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.gitignore
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*.pyc
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changelog.sh
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panoramix
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caravel
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superset
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babel
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.DS_Store
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.coverage
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_build
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build
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*.db
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tmp
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dashed_config.py
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caravel_config.py
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superset_config.py
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local_config.py
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env
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dist
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dashed.egg-info/
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caravel.egg-info/
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superset.egg-info/
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env_py3
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.eggs
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dashed/
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docs/
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app.db
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*.bak
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# Node.js, webpack artifacts
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*.entry.js
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*.js.map
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node_modules
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npm-debug.log
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doc-warnings: yes
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disable:
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- cyclic-import
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- invalid-name
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options:
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docstring-min-length: 10
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pep8:
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full: true
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ignore-paths:
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- docs
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- dashed/migrations/env.py
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- dashed/ascii_art.py
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- ^example/doc_.*\.py$
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- (^|/)docs(/|$)
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python-targets:
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- 2
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- 3
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<a href="../index.html" class="icon icon-home"> Superset's documentation
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<h1>All modules for which code is available</h1>
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<ul><li><a href="superset/jinja_context.html">superset.jinja_context</a></li>
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||||
</ul>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
<footer>
|
||||
|
||||
|
||||
<hr/>
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
|
||||
</p>
|
||||
</div>
|
||||
Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
|
||||
|
||||
</footer>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</section>
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<script type="text/javascript">
|
||||
var DOCUMENTATION_OPTIONS = {
|
||||
URL_ROOT:'../',
|
||||
VERSION:'',
|
||||
COLLAPSE_INDEX:false,
|
||||
FILE_SUFFIX:'.html',
|
||||
HAS_SOURCE: true
|
||||
};
|
||||
</script>
|
||||
<script type="text/javascript" src="../_static/jquery.js"></script>
|
||||
<script type="text/javascript" src="../_static/underscore.js"></script>
|
||||
<script type="text/javascript" src="../_static/doctools.js"></script>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<script type="text/javascript" src="../_static/js/theme.js"></script>
|
||||
|
||||
|
||||
|
||||
|
||||
<script type="text/javascript">
|
||||
jQuery(function () {
|
||||
SphinxRtdTheme.StickyNav.enable();
|
||||
});
|
||||
</script>
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,48 +0,0 @@
|
||||
Druid
|
||||
=====
|
||||
|
||||
Superset works well with Druid, though currently not all
|
||||
advanced features out of Druid are covered. This page clarifies what is
|
||||
covered and what isn't and explains how to use some of the features.
|
||||
|
||||
.. note ::
|
||||
Currently Airbnb runs against Druid ``0.8.x`` and previous /
|
||||
following versions are not tested against.
|
||||
|
||||
Supported
|
||||
'''''''''
|
||||
|
||||
Aggregations
|
||||
------------
|
||||
|
||||
Common aggregations, or Druid metrics can be defined and used in Superset.
|
||||
The first and simpler use case is to use the checkbox matrix expose in your
|
||||
datasource's edit view (``Sources -> Druid Datasources ->
|
||||
[your datasource] -> Edit -> [tab] List Druid Column``).
|
||||
Clicking the ``GroupBy`` and ``Filterable`` checkboxes will make the column
|
||||
appear in the related dropdowns while in explore view. Checking
|
||||
``Count Distinct``, ``Min``, ``Max`` or ``Sum`` will result in creating
|
||||
new metrics that will appear in the ``List Druid Metric`` tab upon saving the
|
||||
datasource. By editing these metrics, you'll notice that they their ``json``
|
||||
element correspond to Druid aggregation definition. You can create your own
|
||||
aggregations manually from the ``List Druid Metric`` tab following Druid
|
||||
documentation.
|
||||
|
||||
.. image:: _static/img/druid_agg.png
|
||||
:scale: 50 %
|
||||
|
||||
Post-Aggregations
|
||||
-----------------
|
||||
|
||||
Druid supports post aggregation and this works in Superset. All you have to
|
||||
do is creating a metric, much like you would create an aggregation manually,
|
||||
but specify ``postagg`` as a ``Metric Type``. You then have to provide a valid
|
||||
json post-aggregation definition (as specified in the Druid docs) in the
|
||||
Json field.
|
||||
|
||||
|
||||
Not yet supported
|
||||
'''''''''''''''''
|
||||
|
||||
- Regex filters
|
||||
- Lookups / joins
|
||||
139
_sources/faq.txt
139
_sources/faq.txt
@@ -1,139 +0,0 @@
|
||||
FAQ
|
||||
===
|
||||
|
||||
|
||||
Can I query/join multiple tables at one time?
