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wordcount

Counting words in different programming languages.

See the article on this project: http://juditacs.github.io/2015/11/26/wordcount.html

or the follow-up article: http://juditacs.github.io/2016/03/19/wordcount2.html

Leaderboard

Full Hungarian Wikipedia

Updated: June 13, 2016

Only the ones that finish are listed. The rest run out of memory.

Rank Experiment CPU seconds User time Maximum memory Contributor
1 java -Xms2G -Xmx8G -classpath java:java/zah-0.6.jar WordCountOptimized 140.64 191.44 4232308
2 java -Xms2G -Xmx8G -classpath java:java/trove-3.0.3.jar WordCountOptimized 183.17 219.39 4391804
3 rust/wordcount/wordcount 208.86 193.34 6966228 Joshua Holmer
4 cpp/wordcount_clang 224.23 211.76 4933968
5 cpp/wordcount 227.74 213.86 4934204 Dmitry Andreev, Matias Fontanini, Judit Acs
6 c/wordcount 270.15 255.95 2453228 gaebor
7 php7.0 php/wordcount.php 285.01 267.78 4174284 Andrey Bukatov, Braun Patrik
8 d/wordcount 297.95 285.76 6363420 Pavel Chebotarev
9 hhvm php/wordcount.php 315.48 294.06 5076280 Andrey Bukatov, Braun Patrik
10 java -Xms2G -Xmx8G -classpath java WordCountBaseline 378.77 577.71 7036308
11 python/wordcount_py2.py 403.6 389.15 3943716 Judit Acs
12 go/bin/wordcount 410.81 418.64 5274756 David Siklosi
13 scala -J-Xms2G -J-Xmx8g -classpath scala Wordcount 546.28 710.57 7075976
14 mono csharp/WordCount.exe 610.95 588.56 4517848 Joe Amenta, Tim Posey, Peter Szel
15 python/wordcount_py2_baseline.py 627.22 606.13 8920684 Judit Acs
16 php5.6 php/wordcount.php 671.8 646.55 12745360 Andrey Bukatov, Braun Patrik
17 perl/wordcount.pl 942.27 915.93 7206948 Larion Garaczi, Judit Acs
18 cpp/wordcount_baseline 1094.24 990.64 6043624 Judit Acs
19 python/wordcount_py3.py 1192.51 1162.29 7771396 Judit Acs
20 lua lua/wordcount.lua 1344.87 1219.83 7255316 daurnimator
21 julia julia/wordcount.jl 1828.39 1789.74 7457092 Attila Zseder, getzdan
22 elixir/wordcount 2326.65 2290.94 12542340 Norbert Melzer
23 bash/wordcount.sh 2561.07 2643.59 13728 Judit Acs

5 million lines from the Hungarian Wikipedia

Updated: April 16, 2016

Rank Experiment CPU seconds User time Maximum memory Contributor
1 rust/wordcount/wordcount 21.23 20.28 990008 Joshua Holmer
2 java -Xmx6G -classpath java:java/trove-3.0.3.jar WordCountOptimized 29.76 34.41 962244 Sam Van Oort
3 d/wordcount 29.79 28.97 752676 Pavel Chebotarev
4 cpp/wordcount 33.58 32.51 758728 Dmitry Andreev, Matias Fontanini, Judit Acs
5 cpp/wordcount_clang 33.84 32.95 758724
6 go/bin/wordcount 38.84 37.86 859284 David Siklosi
7 python/wordcount_py2.py 38.89 37.98 595500 Gabor Szabo
8 c/wordcount 39.64 38.49 427180 gaebor
9 php7.0 php/wordcount.php 41.11 27.13 709756 Andrey Bukatov, Braun Patrik
10 mono csharp/WordCount.exe 48.18 46.37 836040 Joe Amenta, Tim Posey, Peter Szel
11 hhvm php/wordcount.php 51.19 31.72 882584 Andrey Bukatov, Braun Patrik
12 python/wordcount_py2_baseline.py 64.56 63.02 1438388 Judit Acs
13 php5.6 php/wordcount.php 82.56 66.12 2113976 Andrey Bukatov, Braun Patrik
14 java -Xmx6G -classpath java WordCountBaseline 89.12 111.27 1602580 Sam Van Oort, Rick Hendricksen, Dávid Márk Nemeskey
15 python/wordcount_py3.py 98.33 96.62 1246564 Judit Acs
16 perl/wordcount.pl 113.04 110.78 1242056 Larion Garaczi, Judit Acs
17 lua lua/wordcount.lua 132.04 116.02 1210248 daurnimator
18 ruby2.3 ruby/wordcount.rb 137.09 131.31 3847648 Joshua Holmer
19 java -classpath java WordCount 143.01 128.67 1828536
20 julia julia/wordcount.jl 152.88 146.15 2490572 Attila Zseder, getzdan
21 scala -J-Xmx2g -classpath scala Wordcount 174.09 221.95 1457272 Hans van den Bogert
22 elixir/wordcount 184.63 181.5 2590384 Norbert Melzer
23 bash/wordcount.sh 276.14 290.33 13608 Judit Acs
24 haskell/WordCount 295.73 286.33 4216656 Larion Garaczi
25 cpp/wordcount_baseline 354.01 338.81 983292 Judit Acs
26 java -cp clojure.jar clojure.main clojure/wordcount.clj 357.75 379.64 2158764 lverweijen
27 elixir elixir/wordcount.ex 417.88 407.45 2545708
28 nodejs javascript/wordcount.js 578.55 577.19 972904 Laci Kundra
29 nodejs typescript/wordcount.js 628.72 604.63 920764 Braun Patrik

The task

The task is to split a text and count each word's frequency, then print the list sorted by frequency in decreasing order. Ties are printed in alphabetical order.

