mirror of https://github.com/apache/superset.git
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ReStructuredText
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ReStructuredText
.. Licensed to the Apache Software Foundation (ASF) under one
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or more contributor license agreements. See the NOTICE file
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distributed with this work for additional information
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regarding copyright ownership. The ASF licenses this file
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to you under the Apache License, Version 2.0 (the
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"License"); you may not use this file except in compliance
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with the License. You may obtain a copy of the License at
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.. http://www.apache.org/licenses/LICENSE-2.0
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.. Unless required by applicable law or agreed to in writing,
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software distributed under the License is distributed on an
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"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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KIND, either express or implied. See the License for the
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specific language governing permissions and limitations
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under the License.
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Installation & Configuration
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============================
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Getting Started
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---------------
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Superset has deprecated support for Python ``2.*`` and supports
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only ``~=3.6`` to take advantage of the newer Python features and reduce
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the burden of supporting previous versions. We run our test suite
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against ``3.6``, but ``3.7`` is fully supported as well.
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Cloud-native!
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-------------
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Superset is designed to be highly available. It is
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"cloud-native" as it has been designed scale out in large,
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distributed environments, and works well inside containers.
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While you can easily
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test drive Superset on a modest setup or simply on your laptop,
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there's virtually no limit around scaling out the platform.
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Superset is also cloud-native in the sense that it is
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flexible and lets you choose your web server (Gunicorn, Nginx, Apache),
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your metadata database engine (MySQL, Postgres, MariaDB, ...),
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your message queue (Redis, RabbitMQ, SQS, ...),
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your results backend (S3, Redis, Memcached, ...), your caching layer
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(Memcached, Redis, ...), works well with services like NewRelic, StatsD and
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DataDog, and has the ability to run analytic workloads against
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most popular database technologies.
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Superset is battle tested in large environments with hundreds
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of concurrent users. Airbnb's production environment runs inside
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Kubernetes and serves 600+ daily active users viewing over 100K charts a
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day.
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The Superset web server and the Superset Celery workers (optional)
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are stateless, so you can scale out by running on as many servers
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as needed.
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Start with Docker
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-----------------
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.. note ::
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The Docker-related files and documentation has been
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community-contributed and
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is not actively maintained and managed by the core committers working on
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the project. Some issues have been reported as of 2019-01.
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Help and contributions around Docker are welcomed!
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If you know docker, then you're lucky, we have shortcut road for you to
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initialize development environment: ::
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git clone https://github.com/apache/incubator-superset/
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cd incubator-superset/contrib/docker
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# prefix with SUPERSET_LOAD_EXAMPLES=yes to load examples:
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docker-compose run --rm superset ./docker-init.sh
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# you can run this command everytime you need to start superset now:
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docker-compose up
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After several minutes for superset initialization to finish, you can open
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a browser and view `http://localhost:8088` to start your journey.
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From there, the container server will reload on modification of the superset python
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and javascript source code.
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Don't forget to reload the page to take the new frontend into account though.
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See also `CONTRIBUTING.md#building <https://github.com/apache/incubator-superset/blob/master/CONTRIBUTING.md#building>`_,
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for alternative way of serving the frontend.
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It is also possible to run Superset in non-development mode: in the `docker-compose.yml` file remove
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the volumes needed for development and change the variable `SUPERSET_ENV` to `production`.
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If you are attempting to build on a Mac and it exits with 137 you need to increase your docker resources.
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OSX instructions: https://docs.docker.com/docker-for-mac/#advanced (Search for memory)
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Or if you're curious and want to install superset from bottom up, then go ahead.
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See also `contrib/docker/README.md <https://github.com/apache/incubator-superset/blob/master/contrib/docker/README.md>`_
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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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**Ubuntu 18.04** If you have python3.6 installed alongside with python2.7, as is default on **Ubuntu 18.04 LTS**, run this command also: ::
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sudo apt-get install build-essential libssl-dev libffi-dev python3.6-dev python-pip libsasl2-dev libldap2-dev
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otherwise build for ``cryptography`` fails.
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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 cyrus-sasl-devel openldap-devel
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**Mac OS X** If possible, you should upgrade to the latest version of OS X as issues are more likely to be resolved for that version.
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You *will likely need* the latest version of XCode available for your installed version of OS X. You should also install
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the XCode command line tools: ::
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xcode-select --install
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System python is not recommended. Homebrew'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==2.4.2
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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.
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But if it's not installed in your environment for some reason, you can install it
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via the package for your operating systems, 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 3.6+ as venv instead of pyvenv.
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# See https://docs.python.org/3.6/library/venv.html
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python3 -m venv 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 apache-superset
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# Initialize the database
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superset db upgrade
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# Create an admin user (you will be prompted to set a username, first and last name before setting a password)
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$ export FLASK_APP=superset
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flask fab create-admin
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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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# To start a development web server on port 8088, use -p to bind to another port
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superset run -p 8088 --with-threads --reload --debugger
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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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A proper WSGI HTTP Server
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-------------------------
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While you can setup Superset to run on Nginx or Apache, many use
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Gunicorn, preferably in **async mode**, which allows for impressive
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concurrency even and is fairly easy to install and configure. Please
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refer to the
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documentation of your preferred technology to set up this Flask WSGI
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application in a way that works well in your environment. Here's an **async**
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setup known to work well in production: ::
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gunicorn \
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-w 10 \
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-k gevent \
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--timeout 120 \
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-b 0.0.0.0:6666 \
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--limit-request-line 0 \
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--limit-request-field_size 0 \
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--statsd-host localhost:8125 \
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superset:app
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Refer to the
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`Gunicorn documentation <https://docs.gunicorn.org/en/stable/design.html>`_
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for more information.
