,902
file 2,629,406
app 2,588,208
version 2,580,288
assemblyinfo 2,485,708
exception 2,471,403
project 2,432,361
util 2,412,138
user 2,343,408
clock 2,283,091
timex 2,280,225
pci 2,231,228
style 2,226,920
styles 2,212,127

Note that due to memory constraints I made this process slightly lossy. Every 100 projects checked I would check the map and if an identified filename had < 10 counts it was dropped from the list. It could come back for the next run and if there was > 10 at this point it would remain. It shouldn’t happen that often but it is possible the counts may be out by some amount if some common name appeared sparsely in the first batch of repositories before becoming common. In short they are not absolute numbers but should be close enough.

I could have used a trie structure to “compress” the space and gotten absolute numbers for this, but I didn’t feel like writing one and just abused the map slightly to save enough memory and achieve my goal. I am however curious enough to try this out at a later date to see how a trie would perform.

How many repositories appear to be missing a license?

This is an interesting one. Which repositories have an explicit license file somewhere? Note that the lack of a license file here does not mean that the project has none, as it might exist within the README or be indicated through SPDX comment tags in-line. it just means that scc could not find an explicit license file using its own criteria which at time of writing means a file ignoring case named “license”, “licence”, “copying”, “copying3”, “unlicense”, “unlicence”, “license-mit”, “licence-mit” or “copyright”.

Sadly it appears that the vast majority of repositories are missing a license. I would argue that all software should have a license for a variety of reasons but here is someone else’s take on that.

has license count
no 6,502,753
yes 2,597,330

scc-data license count

How many projects use multiple .gitignore files?

Some may not know this but it is possible to have multiple .gitignore files in a git project. Given that fact how many projects use multiple .gitignore files? While we are looking how many have none?

What I did find that was interesting was one project that has 25,794 .gitignore files in its repository. The next highest was 2,547. I have no idea what is going on there. I had a brief look at it and it looks like they are used to allow checking in of the directories but I cannot confirm this.

Bringing this back to something sensible here is a plot of the data up to 20 .gitignore files and close to 99% of the total result.

scc-data process load

Something you would expect would be that the majority of projects would have either 0 or 1 .gitignore files. This is confirmed by the results with a massive drop-off of 10x for projects with 2 .gitignores. What was surprising to me was how many projects have more than a single .gitignore file. The long tail is especially long in this case.

I was also curious as to why some projects had thousands of .gitignore files. One of the main offenders appears to be forks of https://github.com/PhantomX/slackbuilds which all have ~2,547 .gitignore files. However the other repositories with 1000+ ignore files are listed below.

skip table to next section

.gitignore count project count
0 3,628,829
1 4,576,435
2 387,748
3 136,641
4 79,808
5 48,336
6 33,686
7 33,408
8 22,571
9 16,453
10 11,198
11 10,070
12 8,194
13 7,701
14 5,040
15 4,320
16 5,905
17 4,156
18 4,542
19 3,828
20 2,706
21 2,449
22 1,975
23 2,255
24 2,060
25 1,768
26 2,886
27 2,648
28 2,690
29 1,949
30 1,677
31 3,348
32 1,176
33 794
34 1,153
35 845
36 488
37 627
38 533
39 502
40 398
41 370
42 355
43 1,002
44 265
45 262
46 295
47 178
48 384
49 270
50 189
51 435
52 202
53 196
54 325
55 253
56 320
57 126
58 329
59 286
60 292
61 152
62 237
63 163
64 149
65 187
66 164
67 92
68 80
69 138
70 102
71 68
72 62
73 178
74 294
75 89
76 118
77 110
78 319
79 843
80 290
81 162
82 127
83 147
84 170
85 275
86 1,290
87 614
88 4,014
89 2,275
90 775
91 3,630
92 362
93 147
94 110
95 71
96 75
97 62
98 228
99 71
100 174
101 545
102 304
103 212
104 284
105 516
106 236
107 39
108 69
109 131
110 82
111 102
112 465
113 621
114 47
115 59
116 43
117 40
118 43
119 443
120 72
121 42
122 33
123 392
124 66
125 46
126 381
127 19
128 99
129 906
130 52
131 19
132 11
133 99
134 10
135 15
136 6
137 22
138 44
139 33
140 24
141 33
142 39
143 48
144 80
145 20
146 28
147 19
148 17
149 11
150 20
151 57
152 35
153 24
154 31
155 35
156 55
157 89
158 57
159 88
160 18
161 47
162 56
163 36
164 63
165 99
166 44
167 64
168 86
169 70
170 111
171 106
172 25
173 39
174 14
175 25
176 53
177 20
178 56
179 11
180 7
181 40
182 32
183 17
184 68
185 38
186 16
187 3
188 4
189 2
190 12
191 18
192 37
193 9
194 10
195 11
196 18
197 45
198 27
199 11
200 39
201 23
202 37
203 22
204 21
205 7
206 40
207 7
208 8
209 16
210 29
211 20
212 21
213 7
214 4
215 12
217 21
218 13
220 12
221 2
222 15
223 4
224 12
225 9
226 1
227 8
228 3
229 6
230 8
231 31
232 26
233 6
234 17
235 6
236 23
237 1
238 11
239 2
240 10
241 7
242 11
243 1
244 14
245 21
246 3
247 12
248 1
249 6
250 10
251 5
252 18
253 7
254 17
255 4
256 16
257 8
258 24
259 17
260 4
261 1
262 3
263 12
264 3
265 8
267 2
268 1
269 3
271 4
272 1
273 1
274 1
275 3
276 6
279 5
280 1
281 1
284 4
285 1
286 1
288 2
289 1
290 5
291 4
293 7
294 4
295 1
296 1
297 1
299 70
300 2
301 4
302 1
303 7
305 1
306 2
307 2
309 1
310 7
311 1
313 14
316 1
320 1
321 6
322 2
323 3
324 4
327 4
328 2
329 1
330 13
331 5
332 11
333 3
334 1
335 1
336 11
337 1
338 20
339 11
340 2
341 6
342 10
343 37
344 25
345 9
346 32
347 4
348 9
349 7
350 12
351 2
352 5
354 7
358 32
359 7
360 6
361 1
362 21
363 14
364 51
365 17
367 18
368 9
370 7
371 6
372 15
373 1
374 38
375 113
376 57
377 37
378 23
379 87
380 65
382 1
386 2
388 1
391 5
392 1
394 1
397 3
401 1
403 1
408 1
409 2
410 5
411 1
413 4
415 1
418 1
420 1
427 3
428 2
430 2
433 314
437 1
450 2
453 1
468 1
469 1
483 5
484 1
486 1
488 2
489 9
490 4
492 2
493 106
494 3
495 1
496 2
498 1
512 1
539 1
553 1
560 2
570 2
600 1
602 3
643 1
646 2
657 1
663 1
670 1
672 2
729 5
732 1
739 1
744 1
759 1
778 1
819 1
859 1
956 1
959 2
964 2
965 1
973 1
1,133 1
1,186 1
1,267 2
1,523 1
2,535 1
2,536 1
2,537 2
2,539 1
2,540 1
2,541 5
2,542 1
2,545 1
2,547 1
25,794 1

Which language developers have the biggest potty mouth?

