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.. http://www.apache.org/licenses/LICENSE-2.0
.. Unless required by applicable law or agreed to in writing,
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KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
Installation & Configuration
============================
Getting Started
---------------
Superset supports Python versions ``>3.7`` to take advantage of the
newer Python features and reduce the burden of supporting previous versions.
We run our test suite against ``3.7``, with a subset of tests additionally
also being run against ``3.8``.
Cloud-native!
-------------
Superset is designed to be highly available. It is
"cloud-native" as it has been designed scale out in large,
distributed environments, and works well inside containers.
While you can easily
test drive Superset on a modest setup or simply on your laptop,
there's virtually no limit around scaling out the platform.
Superset is also cloud-native in the sense that it is
flexible and lets you choose your web server (Gunicorn, Nginx, Apache),
your metadata database engine (MySQL, Postgres, MariaDB, ...),
your message queue (Redis, RabbitMQ, SQS, ...),
your results backend (S3, Redis, Memcached, ...), your caching layer
(Memcached, Redis, ...), works well with services like NewRelic, StatsD and
DataDog, and has the ability to run analytic workloads against
most popular database technologies.
Superset is battle tested in large environments with hundreds
of concurrent users. Airbnb's production environment runs inside
Kubernetes and serves 600+ daily active users viewing over 100K charts a
day.
The Superset web server and the Superset Celery workers (optional)
are stateless, so you can scale out by running on as many servers
as needed.
Install and Deploy Superset Locally with Docker
-----------------------------------------------
To try Superset locally, the
best-supported currently method is via Docker, using ``docker-compose``. Superset
does not have official support for Windows, so we have provided a VM
workaround below. (We will update this documentation once Windows is
supported.)
**Step 0 - Install a Docker Engine and Docker Compose**
*Mac OSX:*
`Install Docker for Mac <https://docs.docker.com/docker-for-mac/install/>`__, which includes the Docker engine and a recent version of `docker-compose` out of the box.
Once you have Docker for Mac installed, open up the preferences pane for Docker, go to the "Resources" section and increase the allocated memory to 6GB. With only the 2GB of RAM allocated by default, Superset will fail to start.
*Linux:*
`Install Docker on Linux <https://docs.docker.com/engine/install/>`__ by following Docker’s instructions for whichever flavor of Linux suits you.
Because ``docker-compose`` is not installed as part of the base Docker installation on Linux, once you have a working engine, follow the `docker-compose installation instructions <https://docs.docker.com/compose/install/>`__ for Linux.
*Windows:*
NOTE: Windows is currently not a supported environment for Superset installation.
For Windows users, the best option may be to install an Ubuntu Desktop VM via `VirtualBox <https://www.virtualbox.org/>`__ and proceed with the Docker on Linux instructions inside of that VM. It is recommended to assign at least 8GB of RAM to the virtual machine as well as provisioning a hard drive of at least 40GB, so that there will be enough space for both the OS and all of the required dependencies.
**Step 1 - Clone Superset's Github repository**
`Clone Superset's repo <https://github.com/apache/superset>`__
in your terminal with the following command:
.. code:: bash
$ git clone https://github.com/apache/superset.git
Once that command completes successfully, you should see a new
``superset`` folder in your current directory.
**Step 2 - Launch Superset via `docker-compose up`**
Next, `cd` into the folder you created in Step 1:
.. code:: bash
$ cd superset
Once you're in the directory, run the following command:
.. code:: bash
$ docker-compose up
You should see a wall of logging output from the containers being
launched on your machine. Once this output slows to a crawl, you should
have a running instance of Superset on your local machine!
**Step 3 - Log In to Superset**
Your Superset local instance also includes a Postgres server to store
your data and is already pre-loaded with some example datasets that ship
with Superset. You can access Superset now via your web browser by
visiting ``http://localhost:8088``. Note that many browsers now default
to ``https`` - if yours is one of them, please make sure it uses
``http``.
Log in with the default username and password:
::
username: admin
password: admin
Congrats! You have successfully installed Superset!
.. note ::
The Docker-related files and documentation are actively maintained and
managed by the core committers working on the project. Help and contributions
around Docker are welcomed! See also `docker/README.md <https://github.com/apache/superset/blob/master/docker/README.md>`_ for additional information.