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Note that the development web
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server (`superset run` or `flask run`) is not intended for production use.
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If not using gunicorn, you may want to disable the use of flask-compress
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by setting `ENABLE_FLASK_COMPRESS = False` in your `superset_config.py`
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Flask-AppBuilder Permissions
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----------------------------
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By default, every time the Flask-AppBuilder (FAB) app is initialized the
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permissions and views are added automatically to the backend and associated with
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the ‘Admin’ role. The issue, however, is when you are running multiple concurrent
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workers this creates a lot of contention and race conditions when defining
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permissions and views.
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To alleviate this issue, the automatic updating of permissions can be disabled
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by setting `FAB_UPDATE_PERMS = False` (defaults to True).
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In a production environment initialization could take on the following form:
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superset init
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gunicorn -w 10 ... superset:app
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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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the 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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In case that the reverse proxy is used for providing ssl encryption,
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an explicit definition of the `X-Forwarded-Proto` may be required.
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For the Apache webserver this can be set as follows: ::
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RequestHeader set X-Forwarded-Proto "https"
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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_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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WTF_CSRF_ENABLED = True
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# Add endpoints that need to be exempt from CSRF protection
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WTF_CSRF_EXEMPT_LIST = []
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# A CSRF token that expires in 1 year
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WTF_CSRF_TIME_LIMIT = 60 * 60 * 24 * 365
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# Set this API key to enable Mapbox visualizations
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MAPBOX_API_KEY = ''
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All the parameters and default values defined in
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https://github.com/apache/incubator-superset/blob/master/superset/config.py
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can be altered in your local ``superset_config.py`` .
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Administrators will want to
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read through the file to understand what can be configured locally
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as well as the default values in place.
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Since ``superset_config.py`` acts as a Flask configuration module, it
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can be used to alter the settings Flask itself,
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as well as Flask extensions like ``flask-wtf``, ``flask-cache``,
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``flask-migrate``, and ``flask-appbuilder``. Flask App Builder, the web
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framework used by Superset offers many configuration settings. Please consult
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the `Flask App Builder Documentation
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<https://flask-appbuilder.readthedocs.org/en/latest/config.html>`_
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for more information on how to configure it.
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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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In case you need to exempt endpoints from CSRF, e.g. you are running a custom
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auth postback endpoint, you can add them to *WTF_CSRF_EXEMPT_LIST*
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WTF_CSRF_EXEMPT_LIST = ['']
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.. _ref_database_deps:
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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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| Amazon Athena | ``pip install "PyAthenaJDBC>1.0.9"`` | ``awsathena+jdbc://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Amazon Athena | ``pip install "PyAthena>1.2.0"`` | ``awsathena+rest://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Amazon Redshift | ``pip install sqlalchemy-redshift`` | ``redshift+psycopg2://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Apache Drill | ``pip install sqlalchemy-drill`` | For the REST API:`` |
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| | | ``drill+sadrill://`` |
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| | | For JDBC |
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| | | ``drill+jdbc://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Apache Druid | ``pip install pydruid`` | ``druid://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Apache Hive | ``pip install pyhive`` | ``hive://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Apache Impala | ``pip install impyla`` | ``impala://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Apache Kylin | ``pip install kylinpy`` | ``kylin://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Apache Pinot | ``pip install pinotdb`` | ``pinot+http://CONTROLLER:5436/`` |
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| | | ``query?server=http://CONTROLLER:5983/`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Apache Spark SQL | ``pip install pyhive`` | ``jdbc+hive://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| BigQuery | ``pip install pybigquery`` | ``bigquery://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| ClickHouse | ``pip install sqlalchemy-clickhouse`` | |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Elasticsearch | ``pip install elasticsearch-dbapi`` | ``elasticsearch+http://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Exasol | ``pip install sqlalchemy-exasol`` | ``exa+pyodbc://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Google Sheets | ``pip install gsheetsdb`` | ``gsheets://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| IBM Db2 | ``pip install ibm_db_sa`` | ``db2+ibm_db://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| MySQL | ``pip install mysqlclient`` | ``mysql://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| Oracle | ``pip install cx_Oracle`` | ``oracle://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| PostgreSQL | ``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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| Snowflake | ``pip install snowflake-sqlalchemy`` | ``snowflake://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| SQLite | | ``sqlite://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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| SQL Server | ``pip install pymssql`` | ``mssql://`` |
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+------------------+---------------------------------------+-------------------------------------------------+
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||
| Teradata | ``pip install sqlalchemy-teradata`` | ``teradata://`` |
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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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||
Note that many other databases 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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||
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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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|
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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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You can also use `PyAthena` library(no java required) like this ::
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||
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awsathena+rest://{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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See `PyAthena <https://github.com/laughingman7743/PyAthena#sqlalchemy>`_.
|
||
|
||
(Google) BigQuery
|
||
-----------------
|
||
|
||
The connection string for BigQuery looks like this ::
|
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bigquery://{project_id}
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||
|
||
Additionally, you will need to configure authentication via a
|
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Service Account. Create your Service Account via the Google
|
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Cloud Platform control panel, provide it access to the appropriate
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||
BigQuery datasets, and download the JSON configuration file
|
||
for the service account. In Superset, Add a JSON blob to
|
||
the "Secure Extra" field in the database configuration page
|
||
with the following format ::
|
||
|
||
{
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||
"credentials_info": <contents of credentials JSON file>
|
||
}
|
||
|
||
The resulting file should have this structure ::
|
||
|
||
{
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||
"credentials_info": {
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||
"type": "service_account",
|
||
"project_id": "...",
|
||
"private_key_id": "...",
|
||
"private_key": "...",
|
||
"client_email": "...",
|
||
"client_id": "...",
|
||
"auth_uri": "...",
|
||
"token_uri": "...",
|
||
"auth_provider_x509_cert_url": "...",
|
||
"client_x509_cert_url": "...",
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||
}
|
||
}
|
||
|
||
You should then be able to connect to your BigQuery datasets.