Working this out is not an exact science. It falls into the NLP class of problems really. Picking up cursing/swearing or offensive terms using filenames from a defined list is never going to be effective. If you do a simple string contains test you pick up all sorts or normal files such as assemble.sh and such. So to produce the following I pulled a list of curse words, then checked if any files in each project start with one of those values followed by a period. This would mean a file named gangbang.java would be picked up while assemble.sh would not. However this is going to miss all sorts of cases such as pu55syg4rgle.java and other such crude names.

The list I used contained some leet speak such as b00bs and b1tch to try and catch some of the most interesting cases. The full list is here.

While not accurate at all as mentioned it is incredibly fun to see what this produces. So lets start with a list of which languages have the most curse words. However we should probably weight this against how much code exists as well. So here are the top ones.

language filename curse count percent of files
C Header 7,660 0.00126394567906%
Java 7,023 0.00258792635479%
C 6,897 0.00120706524533%
PHP 5,713 0.00283428484703%
JavaScript 4,306 0.00140692338568%
HTML 3,560 0.00177646776919%
Ruby 3,121 0.00223136542655%
JSON 1,598 0.00293688627715%
C++ 1,543 0.00135977378652%
Dart 1,533 0.19129310646%
Rust 1,504 0.038465935524%
Go Template 1,500 0.0792233157387%
SVG 1,234 0.00771043360379%
XML 1,212 0.000875741051608%
Python 1,092 0.00119138129893%
JavaServer Pages 1,037 0.0215440542669%

Interesting! My first thought was “those naughty C developers!” but as it turns out while they have a high count they write so much code it probably isn’t that big a deal. However pretty clearly Dart developers have an axe to grind! If you know someone coding in Dart you may want to go offer them a hug.

I also want to know what are the most commonly used curse words. Lets see how dirty a mind we have collectively. A few of the top ones I could see being legitimate names (if you squint), but the majority would certainly produce few comments in a PR and a raised eyebrow.

word count
ass 11,358
knob 10,368
balls 8,001
xxx 7,205
sex 5,021
nob 3,385
pawn 2,919
hell 2,819
crap 1,112
anal 950
snatch 885
fuck 572
poop 510
cox 476
shit 383
lust 367
butt 265
bum 151
bugger 132
pron 121
cum 118
cok 112
damn 105

Note that some of the more offensive words in the list did have matching filenames which I find rather shocking considering what they were. Thankfully they were not very common and didn’t make my list above which was limited to those which had counts over 100. I am hoping that those files only exist for testing allow/deny lists and such.

Longest files by lines per language

As you would probably expect Plain Text, SQL, XML, JSON and CSV take the top positions of this one, seeing as they usually contain meta-data, database dumps and the like.

NB Some of the links below MAY not translate 100% due to throwing away some information when I created the files. Most should work, but a few you may need to mangle the URL to resolve.