OS dependencies
---------------
Superset stores database connection information in its metadata database.
For that purpose, we use the ``cryptography`` Python library to encrypt
connection passwords. Unfortunately, this library has OS level dependencies.
You may want to attempt the next step
("Superset installation and initialization") and come back to this step if
you encounter an error.
Here's how to install them:
For **Debian** and **Ubuntu**, the following command will ensure that
the required dependencies are installed: ::
sudo apt-get install build-essential libssl-dev libffi-dev python-dev python-pip libsasl2-dev libldap2-dev
**Ubuntu 20.04** the following command will ensure that the required dependencies are installed: ::
sudo apt-get install build-essential libssl-dev libffi-dev python3-dev python3-pip libsasl2-dev libldap2-dev
otherwise build for ``cryptography`` fails.
For **Fedora** and **RHEL-derivatives**, the following command will ensure
that the required dependencies are installed: ::
sudo yum upgrade python-setuptools
sudo yum install gcc gcc-c++ libffi-devel python-devel python-pip python-wheel openssl-devel cyrus-sasl-devel openldap-devel
**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.
You *will likely need* the latest version of XCode available for your installed version of OS X. You should also install
the XCode command line tools: ::
xcode-select --install
System python is not recommended. Homebrew's python also ships with pip: ::
brew install pkg-config libffi openssl python
env LDFLAGS="-L$(brew --prefix openssl)/lib" CFLAGS="-I$(brew --prefix openssl)/include" pip install cryptography==2.4.2
**Windows** isn't officially supported at this point, but if you want to
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: ::
C:\> pip install cryptography
# You may also have to create C:\Temp
C:\> md C:\Temp
Python virtualenv
-----------------
It is recommended to install Superset inside a virtualenv. Python 3 already ships virtualenv.
But if it's not installed in your environment for some reason, you can install it
via the package for your operating systems, otherwise you can install from pip: ::
pip install virtualenv
You can create and activate a virtualenv by: ::
# virtualenv is shipped in Python 3.6+ as venv instead of pyvenv.
# See https://docs.python.org/3.6/library/venv.html
python3 -m venv venv
. venv/bin/activate
On Windows the syntax for activating it is a bit different: ::
venv\Scripts\activate
Once you activated your virtualenv everything you are doing is confined inside the virtualenv.
To exit a virtualenv just type ``deactivate``.
Python's setup tools and pip
----------------------------
Put all the chances on your side by getting the very latest ``pip``
and ``setuptools`` libraries.::
pip install --upgrade setuptools pip
Superset installation and initialization
----------------------------------------
Follow these few simple steps to install Superset.::
# Install superset
pip install apache-superset
# Initialize the database
superset db upgrade
# Create an admin user (you will be prompted to set a username, first and last name before setting a password)
$ export FLASK_APP=superset
superset fab create-admin
# Load some data to play with
superset load_examples
# Create default roles and permissions
superset init
# To start a development web server on port 8088, use -p to bind to another port
superset run -p 8088 --with-threads --reload --debugger
After installation, you should be able to point your browser to the right
hostname:port `http://localhost:8088 <http://localhost:8088>`_, login using
the credential you entered while creating the admin account, and navigate to
`Menu -> Admin -> Refresh Metadata`. This action should bring in all of
your datasources for Superset to be aware of, and they should show up in
`Menu -> Datasources`, from where you can start playing with your data!
A proper WSGI HTTP Server
-------------------------
While you can setup Superset to run on Nginx or Apache, many use
Gunicorn, preferably in **async mode**, which allows for impressive
concurrency even and is fairly easy to install and configure. Please
refer to the
documentation of your preferred technology to set up this Flask WSGI
application in a way that works well in your environment. Here's an **async**
setup known to work well in production: ::
　gunicorn \
-w 10 \
-k gevent \
--timeout 120 \
-b 0.0.0.0:6666 \
--limit-request-line 0 \
--limit-request-field_size 0 \
--statsd-host localhost:8125 \
"superset.app:create_app()"
Refer to the
`Gunicorn documentation <https://docs.gunicorn.org/en/stable/design.html>`_
for more information.
Note that the development web
server (`superset run` or `flask run`) is not intended for production use.