|
||
|
||
To be able to upload data, e.g. sample data, the python library `pandas_gbq` is required.
|
||
|
||
|
||
Elasticsearch
|
||
-------------
|
||
|
||
The connection string for Elasticsearch looks like this ::
|
||
|
||
elasticsearch+http://{user}:{password}@{host}:9200/
|
||
|
||
Using HTTPS ::
|
||
|
||
elasticsearch+https://{user}:{password}@{host}:9200/
|
||
|
||
|
||
Elasticsearch as a default limit of 10000 rows, so you can increase this limit on your cluster
|
||
or set Superset's row limit on config ::
|
||
|
||
ROW_LIMIT = 10000
|
||
|
||
You can query multiple indices on SQLLab for example ::
|
||
|
||
select timestamp, agent from "logstash-*"
|
||
|
||
But, to use visualizations for multiple indices you need to create an alias index on your cluster ::
|
||
|
||
POST /_aliases
|
||
{
|
||
"actions" : [
|
||
{ "add" : { "index" : "logstash-**", "alias" : "logstash_all" } }
|
||
]
|
||
}
|
||
|
||
Then register your table with the ``alias`` name ``logstasg_all``
|
||
|
||
Snowflake
|
||
---------
|
||
|
||
The connection string for Snowflake looks like this ::
|
||
|
||
snowflake://{user}:{password}@{account}.{region}/{database}?role={role}&warehouse={warehouse}
|
||
|
||
The schema is not necessary in the connection string, as it is defined per table/query.
|
||
The role and warehouse can be omitted if defaults are defined for the user, i.e.
|
||
|
||
snowflake://{user}:{password}@{account}.{region}/{database}
|
||
|
||
Make sure the user has privileges to access and use all required
|
||
databases/schemas/tables/views/warehouses, as the Snowflake SQLAlchemy engine does
|
||
not test for user rights during engine creation.
|
||
|
||
See `Snowflake SQLAlchemy <https://github.com/snowflakedb/snowflake-sqlalchemy>`_.
|
||
|
||
Teradata
|
||
---------
|
||
|
||
The connection string for Teradata looks like this ::
|
||
|
||
teradata://{user}:{password}@{host}
|
||
|
||
*Note*: Its required to have Teradata ODBC drivers installed and environment variables configured for proper work of sqlalchemy dialect. Teradata ODBC Drivers available here: https://downloads.teradata.com/download/connectivity/odbc-driver/linux
|
||
|
||
Required environment variables: ::
|
||
|
||
export ODBCINI=/.../teradata/client/ODBC_64/odbc.ini
|
||
export ODBCINST=/.../teradata/client/ODBC_64/odbcinst.ini
|
||
|
||
See `Teradata SQLAlchemy <https://github.com/Teradata/sqlalchemy-teradata>`_.
|
||
|
||
Apache Drill
|
||
------------
|
||
At the time of writing, the SQLAlchemy Dialect is not available on pypi and must be downloaded here:
|
||
`SQLAlchemy Drill <https://github.com/JohnOmernik/sqlalchemy-drill>`_
|
||
|
||
Alternatively, you can install it completely from the command line as follows: ::
|
||
|
||
git clone https://github.com/JohnOmernik/sqlalchemy-drill
|
||
cd sqlalchemy-drill
|
||
python3 setup.py install
|
||
|
||
Once that is done, you can connect to Drill in two ways, either via the REST interface or by JDBC. If you are connecting via JDBC, you must have the
|
||
Drill JDBC Driver installed.
|
||
|
||
The basic connection string for Drill looks like this ::
|
||
|
||
drill+sadrill://{username}:{password}@{host}:{port}/{storage_plugin}?use_ssl=True
|
||
|
||
If you are using JDBC to connect to Drill, the connection string looks like this: ::
|
||
|
||
drill+jdbc://{username}:{password}@{host}:{port}/{storage_plugin}
|
||
|
||
For a complete tutorial about how to use Apache Drill with Superset, see this tutorial:
|
||
`Visualize Anything with Superset and Drill <http://thedataist.com/visualize-anything-with-superset-and-drill/>`_
|
||
|
||
Caching
|
||
-------
|
||
|
||
Superset uses `Flask-Cache <https://pythonhosted.org/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 <https://pypi.python.org/pypi/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``.
|
||
|
||
.. code-block:: python
|
||
|
||
CACHE_CONFIG = {
|
||
'CACHE_TYPE': 'redis',
|
||
'CACHE_DEFAULT_TIMEOUT': 60 * 60 * 24, # 1 day default (in secs)
|
||
'CACHE_KEY_PREFIX': 'superset_results',
|
||
'CACHE_REDIS_URL': 'redis://localhost:6379/0',
|
||
}
|
||
|
||
It is also possible to pass a custom cache initialization function in the
|
||
config to handle additional caching use cases. The function must return an
|
||
object that is compatible with the `Flask-Cache <https://pythonhosted.org/Flask-Cache/>`_ API.
