Installation & Configuration ============================ Getting Started --------------- Superset is tested against Python ``2.7`` and Python ``3.4``. Airbnb currently uses 2.7.* in production. We do not plan on supporting Python ``2.6``. OS dependencies --------------- Superset stores database connection information in its metadata database. For that purpose, we use the ``cryptography`` Python library to encrypt connection passwords. Unfortunately this library has OS level dependencies. You may want to attempt the next step ("Superset installation and initialization") and come back to this step if you encounter an error. Here's how to install them: For **Debian** and **Ubuntu**, the following command will ensure that the required dependencies are installed: :: sudo apt-get install build-essential libssl-dev libffi-dev python-dev python-pip libsasl2-dev libldap2-dev For **Fedora** and **RHEL-derivatives**, the following command will ensure that the required dependencies are installed: :: sudo yum upgrade python-setuptools sudo yum install gcc gcc-c++ libffi-devel python-devel python-pip python-wheel openssl-devel libsasl2-devel openldap-devel **OSX**, system python is not recommended. brew's python also ships with pip :: brew install pkg-config libffi openssl python env LDFLAGS="-L$(brew --prefix openssl)/lib" CFLAGS="-I$(brew --prefix openssl)/include" pip install cryptography==1.7.2 **Windows** isn't officially supported at this point, but if you want to attempt it, download `get-pip.py `_, and run ``python get-pip.py`` which may need admin access. Then run the following: :: C:\> pip install cryptography # You may also have to create C:\Temp C:\> md C:\Temp Python virtualenv ----------------- It is recommended to install Superset inside a virtualenv. Python 3 already ships virtualenv, for Python 2 you need to install it. If it's packaged for your operating systems install it from there otherwise you can install from pip: :: pip install virtualenv You can create and activate a virtualenv by: :: # virtualenv is shipped in Python 3 as pyvenv virtualenv venv . ./venv/bin/activate On windows the syntax for activating it is a bit different: :: venv\Scripts\activate Once you activated your virtualenv everything you are doing is confined inside the virtualenv. To exit a virtualenv just type ``deactivate``. Python's setup tools and pip ---------------------------- Put all the chances on your side by getting the very latest ``pip`` and ``setuptools`` libraries.:: pip install --upgrade setuptools pip Superset installation and initialization ---------------------------------------- Follow these few simple steps to install Superset.:: # Install superset pip install superset # Create an admin user (you will be prompted to set username, first and last name before setting a password) fabmanager create-admin --app superset # Initialize the database superset db upgrade # Load some data to play with superset load_examples # Create default roles and permissions superset init # Start the web server on port 8088, use -p to bind to another port superset runserver # To start a development web server, use the -d switch # superset runserver -d After installation, you should be able to point your browser to the right hostname:port `http://localhost:8088 `_, login using the credential you entered while creating the admin account, and navigate to `Menu -> Admin -> Refresh Metadata`. This action should bring in all of your datasources for Superset to be aware of, and they should show up in `Menu -> Datasources`, from where you can start playing with your data! Please note that *gunicorn*, Superset default application server, does not work on Windows so you need to use the development web server. The development web server though is not intended to be used on production systems so better use a supported platform that can run *gunicorn*. Configuration behind a load balancer ------------------------------------ If you are running superset behind a load balancer or reverse proxy (e.g. NGINX or ELB on AWS), you may need to utilise a healthcheck endpoint so that your load balancer knows if your superset instance is running. This is provided at ``/health`` which will return a 200 response containing "OK" if the webserver is running. If the load balancer is inserting X-Forwarded-For/X-Forwarded-Proto headers, you should set `ENABLE_PROXY_FIX = True` in the superset config file to extract and use the headers. Configuration ------------- To configure your application, you need to create a file (module) ``superset_config.py`` and make sure it is in your PYTHONPATH. Here are some of the parameters you can copy / paste in that configuration module: :: #--------------------------------------------------------- # Superset specific config #--------------------------------------------------------- ROW_LIMIT = 5000 SUPERSET_WORKERS = 4 SUPERSET_WEBSERVER_PORT = 8088 #--------------------------------------------------------- #--------------------------------------------------------- # Flask App Builder configuration #--------------------------------------------------------- # Your App secret key SECRET_KEY = '\2\1thisismyscretkey\1\2\e\y\y\h' # The SQLAlchemy connection string to your database backend # This connection defines the path to the database that stores your # superset metadata (slices, connections, tables, dashboards, ...). # Note that the connection information to connect to the datasources # you want to explore are managed directly in the web UI SQLALCHEMY_DATABASE_URI = 'sqlite:////path/to/superset.db' # Flask-WTF flag for CSRF CSRF_ENABLED = True # Set this API key to enable Mapbox visualizations MAPBOX_API_KEY = '' This file also allows you to define