skip table to next section

language filename lines
Plain Text 1366100696temp.txt 347,671,811
PHP phpfox_error_log_04_04_12_3d4b11f6ee2a89fd5ace87c910cee04b.php 121,930,973
HTML yo.html 54,596,752
LEX l 39,743,785
XML dblp.xml 39,445,222
Autoconf 21-t2.in 33,526,784
CSV ontology.csv 31,946,031
Prolog top500_full.p 22,428,770
JavaScript mirus.js 22,023,354
JSON douglasCountyVoterRegistration.json 21,104,668
Game Maker Language lg.gml 13,302,632
C Header trk6data.h 13,025,371
Objective C++ review-1.mm 12,788,052
SQL newdump.sql 11,595,909
Patch clook_iosched-team01.patch 10,982,879
YAML data.yml 10,764,489
SVG large-file.svg 10,485,763
Sass large_empty.scss 10,000,000
Assembly J.s 8,388,608
LaTeX tex 8,316,556
C++ Header primpoly_impl.hh 8,129,599
Lisp simN.lsp 7,233,972
Perl aimlCore3.pl 6,539,759
SAS output.sas 5,874,153
C CathDomainDescriptionFile.v3.5.c 5,440,052
Lua giant.lua 5,055,019
R disambisearches.R 4,985,492
MUMPS ref.mps 4,709,289
HEX combine.hex 4,194,304
Python mappings.py 3,064,594
Scheme atomspace.scm 3,027,366
C++ Int.cpp 2,900,609
Properties File nuomi_active_user_ids.properties 2,747,671
Alex Dalek.X 2,459,209
TCL TCL 2,362,970
Ruby smj_12_2004.rb 2,329,560
Wolfram hmm.nb 2,177,422
Brainfuck BF 2,097,158
TypeScript all_6.ts 1,997,637
Module-Definition matrix.def 1,948,817
LESS less 1,930,356
Objective C faster.m 1,913,966
Org default.org 1,875,096
Jupyter ReHDDM - AllGo sxFits-Copy0.ipynb 1,780,197
Specman e twitter.e 1,768,135
F* Pan_troglodytes_monomers.fst 1,739,878
Systemd video_clean_lower_tokenized.target 1,685,570
V ImageMazeChannelValueROM.v 1,440,068
Markdown eukaryota.md 1,432,161
TeX japanischtest.tex 1,337,456
Forth europarl.tok.fr 1,288,074
Shell add_commitids_to_src.sh 1,274,873
SKILL hijacked.il 1,187,701
CSS 7f116c3.css 1,170,216
C# Form1.cs 1,140,480
gitignore .gitignore 1,055,167
Boo 3.out.tex 1,032,145
Java Monster.java 1,000,019
ActionScript as 1,000,000
MSBuild train.props 989,860
D D 883,308
Coq CompiledDFAs.v 873,354
Clojure raw-data.clj 694,202
Swig 3DEditor.i 645,117
Happy y 624,673
GLSL capsid.vert 593,618
Verilog pipeline.vg 578,418
Standard ML (SML) Ambit3-HRVbutNoHR.sml 576,071
SystemVerilog bitcoinminer.v 561,974
Visual Basic linqStoreProcs.designer.vb 561,067
Go info.go 559,236
Expect Argonne_hourly_dewpoint.exp 552,269
Erlang sdh_analogue_data.erl 473,924
Makefile Makefile 462,433
QML 2005.qml 459,113
SPDX linux-coreos.spdx 444,743
VHDL cpuTest.vhd 442,043
ASP.NET AllProducts.aspx 438,423
XML Schema AdvanceShipNotices.xsd 436,055
Elixir gene.train.with.rare.ex 399,995
Macromedia eXtensible Markup Language StaticFlex4PerformanceTest20000.mxml 399,821
Ada bmm_top.adb 390,275
TypeScript Typings dojox.d.ts 384,171
Pascal FHIR.R4.Resources.pas 363,291
COBOL cpy 358,745
Basic excel-vba-streams-#1.bas 333,707
Visual Basic for Applications Dispatcher.cls 332,266
Puppet main_110.pp 314,217
FORTRAN Legacy f 313,599
OCaml Pent.ML 312,749
FORTRAN Modern slatec.f90 298,677
CoffeeScript dictionary.coffee 271,378
Nix hackage-packages.nix 259,940
Intel HEX epdc_ED060SCE.fw.ihex 253,836
Scala models_camaro.sc 253,559
Julia IJulia 0.jl 221,058
SRecode Template espell.srt 216,243
sed CSP-2004fe.SED 214,290
ReStructuredText S40HO033.rst 211,403
Bosque world_dem_5arcmin_geo.bsq 199,238
Emacs Lisp ubermacros.el 195,861
F# Ag_O1X5.5_O2X0.55.eam.fs 180,008
GDScript 72906.gd 178,628
Gherkin Specification feature 175,229
Haskell Excel.hs 173,039
Dart surnames_list.dart 153,144
Bazel matplotlib_1.3.1-1_amd64-20140427-1441.build 149,234
Haxe elf-x86id.hx 145,800
IDL all-idls.idl 129,435
LD Script kernel_partitions.lds 127,187
Monkey C LFO_BT1-point.mc 120,881
Modula3 tpch22.m3 120,185
Batch EZhunter.cmd 119,341
Rust data.rs 114,408
Ur/Web dict.ur-en.ur 113,911
Unreal Script orfs.derep_id97.uc 110,737
Groovy groovy 100,297
Smarty Template assign.100000.tpl 100,002
Bitbake bb 100,000
BASH palmer-master-thesis.bash 96,911
PSL Assertion test_uno.psl 96,253
ASP sat_gbie_01.asp 95,144
Protocol Buffers select1.proto 89,796
Report Definition Language ACG.rdl 84,666
Powershell PresentationFramework.ps1 83,861
Jinja jinja2 76,040
AWK words-large.awk 69,964
LOLCODE lol 67,520
Wren reuse_constants.wren 65,550
JSX AEscript.jsx 65,108
Rakefile seed.rake 63,000
Stata .31113.do 60,343
Vim Script ddk.vim 60,282
Swift Google.Protobuf.UnittestEnormousDescriptor.proto.swift 60,236
Korn Shell attachment-0002.ksh 58,298
AsciiDoc index.adoc 52,627
Freemarker Template designed.eml.ftl 52,160
Cython CALC.pex.netlist.CALC.pxi 50,283
m4 ax.m4 47,828
Extensible Stylesheet Language Transformations green_ccd.xslt 37,247
License copyright 37,205
JavaServer Pages 1MB.jsp 36,007
Document Type Definition bookmap.dtd 32,815
Fish Godsay.fish 31,112
ClojureScript core.cljs 31,013
Robot Framework robot 30,460
Processing data.pde 30,390
Ruby HTML big_table.rhtml 29,306
ColdFusion spreadsheet2009Q1.cfm 27,974
CMake ListOfVistARoutines.cmake 27,550
ATS test06.dats 24,350
Nim windows.nim 23,949
Vue Ogre.vue 22,916
Razor validationerror.cshtml 22,832
Spice Netlist input6.ckt 22,454
Isabelle WooLam_cert_auto.thy 22,312
XAML SymbolDrawings.xaml 20,764
Opalang p4000_g+5.0_m0.0_t00_st_z+0.00_a+0.00_c+0.00_n+0.00_o+0.00_r+0.00_s+0.00.opa 20,168
TOML too_large.toml 20,000
Madlang evgg.mad 19,416
Stylus test.styl 19,127
Go Template html-template.tmpl 19,016
AutoHotKey glext.ahk 18,036
ColdFusion CFScript IntakeHCPCIO.cfc 17,606
Zsh _oc.zsh 17,307
Twig Template show.html.twig 16,320
ABAP ZRIM01F01.abap 16,029
Elm 57chevy.elm 14,968
Kotlin _Arrays.kt 14,396
Varnish Configuration 40_generic_attacks.vcl 13,367
Mustache huge.mustache 13,313
Alloy output.als 12,168
Device Tree tegra132-flounder-emc.dtsi 11,893
MQL4 PhD Appsolute System.mq4 11,280
Jade fugue.jade 10,711
Q# in_navegador.qs 10,025
JSONL train.jsonl 10,000
Flow9 graph2.flow 9,902
Vala mwp.vala 8,765
Handlebars theme.scss.hbs 8,259
Crystal CR 8,084
C Shell plna.csh 8,000
Hamlet hamlet 7,882
BuildStream biometrics.bst 7,746
Mako verificaciones.mako 7,306
Agda Pifextra.agda 6,483
Thrift concourse.thrift 6,471
Fragment Shader File ms812_bseqoslabel_l.fsh 6,269
Cargo Lock Cargo.lock 6,202
Xtend UMLSlicerAspect.xtend 5,936
Arvo test-extra-large.avsc 5,378
Scons SConstruct 5,272
Closure Template buckconfig.soy 5,189
GN BUILD.gn 4,653
Softbridge Basic owptext.sbl 4,646
PKGBUILD PKGBUILD 4,636
Oz StaticAnalysis.oz 4,500
Lucius bootstrap.lucius 3,992
Ceylon RedHatTransformer.ceylon 3,907
Creole MariaDB_Manager_Monitors.creole 3,855
Luna Base.luna 3,731
Gradle dependencies.gradle 3,612
MQL Header IncGUI.mqh 3,544
Cabal smartword.cabal 3,452
Emacs Dev Env ede 3,400
Meson meson.build 3,264
nuspec Npm.js.nuspec 2,823
Game Maker Project LudumDare.yyp 2,679
Julius default-layout.julius 2,454
Idris ring_reduce.idr 2,434
Alchemist out.lmf-dos.crn 2,388
MQL5 DTS1-Build_814.1_B-test~.mq5 2,210
Android Interface Definition Language ITelephony.aidl 2,005
Vertex Shader File sdk_macros.vsh 1,922
Lean interactive.lean 1,664
Jenkins Buildfile Jenkinsfile 1,559
FIDL amb.in.fidl 1,502
Pony scenery.pony 1,497
PureScript prelude.purs 1,225
TaskPaper task-3275.taskpaper 1,196
Dockerfile Dockerfile 1,187
Janet Janet 1,158
Futhark math.fut 990
Zig main.zig 903
XCode Config Project-Shared.xcconfig 522
JAI LCregistryFile.jai 489
QCL bwt.qcl 447
Ur/Web Project reader.urp 346
Cassius default-layout.cassius 313
Docker ignore .dockerignore 311
Dhall largeExpressionA.dhall 254
ignore .ignore 192
Bitbucket Pipeline bitbucket-pipelines.yml 181
Just Justfile 95
Verilog Args File or1200.irunargs 60
Polly polly 26

Whats the most complex file in each language?