|
||
|
||
.. code-block:: python
|
||
|
||
from custom_caching import CustomCache
|
||
|
||
def init_cache(app):
|
||
"""Takes an app instance and returns a custom cache backend"""
|
||
config = {
|
||
'CACHE_DEFAULT_TIMEOUT': 60 * 60 * 24, # 1 day default (in secs)
|
||
'CACHE_KEY_PREFIX': 'superset_results',
|
||
}
|
||
return CustomCache(app, config)
|
||
|
||
CACHE_CONFIG = init_cache
|
||
|
||
Superset has a Celery task that will periodically warm up the cache based on
|
||
different strategies. To use it, add the following to the `CELERYBEAT_SCHEDULE`
|
||
section in `config.py`:
|
||
|
||
.. code-block:: python
|
||
|
||
CELERYBEAT_SCHEDULE = {
|
||
'cache-warmup-hourly': {
|
||
'task': 'cache-warmup',
|
||
'schedule': crontab(minute=0, hour='*'), # hourly
|
||
'kwargs': {
|
||
'strategy_name': 'top_n_dashboards',
|
||
'top_n': 5,
|
||
'since': '7 days ago',
|
||
},
|
||
},
|
||
}
|
||
|
||
This will cache all the charts in the top 5 most popular dashboards every hour.
|
||
For other strategies, check the `superset/tasks/cache.py` file.
|
||
|
||
|
||
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:: images/tutorial/add_db.png
|
||
:scale: 30 %
|
||
|
||
This JSON string contains extra configuration elements. The ``engine_params``
|
||
object gets unpacked into the
|
||
`sqlalchemy.create_engine <https://docs.sqlalchemy.org/en/latest/core/engines.html#sqlalchemy.create_engine>`_ call,
|
||
while the ``metadata_params`` get unpacked into the
|
||
`sqlalchemy.MetaData <https://docs.sqlalchemy.org/en/rel_1_2/core/metadata.html#sqlalchemy.schema.MetaData>`_ call. Refer to the SQLAlchemy docs for more information.
|
||
|
||
.. note:: If your using CTAS on SQLLab and PostgreSQL
|
||
take a look at :ref:`ref_ctas_engine_config` for specific ``engine_params``.
|
||
|
||
|
||
Schemas (Postgres & Redshift)
|
||
-----------------------------
|
||
|
||
Postgres and Redshift, as well as other databases,
|
||
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.
|
||
|
||
|
||
External Password store for SQLAlchemy connections
|
||
--------------------------------------------------
|
||
It is possible to use an external store for you database passwords. This is
|
||
useful if you a running a custom secret distribution framework and do not wish
|
||
to store secrets in Superset's meta database.
|
||
|
||
Example:
|
||
Write a function that takes a single argument of type ``sqla.engine.url`` and returns
|
||
the password for the given connection string. Then set ``SQLALCHEMY_CUSTOM_PASSWORD_STORE``
|
||
in your config file to point to that function. ::
|
||
|
||
def example_lookup_password(url):
|
||
secret = <<get password from external framework>>
|
||
return 'secret'
|
||
|
||
SQLALCHEMY_CUSTOM_PASSWORD_STORE = example_lookup_password
|
||
|
||
A common pattern is to use environment variables to make secrets available.
|
||
``SQLALCHEMY_CUSTOM_PASSWORD_STORE`` can also be used for that purpose. ::
|
||
|
||
def example_password_as_env_var(url):
|
||
# assuming the uri looks like
|
||
# mysql://localhost?superset_user:{SUPERSET_PASSWORD}
|
||
return url.password.format(os.environ)
|
||
|
||
SQLALCHEMY_CUSTOM_PASSWORD_STORE = example_password_as_env_var
|
||
|
||
|
||
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
|
||
`Sources -> Druid Clusters` menu by hitting the + sign.
|
||
|
||
* Once the Druid cluster connection information is entered, hit the
|
||
`Sources -> Refresh Druid 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)
|
||
|
||
|
||
Presto
|
||
------
|
||
|
||
By default Superset assumes the most recent version of Presto is being used when
|
||
querying the datasource. If you're using an older version of presto, you can configure
|
||
it in the ``extra`` parameter::
|
||
|
||
{
|
||
"version": "0.123"
|
||
}
|
||
|
||
|
||
Exasol
|
||
---------
|
||
|
||
The connection string for Exasol looks like this ::
|
||
|
||
exa+pyodbc://{user}:{password}@{host}
|
||
|
||
*Note*: It's required to have Exasol ODBC drivers installed for the sqlalchemy dialect to work properly. Exasol ODBC Drivers available are here: https://www.exasol.com/portal/display/DOWNLOAD/Exasol+Download+Section
|
||
|
||
Example config (odbcinst.ini can be left empty) ::
|
||
|
||
$ cat $/.../path/to/odbc.ini
|
||
[EXAODBC]
|
||
DRIVER = /.../path/to/driver/EXASOL_driver.so
|
||
EXAHOST = host:8563
|
||
EXASCHEMA = main
|
||
|
||
See `SQLAlchemy for Exasol <https://github.com/blue-yonder/sqlalchemy_exasol>`_.
|
||
|
||
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 <https://flask-cors.corydolphin.com/en/latest/api.html#extension>`)
|
||
|
||
|
||
Domain Sharding
|
||
---------------
|
||
|
||
Chrome allows up to 6 open connections per domain at a time. When there are more
|
||
than 6 slices in dashboard, a lot of time fetch requests are queued up and wait for
|
||
next available socket. `PR 5039 <https://github.com/apache/incubator-superset/pull/5039>`_ adds domain sharding to Superset,
|
||
and this feature will be enabled by configuration only (by default Superset
|
||
doesn't allow cross-domain request).