configuration parameters used by Flask App Builder, the web framework used by Superset. Please consult the `Flask App Builder Documentation `_ for more information on how to configure Superset. Please make sure to change: * *SQLALCHEMY_DATABASE_URI*, by default it is stored at *~/.superset/superset.db* * *SECRET_KEY*, to a long random string Database dependencies --------------------- Superset does not ship bundled with connectivity to databases, except for Sqlite, which is part of the Python standard library. You'll need to install the required packages for the database you want to use as your metadata database as well as the packages needed to connect to the databases you want to access through Superset. Here's a list of some of the recommended packages. +---------------+-------------------------------------+-------------------------------------------------+ | database | pypi package | SQLAlchemy URI prefix | +===============+=====================================+=================================================+ | MySQL | ``pip install mysqlclient`` | ``mysql://`` | +---------------+-------------------------------------+-------------------------------------------------+ | Postgres | ``pip install psycopg2`` | ``postgresql+psycopg2://`` | +---------------+-------------------------------------+-------------------------------------------------+ | Presto | ``pip install pyhive`` | ``presto://`` | +---------------+-------------------------------------+-------------------------------------------------+ | Oracle | ``pip install cx_Oracle`` | ``oracle://`` | +---------------+-------------------------------------+-------------------------------------------------+ | sqlite | | ``sqlite://`` | +---------------+-------------------------------------+-------------------------------------------------+ | Redshift | ``pip install sqlalchemy-redshift`` | ``postgresql+psycopg2://`` | +---------------+-------------------------------------+-------------------------------------------------+ | MSSQL | ``pip install pymssql`` | ``mssql://`` | +---------------+-------------------------------------+-------------------------------------------------+ | Impala | ``pip install impyla`` | ``impala://`` | +---------------+-------------------------------------+-------------------------------------------------+ | SparkSQL | ``pip install pyhive`` | ``jdbc+hive://`` | +---------------+-------------------------------------+-------------------------------------------------+ | Greenplum | ``pip install psycopg2`` | ``postgresql+psycopg2://`` | +---------------+-------------------------------------+-------------------------------------------------+ | Athena | ``pip install "PyAthenaJDBC>1.0.9"``| ``awsathena+jdbc://`` | +---------------+-------------------------------------+-------------------------------------------------+ | Vertica | ``pip install | ``vertica+vertica_python://`` | | | sqlalchemy-vertica-python`` | | +---------------+-------------------------------------+-------------------------------------------------+ | ClickHouse | ``pip install | ``clickhouse://`` | | | sqlalchemy-clickhouse`` | | +---------------+-------------------------------------+-------------------------------------------------+ Note that many other database are supported, the main criteria being the existence of a functional SqlAlchemy dialect and Python driver. Googling the keyword ``sqlalchemy`` in addition of a keyword that describes the database you want to connect to should get you to the right place. (AWS) Athena ------------ This currently relies on an unreleased future version of `PyAthenaJDBC `_. If you're adventurous or simply impatient, you can install directly from git: :: pip install git+https://github.com/laughingman7743/PyAthenaJDBC@support_sqlalchemy The connection string for Athena looks like this :: awsathena+jdbc://{aws_access_key_id}:{aws_secret_access_key}@athena.{region_name}.amazonaws.com/{schema_name}?s3_staging_dir={s3_staging_dir}&... Where you need to escape/encode at least the s3_staging_dir, i.e., :: s3://... -> s3%3A//... Caching ------- Superset uses `Flask-Cache `_ for caching purpose. Configuring your caching backend is as easy as providing a ``CACHE_CONFIG``, constant in your ``superset_config.py`` that complies with the Flask-Cache specifications. Flask-Cache supports multiple caching backends (Redis, Memcached, SimpleCache (in-memory), or the local filesystem). If you are going to use Memcached please use the `pylibmc` client library as `python-memcached` does not handle storing binary data correctly. If you use Redis, please install the `redis `_ Python package: :: pip install redis For setting your timeouts, this is done in the Superset metadata and goes up the "timeout searchpath", from your slice configuration, to your data source's configuration, to your database's and ultimately falls back into your global default defined in ``CACHE_CONFIG``. Deeper SQLAlchemy integration ----------------------------- It is possible to tweak the database connection information using the parameters exposed by SQLAlchemy. In the ``Database`` edit view, you will find an ``extra`` field as a ``JSON`` blob. .. image:: _static/img/tutorial/add_db.png :scale: 30 % This JSON string contains extra configuration elements. The ``engine_params`` object gets unpacked into the `sqlalchemy.create_engine `_ call, while the ``metadata_params`` get unpacked into the `sqlalchemy.MetaData `_ call. Refer to the SQLAlchemy docs for more information. Schemas (Postgres & Redshift) ----------------------------- Postgres and Redshift, as well as other database, use the concept of **schema** as a logical entity on top of the **database**. For Superset to connect to a specific