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Getting started

The supported environment for Timesketch development is Docker.

Note: Exclamation mark ! denotes commands that should run in the docker container shell, dollar sign $ denotes commands to run in your local shell.

Locations and concepts

  • Timesketch provides a webinterface and a REST API
  • The configurations is located at /data sourcecode folder
  • The front end uses Vue.js framework and is stored at /timesketch/frontend
  • Code that is used in potentially multiple locations is stored in /timesketch/lib
  • Analyzers are located at /timesketch/lib/analyzers
  • The API methods are defined in /timesketch/api
  • API client code is in /api_client/python/timesketch_api_client
  • Data models are defined in /timesketch/models

Setting up your development environment

Start a shell, change to the timesketch/docker/dev directory

$ git clone timesketch
$ cd timesketch/docker/dev
$ docker-compose up

Wait a few minutes for the installation script to complete.

$ docker-compose logs timesketch
Attaching to timesketch-dev
timesketch-dev         | Obtaining file:///usr/local/src/timesketch
timesketch-dev         | Installing collected packages: timesketch
timesketch-dev         |   Running setup.py develop for timesketch
timesketch-dev         | Successfully installed timesketch
timesketch-dev         | User dev created/updated
timesketch-dev         | Timesketch development server is ready!

Add a user to your Timesketch server (this will add a user dev with password dev)

$ docker-compose exec timesketch tsctl create-user dev --password dev
User dev created/updated

Now, start the gunicon server that will serve the Timsketch WSGI app

In one shell:

$ docker-compose exec timesketch gunicorn --reload -b 0.0.0.0:5000 --log-file - --timeout 120 timesketch.wsgi:application
[2021-05-25 16:36:32 +0000] [94] [INFO] Starting gunicorn 19.10.0
[2021-05-25 16:36:32 +0000] [94] [INFO] Listening at: http://0.0.0.0:5000 (94)
[2021-05-25 16:36:32 +0000] [94] [INFO] Using worker: sync
/usr/lib/python3.8/os.py:1023: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used
  return io.open(fd, *args, **kwargs)
[2021-05-25 16:36:32 +0000] [102] [INFO] Booting worker with pid: 102
[2021-05-25 16:36:33,343] timesketch.wsgi_server/INFO Metrics server enabled

By now, you should be able to point your browser to http://localhost:5000/ and log in with the username and password combination you specified earlier. Any changes to Python files (e.g. in the timesketch/api/v1 directory tree) will be picked up automatically.

Celery workers

Although they are written in Python, changes on importers, analyzers and other asynchronous elements of the codebase are not picked up by the Gunicorn servers but by Celery workers.

If you're planning to work on those (or even just import timelines into your Timesketch instance), you'll need to launch a Celery worker, and re-launch it every time you bring changes to its code.

In a new shell, run the following:

$ docker-compose exec timesketch celery -A timesketch.lib.tasks worker --loglevel info

Restarting

To restart the webserver and celery workers, stop the execution. Depending on your system ctrl+c will do it. Then start them both as outlined before with:

$ docker-compose exec timesketch gunicorn --reload -b 0.0.0.0:5000 --log-file - --timeout 120 timesketch.wsgi:application
$ docker-compose exec timesketch celery -A timesketch.lib.tasks worker --loglevel info

API development

Exposing new functionality via the API starts at /timesketch/api/v1/routes.py. In that file the different routes / endpoints are defined that can be used. Typically every route has a dedicated Resource file in /timesketch/api/v1/resources.

A resource can have GET as well as POSTor other HTTP methods each defined in the same resource file. A good example of a resource that has a mixture is /timesketch/api/v1/resources/archive.py.

To write tests for the resource, add a section in /timesketch/api/v1/resources_test.py

Error handling

It is recommended to expose the error with as much detail as possible to the user / tool that is trying to access the resource.

For example the following will give a human readable information as well as a HTTP status code that client code can react on

if not sketch:
            abort(HTTP_STATUS_CODE_NOT_FOUND, 'No sketch found with this ID.')

On the opposite side the following is not recommended:

if not sketch:
            abort(HTTP_STATUS_CODE_BAD_REQUEST, 'Error')

Writing documentation

Writing documentation is critical for others to use your features, so we encourage to write documentation along side with shipping new features.

The documentation is auto generated by a Github workflow https://github.com/google/timesketch/blob/master/.github/workflows/mkdocs.yml which will execute mkdocs gh-deploy --forceand deploy changes to timesketch.org.

To test mkdocs locally, run the following in your container:

! cd /usr/local/src/timesketch
! pip3 install mkdocs mkdocs-material mkdocs-redirects
! mkdocs serve

And visit the results / review remarks, warnings or errors from mkdocs.

Formatting

Before merging a pull request, we expect the code to be formatted in a certain manner. You can use VS Code extensions to make your life easier in formatting your files. For example: * Vetur for Vue files * Python and Black for Python files.

Formatting Python files

We use black to format Python files. black is the uncompromising Python code formatter. There are two ways to use it: 1. Manually from the command line: * Install black following the official black documentation. * Format your file by running this command: $ black path/to/python/file 2. Automatically from VS Code: * Download the VS Code extension Python. * Navigate to Code -> Preferences -> Settings and search for Python Formatting Provider. Then, select black from the dropdown menu. * Enable the Format on Save option to automatically format your files every time you save them.