|
||||
---------------------------------------------
|
||||
Not directly no. A Superset SQLAlchemy datasource can only be a single table
|
||||
or a view.
|
||||
|
||||
When working with tables, the solution would be to materialize
|
||||
a table that contains all the fields needed for your analysis, most likely
|
||||
through some scheduled batch process.
|
||||
|
||||
A view is a simple logical layer that abstract an arbitrary SQL queries as
|
||||
a virtual table. This can allow you to join and union multiple tables, and
|
||||
to apply some transformation using arbitrary SQL expressions. The limitation
|
||||
there is your database performance as Superset effectively will run a query
|
||||
on top of your query (view). A good practice may be to limit yourself to
|
||||
joining your main large table to one or many small tables only, and avoid
|
||||
using ``GROUP BY`` where possible as Superset will do its own ``GROUP BY`` and
|
||||
doing the work twice might slow down performance.
|
||||
|
||||
Whether you use a table or a view, the important factor is whether your
|
||||
database is fast enough to serve it in an interactive fashion to provide
|
||||
a good user experience in Superset.
|
||||
|
||||
|
||||
How BIG can my data source be?
|
||||
------------------------------
|
||||
|
||||
It can be gigantic! As mentioned above, the main criteria is whether your
|
||||
database can execute queries and return results in a time frame that is
|
||||
acceptable to your users. Many distributed databases out there can execute
|
||||
queries that scan through terabytes in an interactive fashion.
|
||||
|
||||
|
||||
How do I create my own visualization?
|
||||
-------------------------------------
|
||||
|
||||
We are planning on making it easier to add new visualizations to the
|
||||
framework, in the meantime, we've tagged a few pull requests as
|
||||
``example`` to give people examples of how to contribute new
|
||||
visualizations.
|
||||
|
||||
https://github.com/airbnb/superset/issues?q=label%3Aexample+is%3Aclosed
|
||||
|
||||
|
||||
Why are my queries timing out?
|
||||
------------------------------
|
||||
|
||||
If you are seeing timeouts (504 Gateway Time-out) when running queries,
|
||||
it's because the web server is timing out web requests. If you want to
|
||||
increase the default (50), you can specify the timeout when starting the
|
||||
web server with the ``-t`` flag, which is expressed in seconds.
|
||||
|
||||
``superset runserver -t 300``
|
||||
|
||||
|
||||
Why is the map not visible in the mapbox visualization?
|
||||
-------------------------------------------------------
|
||||
|
||||
You need to register to mapbox.com, get an API key and configure it as
|
||||
``MAPBOX_API_KEY`` in ``superset_config.py``.
|
||||
|
||||
|
||||
How to add dynamic filters to a dashboard?
|
||||
------------------------------------------
|
||||
|
||||
It's easy: use the ``Filter Box`` widget, build a slice, and add it to your
|
||||
dashboard.
|
||||
|
||||
The ``Filter Box`` widget allows you to define a query to populate dropdowns
|
||||
that can be use for filtering. To build the list of distinct values, we
|
||||
run a query, and sort the result by the metric you provide, sorting
|
||||
descending.
|
||||
|
||||
The widget also has a checkbox ``Date Filter``, which enables time filtering
|
||||
capabilities to your dashboard. After checking the box and refreshing, you'll
|
||||
see a ``from`` and a ``to`` dropdown show up.
|
||||
|
||||
By default, the filtering will be applied to all the slices that are built
|
||||
on top of a datasource that shares the column name that the filter is based
|
||||
on. It's also a requirement for that column to be checked as "filterable"
|
||||
in the column tab of the table editor.
|
||||
|
||||
But what about if you don't want certain widgets to get filtered on your
|
||||
dashboard? You can do that by editing your dashboard, and in the form,
|
||||
edit the ``JSON Metadata`` field, more specifically the
|
||||
``filter_immune_slices`` key, that receives an array of sliceIds that should
|
||||
never be affected by any dashboard level filtering.
|
||||
|
||||
|
||||
..code::
|
||||
|
||||
{
|
||||
"filter_immune_slices": [324, 65, 92],
|
||||
"expanded_slices": {},
|
||||
"filter_immune_slice_fields": {
|
||||
"177": ["country_name", "__from", "__to"],
|
||||
"32": ["__from", "__to"]
|
||||
}
|
||||
}
|
||||
|
||||
In the json blob above, slices 324, 65 and 92 won't be affected by any
|
||||
dashboard level filtering.
