mirror of
https://github.com/apache/superset.git
synced 2024-09-12 00:29:39 -04:00
* db_engine_specs: added engine spec for clickhouse * models: group by time without join on subquery if this is enabled, it will remove the subquery when grouping by time, and group by time will be rewritten as group by timeslot, ... this particularly fixes clickhouse backend * docs: added clickhouse * models: allow using secondary dttm columns as constraints * superset/forms: configurable default slice since time * superset/models: respect dialect quoting * dttm handling, codeclimate tweaks
430 lines
17 KiB
ReStructuredText
430 lines
17 KiB
ReStructuredText
Installation & Configuration
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============================
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Getting Started
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---------------
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Superset is tested against Python ``2.7`` and Python ``3.4``.
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Airbnb currently uses 2.7.* in production. We do not plan on supporting
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Python ``2.6``.
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OS dependencies
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---------------
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Superset stores database connection information in its metadata database.
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For that purpose, we use the ``cryptography`` Python library to encrypt
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connection passwords. Unfortunately this library has OS level dependencies.
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You may want to attempt the next step
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("Superset installation and initialization") and come back to this step if
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you encounter an error.
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Here's how to install them:
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For **Debian** and **Ubuntu**, the following command will ensure that
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the required dependencies are installed: ::
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sudo apt-get install build-essential libssl-dev libffi-dev python-dev python-pip libsasl2-dev libldap2-dev
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For **Fedora** and **RHEL-derivatives**, the following command will ensure
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that the required dependencies are installed: ::
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sudo yum upgrade python-setuptools
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sudo yum install gcc gcc-c++ libffi-devel python-devel python-pip python-wheel openssl-devel libsasl2-devel openldap-devel
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**OSX**, system python is not recommended. brew's python also ships with pip ::
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brew install pkg-config libffi openssl python
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env LDFLAGS="-L$(brew --prefix openssl)/lib" CFLAGS="-I$(brew --prefix openssl)/include" pip install cryptography==1.7.2
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**Windows** isn't officially supported at this point, but if you want to
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attempt it, download `get-pip.py <https://bootstrap.pypa.io/get-pip.py>`_, and run ``python get-pip.py`` which may need admin access. Then run the following: ::
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C:\> pip install cryptography
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# You may also have to create C:\Temp
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C:\> md C:\Temp
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Python virtualenv
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-----------------
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It is recommended to install Superset inside a virtualenv. Python 3 already ships virtualenv, for
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Python 2 you need to install it. If it's packaged for your operating systems install it from there
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otherwise you can install from pip: ::
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pip install virtualenv
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You can create and activate a virtualenv by: ::
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# virtualenv is shipped in Python 3 as pyvenv
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virtualenv venv
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. ./venv/bin/activate
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On windows the syntax for activating it is a bit different: ::
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venv\Scripts\activate
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Once you activated your virtualenv everything you are doing is confined inside the virtualenv.
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To exit a virtualenv just type ``deactivate``.
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Python's setup tools and pip
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----------------------------
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Put all the chances on your side by getting the very latest ``pip``
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and ``setuptools`` libraries.::
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pip install --upgrade setuptools pip
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Superset installation and initialization
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----------------------------------------
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Follow these few simple steps to install Superset.::
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# Install superset
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pip install superset
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# Create an admin user (you will be prompted to set username, first and last name before setting a password)
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fabmanager create-admin --app superset
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# Initialize the database
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superset db upgrade
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# Load some data to play with
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superset load_examples
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# Create default roles and permissions
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superset init
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# Start the web server on port 8088, use -p to bind to another port
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superset runserver
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# To start a development web server, use the -d switch
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# superset runserver -d
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After installation, you should be able to point your browser to the right
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hostname:port `http://localhost:8088 <http://localhost:8088>`_, login using
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the credential you entered while creating the admin account, and navigate to
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`Menu -> Admin -> Refresh Metadata`. This action should bring in all of
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your datasources for Superset to be aware of, and they should show up in
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`Menu -> Datasources`, from where you can start playing with your data!
