wordcount

Counting words in different programming languages.

Leaderboard

Updated: 26-11-2015

On the full Hungarian Wikisource:

Rank	Experiment	CPU seconds	User time	Maximum memory
1	cpp/wc_vector	15.96	12.24	244404
2	cpp/wc_hash_nosync	18.81	15.02	327184
3	python/wordcount_py2.py	20.02	19.58	554352
4	cpp/wc_baseline_hash	24.23	20.45	343276
5	java -classpath java WordCount	27.37	31.39	545940
6	java -classpath java WordCountEntries	28.13	32.23	540276
7	python/wordcount_py3.py	33.06	32.59	482988
8	cpp/wc_baseline	35.1	31.25	355692
9	perl/wordcount.pl	39.15	38.66	447000
10	php php/wordcount.php	39.82	35.58	781668
11	nodejs javascript/wordcount.js	72.93	67.14	1005116
12	bash/wordcount.sh	82.36	93.9	12924
13	julia julia/wordcount.jl	94.46	93.51	725232

On a smaller dataset:

Rank	Experiment	CPU seconds	User time	Maximum memory
1	cpp/wc_vector	7.14	5.32	126268
2	cpp/wc_hash_nosync	8.04	6.07	163404
3	python/wordcount_py2.py	8.68	8.48	280616
4	cpp/wc_baseline_hash	10.18	8.14	171656
5	java -classpath java WordCount	13.56	15.66	404064
6	java -classpath java WordCountEntries	13.8	15.77	398768
7	cpp/wc_baseline	13.9	12.0	178084
8	python/wordcount_py3.py	14.14	13.86	245164
9	php php/wordcount.php	15.16	13.15	396516
10	perl/wordcount.pl	17.04	16.79	225352
11	nodejs javascript/wordcount.js	27.9	24.93	577472
12	bash/wordcount.sh	34.92	40.51	10768

Format

All programs should read from STDIN and write to STDOUT. The input is always encoded in UTF-8. 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

scripts/create_input.sh downloads 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.

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

Using the provided scripts

Installation

There are two ways to install all the dependencies:

Build a Docker image with the provided Dockerfile, which installs all the required packages.
Install them manually via a package manager. The Docker image is an Ubuntu image but the same packages work for me on Manjaro Linux as well. Use this command on Ubuntu to install all dependencies, but be prepared for a lot of new packages. You've been warned.

sudo apt-get install wget gcc python npm perl php5 git default-jdk time

Docker image

You can run the experiment in a Docker container. The Dockerfile is provided, run:

docker build -t wordcount --rm .

This might take a while.

Load the image into a container:

docker run -it wordcount bash

You should see the cloned directory in /root

cd wordcount

Downloading the dataset

bash scripts/create_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"

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 save the results to results.txt.

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

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:

Experiment	CPU seconds	User time	Maximum memory
cpp/wc_vector	2.68	2.37	32168
python/wordcount_py2.py	2.68	2.61	71512
bash/wordcount.sh	3.0	4.19	10820

Adding a new program

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

add dependencies to the Dockerfile. Basically add the package to the existing apt-get package list.
if it needs compiling or any other setup method, add it to scripts/build.sh
add the actual invoke command to run_commands.txt

Adding your program to this experiment

Make sure all dependencies are installed via standard packages and your code compiles.
Pass at least the first 3 tests successfully.
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.

Old notes for manual building and running

Javascript

Nodejs and npm are needed. Install dependencies:

cd javascript
npm install

Run:

node index.js

BASH

Set the LC_COLLATE variable to C to consider non-alphanumeric characters when sorting:

export LC_COLLATE=C