Once again these values are not directly comparable to each other, but it is interesting to see what is considered the most complex in each language.

Some of these files are absolute monsters. For example consider the most complex C++ file I found COLLADASaxFWLColladaParserAutoGen15PrivateValidation.cpp which is 28.3 MB of compiler hell (and thankfully appears to be generated).

NB Some of the links below MAY not translate 100% due to throwing away some information when I created the files. Most should work, but a few you may need to mangle the URL to resolve.

skip table to next section

language filename complexity
C++ COLLADASaxFWLColladaParserAutoGen15PrivateValidation.cpp 682,001
JavaScript blocks.js 582,070
C Header bigmofoheader.h 465,589
C fmFormula.c 445,545
Objective C faster.m 409,792
SQL dump20120515.sql 181,146
ASP.NET results-i386.master 164,528
Java ConcourseService.java 139,020
TCL 68030_TK.tcl 136,578
C++ Header TPG_hardcoded.hh 129,465
TypeScript Typings all.d.ts 127,785
SVG Class Diagram2.svg 105,353
Lua luaFile1000kLines.lua 102,960
PHP fopen.php 100,000
Org 2015-02-25_idfreeze-2.org 63,326
Ruby all_search_helpers.rb 60,375
Scheme test.ss 50,000
Stata .31113.do 48,600
Elixir pmid.sgd.crawl.ex 46,479
Brainfuck Poll.bf 41,399
Perl r1d7.pl 41,128
Go segment_words_prod.go 34,715
Python lrparsing-sqlite.py 34,700
Module-Definition wordnet3_0.def 32,008
Clojure raw-data.clj 29,950
C# Matrix.Product.Generated.cs 29,675
D parser.d 27,249
FORTRAN Modern euitm_routines_407c.f90 27,161
Puppet sqlite3.c.pp 25,753
SystemVerilog 6s131.sv 24,300
Autoconf Makefile.in 23,183
Specman e hansards.e 20,893
Smarty Template test-include-09.tpl 20,000
TypeScript JSONiqParser.ts 18,162
V altera_mf.v 13,584
F* slayer-3.fst 13,428
TeX definitions.tex 13,342
Swift Google.Protobuf.UnittestEnormousDescriptor.proto.swift 13,017
Assembly all-opcodes.s 12,800
Bazel firebird2.5_2.5.2.26540.ds4-10_amd64-20140427-2159.build 12,149
FORTRAN Legacy lm67.F 11,837
R Rallfun-v36.R 11,287
ActionScript AccessorSpray.as 10,804
Haskell Tags.hs 10,444
Prolog books_save.p 10,243
Dart DartParser.dart 9,606
VHDL unisim_VITAL.vhd 9,590
Batch test.bat 9,424
Boo compman.tex 9,280
Coq NangateOpenCellLibrary.v 8,988
Shell i3_completion.sh 8,669
Kotlin 1.kt 7,388
JSX typescript-parser.jsx 7,123
Makefile Makefile 6,642
Emacs Lisp bible.el 6,345
Objective C++ set.mm 6,285
OCaml sparcrec.ml 6,285
Expect condloadstore.stdout.exp 6,144
SAS import_REDCap.sas 5,783
Julia pilot-2013-05-14.jl 5,599
Cython types.pyx 5,278
Modula3 tpch22.m3 5,182
Haxe T1231.hx 5,110
Visual Basic for Applications Coverage.cls 5,029
Lisp simN.lsp 4,994
Scala SpeedTest1MB.sc 4,908
Groovy ZulTagLib.groovy 4,714
Powershell PresentationFramework.ps1 4,108
Ada bhps-print_full_version.adb 3,961
JavaServer Pages sink_jq.jsp 3,850
GN patch-third_partyffmpegffmpeg_generated.gni 3,742
Basic MSA_version116_4q.bas 3,502
Pascal Python_StdCtrls.pas 3,399
Standard ML (SML) arm.sml 3,375
Erlang lipsum.hrl 3,228
ASP mylib.asp 3,149
CSS three-viewer.css 3,071
Unreal Script ScriptedPawn.uc 2,909
CoffeeScript game.coffee 2,772
AutoHotKey fishlog5.93.ahk 2,764
MQL4 PhD Appsolute System.mq4 2,738
Processing Final.pde 2,635
Isabelle StdInst.thy 2,401
Razor Checklist.cshtml 2,341
Sass _multi-color-css-stackicons-social.scss 2,325
Vala valaccodebasemodule.vala 2,100
MSBuild all.props 2,008
Rust ffi.rs 1,928
QML Dots.qml 1,875
F# test.fsx 1,826
Vim Script netrw.vim 1,790
Korn Shell attachment.ksh 1,773
Vue vue 1,738
sed SED 1,699
GLSL comp 1,699
Nix auth.nix 1,615
Mustache template.mustache 1,561
Bitbake my-2010.bb 1,549
Ur/Web votes.ur 1,515
BASH pgxc_ctl.bash 1,426
MQL Header hanoverfunctions.mqh 1,393
Visual Basic LGMDdataDataSet.Designer.vb 1,369
Q# flfacturac.qs 1,359
C Shell regtest_hwrf.csh 1,214
MQL5 DTS1-Build_814.1_B-test~.mq5 1,186
Xtend Parser.xtend 1,116
Nim disas.nim 1,098
CMake MacroOutOfSourceBuild.cmake 1,069
Protocol Buffers configure.proto 997
SKILL switch.il 997
COBOL geekcode.cob 989
Game Maker Language hydroEx_River.gml 982
Gherkin Specification upload_remixed_program_again_complex.feature 959
Alloy battleformulas.als 948
Bosque recover.bsq 924
ColdFusion jquery.js.cfm 920
Stylus buttron.styl 866
ColdFusion CFScript apiUtility.cfc 855
Verilog exec_matrix.vh 793
Freemarker Template DefaultScreenMacros.html.ftl 771
Crystal lexer.cr 753
Forth e4 690
Monkey C mc 672
Rakefile import.rake 652
Zsh zshrc 649
Ruby HTML ext_report.rhtml 633
Handlebars templates.handlebars 557
SRecode Template Al3SEbeK61s.srt 535
Scons SConstruct 522
Agda Square.agda 491
Ceylon runtime.ceylon 467
Julius default-layout.julius 436
Wolfram qmSolidsPs8dContourPlot.nb 417
Cabal parconc-examples.cabal 406
Fragment Shader File flappybird.fsh 349
ATS ats_staexp2_util1.dats 311
Jinja php.ini.j2 307
Opalang unicode.opa 306
Twig Template product_form.twig 296
ClojureScript core.cljs 271
Hamlet hamlet 270
Oz StaticAnalysis.oz 267
Elm Indexer.elm 267
Meson meson.build 248
ABAP ZRFFORI99.abap 244
Dockerfile Dockerfile 243
Wren repl.wren 242
Fish fisher.fish 217
Emacs Dev Env ede 211
GDScript tiled_map.gd 195
IDL bgfx.idl 187
Jade docs.jade 181
PureScript List.purs 180
XAML Midnight.xaml 179
Flow9 TypeMapper.js.flow 173
Idris Utils.idr 166
PSL Assertion pre_dec.psl 162
Lean kernel.lean 161
MUMPS link.mps 161
Vertex Shader File base.vsh 152
Go Template code-generator.tmpl 148
Mako pokemon.mako 137
Closure Template template.soy 121
Zig main.zig 115
TOML telex_o.toml 100
Softbridge Basic asm.sbl 98
QCL bwt.qcl 96
Futhark math.fut 86
Pony jstypes.pony 70
LOLCODE LOLTracer.lol 61
Alchemist alchemist.crn 55
Madlang Copying.MAD 44
LD Script plugin.lds 39
Device Tree dts 22
FIDL GlobalCapabilitiesDirectory.fidl 19
JAI LICENSE.jai 18
Just Justfile 7
Android Interface Definition Language aidl 3
Ur/Web Project jointSpace.urp 2
Spice Netlist GRI30.CKT 2