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Please note that *gunicorn*, Superset default application server, does not
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work on Windows so you need to use the development web server.
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The development web server though is not intended to be used on production systems
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so better use a supported platform that can run *gunicorn*.
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Configuration behind a load balancer
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------------------------------------
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If you are running superset behind a load balancer or reverse proxy (e.g. NGINX
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or ELB on AWS), you may need to utilise a healthcheck endpoint so that your
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load balancer knows if your superset instance is running. This is provided
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at ``/health`` which will return a 200 response containing "OK" if the
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webserver is running.
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If the load balancer is inserting X-Forwarded-For/X-Forwarded-Proto headers, you
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should set `ENABLE_PROXY_FIX = True` in the superset config file to extract and use
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the headers.
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Configuration
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-------------
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To configure your application, you need to create a file (module)
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``superset_config.py`` and make sure it is in your PYTHONPATH. Here are some
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of the parameters you can copy / paste in that configuration module: ::
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#---------------------------------------------------------
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# Superset specific config
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#---------------------------------------------------------
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ROW_LIMIT = 5000
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SUPERSET_WORKERS = 4
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SUPERSET_WEBSERVER_PORT = 8088
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#---------------------------------------------------------
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#---------------------------------------------------------
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# Flask App Builder configuration
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#---------------------------------------------------------
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# Your App secret key
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SECRET_KEY = '\2\1thisismyscretkey\1\2\e\y\y\h'
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# The SQLAlchemy connection string to your database backend
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# This connection defines the path to the database that stores your
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# superset metadata (slices, connections, tables, dashboards, ...).
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# Note that the connection information to connect to the datasources
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# you want to explore are managed directly in the web UI
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SQLALCHEMY_DATABASE_URI = 'sqlite:////path/to/superset.db'
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# Flask-WTF flag for CSRF
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CSRF_ENABLED = True
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# Set this API key to enable Mapbox visualizations
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MAPBOX_API_KEY = ''
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This file also allows you to define configuration parameters used by
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Flask App Builder, the web framework used by Superset. Please consult
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the `Flask App Builder Documentation
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<http://flask-appbuilder.readthedocs.org/en/latest/config.html>`_
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for more information on how to configure Superset.
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Please make sure to change:
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* *SQLALCHEMY_DATABASE_URI*, by default it is stored at *~/.superset/superset.db*
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* *SECRET_KEY*, to a long random string
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Database dependencies
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---------------------
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Superset does not ship bundled with connectivity to databases, except
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for Sqlite, which is part of the Python standard library.
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You'll need to install the required packages for the database you
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want to use as your metadata database as well as the packages needed to
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connect to the databases you want to access through Superset.
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Here's a list of some of the recommended packages.
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+---------------+-------------------------------------+-------------------------------------------------+
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| database | pypi package | SQLAlchemy URI prefix |
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+===============+=====================================+=================================================+
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| MySQL | ``pip install mysqlclient`` | ``mysql://`` |
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+---------------+-------------------------------------+-------------------------------------------------+
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| Postgres | ``pip install psycopg2`` | ``postgresql+psycopg2://`` |
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+---------------+-------------------------------------+-------------------------------------------------+
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| Presto | ``pip install pyhive`` | ``presto://`` |
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+---------------+-------------------------------------+-------------------------------------------------+
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| Oracle | ``pip install cx_Oracle`` | ``oracle://`` |
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+---------------+-------------------------------------+-------------------------------------------------+
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| sqlite | | ``sqlite://`` |
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+---------------+-------------------------------------+-------------------------------------------------+
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| Redshift | ``pip install sqlalchemy-redshift`` | ``postgresql+psycopg2://`` |
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+---------------+-------------------------------------+-------------------------------------------------+
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| MSSQL | ``pip install pymssql`` | ``mssql://`` |
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+---------------+-------------------------------------+-------------------------------------------------+
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| Impala | ``pip install impyla`` | ``impala://`` |
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+---------------+-------------------------------------+-------------------------------------------------+
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| SparkSQL | ``pip install pyhive`` | ``jdbc+hive://`` |
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+---------------+-------------------------------------+-------------------------------------------------+
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| Greenplum | ``pip install psycopg2`` | ``postgresql+psycopg2://`` |
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+---------------+-------------------------------------+-------------------------------------------------+
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| Athena | ``pip install "PyAthenaJDBC>1.0.9"``| ``awsathena+jdbc://`` |
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+---------------+-------------------------------------+-------------------------------------------------+
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| Vertica | ``pip install | ``vertica+vertica_python://`` |
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| | sqlalchemy-vertica-python`` | |
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+---------------+-------------------------------------+-------------------------------------------------+
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| ClickHouse | ``pip install | ``clickhouse://`` |
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| | sqlalchemy-clickhouse`` | |
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+---------------+-------------------------------------+-------------------------------------------------+
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Note that many other database are supported, the main criteria being the
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existence of a functional SqlAlchemy dialect and Python driver. Googling
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the keyword ``sqlalchemy`` in addition of a keyword that describes the
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database you want to connect to should get you to the right place.