|
||
|
||
* ``SUPERSET_WEBSERVER_DOMAINS``: list of allowed hostnames for domain sharding feature. default `None`
|
||
|
||
|
||
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/*
|
||
|
||
Event Logging
|
||
-------------
|
||
|
||
Superset by default logs special action event on it's database. These log can be accessed on the UI navigating to
|
||
"Security" -> "Action Log". You can freely customize these logs by implementing your own event log class.
|
||
|
||
Example of a simple JSON to Stdout class::
|
||
|
||
class JSONStdOutEventLogger(AbstractEventLogger):
|
||
|
||
def log(self, user_id, action, *args, **kwargs):
|
||
records = kwargs.get('records', list())
|
||
dashboard_id = kwargs.get('dashboard_id')
|
||
slice_id = kwargs.get('slice_id')
|
||
duration_ms = kwargs.get('duration_ms')
|
||
referrer = kwargs.get('referrer')
|
||
|
||
for record in records:
|
||
log = dict(
|
||
action=action,
|
||
json=record,
|
||
dashboard_id=dashboard_id,
|
||
slice_id=slice_id,
|
||
duration_ms=duration_ms,
|
||
referrer=referrer,
|
||
user_id=user_id
|
||
)
|
||
print(json.dumps(log))
|
||
|
||
|
||
Then on Superset's config pass an instance of the logger type you want to use.
|
||
|
||
EVENT_LOGGER = JSONStdOutEventLogger()
|
||
|
||
|
||
Upgrading
|
||
---------
|
||
|
||
Upgrading should be as straightforward as running::
|
||
|
||
pip install apache-superset --upgrade
|
||
superset db upgrade
|
||
superset init
|
||
|
||
We recommend to follow standard best practices when upgrading Superset, such
|
||
as taking a database backup prior to the upgrade, upgrading a staging
|
||
environment prior to upgrading production, and upgrading production while less
|
||
users are active on the platform.
|
||
|
||
.. note ::
|
||
Some upgrades may contain backward-incompatible changes, or require
|
||
scheduling downtime, when that is the case, contributors attach notes in
|
||
``UPDATING.md`` in the repository. It's recommended to review this
|
||
file prior to running an upgrade.
|
||
|
||
|
||
Celery Tasks
|
||
------------
|
||
|
||
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 consists of:
|
||
|
||
* one or many Superset workers (which is implemented as a Celery worker), and
|
||
can be started with the ``celery worker`` command, run
|
||
``celery 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',
|
||
'superset.tasks',
|
||
)
|
||
CELERY_RESULT_BACKEND = 'redis://localhost:6379/0'
|
||
CELERYD_LOG_LEVEL = 'DEBUG'
|
||
CELERYD_PREFETCH_MULTIPLIER = 10
|
||
CELERY_ACKS_LATE = True
|
||
CELERY_ANNOTATIONS = {
|
||
'sql_lab.get_sql_results': {
|
||
'rate_limit': '100/s',
|
||
},
|
||
'email_reports.send': {
|
||
'rate_limit': '1/s',
|
||
'time_limit': 120,
|
||
'soft_time_limit': 150,
|
||
'ignore_result': True,
|
||
},
|
||
}
|
||
CELERYBEAT_SCHEDULE = {
|
||
'email_reports.schedule_hourly': {
|
||
'task': 'email_reports.schedule_hourly',
|
||
'schedule': crontab(minute=1, hour='*'),
|
||
},
|
||
}
|
||
|
||
CELERY_CONFIG = CeleryConfig
|
||
|
||
* To start a Celery worker to leverage the configuration run: ::
|
||
|
||
celery worker --app=superset.tasks.celery_app:app --pool=prefork -O fair -c 4
|
||
|
||
* To start a job which schedules periodic background jobs, run ::
|
||
|
||
celery beat --app=superset.tasks.celery_app:app
|
||
|
||
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')
|
||
|
||
For performance gains, `MessagePack <https://github.com/msgpack/msgpack-python>`_
|
||
and `PyArrow <https://arrow.apache.org/docs/python/>`_ are now used for results
|
||
serialization. This can be disabled by setting ``RESULTS_BACKEND_USE_MSGPACK = False``
|
||
in your configuration, should any issues arise. Please clear your existing results
|
||
cache store when upgrading an existing environment.
|
||
|
||
**Important notes**
|
||
|
||
* It is important that all the worker nodes and web servers in
|
||
the Superset cluster share a common metadata database.
|
||
This means that SQLite will not work in this context since it has
|
||
limited support for concurrency and
|
||
typically lives on the local file system.
|
||
|
||
* There should only be one instance of ``celery beat`` running in your
|
||
entire setup. If not, background jobs can get scheduled multiple times
|
||
resulting in weird behaviors like duplicate delivery of reports,
|
||
higher than expected load / traffic etc.
|
||
|
||
* SQL Lab will only run your queries asynchronously if you enable
|
||
"Asynchronous Query Execution" in your database settings.