|
||||
|
||||
Now note the ``filter_immune_slice_fields`` key. This one allows you to
|
||||
be more specific and define for a specific slice_id, which filter fields
|
||||
should be disregarded.
|
||||
|
||||
Note the use of the ``__from`` and ``__to`` keywords, those are reserved
|
||||
for dealing with the time boundary filtering mentioned above.
|
||||
|
||||
But what happens with filtering when dealing with slices coming from
|
||||
different tables or databases? If the column name is shared, the filter will
|
||||
be applied, it's as simple as that.
|
||||
|
||||
Why does fabmanager or superset freezed/hung/not responding when started (my home directory is NFS mounted)?
|
||||
-----------------------------------------------------------------------------------------
|
||||
superset creates and uses an sqlite database at ``~/.superset/superset.db``. Sqlite is known to `don't work well if used on NFS`__ due to broken file locking implementation on NFS.
|
||||
|
||||
__ https://www.sqlite.org/lockingv3.html
|
||||
|
||||
One work around is to create a symlink from ~/.superset to a directory located on a non-NFS partition.
|
||||
|
||||
Another work around is to change where superset stores the sqlite database by adding ``SQLALCHEMY_DATABASE_URI = 'sqlite:////new/localtion/superset.db'`` in superset_config.py (create the file if needed), then adding the directory where superset_config.py lives to PYTHONPATH environment variable (e.g. ``export PYTHONPATH=/opt/logs/sandbox/airbnb/``).
|
||||
|
||||
How do I add new columns to an existing table
|
||||
---------------------------------------------
|
||||
|
||||
Table schemas evolve, and Superset needs to reflect that. It's pretty common
|
||||
in the life cycle of a dashboard to want to add a new dimension or metric.
|
||||
To get Superset to discover your new columns, all you have to do is to
|
||||
go to ``Menu -> Sources -> Tables``, click the ``edit`` icon next to the
|
||||
table who's schema has changed, and hit ``Save`` from the ``Detail`` tab.
|
||||
Behind the scene, the new columns will get merged it. Following this,
|
||||
you may want to
|
||||
re-edit the table afterwards to configure the ``Column`` tab, check the
|
||||
appropriate boxes and save again.
|
||||
@@ -1,89 +0,0 @@
|
||||
Gallery
|
||||
=======
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/line.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/bubble.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/table.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/pie.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/bar.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/world_map.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/sankey.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/word_cloud.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/filter_box.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/pivot_table.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/directed_force.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/compare.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/sunburst.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/area.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/big_number.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/big_number_total.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/bullet.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/dist_bar.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/heatmap.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/markup.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/para.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/iframe.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/box_plot.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/treemap.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/cal_heatmap.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/horizon.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/mapbox.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/separator.png
|
||||
:scale: 25 %
|
||||
|
||||
.. image:: _static/img/viz_thumbnails/histogram.png
|
||||
:scale: 25 %
|
||||
@@ -1,74 +0,0 @@
|
||||
.. image:: _static/img/s.png
|
||||
|
||||
Superset's documentation
|
||||
''''''''''''''''''''''''
|
||||
|
||||
Superset is a data exploration platform designed to be visual, intuitive
|
||||
and interactive.
|
||||
|
||||
|
||||
----------------
|
||||
|
||||
.. warning:: This project was originally named Panoramix, was renamed to
|
||||
Caravel in March 2016, and is currently named Superset as of November 2016
|
||||
|
||||
Overview
|
||||
=======================================
|
||||
|
||||
Features
|
||||
---------
|
||||
|
||||
- A rich set of data visualizations, integrated from some of the best
|
||||
visualization libraries
|
||||
- Create and share simple dashboards
|
||||
- An extensible, high-granularity security/permission model allowing
|
||||
intricate rules on who can access individual features and the dataset
|
||||
- Enterprise-ready authentication with integration with major authentication
|
||||
providers (database, OpenID, LDAP, OAuth & REMOTE_USER through
|
||||
Flask AppBuilder)
|
||||
- A simple semantic layer, allowing users to control how data sources are
|
||||
displayed in the UI by defining which fields should show up in which
|
||||
drop-down and which aggregation and function metrics are made available
|
||||
to the user
|
||||
- Integration with most RDBMS through SqlAlchemy
|
||||
- Deep integration with Druid.io
|
||||
|
||||
------
|
||||
|
||||
.. image:: https://camo.githubusercontent.com/82e264ef777ba06e1858766fe3b8817ee108eb7e/687474703a2f2f672e7265636f726469742e636f2f784658537661475574732e676966
|
||||
|
||||
------
|
||||
|
||||
.. image:: https://camo.githubusercontent.com/4991ff37a0005ea4e4267919a52786fda82d2d21/687474703a2f2f672e7265636f726469742e636f2f755a6767594f645235672e676966
|
||||
|
||||
------
|
||||
|
||||
.. image:: https://camo.githubusercontent.com/a389af15ac1e32a3d0fee941b4c62c850b1d583b/687474703a2f2f672e7265636f726469742e636f2f55373046574c704c76682e676966
|
||||
|
||||
------
|
||||
|
||||
|
||||
Contents
|
||||
---------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
installation
|
||||
tutorial
|
||||
security
|
||||
sqllab
|
||||
visualization
|
||||
videos
|
||||
gallery
|
||||
druid
|
||||
faq
|
||||
|
||||
|
||||
Indices and tables
|
||||
------------------
|
||||
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
|
||||
@@ -1,475 +0,0 @@
|
||||
Installation & Configuration
|
||||
============================
|
||||
|
||||
Getting Started
|
||||
---------------
|
||||
|
||||
Superset is tested against Python ``2.7`` and Python ``3.4``.