schema, there's a **schema** parameter you can set in the table form. SSL Access to databases ----------------------- This example worked with a MySQL database that requires SSL. The configuration may differ with other backends. This is what was put in the ``extra`` parameter :: { "metadata_params": {}, "engine_params": { "connect_args":{ "sslmode":"require", "sslrootcert": "/path/to/my/pem" } } } Druid ----- * From the UI, enter the information about your clusters in the ``Admin->Clusters`` menu by hitting the + sign. * Once the Druid cluster connection information is entered, hit the ``Admin->Refresh Metadata`` menu item to populate * Navigate to your datasources Note that you can run the ``superset refresh_druid`` command to refresh the metadata from your Druid cluster(s) CORS ----- The extra CORS Dependency must be installed: superset[cors] The following keys in `superset_config.py` can be specified to configure CORS: * ``ENABLE_CORS``: Must be set to True in order to enable CORS * ``CORS_OPTIONS``: options passed to Flask-CORS (`documentation `) MIDDLEWARE ---------- Superset allows you to add your own middleware. To add your own middleware, update the ``ADDITIONAL_MIDDLEWARE`` key in your `superset_config.py`. ``ADDITIONAL_MIDDLEWARE`` should be a list of your additional middleware classes. For example, to use AUTH_REMOTE_USER from behind a proxy server like nginx, you have to add a simple middleware class to add the value of ``HTTP_X_PROXY_REMOTE_USER`` (or any other custom header from the proxy) to Gunicorn's ``REMOTE_USER`` environment variable: :: class RemoteUserMiddleware(object): def __init__(self, app): self.app = app def __call__(self, environ, start_response): user = environ.pop('HTTP_X_PROXY_REMOTE_USER', None) environ['REMOTE_USER'] = user return self.app(environ, start_response) ADDITIONAL_MIDDLEWARE = [RemoteUserMiddleware, ] *Adapted from http://flask.pocoo.org/snippets/69/* Upgrading --------- Upgrading should be as straightforward as running:: pip install superset --upgrade superset db upgrade superset init SQL Lab ------- SQL Lab is a powerful SQL IDE that works with all SQLAlchemy compatible databases. By default, queries are executed in the scope of a web request so they may eventually timeout as queries exceed the maximum duration of a web request in your environment, whether it'd be a reverse proxy or the Superset server itself. On large analytic databases, it's common to run queries that execute for minutes or hours. To enable support for long running queries that execute beyond the typical web request's timeout (30-60 seconds), it is necessary to configure an asynchronous backend for Superset which consist of: * one or many Superset worker (which is implemented as a Celery worker), and can be started with the ``superset worker`` command, run ``superset worker --help`` to view the related options * a celery broker (message queue) for which we recommend using Redis or RabbitMQ * a results backend that defines where the worker will persist the query results Configuring Celery requires defining a ``CELERY_CONFIG`` in your ``superset_config.py``. Both the worker and web server processes should have the same configuration. .. code-block:: python class CeleryConfig(object): BROKER_URL = 'redis://localhost:6379/0' CELERY_IMPORTS = ('superset.sql_lab', ) CELERY_RESULT_BACKEND = 'redis://localhost:6379/0' CELERY_ANNOTATIONS = {'tasks.add': {'rate_limit': '10/s'}} CELERY_CONFIG = CeleryConfig To setup a result backend, you need to pass an instance of a derivative of ``werkzeug.contrib.cache.BaseCache`` to the ``RESULTS_BACKEND`` configuration key in your ``superset_config.py``. It's possible to use Memcached, Redis, S3 (https://pypi.python.org/pypi/s3werkzeugcache), memory or the file system (in a single server-type setup or for testing), or to write your own caching interface. Your ``superset_config.py`` may look something like: .. code-block:: python # On S3 from s3cache.s3cache import S3Cache S3_CACHE_BUCKET = 'foobar-superset' S3_CACHE_KEY_PREFIX = 'sql_lab_result' RESULTS_BACKEND = S3Cache(S3_CACHE_BUCKET, S3_CACHE_KEY_PREFIX) # On Redis from werkzeug.contrib.cache import RedisCache RESULTS_BACKEND = RedisCache( host='localhost', port=6379, key_prefix='superset_results') Also note that SQL Lab supports Jinja templating in queries, and that it's possible to overload the default Jinja context in your environment by defining the ``JINJA_CONTEXT_ADDONS`` in your superset configuration. Objects referenced in this dictionary are made available for users to use in their SQL. .. code-block:: python JINJA_CONTEXT_ADDONS = { 'my_crazy_macro': lambda x: x*2, } Making your own build --------------------- For more advanced users, you may want to build Superset from sources. That would be the case if you fork the project to add features specific to your environment.:: # assuming $SUPERSET_HOME as the root of the repo cd $SUPERSET_HOME/superset/assets npm install npm run build cd $SUPERSET_HOME python setup.py install Blueprints ---------- `Blueprints are Flask's reusable apps `_. Superset allows you to specify an array of Blueprints an array of Blueprints in your ``superset_config`` module. Here's an example on how this can work with a simple Blueprint. By doing so, you can expect Superset to serve a page that says "OK" at the ``/simple_page`` url. This can allow you to run other things such as custom data visualization applications alongside Superset, on the same server. ..code :: from flask import Blueprint simple_page = Blueprint('simple_page', __name__, template_folder='templates') @simple_page.route('/', defaults={'page': 'index'}) @simple_page.route('/') def show(page): return "Ok" BLUEPRINTS = [simple_page]