If not using gunicorn, you may want to disable the use of flask-compress
by setting `COMPRESS_REGISTER = False` in your `superset_config.py`
Configuration behind a load balancer
------------------------------------
If you are running superset behind a load balancer or reverse proxy (e.g. NGINX
or ELB on AWS), you may need to utilise a healthcheck endpoint so that your
load balancer knows if your superset instance is running. This is provided
at ``/health`` which will return a 200 response containing "OK" if the
the webserver is running.
If the load balancer is inserting X-Forwarded-For/X-Forwarded-Proto headers, you
should set `ENABLE_PROXY_FIX = True` in the superset config file to extract and use
the headers.
In case that the reverse proxy is used for providing ssl encryption,
an explicit definition of the `X-Forwarded-Proto` may be required.
For the Apache webserver this can be set as follows: ::
RequestHeader set X-Forwarded-Proto "https"
Configuration
-------------
To configure your application, you need to create a file (module)
``superset_config.py`` and make sure it is in your PYTHONPATH. Here are some
of the parameters you can copy / paste in that configuration module: ::
#---------------------------------------------------------
# Superset specific config
#---------------------------------------------------------
ROW_LIMIT = 5000
SUPERSET_WEBSERVER_PORT = 8088
#---------------------------------------------------------
#---------------------------------------------------------
# Flask App Builder configuration
#---------------------------------------------------------
# Your App secret key
SECRET_KEY = '\2\1thisismyscretkey\1\2\e\y\y\h'
# The SQLAlchemy connection string to your database backend
# This connection defines the path to the database that stores your
# superset metadata (slices, connections, tables, dashboards, ...).
# Note that the connection information to connect to the datasources
# you want to explore are managed directly in the web UI
SQLALCHEMY_DATABASE_URI = 'sqlite:////path/to/superset.db'
# Flask-WTF flag for CSRF
WTF_CSRF_ENABLED = True
# Add endpoints that need to be exempt from CSRF protection
WTF_CSRF_EXEMPT_LIST = []
# A CSRF token that expires in 1 year
WTF_CSRF_TIME_LIMIT = 60 * 60 * 24 * 365
# Set this API key to enable Mapbox visualizations
MAPBOX_API_KEY = ''
All the parameters and default values defined in
https://github.com/apache/superset/blob/master/superset/config.py
can be altered in your local ``superset_config.py`` .
Administrators will want to
read through the file to understand what can be configured locally
as well as the default values in place.
Since ``superset_config.py`` acts as a Flask configuration module, it
can be used to alter the settings Flask itself,
as well as Flask extensions like ``flask-wtf``, ``flask-caching``,
``flask-migrate``, and ``flask-appbuilder``. Flask App Builder, the web
framework used by Superset offers many configuration settings. Please consult
the `Flask App Builder Documentation
<https://flask-appbuilder.readthedocs.org/en/latest/config.html>`_
for more information on how to configure it.
Make sure to change:
* *SQLALCHEMY_DATABASE_URI*, by default it is stored at *~/.superset/superset.db*
* *SECRET_KEY*, to a long random string
In case you need to exempt endpoints from CSRF, e.g. you are running a custom
auth postback endpoint, you can add them to *WTF_CSRF_EXEMPT_LIST*
WTF_CSRF_EXEMPT_LIST = ['']
.. _ref_database_deps:
Caching
-------
Superset uses `Flask-Caching <https://flask-caching.readthedocs.io/>`_ for
caching purpose. Configuring your caching backend is as easy as providing
``CACHE_CONFIG`` and ``DATA_CACHE_CONFIG`, constants in ``superset_config.py``
that complies with `the Flask-Caching specifications <https://flask-caching.readthedocs.io/en/latest/#configuring-flask-caching>`_.
Flask-Caching 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 chart data, Superset goes up a “timeout search path”, from a slice's configuration
to the datasource’s, the database’s, then ultimately falls back to the global default
defined in ``DATA_CACHE_CONFIG``.
.. code-block:: python
DATA_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-Caching <https://flask-caching.readthedocs.io/>`_ API.
.. code-block:: python
from custom_caching import CustomCache
def init_data_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)
DATA_CACHE_CONFIG = init_data_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.