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(AWS) Athena
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------------
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This currently relies on an unreleased future version of `PyAthenaJDBC <https://github.com/laughingman7743/PyAthenaJDBC>`_. If you're adventurous or simply impatient, you can install directly from git: ::
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pip install git+https://github.com/laughingman7743/PyAthenaJDBC@support_sqlalchemy
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The connection string for Athena looks like this ::
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awsathena+jdbc://{aws_access_key_id}:{aws_secret_access_key}@athena.{region_name}.amazonaws.com/{schema_name}?s3_staging_dir={s3_staging_dir}&...
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Where you need to escape/encode at least the s3_staging_dir, i.e., ::
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s3://... -> s3%3A//...
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Caching
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-------
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Superset uses `Flask-Cache <https://pythonhosted.org/Flask-Cache/>`_ for
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caching purpose. Configuring your caching backend is as easy as providing
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a ``CACHE_CONFIG``, constant in your ``superset_config.py`` that
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complies with the Flask-Cache specifications.
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Flask-Cache supports multiple caching backends (Redis, Memcached,
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SimpleCache (in-memory), or the local filesystem). If you are going to use
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Memcached please use the `pylibmc` client library as `python-memcached` does
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not handle storing binary data correctly. If you use Redis, please install
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the `redis <https://pypi.python.org/pypi/redis>`_ Python package: ::
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pip install redis
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For setting your timeouts, this is done in the Superset metadata and goes
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up the "timeout searchpath", from your slice configuration, to your
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data source's configuration, to your database's and ultimately falls back
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into your global default defined in ``CACHE_CONFIG``.
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Deeper SQLAlchemy integration
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-----------------------------
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It is possible to tweak the database connection information using the
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parameters exposed by SQLAlchemy. In the ``Database`` edit view, you will
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find an ``extra`` field as a ``JSON`` blob.
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.. image:: _static/img/tutorial/add_db.png
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:scale: 30 %
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This JSON string contains extra configuration elements. The ``engine_params``
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object gets unpacked into the
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`sqlalchemy.create_engine <http://docs.sqlalchemy.org/en/latest/core/engines.html#sqlalchemy.create_engine>`_ call,
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while the ``metadata_params`` get unpacked into the
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`sqlalchemy.MetaData <http://docs.sqlalchemy.org/en/rel_1_0/core/metadata.html#sqlalchemy.schema.MetaData>`_ call. Refer to the SQLAlchemy docs for more information.
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Schemas (Postgres & Redshift)
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-----------------------------
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Postgres and Redshift, as well as other database,
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use the concept of **schema** as a logical entity
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on top of the **database**. For Superset to connect to a specific schema,
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there's a **schema** parameter you can set in the table form.
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SSL Access to databases
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-----------------------
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This example worked with a MySQL database that requires SSL. The configuration
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may differ with other backends. This is what was put in the ``extra``
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parameter ::
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{
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"metadata_params": {},
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"engine_params": {
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"connect_args":{
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"sslmode":"require",
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"sslrootcert": "/path/to/my/pem"
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}
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}
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}
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Druid
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-----
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* From the UI, enter the information about your clusters in the
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``Admin->Clusters`` menu by hitting the + sign.