Whats the most complex file weighted against lines?

This sounds good in practice, but in reality… anything minified or with no newlines skews the results making this one effectively pointless. As such I have not included this calculation. I have however created an issue inside scc to support detection of minified code so it can be removed from the calculation results https://github.com/boyter/scc/issues/91

It’s probably possible to infer this using just the data at hand, but id like to make it a more robust check that anyone using scc can benefit from.

Whats the most commented file in each language?

Whats the most commented file in each language? I have no idea what sort of information you can get out of this that might be useful but it is interesting to have a look.

NB Some of the links below MAY not translate 100% due to throwing away some information when I created the files. Most should work, but a few you may need to mangle the URL to resolve.

skip table to next section

language filename comment lines
Prolog ts-with-score-multiplier.p 5,603,870
C testgen.c 1,705,508
Python Untitled0.py 1,663,466
JavaScript 100MB.js 1,165,656
SVG p4-s3_I369600.svg 1,107,955
SQL test.sql 858,993
C Header head.h 686,587
C++ ResidueTopology.cc 663,024
Autoconf square_detector_local.in 625,464
TypeScript reallyLargeFile.ts 583,708
LEX polaris-xp900.l 457,288
XML Test1-CDL-soapui-project.xml 411,321
HTML todos_centros.html 366,776
Pascal FHIR.R4.Resources.pas 363,289
SystemVerilog mkToplevelBT64.v 338,042
PHP lt.php 295,054
TypeScript Typings dojox.d.ts 291,002
Verilog CVP14_synth.vg 264,649
Lua objects.lua 205,006
V TestDataset01-functional.v 201,973
Java FinalPackage.java 198,035
C++ Header test_cliprdr_channel_xfreerdp_full_authorisation.hpp 196,958
Shell add_commitids_to_src.sh 179,223
C# ItemId.cs 171,944
FORTRAN Modern slatec.f90 169,817
Assembly HeavyWeather.asm 169,645
Module-Definition top_level.final.def 139,150
FORTRAN Legacy dlapack.f 110,640
VHDL cpuTest.vhd 107,882
Groovy groovy 98,985
IDL all-idls.idl 91,771
Wolfram K2KL.nb 90,224
Go frequencies.go 89,661
Scheme s7test.scm 88,907
D coral.jar.d 80,674
Coq cycloneiv_hssi_atoms.v 74,936
Specman e sysobjs.e 65,146
Puppet sqlite3.c.pp 63,656
Wren many_globals.wren 61,388
Boo sun95.tex 57,018
Ruby bigfile.rb 50,000
Objective C job_sub011.m 44,788
CSS screener.css 43,785
Swig CIDE.I 37,235
Fish Godsay.fish 31,103
Sass sm30_kernels.sass 30,306
CoffeeScript tmp.coffee 29,088
Erlang nci_ft_ricm_dul_SUITE.erl 28,306
Lisp km_2-5-33.lisp 27,579
YAML ciudades.yml 27,168
R PhyloSimSource.R 26,023
Scala GeneratedRedeclTests.scala 24,647
Emacs Lisp pjb-java.el 24,375
Haskell Dipole80.hs 24,245
ATS test06.dats 24,179
m4 ax.m4 22,675
ActionScript __2E_str95.as 21,173
Objective C++ edges-new.mm 20,789
Visual Basic clsProjections.vb 20,641
TCL 68030_TK.tcl 20,616
Nix nix 19,605
Perl LF_aligner_3.12_with_modules.pl 18,013
Ada amf-internals-tables-uml_metamodel-objects.adb 14,535
Batch MAS_0.6_en.cmd 14,402
OCaml code_new.ml 13,648
LaTeX pm3dcolors.tex 13,092
Properties File messages_ar_SA.properties 13,074
MSBuild ncrypto.csproj 11,302
ASP.NET GallerySettings.ascx 10,969
Powershell mail_imap.ps1 10,798
Standard ML (SML) TCP1_hostLTSScript.sml 10,790
Dart html_dart2js.dart 10,547
AutoHotKey studio.ahk 10,391
Expect Navigator.exp 10,063
Julia PETScRealSingle.jl 9,417
Makefile Makefile 9,204
Forth europarl.lowercased.fr 9,107
ColdFusion js.cfm 8,786
TeX hyperref.sty 8,591
Opalang i18n_language.opa 7,860
LESS _variables.less 7,394
Swift CodeSystems.swift 6,847
Bazel gcc-mingw-w64_12_amd64-20140427-2100.build 6,429
Kotlin _Arrays.kt 5,887
SAS 202_002_Stream_DQ_DRVT.sas 5,597
Haxe CachedRowSetImpl.hx 5,438
Rust lrgrammar.rs 5,150
Monkey C mc 5,044
Cython pcl_common_172.pxd 5,030
Nim disas.nim 4,547
Game Maker Language gm_spineapi.gml 4,345
ABAP ZACO19U_SHOP_NEW_1.abap 4,244
XAML Raumplan.xaml 4,193
Razor Privacy.cshtml 4,092
Varnish Configuration 46_slr_et_rfi_attacks.vcl 3,924
Basic MSA_version116_4q.bas 3,892
Isabelle Pick.thy 3,690
Protocol Buffers metrics_constants.proto 3,682
BASH bashrc 3,606
Clojure all-playlists-output.clj 3,440
F# GenericMatrixDoc.fs 3,383
Thrift NoteStore.thrift 3,377
COBOL db2ApiDf.cbl 3,319
JavaServer Pages sink_jq.jsp 3,204
Modula3 gdb.i3 3,124
Visual Basic for Applications HL7xmlBuilder.cls 2,987
Oz timing.oz 2,946
Closure Template buckconfig.soy 2,915
Agda Pifextra.agda 2,892
Stata R2_2cleaningprocess.do 2,660
ColdFusion CFScript Intake.cfc 2,578
Luna Base.luna 2,542
Unreal Script UIRoot.uc 2,449
CMake cmake 2,425
Org lens-wsn.org 2,417
Flow9 index.js.flow 2,361