Caching Thumbnails
------------------
This is an optional feature that can be turned on by activating its feature flag on config:
.. code-block:: python
FEATURE_FLAGS = {
"THUMBNAILS": True,
"THUMBNAILS_SQLA_LISTENERS": True,
}
For this feature you will need a cache system and celery workers. All thumbnails are stored on cache and are processed
asynchronously by the workers.
An example config where images are stored on S3 could be:
.. code-block:: python
from flask import Flask
from s3cache.s3cache import S3Cache
...
class CeleryConfig(object):
BROKER_URL = "redis://localhost:6379/0"
CELERY_IMPORTS = ("superset.sql_lab", "superset.tasks", "superset.tasks.thumbnails")
CELERY_RESULT_BACKEND = "redis://localhost:6379/0"
CELERYD_PREFETCH_MULTIPLIER = 10
CELERY_ACKS_LATE = True
CELERY_CONFIG = CeleryConfig
def init_thumbnail_cache(app: Flask) -> S3Cache:
return S3Cache("bucket_name", 'thumbs_cache/')
THUMBNAIL_CACHE_CONFIG = init_thumbnail_cache
# Async selenium thumbnail task will use the following user
THUMBNAIL_SELENIUM_USER = "Admin"
Using the above example cache keys for dashboards will be `superset_thumb__dashboard__{ID}`
You can override the base URL for selenium using:
.. code-block:: python
WEBDRIVER_BASEURL = "https://superset.company.com"
Additional selenium web drive config can be set using `WEBDRIVER_CONFIGURATION`
You can implement a custom function to authenticate selenium, the default uses flask-login session cookie.
An example of a custom function signature:
.. code-block:: python
def auth_driver(driver: WebDriver, user: "User") -> WebDriver:
pass
Then on config:
.. code-block:: python
WEBDRIVER_AUTH_FUNC = auth_driver
Database dependencies
---------------------
Superset does not ship bundled with connectivity to databases, except
for Sqlite, which is part of the Python standard library.
You'll need to install the required packages for the database you
want to use as your metadata database as well as the packages needed to
connect to the databases you want to access through Superset.
Here's a list of some of the recommended packages.
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Database | PyPI package | SQLAlchemy URI prefix |
+==================+===================================================================+=================================================+
| Amazon Athena | ``"apache-superset[athena]"`` | ``awsathena+jdbc://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Amazon Redshift | ``"apache-superset[redshift]"`` | ``redshift+psycopg2://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Apache Drill | ``"apache-superset[drill]"`` | For the REST API:`` |
| | | ``drill+sadrill://`` |
| | | For JDBC |
| | | ``drill+jdbc://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Apache Druid | ``"apache-superset[druid]"`` | ``druid://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Apache Hive | ``"apache-superset[hive]"`` | ``hive://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Apache Impala | ``"apache-superset[impala]"`` | ``impala://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Apache Kylin | ``"apache-superset[kylin]"`` | ``kylin://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Apache Pinot | ``"apache-superset[pinot]"`` | ``pinot+http://CONTROLLER:5436/`` |
| | | ``query?server=http://CONTROLLER:5983/`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Apache Solr | ``pip install sqlalchemy-solr`` | ``solr://`` |
+------------------+---------------------------------------+-----------------------------------------------------------------------------+
| Apache Spark SQL | ``"apache-superset[hive]"`` | ``jdbc+hive://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| BigQuery | ``"apache-superset[bigquery]"`` | ``bigquery://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| ClickHouse | ``"apache-superset[clickhouse]"`` | |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| CockroachDB | ``"apache-superset[cockroachdb]"`` | ``cockroachdb://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Dremio | ``"apache-superset[dremio]"`` | ``dremio://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Elasticsearch | ``"apache-superset[elasticsearch]"`` | ``elasticsearch+http://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Exasol | ``"apache-superset[exasol]"`` | ``exa+pyodbc://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Google Sheets | ``"apache-superset[gsheets]"`` | ``gsheets://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| IBM Db2 | ``"apache-superset[db2]"`` | ``db2+ibm_db://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| MySQL | ``"apache-superset[mysql]"`` | ``mysql://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Oracle | ``"apache-superset[oracle]"`` | ``oracle://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| PostgreSQL | ``"apache-superset[postgres]"`` | ``postgresql+psycopg2://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Presto | ``"apache-superset[presto]"`` | ``presto://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| SAP HANA | ``"apache-superset[hana]"`` | ``hana://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Snowflake | ``"apache-superset[snowflake]"`` | ``snowflake://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| SQLite | | ``sqlite://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| SQL Server | ``"apache-superset[mssql]"`` | ``mssql://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Teradata | ``"apache-superset[teradata]"`` | ``teradata://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
| Vertica | ``"apache-superset[vertical]"`` | ``vertica+vertica_python://`` |
+------------------+-------------------------------------------------------------------+-------------------------------------------------+
Note that many other databases are supported, the main criteria being the
existence of a functional SqlAlchemy dialect and Python driver. Googling
the keyword ``sqlalchemy`` in addition of a keyword that describes the
database you want to connect to should get you to the right place.