|
||
|
||
|
||
Email Reports
|
||
-------------
|
||
Email reports allow users to schedule email reports for
|
||
|
||
* chart and dashboard visualization (Attachment or inline)
|
||
* chart data (CSV attachment on inline table)
|
||
|
||
**Setup**
|
||
|
||
Make sure you enable email reports in your configuration file
|
||
|
||
.. code-block:: python
|
||
|
||
ENABLE_SCHEDULED_EMAIL_REPORTS = True
|
||
|
||
Now you will find two new items in the navigation bar that allow you to schedule email
|
||
reports
|
||
|
||
* Manage -> Dashboard Emails
|
||
* Manage -> Chart Email Schedules
|
||
|
||
Schedules are defined in crontab format and each schedule
|
||
can have a list of recipients (all of them can receive a single mail,
|
||
or separate mails). For audit purposes, all outgoing mails can have a
|
||
mandatory bcc.
|
||
|
||
In order get picked up you need to configure a celery worker and a celery beat
|
||
(see section above "Celery Tasks"). Your celery configuration also
|
||
needs an entry ``email_reports.schedule_hourly`` for ``CELERYBEAT_SCHEDULE``.
|
||
|
||
To send emails you need to configure SMTP settings in your configuration file. e.g.
|
||
|
||
.. code-block:: python
|
||
|
||
EMAIL_NOTIFICATIONS = True
|
||
|
||
SMTP_HOST = "email-smtp.eu-west-1.amazonaws.com"
|
||
SMTP_STARTTLS = True
|
||
SMTP_SSL = False
|
||
SMTP_USER = "smtp_username"
|
||
SMTP_PORT = 25
|
||
SMTP_PASSWORD = os.environ.get("SMTP_PASSWORD")
|
||
SMTP_MAIL_FROM = "insights@komoot.com"
|
||
|
||
|
||
To render dashboards you need to install a local browser on your superset instance
|
||
|
||
* `geckodriver <https://github.com/mozilla/geckodriver>`_ and Firefox is preferred
|
||
* `chromedriver <http://chromedriver.chromium.org/>`_ is a good option too
|
||
|
||
You need to adjust the ``EMAIL_REPORTS_WEBDRIVER`` accordingly in your configuration.
|
||
|
||
You also need to specify on behalf of which username to render the dashboards. In general
|
||
dashboards and charts are not accessible to unauthorized requests, that is why the
|
||
worker needs to take over credentials of an existing user to take a snapshot. ::
|
||
|
||
EMAIL_REPORTS_USER = 'username_with_permission_to_access_dashboards'
|
||
|
||
|
||
**Important notes**
|
||
|
||
* Be mindful of the concurrency setting for celery (using ``-c 4``).
|
||
Selenium/webdriver instances can consume a lot of CPU / memory on your servers.
|
||
|
||
* In some cases, if you notice a lot of leaked ``geckodriver`` processes, try running
|
||
your celery processes with ::
|
||
|
||
celery worker --pool=prefork --max-tasks-per-child=128 ...
|
||
|
||
* It is recommended to run separate workers for ``sql_lab`` and
|
||
``email_reports`` tasks. Can be done by using ``queue`` field in ``CELERY_ANNOTATIONS``
|
||
|
||
* Adjust ``WEBDRIVER_BASEURL`` in your config if celery workers can't access superset via its
|
||
default value ``http://0.0.0.0:8080/`` (notice the port number 8080, many other setups use
|
||
port 8088).
|
||
|
||
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. In such cases, it is preferred to use ``celery`` to run the queries
|
||
in the background. Please follow the examples/notes mentioned above to get your
|
||
celery setup working.
|
||
|
||
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,
|
||
}
|
||
|
||
SQL Lab also includes a live query validation feature with pluggable backends.
|
||
You can configure which validation implementation is used with which database
|
||
engine by adding a block like the following to your config.py:
|
||
|
||
.. code-block:: python
|
||
|
||
FEATURE_FLAGS = {
|
||
'SQL_VALIDATORS_BY_ENGINE': {
|
||
'presto': 'PrestoDBSQLValidator',
|
||
}
|
||
}
|
||
|
||
The available validators and names can be found in `sql_validators/`.
|
||
|
||
**Scheduling queries**
|
||
|
||
You can optionally allow your users to schedule queries directly in SQL Lab.
|
||
This is done by addding extra metadata to saved queries, which are then picked
|
||
up by an external scheduled (like [Apache Airflow](https://airflow.apache.org/)).
|
||
|
||
To allow scheduled queries, add the following to your `config.py`:
|
||
|
||
.. code-block:: python
|
||
|
||
FEATURE_FLAGS = {
|
||
# Configuration for scheduling queries from SQL Lab. This information is
|
||
# collected when the user clicks "Schedule query", and saved into the `extra`
|
||
# field of saved queries.
|
||
# See: https://github.com/mozilla-services/react-jsonschema-form
|
||
'SCHEDULED_QUERIES': {
|
||
'JSONSCHEMA': {
|
||
'title': 'Schedule',
|
||
'description': (
|
||
'In order to schedule a query, you need to specify when it '
|
||
'should start running, when it should stop running, and how '
|
||
'often it should run. You can also optionally specify '
|
||
'dependencies that should be met before the query is '
|
||
'executed. Please read the documentation for best practices '
|
||
'and more information on how to specify dependencies.'