MQL Header IncGUI.mqh 2,352
JSX ContactSheetII.jsx 2,243
MQL4 PhD Appsolute System.mq4 2,061
Ruby HTML FinalOral-Old.Rhtml 2,061
GDScript group.gd 2,023
Processing testcode.pde 2,014
PSL Assertion 2016-08-16.psl 2,011
ASP c_system_plugin.asp 1,878
AWK dic-generator.awk 1,732
Jinja php.ini.j2 1,668
Zsh .zshrc 1,588
Q# in_navegador.qs 1,568
sed Makefile.sed 1,554
Stylus popup.styl 1,550
Bitbake Doxyfile.bb 1,533
Rakefile samples.rake 1,509
Gherkin Specification WorkflowExecution.feature 1,421
Crystal string.cr 1,412
Android Interface Definition Language ITelephony.aidl 1,410
Xtend Properties.xtend 1,363
SKILL DT_destub.il 1,181
Madlang .config.mad 1,137
Spice Netlist APEXLINEAR.ckt 1,114
QML MainFULL.qml 1,078
GLSL subPlanetNoise.frag 1,051
Ur/Web initial.ur 1,018
Alloy TransactionFeatureFinal.als 1,012
Vala puzzle-piece.vala 968
Smarty Template Ensau.tpl 965
Mako jobs.mako 950
TOML traefik.toml 938
gitignore .gitignore 880
Elixir macros.ex 832
GN rules.gni 827
Korn Shell lx_distro_install.ksh 807
LD Script vmlinux.lds 727
Scons SConstruct 716
Handlebars Consent-Form.handlebars 714
Device Tree ddr4-common.dtsi 695
FIDL amb.in.fidl 686
Julius glMatrix.julius 686
C Shell setup_grid.csh 645
Lean perm.lean 642
Idris Overview.idr 637
PureScript Array.purs 631
Freemarker Template result_softwares.ftl 573
ClojureScript lt-cljs-tutorial.cljs 518
Fragment Shader File bulb.fsh 464
Elm Attributes.elm 434
Jade index.jade 432
Vue form.vue 418
Gradle build.gradle 416
Lucius bootstrap.lucius 404
Go Template fast-path.go.tmpl 400
Meson meson.build 306
F* Crypto.Symmetric.Poly1305.Bignum.Lemmas.Part1.fst 289
Ceylon IdeaCeylonParser.ceylon 286
MQL5 ZigzagPattern_oldest.mq5 282
XCode Config Project-Shared.xcconfig 265
Futhark blackscholes.fut 257
Pony scenery.pony 252
Vertex Shader File CC3TexturableRigidBones.vsh 205
Softbridge Basic greek.sbl 192
Cabal deeplearning.cabal 180
nuspec Xamarin.Auth.XamarinForms.nuspec 156
Dockerfile Dockerfile 152
Mustache models_list.mustache 141
LOLCODE LOLTracer.lol 139
BuildStream astrobib.bst 120
Janet Janet 101
Cassius xweek.cassius 94
Docker ignore .dockerignore 92
Hamlet upload.hamlet 90
QCL mod.qcl 88
Dhall nix.bash.dhall 86
ignore .ignore 60
Just Justfile 46
SRecode Template srecode-test.srt 35
Bitbucket Pipeline bitbucket-pipelines.yml 30
Ur/Web Project reader.urp 22
Alchemist ctrl.crn 16
Zig main.zig 12
MUMPS mps 11
Bosque bosque.bsq 8
Report Definition Language example.rdl 4
Emacs Dev Env Project.ede 3
Cargo Lock Cargo.lock 2
JAI thekla_atlas.jai 1

How many “pure” projects

Assuming you define pure to mean a project that has 1 language in it. Of course that would not be very interesting by itself, so lets see what the spread is. As it turns out most projects have fewer than 25 languages in them with most in the less than 10 bracket.

The peak in the below graph is for 4 languages.

Of course pure projects might only have one programming language, but have lots of supporting other formats such as markdown, json, yml, css, .gitignore which are picked up by scc. It’s probably reasonable to assume that any project with less than 5 languages is “pure” (for some level of purity) and as it turns out is just over half the total data set. Of course your definition of purity might be different to mine so feel free to adjust to whatever number you like.

What suprises me is an odd bump around 34-35 languages. I have no reasonable explanation as to why this might be the case and it probably warrents some investigation.

scc-data pure projects

The full list of results is included below.

skip table to next section

language count project count
1 886,559
2 951,009
3 989,025
4 1,070,987
5 1,012,686
6 845,898
7 655,510
8 542,625
9 446,278
10 392,212
11 295,810
12 204,291
13 139,021
14 110,204
15 87,143
16 67,602
17 61,936
18 44,874
19 34,740
20 32,041
21 25,416
22 24,986
23 23,634
24 16,614
25 13,823
26 10,998
27 9,973
28 6,807
29 7,929
30 6,223
31 5,602
32 6,614
33 12,155
34 15,375
35 7,329
36 6,227
37 4,158
38 3,744
39 3,844
40 1,570
41 1,041
42 746
43 1,037
44 1,363
45 934
46 545
47 503
48 439
49 393
50 662
51 436
52 863
53 393
54 684
55 372
56 366
57 842
58 398
59 206
60 208
61 177
62 377
63 450
64 341
65 86
66 78
67 191
68 280
69 61
70 209
71 330
72 171
73 190
74 142
75 102
76 32
77 57
78 50
79 26
80 31
81 63
82 38
83 26
84 72
85 205
86 73
87 67
88 21
89 15
90 6
91 12
92 10
93 8
94 16
95 24
96 7
97 30
98 4
99 1
100 6
101 7
102 16
103 1
104 5
105 1
106 19
108 2
109 2
110 1
111 3
112 1
113 1
114 3
115 5
116 5
118 1
120 5
124 1
125 1
131 2
132 1
134 2
136 1
137 1
138 1
142 1
143 2
144 1
158 1
159 2