PostgreSQL
------------
The connection string for PostgreSQL looks like this ::
postgresql+psycopg2://{username}:{password}@{host}:{port}/{database}
Additional may be configured via the ``extra`` field under ``engine_params``.
If you would like to enable mutual SSL here is a sample configuration:
.. code-block:: json
{
"metadata_params": {},
"engine_params": {
"connect_args":{
"sslmode": "require",
"sslrootcert": "/path/to/root_cert"
}
}
}
If the key ``sslrootcert`` is present the server's certificate will be verified to be signed by the same Certificate Authority (CA).
If you would like to enable mutual SSL here is a sample configuration:
.. code-block:: json
{
"metadata_params": {},
"engine_params": {
"connect_args":{
"sslmode": "require",
"sslcert": "/path/to/client_cert",
"sslkey": "/path/to/client_key",
"sslrootcert": "/path/to/root_cert"
}
}
}
See `psycopg2 SQLAlchemy <https://docs.sqlalchemy.org/en/13/dialects/postgresql.html#module-sqlalchemy.dialects.postgresql.psycopg2>`_.
Hana
------------
The connection string for Hana looks like this ::
hana://{username}:{password}@{host}:{port}
(AWS) Athena
------------
The connection string for Athena looks like this ::
awsathena+jdbc://{aws_access_key_id}:{aws_secret_access_key}@athena.{region_name}.amazonaws.com/{schema_name}?s3_staging_dir={s3_staging_dir}&...
Where you need to escape/encode at least the s3_staging_dir, i.e., ::
s3://... -> s3%3A//...
You can also use `PyAthena` library(no java required) like this ::
awsathena+rest://{aws_access_key_id}:{aws_secret_access_key}@athena.{region_name}.amazonaws.com/{schema_name}?s3_staging_dir={s3_staging_dir}&...
See `PyAthena <https://github.com/laughingman7743/PyAthena#sqlalchemy>`_.
(Google) BigQuery
-----------------
The connection string for BigQuery looks like this ::
bigquery://{project_id}
Additionally, you will need to configure authentication via a
Service Account. Create your Service Account via the Google
Cloud Platform control panel, provide it access to the appropriate
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 ::
{
"credentials_info": <contents of credentials JSON file>
}
The resulting file should have this structure ::
{
"credentials_info": {
"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": "...",
}
}
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.
Apache Solr
------------
The connection string for Apache Solr looks like this ::
solr://{username}:{password}@{host}:{port}/{server_path}/{collection}[/?use_ssl=true|false]
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/role rights during engine creation by default. However, when
pressing the "Test Connection" button in the Create or Edit Database dialog,
user/role credentials are validated by passing `"validate_default_parameters": True`
to the `connect()` method during engine creation. If the user/role is not authorized
to access the database, an error is recorded in the Superset logs.
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/>`_
Deeper SQLAlchemy integration
-----------------------------
It is possible to tweak the database connection information using the
parameters exposed by SQLAlchemy. In the ``Database`` edit view, you will
find an ``extra`` field as a ``JSON`` blob.
.. image:: _static/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 are 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
-----
The native Druid connector (behind the ``DRUID_IS_ACTIVE`` feature flag)
is slowly getting deprecated in favor of the SQLAlchemy/DBAPI connector made
available in the ``pydruid`` library.