Rules

  • the input is read from STDIN
  • the input is always encoded in UTF-8
  • output is printed to STDOUT
  • break only on space, tab and newline (do not break on non-breaking space)
  • do not write anything to STDERR
  • the output is tab-separated
  • sort by frequency AND secondary sort in alphabetical order
  • try to write simple code with few dependencies
    • standard library
  • single-thread is preferred but you can add multi-threaded or multicore versions too

The output should contain lines like this:

freqword <tab> freq

Example

$ echo "apple pear apple art" | python2 python/wordcount.py
apple   2
art     1
pear    1

Test corpus: Hungarian Wikisource and Wikipedia

scripts/create_input.sh downloads and unpacks the latest Hungarian Wikisource XML dump. Why Wikisource? It's not too small not too large and more importantly, it's valid utf8. Why Hungarian? There are many non-ascii characters and the number of different word types is high.

scripts/create_large_input.sh downloads and unpacks the latest Hungarian Wikipedia. This is the largest input used for comparison, see the first leaderboard.

Usage

To test on a small sample:

time cat data/huwikisource-latest-pages-meta-current.xml | head -10000 | python3 python/wordcount_py3.py > python_out

Installation with Docker

I strongly recommend building the Docker image instead of installing every package manually, but it's possible to install the dependencies manually. See the installation commands in Dockerfile.

Docker image

You can run the experiment in a Docker container.

Pull from DockerHub & tag it:

docker pull svanoort/allthelanguages:latest && docker tag svanoort/allthelanguages:latest allthelanguages

If you wish to build locally (this while take quite a while):

`docker build -t allthelanguages --rm .`

In either case, this requires quite a bit of storage, currently about 2.4 GB.

Run the image, mounting the local directory into the working directory of the docker file as a volume:

`docker run -h DOCKER -it --rm -v $(pwd):/allthelanguages allthelanguages as_user.sh $(id -un) $(id -u) $(id -gn) $(id -g)`

Changes you made in the folder will show up in the docker container, and any output (builds, results) will write to the folder as well. All permissions are maintained.

Note that if you're building node.js/typescript, it needs root (non-sudo) access so you'll need to do it this way, which creates files with root as the owner (you'll need to chown them):

`docker run -h DOCKER -it --rm -v $(pwd):/allthelanguages allthelanguages`

Downloading the dataset

bash scripts/create_input.sh

or the full dataset:

bash scripts/create_large_input.sh

Compile/build/whatever the wordcount scripts

bash scripts/build.sh

Run tests on one language

scripts/test.sh runs all tests for one language, well actually for a single command.

bash scripts/test.sh "python2 python/wordcount_py2.py"

Or

bash scripts/test.sh python/wordcount_py2.py

if the file is executable and has a valid shebang line.

The script either prints OK or the list of failed tests and a final FAIL.

Run tests on all languages

All commands are listed in the file run_commands.txt and the script scripts/test_all.sh runs test.sh with each command:

bash scripts/test_all.sh

Run the actual experiment on a larger dataset

If all tests are passed, the scripts work reasonably well. This does not mean that all output will be the same, see the full test later. For now, we consider them good enough for testing.

This command will run each test twice and append the results to results.txt. It's possible to add a comment at the end of each line.

bash scripts/compare.sh data/huwikisource-latest-pages-meta-current.xml 2 "full huwikisource"

Or test it on a part of huwikisource:

bash scripts/compare.sh <( head -10000 data/huwikisource-latest-pages-meta-current.xml) 1

Results.txt in a tab separated file that can be formatted to a Markdown table with this command:

cat results.txt | python2 scripts/evaluate_results.py

This scripts prints the fastest run for each command in a markup table like this:

Rank Experiment CPU seconds User time Maximum memory Contributor
1 rust/wordcount/wordcount 20.57 19.79 990008 Joshua Holmer
2 cpp/wc_vector 33.3 31.93 775952 Matias Fontanini, Judit Acs
3 python/wordcount_py2gabor.py 40.13 38.71 594800 Gabor Szabo

Adding a new program

Adding a new programming language or a new version for an existing programming language consists of the following steps:

  1. Add dependencies to the Dockerfile. Basically add the package to the existing apt-get package list.
  2. If it needs compiling or any other setup method, add it to scripts/build.sh
  3. Add the actual invoke command to run_commands.txt
  4. If your executable differs from the source file, add the executable - source code mapping to binary_mapping.txt. This is used by scripts/evaluate_results.py for finding out the contributors of each program. The file is tab-separated.

Adding your program to this experiment

  1. Make sure all dependencies are installed via standard packages and your code compiles.
  2. Your code passes all the tests.
  3. Make sure it runs for less than two minutes for 100,000 lines of text. If it is slower, it doesn't make much sense to add it.