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* Once the Druid cluster connection information is entered, hit the
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``Admin->Refresh Metadata`` menu item to populate
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* Navigate to your datasources
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Note that you can run the ``superset refresh_druid`` command to refresh the
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metadata from your Druid cluster(s)
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CORS
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-----
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The extra CORS Dependency must be installed:
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superset[cors]
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The following keys in `superset_config.py` can be specified to configure CORS:
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* ``ENABLE_CORS``: Must be set to True in order to enable CORS
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* ``CORS_OPTIONS``: options passed to Flask-CORS (`documentation <http://flask-cors.corydolphin.com/en/latest/api.html#extension>`)
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MIDDLEWARE
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----------
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Superset allows you to add your own middleware. To add your own middleware, update the ``ADDITIONAL_MIDDLEWARE`` key in
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your `superset_config.py`. ``ADDITIONAL_MIDDLEWARE`` should be a list of your additional middleware classes.
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For example, to use AUTH_REMOTE_USER from behind a proxy server like nginx, you have to add a simple middleware class to
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add the value of ``HTTP_X_PROXY_REMOTE_USER`` (or any other custom header from the proxy) to Gunicorn's ``REMOTE_USER``
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environment variable: ::
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class RemoteUserMiddleware(object):
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def __init__(self, app):
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self.app = app
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def __call__(self, environ, start_response):
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user = environ.pop('HTTP_X_PROXY_REMOTE_USER', None)
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environ['REMOTE_USER'] = user
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return self.app(environ, start_response)
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ADDITIONAL_MIDDLEWARE = [RemoteUserMiddleware, ]
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*Adapted from http://flask.pocoo.org/snippets/69/*
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Upgrading
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---------
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Upgrading should be as straightforward as running::
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pip install superset --upgrade
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superset db upgrade
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superset init
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SQL Lab
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-------
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SQL Lab is a powerful SQL IDE that works with all SQLAlchemy compatible
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databases out there. By default, queries are run in a web request, and
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may eventually timeout as queries exceed the maximum duration of a web
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request in your environment, whether it'd be a reverse proxy or the Superset
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server itself.
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In the modern analytics world, it's not uncommon to run large queries that
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run for minutes or hours.
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To enable support for long running queries that
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execute beyond the typical web request's timeout (30-60 seconds), it is
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necessary to deploy an asynchronous backend, which consist of one or many
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Superset worker, which is implemented as a Celery worker, and a Celery
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broker for which we recommend using Redis or RabbitMQ.
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It's also preferable to setup an async result backend as a key value store
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that can hold the long-running query results for a period of time. More
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details to come as to how to set this up here soon.
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SQL Lab supports templating in queries, and it's possible to override
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the default Jinja context in your environment by defining the
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``JINJA_CONTEXT_ADDONS`` in your superset configuration. Objects referenced
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in this dictionary are made available for users to use in their SQL.
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Making your own build
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---------------------
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For more advanced users, you may want to build Superset from sources. That
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would be the case if you fork the project to add features specific to
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your environment.::
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# assuming $SUPERSET_HOME as the root of the repo
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cd $SUPERSET_HOME/superset/assets
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npm install
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npm run build
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cd $SUPERSET_HOME
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python setup.py install
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Blueprints
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----------
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`Blueprints are Flask's reusable apps <http://flask.pocoo.org/docs/0.12/blueprints/>`_.
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Superset allows you to specify an array of Blueprints
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an array of Blueprints in your ``superset_config`` module. Here's
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an example on how this can work with a simple Blueprint. By doing
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so, you can expect Superset to serve a page that says "OK"
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at the ``/simple_page`` url. This can allow you to run other things such
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as custom data visualization applications alongside Superset, on the
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same server.
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..code ::
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from flask import Blueprint
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simple_page = Blueprint('simple_page', __name__,
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template_folder='templates')
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@simple_page.route('/', defaults={'page': 'index'})
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@simple_page.route('/<page>')
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def show(page):
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return "Ok"
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BLUEPRINTS = [simple_page]
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