Linux + Docker

  1. Install Docker.

  2. Pull the image:

    $ sudo docker pull scrapinghub/splash
  3. Start the container:

    $ sudo docker run -p 5023:5023 -p 8050:8050 -p 8051:8051 scrapinghub/splash
  4. Splash is now available at at ports 8050 (http), 8051 (https) and 5023 (telnet).

OS X + Docker

  1. Install Docker (via Boot2Docker).

  2. Pull the image:

    $ docker pull scrapinghub/splash
  3. Start the container:

    $ docker run -p 5023:5023 -p 8050:8050 -p 8051:8051 scrapinghub/splash
  4. Figure out the ip address of boot2docker:

    $ boot2docker ip
    The VM's Host only interface IP address is:
  5. Splash is available at the returned IP address at ports 8050 (http), 8051 (https) and 5023 (telnet).

Ubuntu 12.04 (manual way)

  1. Install system dependencies:

    $ sudo add-apt-repository -y ppa:pi-rho/security
    $ sudo apt-get update
    $ sudo apt-get install libre2-dev
    $ sudo apt-get install netbase ca-certificates liblua5.2-dev \
                           python python-dev build-essential libicu48 \
                           xvfb libqt4-webkit python-twisted python-qt4
  2. TODO: install Python dependencies using pip, clone repo, chdir to it, start splash.

To run the server execute the following command:

python -m splash.server

Run python -m splash.server --help to see options available.

By default, Splash API endpoints listen to port 8050 on all available IPv4 addresses. To change the port use --port option:

python -m splash.server --port=5000


# install PyQt4 (Splash is tested on PyQT 4.9.x and PyQT 4.11.x)
# and the following packages:
adblockparser >= 0.4
re2 >= 0.2.21

# for scripting support
lupa >= 1.1
funcparserlib >= 0.3.6

# the following libraries are only required by tests
requests >= 1.0
jsonschema >= 2.0

Splash Versions

docker pull scrapinghub/splash will give you the latest stable Splash release. To obtain the latest development version use docker pull scrapinghub/splash:master. Specific Splash versions are also available, e.g. docker pull scrapinghub/splash:1.5.

Customizing Dockerized Splash

Passing Custom Options

To run Splash with custom options pass them to docker run. For example, let’s increase log verbosity:

$ docker run -p 8050:8050 scrapinghub/splash -v3

To see all possible options pass --help. Not all options will work the same inside Docker: changing ports doesn’t make sense (use docker run options instead), and paths are paths in the container.

Folders Sharing

To set custom Request Filters use -v Docker option. First, create a folder with request filters on your local filesystem, then make it available to the container:

$ docker run -p 8050:8050 -v <my-filters-dir>:/etc/splash/filters scrapinghub/splash

Replace <my-filters-dir> with a path of your local folder with request filters.

Docker Data Volume Containers can also be used. Check for more info.

Proxy Profiles and Javascript Profiles can be added in a similar way:

$ docker run -p 8050:8050 \
      -v <my-proxy-profiles-dir>:/etc/splash/proxy-profiles \
      -v <my-js-profiles-dir>:/etc/splash/js-profiles \

To setup Custom Lua Modules mount a folder to /etc/splash/lua_modules. If you use a Lua sandbox (default) don’t forget to list safe modules using --lua-sandbox-allowed-modules option:

$ docker run -p 8050:8050 \
      -v <my-lua-modules-dir>:/etc/splash/lua_modules \
      --lua-sandbox-allowed-modules 'module1;module2' \


Folder sharing (-v option) doesn’t work on OS X and Windows (see It should be fixed in future Docker & Boot2Docker releases. For now use one of the workarounds mentioned in issue comments or clone Splash repo and customize its Dockerfile.

Splash in Production

In production you may want to daemonize Splash, start it on boot and restart on failures. Since Docker 1.2 an easy way to do this is to use --restart and -d options together; another way to do that is to use standard tools like upstart, systemd or supervisor.


--restart option won’t work without -d.

Please also take into account the memory usage: Splash uses an unbound in-memory cache and so it will eventually consume all RAM. A workaround is to restart the process when it uses too much memory; there is Splash --maxrss option for that. You can also add Docker --memory option to the mix.

In production it is a good idea to pin Splash version - instead of scrapinghub/splash it is usually better to use something like scrapinghub/splash:1.6.

The final command for starting a long-running Splash server which uses up to 4GB RAM and daemonizes & restarts itself could look like this:

$ docker run -d -p 8050:8050 --memory=4.5G --restart=always scrapinghub/splash:1.6 --maxrss 4000

Building Local Docker Images

To build your own Docker image, checkout Splash source code using git, then execute the following command from Splash source root:

$ docker build -t my-local-splash .

To build Splash-Jupyter Docker image use this command:

$ docker build -t my-local-splash-jupyter -f  dockerfiles/splash-jupyter/Dockerfile .

You may have to change FROM line in dockerfiles/splash-jupyter/Dockerfile if you want it to be based on your local Splash Docker container.