|
||
),
|
||
'type': 'object',
|
||
'properties': {
|
||
'output_table': {
|
||
'type': 'string',
|
||
'title': 'Output table name',
|
||
},
|
||
'start_date': {
|
||
'type': 'string',
|
||
'title': 'Start date',
|
||
# date-time is parsed using the chrono library, see
|
||
# https://www.npmjs.com/package/chrono-node#usage
|
||
'format': 'date-time',
|
||
'default': 'tomorrow at 9am',
|
||
},
|
||
'end_date': {
|
||
'type': 'string',
|
||
'title': 'End date',
|
||
# date-time is parsed using the chrono library, see
|
||
# https://www.npmjs.com/package/chrono-node#usage
|
||
'format': 'date-time',
|
||
'default': '9am in 30 days',
|
||
},
|
||
'schedule_interval': {
|
||
'type': 'string',
|
||
'title': 'Schedule interval',
|
||
},
|
||
'dependencies': {
|
||
'type': 'array',
|
||
'title': 'Dependencies',
|
||
'items': {
|
||
'type': 'string',
|
||
},
|
||
},
|
||
},
|
||
},
|
||
'UISCHEMA': {
|
||
'schedule_interval': {
|
||
'ui:placeholder': '@daily, @weekly, etc.',
|
||
},
|
||
'dependencies': {
|
||
'ui:help': (
|
||
'Check the documentation for the correct format when '
|
||
'defining dependencies.'
|
||
),
|
||
},
|
||
},
|
||
'VALIDATION': [
|
||
# ensure that start_date <= end_date
|
||
{
|
||
'name': 'less_equal',
|
||
'arguments': ['start_date', 'end_date'],
|
||
'message': 'End date cannot be before start date',
|
||
# this is where the error message is shown
|
||
'container': 'end_date',
|
||
},
|
||
],
|
||
# link to the scheduler; this example links to an Airflow pipeline
|
||
# that uses the query id and the output table as its name
|
||
'linkback': (
|
||
'https://airflow.example.com/admin/airflow/tree?'
|
||
'dag_id=query_${id}_${extra_json.schedule_info.output_table}'
|
||
),
|
||
},
|
||
}
|
||
|
||
This feature flag is based on [react-jsonschema-form](https://github.com/mozilla-services/react-jsonschema-form),
|
||
and will add a button called "Schedule Query" to SQL Lab. When the button is
|
||
clicked, a modal will show up where the user can add the metadata required for
|
||
scheduling the query.
|
||
|
||
This information can then be retrieved from the endpoint `/savedqueryviewapi/api/read`
|
||
and used to schedule the queries that have `scheduled_queries` in their JSON
|
||
metadata. For schedulers other than Airflow, additional fields can be easily
|
||
added to the configuration file above.
|
||
|
||
Celery Flower
|
||
-------------
|
||
Flower is a web based tool for monitoring the Celery cluster which you can
|
||
install from pip: ::
|
||
|
||
pip install flower
|
||
|
||
and run via: ::
|
||
|
||
celery flower --app=superset.tasks.celery_app:app
|
||
|
||
Building from source
|
||
---------------------
|
||
|
||
More advanced users may want to build Superset from sources. That
|
||
would be the case if you fork the project to add features specific to
|
||
your environment. See `CONTRIBUTING.md#setup-local-environment-for-development <https://github.com/apache/incubator-superset/blob/master/CONTRIBUTING.md#setup-local-environment-for-development>`_.
|
||
|
||
Blueprints
|
||
----------
|
||
|
||
`Blueprints are Flask's reusable apps <https://flask.palletsprojects.com/en/1.0.x/tutorial/views/>`_.
|
||
Superset allows you to specify an array of Blueprints
|
||
in your ``superset_config`` module. Here's
|
||
an example of 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-block:: python
|
||
|
||
from flask import Blueprint
|
||
simple_page = Blueprint('simple_page', __name__,
|
||
template_folder='templates')
|
||
@simple_page.route('/', defaults={'page': 'index'})
|
||
@simple_page.route('/<page>')
|
||
def show(page):
|
||
return "Ok"
|
||
|
||
BLUEPRINTS = [simple_page]
|
||
|
||
StatsD logging
|
||
--------------
|
||
|
||
Superset is instrumented to log events to StatsD if desired. Most endpoints hit
|
||
are logged as well as key events like query start and end in SQL Lab.
|
||
|
||
To setup StatsD logging, it's a matter of configuring the logger in your
|
||
``superset_config.py``.
|
||
|
||
.. code-block:: python
|
||
|
||
from superset.stats_logger import StatsdStatsLogger
|
||
STATS_LOGGER = StatsdStatsLogger(host='localhost', port=8125, prefix='superset')
|
||
|
||
Note that it's also possible to implement you own logger by deriving
|
||
``superset.stats_logger.BaseStatsLogger``.
|
||
|
||
|
||
Install Superset with helm in Kubernetes
|
||
----------------------------------------
|
||
|
||
You can install Superset into Kubernetes with Helm <https://helm.sh/>. The chart is
|
||
located in ``install/helm``.
|
||
|
||
To install Superset into your Kubernetes:
|
||
|
||
.. code-block:: bash
|
||
|
||
helm upgrade --install superset ./install/helm/superset
|
||
|
||
Note that the above command will install Superset into ``default`` namespace of your Kubernetes cluster.
|
||
|
||
Custom OAuth2 configuration
|
||
---------------------------
|
||
|
||
Beyond FAB supported providers (github, twitter, linkedin, google, azure), its easy to connect Superset with other OAuth2 Authorization Server implementations that support "code" authorization.
|
||
|
||
The first step: Configure authorization in Superset ``superset_config.py``.