Projects with TypeScript but not JavaScript

Ah the modern world of TypeScript. But for projects that are using TypeScipt how many are using TypeScript exclusively?

pure TypeScript projects
27,026 projects

Have to admit, I am a little surprised by that number. While I understand mixing JavaScript with TypeScript is fairly common I would have thought there would be more projects using the new hotness. This may however be mostly down to the projects I was able to pull though and I suspect a refreshed project list with newer projects would change this number drastically.

Anyone using CoffeeScript and TypeScript?

using TypeScript and CoffeeScript
7,849 projects

I have a feeling some TypeScript developers are dry heaving at the very thought of this. If it is of any comfort I suspect most of these projects are things like scc which uses examples of all languages mixed together for testing purposes.

What’s the typical path length, broken up by language

Given that you can either dump all the files you need in a single directory, or span them out using file paths whats the typical path length and number of directories?

This is done by counting the number of path separators / for each file and its location and averaging it out. I didn’t know what to expect here other that I would expect java to be close to the top as its file paths are usually quite deep.

skip table to next section

language average path length
ABAP 4.406555175781266
ASP 6.372800350189209
ASP.NET 7.25
ATS 4.000007286696899
AWK 4.951896171638623
ActionScript 8.139775436837226
Ada 4.00042700953189
Agda 3.9126438455441743
Alchemist 3.507827758789091
Alex 5.000001311300139
Alloy 5.000488222547574
Android Interface Definition Language 11.0048217363656
Arvo 5.9999994741776135
AsciiDoc 3.5
Assembly 4.75
AutoHotKey 2.2087400984292067
Autoconf 5.8725585937792175
BASH 2.1289059027401294
Basic 3.003903865814209
Batch 6.527053831937014
Bazel 3.18005371087348
Bitbake 2.015624999069132
Bitbucket Pipeline 2.063491820823401
Boo 4.010679721835899
Bosque 4.98316764831543
Brainfuck 4.2025654308963425
BuildStream 3.4058846323741645
C 4.923767089530871
C Header 4.8744963703211965
C Shell 3.027952311891569
C# 3.9303305113013427
C++ 3.765686050057411
C++ Header 5.0468749664724015
CMake 4.474763816174707
COBOL 2.718678008809146
CSS 3.158353805542812
CSV 2.0005474090593514
Cabal 2.0234456174658693
Cargo Lock 2.602630615232607
Cassius 3.56445312181134
Ceylon 4.750730359584461
Clojure 3.992209411809762
ClojureScript 4.905477865257108
Closure Template 6.800760253008946
CoffeeScript 4.503051759227674
ColdFusion 6.124976545410084
ColdFusion CFScript 6.188602089623717
Coq 4.000243186950684
Creole 3.124526690922411
Crystal 3.1243934621916196
Cython 5.219657994911814
D 9.291626930357722
Dart 3.939864161220478
Device Tree 6.530643464186369
Dhall 0.12061593477278201
Docker ignore 2.9984694408020562
Dockerfile 3.1281526535752064
Document Type Definition 6.3923129292499254
Elixir 3.9999989270017977
Elm 2.968016967181992
Emacs Dev Env 4.750648772301943
Emacs Lisp 2.0156250001746203
Erlang 4.756546300111156
Expect 5.126907349098477
Extensible Stylesheet Language Transformations 4.519531239055546
F# 5.752862453457055
F* 4.063724638864983
FIDL 4.484130888886213
FORTRAN Legacy 6.117128185927898
FORTRAN Modern 5.742561882347131
Fish 3.993835387425861
Flow9 9.462829245721366
Forth 4.016601327653859
Fragment Shader File 3.8598623261805187
Freemarker Template 11.122007250069213
Futhark 6.188476562965661
GDScript 3.2812499999872675
GLSL 6.6093769371505005
GN 3.497192621218512
Game Maker Language 4.968749999941792
Game Maker Project 3.8828125
Gherkin Specification 3.999099795268081
Go 3.9588454874029275
Go Template 4
Gradle 2.655930499769198
Groovy 11.499969503013528
HEX 3.98394775342058
HTML 4.564478578133282
Hamlet 3.4842224120074867
Handlebars 4.998766578761208
Happy 5.699636149570479
Haskell 2.000140870587468
Haxe 5.999999999999997
IDL 6.249999993495294
Idris 3.515075657458509
Intel HEX 3.983397483825683
Isabelle 4.18351352773584
JAI 7.750007518357038
JSON 3.9999972562254724
JSONL 5.751412352804029
JSX 5.0041952044625715
Jade 4.744544962807595
Janet 3.0312496423721313
Java 11.265740856469563
JavaScript 4.242187985224513
JavaServer Pages 7.999993488161865
Jenkins Buildfile 2.000000000087315
Jinja 6.937498479846909
Julia 3.9999848530092095
Julius 3.187606761406953
Jupyter 2.375
Just 4.312155187124516
Korn Shell 7.0685427486899925
Kotlin 6.455277973786039
LD Script 5.015594720376608
LESS 5.999999999999886
LEX 5.6996263030493495
LOLCODE 3.722656242392418
LaTeX 4.499990686770616
Lean 4.1324310302734375
License 4.7715609660297105
Lisp 6.00048828125
Lua 3.999999057474633
Lucius 3.0000303482974573
Luna 4.758178874869392
MQL Header 5.421851994469764
MQL4 5.171874999953652
MQL5 4.069171198975555
MSBuild 4.8931884765733855
MUMPS 4.999999672174454
Macromedia eXtensible Markup Language 3.9139365140181326
Madlang 3.625
Makefile 4.717208385332443
Mako 4.0349732004106045
Markdown 2.25
Meson 3.342019969206285
Modula3 3.980173215190007
Module-Definition 8.875000973076205
Monkey C 3.0672508481368164
Mustache 6.000003708292297
Nim 3.7500824918105313
Nix 2.0307619677526234
OCaml 3.269392550457269
Objective C 3.526367187490962
Objective C++ 5.000000834608569
Opalang 4.500069382134143
Org 5.953919619084296
Oz 4.125
PHP 7.999984720368943
PKGBUILD 4.875488281252839
PSL Assertion 5.004394620715175
Pascal 5.0781240425935845
Patch 3.999999999999819
Perl 4.691352904239976
Plain Text 5.247085583343509
Polly 2.953125
Pony 2.9688720703125
Powershell 4.596205934882159
Processing 3.999931812300937
Prolog 4.4726600636568055
Properties File 3.5139240025278604
Protocol Buffers 6.544742336542192
Puppet 6.662078857422106
PureScript 4.000007774680853
Python 5.4531080610843805
Q# 3.7499999999999996
QCL 2.992309644818306
QML 7.042003512360623
R 3.0628376582587578
Rakefile 4.78515574071335
Razor 8.062499530475186
ReStructuredText 5.061766624473476
Report Definition Language 5.996573380834889
Robot Framework 4.0104638249612155
Ruby 5.1094988621717725
Ruby HTML 5.57654969021678
Rust 3.2265624976654292
SAS 4.826202331129183
SKILL 6.039547920227052
SPDX 4.000203706655157
SQL 7.701822280883789
SRecode Template 3.500030428171159
SVG 5.217570301278483
Sass 6.000000000056957
Scala 4.398563579539738
Scheme 6.999969714792911
Scons 5.010994006631478
Shell 4.988665378738929
Smarty Template 5.000527858268356
Softbridge Basic 4.87873840331963
Specman e 5.765624999999318
Spice Netlist 3.9687499998835882
Standard ML (SML) 4.031283043158929
Stata 6.27345275902178
Stylus 3.5000006667406485
Swift 3
Swig 5.246093751920853
SystemVerilog 2.9995259092956985
Systemd 3.9960937500000284
TCL 2.508188682367951
TOML 2.063069331460588
TaskPaper 2.003804363415667
TeX 3.500000000931251
Thrift 4.956119492650032
Twig Template 8.952746974652655
TypeScript 4.976589231140677
TypeScript Typings 5.832031190521718
Unreal Script 4.22499089783372
Ur/Web 4.41992186196147
Ur/Web Project 5.1147780619789955
V 4.251464832544997
VHDL 4.000000961231823
Vala 3.99804687498741
Varnish Configuration 4.006103516563625
Verilog 3.6906727683381173
Verilog Args File 8.93109059158814
Vertex Shader File 3.8789061926163697
Vim Script 3.9995117782528147
Visual Basic 4.5
Visual Basic for Applications 3.6874962672526417
Vue 7.752930045514701
Wolfram 3.075198844074798
Wren 4
XAML 4.515627968764219
XCode Config 6.969711296260638
XML 6
XML Schema 5.807670593268995
Xtend 4.315674404631856
YAML 3.2037304108964673
Zig 3.4181210184442534
Zsh 2.0616455049940288
gitignore 2.51172685490884
ignore 10.6434326171875
m4 3.7519528857323934
nuspec 4.109375
sed 4.720429063539986