To use a custom SSL certificate to validate HTTPS requests, the certificate
contents can be entered in the ``Root Certificate`` field in the Database
dialog. When using a custom certificate, ``pydruid`` will automatically use
``https`` scheme. To disable SSL verification add the following to extras:
``engine_params": {"connect_args": {"scheme": "https", "ssl_verify_cert": false}}``
Dremio
------
Install the following dependencies to connect to Dremio:
* Dremio SQLAlchemy: ``pip install sqlalchemy_dremio``
* If you receive any errors during the installation of ``sqlalchemy_dremio``, make sure to install the prerequisites for PyODBC properly by following the instructions for your OS here: https://github.com/narendrans/sqlalchemy_dremio#installation
* Dremio's ODBC driver: https://www.dremio.com/drivers/
Example SQLAlchemy URI: ``dremio://dremio:dremio123@localhost:31010/dremio``
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:
.. code-block:: text
pip install apache-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/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 its database. These logs 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 --app=superset.tasks.celery_app:app worker --pool=prefork -O fair -c 4
* To start a job which schedules periodic background jobs, run ::
celery --app=superset.tasks.celery_app:app beat
To setup a result backend, you need to pass an instance of a derivative
of ``from cachelib.base.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 cachelib.redis 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.
**Async queries for dashboards and Explore**
It's also possible to configure database queries for charts to operate in `async` mode.
This is especially useful for dashboards with many charts that may otherwise be affected
by browser connection limits. To enable async queries for dashboards and Explore, the
following dependencies are required:
- Redis 5.0+ (the feature utilizes `Redis Streams <https://redis.io/topics/streams-intro>`_)
- Cache backends enabled via the ``CACHE_CONFIG`` and ``DATA_CACHE_CONFIG`` config settings
- Celery workers configured and running to process async tasks
The following configuration settings are available for async queries (see config.py for default values)
- ``GLOBAL_ASYNC_QUERIES`` (feature flag) - enable or disable async query operation
- ``GLOBAL_ASYNC_QUERIES_REDIS_CONFIG`` - Redis connection info
- ``GLOBAL_ASYNC_QUERIES_REDIS_STREAM_PREFIX`` - the prefix used with Redis Streams
- ``GLOBAL_ASYNC_QUERIES_REDIS_STREAM_LIMIT`` - the maximum number of events for each user-specific event stream (FIFO eviction)
- ``GLOBAL_ASYNC_QUERIES_REDIS_STREAM_LIMIT_FIREHOSE`` - the maximum number of events for all users (FIFO eviction)
- ``GLOBAL_ASYNC_QUERIES_JWT_COOKIE_NAME`` - the async query feature uses a `JWT <https://tools.ietf.org/html/rfc7519>`_ cookie for authentication, this setting is the cookie's name
- ``GLOBAL_ASYNC_QUERIES_JWT_COOKIE_SECURE`` - JWT cookie secure option
- ``GLOBAL_ASYNC_QUERIES_JWT_COOKIE_DOMAIN`` - JWT cookie domain option (`see docs for set_cookie <https://tedboy.github.io/flask/interface_api.response_object.html#flask.Response.set_cookie>`
- ``GLOBAL_ASYNC_QUERIES_JWT_SECRET`` - JWT's use a secret key to sign and validate the contents. This value should be at least 32 bytes and have sufficient randomness for proper security
- ``GLOBAL_ASYNC_QUERIES_TRANSPORT`` - available options: "polling" (HTTP, default), "ws" (WebSocket, requires running superset-websocket server)
- ``GLOBAL_ASYNC_QUERIES_POLLING_DELAY`` - the time (in ms) between polling requests
More information on the async query feature can be found in `SIP-39 <https://github.com/apache/superset/issues/9190>`_.
**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
This flag enables some permissions that are stored in your database, so you'll want to run `superset init` again if you are running this in a dev environment.
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 ``WEBDRIVER_TYPE`` 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,
}
Default values for jinja templates can be specified via ``Parameters`` menu in the SQL Lab user interface.
In the UI you can assign a set of parameters as JSON
.. code-block:: JSON
{
"my_table": "foo"
}
The parameters become available in your SQL (example:SELECT * FROM {{ my_table }} ) by using Jinja templating syntax.
SQL Lab template parameters are stored with the dataset as TEMPLATE PARAMETERS.
There is a special ``_filters`` parameter which can be used to test filters used in the jinja template.