|
||
|
||
.. code-block:: python
|
||
|
||
AUTH_TYPE = AUTH_OAUTH
|
||
OAUTH_PROVIDERS = [
|
||
{ 'name':'egaSSO',
|
||
'token_key':'access_token', # Name of the token in the response of access_token_url
|
||
'icon':'fa-address-card', # Icon for the provider
|
||
'remote_app': {
|
||
'consumer_key':'myClientId', # Client Id (Identify Superset application)
|
||
'consumer_secret':'MySecret', # Secret for this Client Id (Identify Superset application)
|
||
'request_token_params':{
|
||
'scope': 'read' # Scope for the Authorization
|
||
},
|
||
'access_token_method':'POST', # HTTP Method to call access_token_url
|
||
'access_token_params':{ # Additional parameters for calls to access_token_url
|
||
'client_id':'myClientId'
|
||
},
|
||
'access_token_headers':{ # Additional headers for calls to access_token_url
|
||
'Authorization': 'Basic Base64EncodedClientIdAndSecret'
|
||
},
|
||
'base_url':'https://myAuthorizationServer/oauth2AuthorizationServer/',
|
||
'access_token_url':'https://myAuthorizationServer/oauth2AuthorizationServer/token',
|
||
'authorize_url':'https://myAuthorizationServer/oauth2AuthorizationServer/authorize'
|
||
}
|
||
}
|
||
]
|
||
|
||
# Will allow user self registration, allowing to create Flask users from Authorized User
|
||
AUTH_USER_REGISTRATION = True
|
||
|
||
# The default user self registration role
|
||
AUTH_USER_REGISTRATION_ROLE = "Public"
|
||
|
||
Second step: Create a `CustomSsoSecurityManager` that extends `SupersetSecurityManager` and overrides `oauth_user_info`:
|
||
|
||
.. code-block:: python
|
||
|
||
from superset.security import SupersetSecurityManager
|
||
|
||
class CustomSsoSecurityManager(SupersetSecurityManager):
|
||
|
||
def oauth_user_info(self, provider, response=None):
|
||
logging.debug("Oauth2 provider: {0}.".format(provider))
|
||
if provider == 'egaSSO':
|
||
# As example, this line request a GET to base_url + '/' + userDetails with Bearer Authentication,
|
||
# and expects that authorization server checks the token, and response with user details
|
||
me = self.appbuilder.sm.oauth_remotes[provider].get('userDetails').data
|
||
logging.debug("user_data: {0}".format(me))
|
||
return { 'name' : me['name'], 'email' : me['email'], 'id' : me['user_name'], 'username' : me['user_name'], 'first_name':'', 'last_name':''}
|
||
...
|
||
|
||
This file must be located at the same directory than ``superset_config.py`` with the name ``custom_sso_security_manager.py``.
|
||
|
||
Then we can add this two lines to ``superset_config.py``:
|
||
|
||
.. code-block:: python
|
||
|
||
from custom_sso_security_manager import CustomSsoSecurityManager
|
||
CUSTOM_SECURITY_MANAGER = CustomSsoSecurityManager
|
||
|
||
Feature Flags
|
||
-------------
|
||
|
||
Because of a wide variety of users, Superset has some features that are not enabled by default. For example, some users have stronger security restrictions, while some others may not. So Superset allow users to enable or disable some features by config. For feature owners, you can add optional functionalities in Superset, but will be only affected by a subset of users.
|
||
|
||
You can enable or disable features with flag from ``superset_config.py``:
|
||
|
||
.. code-block:: python
|
||
|
||
DEFAULT_FEATURE_FLAGS = {
|
||
'CLIENT_CACHE': False,
|
||
'ENABLE_EXPLORE_JSON_CSRF_PROTECTION': False,
|
||
'PRESTO_EXPAND_DATA': False,
|
||
}
|
||
|
||
Here is a list of flags and descriptions:
|
||
|
||
* ENABLE_EXPLORE_JSON_CSRF_PROTECTION
|
||
|
||
* For some security concerns, you may need to enforce CSRF protection on all query request to explore_json endpoint. In Superset, we use `flask-csrf <https://sjl.bitbucket.io/flask-csrf/>`_ add csrf protection for all POST requests, but this protection doesn't apply to GET method.
|
||
|
||
* When ENABLE_EXPLORE_JSON_CSRF_PROTECTION is set to true, your users cannot make GET request to explore_json. The default value for this feature False (current behavior), explore_json accepts both GET and POST request. See `PR 7935 <https://github.com/apache/incubator-superset/pull/7935>`_ for more details.
|
||
|
||
* PRESTO_EXPAND_DATA
|
||
|
||
* When this feature is enabled, nested types in Presto will be expanded into extra columns and/or arrays. This is experimental, and doesn't work with all nested types.
|
||
|
||
|
||
SIP-15
|
||
------
|
||
|
||
`SIP-15 <https://github.com/apache/incubator-superset/issues/6360>`_ aims to ensure that time intervals are handled in a consistent and transparent manner for both the Druid and SQLAlchemy connectors.
|
||
|
||
Prior to SIP-15 SQLAlchemy used inclusive endpoints however these may behave like exclusive for string columns (due to lexicographical ordering) if no formatting was defined and the column formatting did not conform to an ISO 8601 date-time (refer to the SIP for details).
|
||
|
||
To remedy this rather than having to define the date/time format for every non-IS0 8601 date-time column, once can define a default column mapping on a per database level via the ``extra`` parameter ::
|
||
|
||
{
|
||
"python_date_format_by_column_name": {
|
||
"ds": "%Y-%m-%d"
|
||
}
|
||
}
|
||
|
||
Additionally to aid with transparency the current endpoint behavior is explicitly called out in the chart time range (post SIP-15 this will be [start, end) for all connectors and databases). One can override the defaults on a per database level via the ``extra``
|
||
parameter ::
|
||
|
||
{
|
||
"time_range_endpoints": ["inclusive", "inclusive"]
|
||
}
|