YAML or YML?

Sometime back on the company slack there was a “discussion” with many dying on one hill or the other over the use of .yaml or .yml

The debate can finally(?) be ended. Although I suspect some will still prefer to die on their chosen hill.

extension count
yaml 3,572,609
yml 14,076,349

Upper lower or mixed case?

What case style is used on filenames? This includes the extension so you would expect it to be mostly mixed case.

style count
mixed 9,094,732
lower 2,476
upper 2,875

Which of course is not very interesting because generally file extensions are lowercase. What about if we ignore the file extension?

style count
mixed 8,104,053
lower 347,458
upper 614,922

Not what I would have expected. Mostly mixed is normal, but I would have thought lower would be more popular.

Java Factories

Another one that came up in the internal company slack when looking through some old Java code. I thought why not add a check for any Java code that has Factory, FactoryFactory or FactoryFactoryFactory in the name. The idea being to see how many factories are out there.

type count percent
not factory 271,375,574 97.9%
factory 5,695,568 2.09%
factoryfactory 25,316 0.009%
factoryfactoryfactory 0 0%

So slightly over 2% of all the Java code that I checked appeared to be a factory or factoryfactory. Thankfully there are no factoryfactoryfactories and perhaps that joke can finally die, although I am sure at least one non-ironic one exist somewhere in some Java 5 monolith that makes more money every day than I will see over my entire working life.

Ignore files

The .ignore file idea was hammered out by burntsushi and ggreer in a Hacker News thread and is possibly one of the greatest cases of “competing” open source tools working together to a good outcome and done in record time. It has become the defacto way to add things into source control yet have tools ignore them. As it turns out scc also implements .ignore files but counts them as well. Lets see how well the idea has spread.

skip table to next section

.ignore count project count
0 9,088,796
1 7,848
2 1,258
3 508
4 333
5 43
6 130
7 8
8 14
9 83
10 49
11 35
12 112
13 736
15 4
17 1
18 4
20 2
21 1
23 2
24 3
26 2
27 1
34 31
35 19
36 9
38 2
39 1
43 12
44 1
45 2
46 5
49 7
50 7
51 12
52 2

Future ideas

Id love to do some analysis of tabs vs spaces. Scanning for things like AWS AKIA keys and the like would be pretty neat as well. Id also love to expand out the bitbucket and gitlab coverage and get it broken down via each to see if groups of developers from different camps hang out in different areas.

Shortcomings id love to overcome in the above if I decide to do this again.

So why bother?

Well I can take some of this information and plug it into searchcode.com and scc. Even if only some useful data points. The stated goal was pretty much this and it is potentially very useful to know how your project compares to others. Besides it was a fun way to spend a few days solving some interesting problems. Also I think it is safe to say that scc is a fairly reliable tool at this point.

In addition, I am working on a tool that helps senior-developer or manager types analyze code looking for languages, large files, flaws etc… with the assumption you have to watch multiple repositories. You put in some code and it will tell you how maintainable it is and what skills you need to maintain it. Useful for determining if you should buy or maintain some code-base and getting an overview of what your development team is producing. Should in theory help teams scale through shared resources. Something like AWS Macie but for code is the angle I am working with. It’s something I need for my day job and I suspect others may find use in it, or at least thats the theory.

I should probably put an email sign up for that here at some point to gather interest for that.

Raw / Processed Files

I have included a link to the processed files (20 MB) for those who wish to do their own analysis and corrections. If someone wants to host the raw files to allow others to download it let me know. It is a 83 GB tar.gz file which uncompressed is just over 1 TB in size. It contents consists of just over 9 million JSON files of various sizes.

EDIT - A few very kind souls have offered to host the file, the locations are included below

For hosting that tar.gz file, thanks to CNCF for providing that server to xet7 for Wekan development.