.. code-block:: JSON
{
"_filters": [ {
"col": "action_type",
"op": "IN",
"val": ["sell", "buy"]
} ]
}
.. code-block:: python
SELECT action, count(*) as times
FROM logs
WHERE
action in ({{ "'" + "','".join(filter_values('action_type')) + "'" }})
GROUP BY action
Note ``_filters`` is not stored with the dataset. It's only used within the SQL Lab UI.
Besides default Jinja templating, SQL lab also supports self-defined template
processor by setting the ``CUSTOM_TEMPLATE_PROCESSORS`` in your superset configuration.
The values in this dictionary overwrite the default Jinja template processors of the
specified database engine.
The example below configures a custom presto template processor which implements
its own logic of processing macro template with regex parsing. It uses ``$`` style
macro instead of ``{{ }}`` style in Jinja templating. By configuring it with
``CUSTOM_TEMPLATE_PROCESSORS``, sql template on presto database is processed
by the custom one rather than the default one.
.. code-block:: python
def DATE(
ts: datetime, day_offset: SupportsInt = 0, hour_offset: SupportsInt = 0
) -> str:
"""Current day as a string."""
day_offset, hour_offset = int(day_offset), int(hour_offset)
offset_day = (ts + timedelta(days=day_offset, hours=hour_offset)).date()
return str(offset_day)
class CustomPrestoTemplateProcessor(PrestoTemplateProcessor):
"""A custom presto template processor."""
engine = "presto"
def process_template(self, sql: str, **kwargs) -> str:
"""Processes a sql template with $ style macro using regex."""
# Add custom macros functions.
macros = {
"DATE": partial(DATE, datetime.utcnow())
} # type: Dict[str, Any]
# Update with macros defined in context and kwargs.
macros.update(self.context)
macros.update(kwargs)
def replacer(match):
"""Expand $ style macros with corresponding function calls."""
macro_name, args_str = match.groups()
args = [a.strip() for a in args_str.split(",")]
if args == [""]:
args = []
f = macros[macro_name[1:]]
return f(*args)
macro_names = ["$" + name for name in macros.keys()]
pattern = r"(%s)\s*\(([^()]*)\)" % "|".join(map(re.escape, macro_names))
return re.sub(pattern, replacer, sql)
CUSTOM_TEMPLATE_PROCESSORS = {
CustomPrestoTemplateProcessor.engine: CustomPrestoTemplateProcessor
}
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 adding 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/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 the ``helm`` directory.
To install Superset in your Kubernetes cluster with Helm 3, run:
.. code-block:: bash
helm dep install ./helm/superset
helm upgrade --install superset ./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': {
'client_id':'myClientId', # Client Id (Identify Superset application)
'client_secret':'MySecret', # Secret for this Client Id (Identify Superset application)
'client_kwargs':{
'scope': 'read' # Scope for the Authorization
},
'access_token_params':{ # Additional parameters for calls to access_token_url
'client_id':'myClientId'
},
'api_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
import logging
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
FEATURE_FLAGS = {
'CLIENT_CACHE': False,
'ENABLE_EXPLORE_JSON_CSRF_PROTECTION': False,
'PRESTO_EXPAND_DATA': False,
}
A current list of feature flags can be found in `RESOURCES/FEATURE_FLAGS.md`
SIP-15
------
`SIP-15 <https://github.com/apache/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"
}
}
**New deployments**
All new Superset deployments should enable SIP-15 via,
.. code-block:: python
SIP_15_ENABLED = True
**Existing deployments**
Given that it is not apparent whether the chart creator was aware of the time range inconsistencies (and adjusted the endpoints accordingly) changing the behavior of all charts is overly aggressive. Instead SIP-15 proivides a soft transistion allowing producers (chart owners) to see the impact of the proposed change and adjust their charts accordingly.
Prior to enabling SIP-15 existing deployments should communicate to their users the impact of the change and define a grace period end date (exclusive of course) after which all charts will conform to the [start, end) interval, i.e.,
.. code-block:: python
from dateime import date
SIP_15_ENABLED = True
SIP_15_GRACE_PERIOD_END = date(<YYYY>, <MM>, <DD>)
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"]
}
Note in a future release the interim SIP-15 logic will be removed (including the ``time_grain_endpoints`` form-data field) via a code change and Alembic migration.