|
||||
Airbnb currently uses 2.7.* in production. We do not plan on supporting
|
||||
Python ``2.6``.
|
||||
|
||||
|
||||
OS dependencies
|
||||
---------------
|
||||
|
||||
Superset stores database connection information in its metadata database.
|
||||
For that purpose, we use the ``cryptography`` Python library to encrypt
|
||||
connection passwords. Unfortunately this library has OS level dependencies.
|
||||
|
||||
You may want to attempt the next step
|
||||
("Superset installation and initialization") and come back to this step if
|
||||
you encounter an error.
|
||||
|
||||
Here's how to install them:
|
||||
|
||||
For **Debian** and **Ubuntu**, the following command will ensure that
|
||||
the required dependencies are installed: ::
|
||||
|
||||
sudo apt-get install build-essential libssl-dev libffi-dev python-dev python-pip libsasl2-dev libldap2-dev
|
||||
|
||||
For **Fedora** and **RHEL-derivatives**, the following command will ensure
|
||||
that the required dependencies are installed: ::
|
||||
|
||||
sudo yum upgrade python-setuptools
|
||||
sudo yum install gcc gcc-c++ libffi-devel python-devel python-pip python-wheel openssl-devel libsasl2-devel openldap-devel
|
||||
|
||||
**OSX**, system python is not recommended. brew's python also ships with pip ::
|
||||
|
||||
brew install pkg-config libffi openssl python
|
||||
env LDFLAGS="-L$(brew --prefix openssl)/lib" CFLAGS="-I$(brew --prefix openssl)/include" pip install cryptography==1.7.2
|
||||
|
||||
**Windows** isn't officially supported at this point, but if you want to
|
||||
attempt it, download `get-pip.py <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, for
|
||||
Python 2 you need to install it. If it's packaged for your operating systems install it from there
|
||||
otherwise you can install from pip: ::
|
||||
|
||||
pip install virtualenv
|
||||
|
||||
You can create and activate a virtualenv by: ::
|
||||
|
||||
# virtualenv is shipped in Python 3 as pyvenv
|
||||
virtualenv venv
|
||||
. ./venv/bin/activate
|
||||
|
||||
On windows the syntax for activating it is a bit different: ::
|
||||
|
||||
venv\Scripts\activate
|
||||
|
||||
Once you activated your virtualenv everything you are doing is confined inside the virtualenv.
|
||||
To exit a virtualenv just type ``deactivate``.
|
||||
|
||||
Python's setup tools and pip
|
||||
----------------------------
|
||||
Put all the chances on your side by getting the very latest ``pip``
|
||||
and ``setuptools`` libraries.::
|
||||
|
||||
pip install --upgrade setuptools pip
|
||||
|
||||
Superset installation and initialization
|
||||
----------------------------------------
|
||||
Follow these few simple steps to install Superset.::
|
||||
|
||||
# Install superset
|
||||
pip install superset
|
||||
|
||||
# Create an admin user (you will be prompted to set username, first and last name before setting a password)
|
||||
fabmanager create-admin --app superset
|
||||
|
||||
# Initialize the database
|
||||
superset db upgrade
|
||||
|
||||
# Load some data to play with
|
||||
superset load_examples
|
||||
|
||||
# Create default roles and permissions
|
||||
superset init
|
||||
|
||||
# Start the web server on port 8088, use -p to bind to another port
|
||||
superset runserver
|
||||
|
||||
# To start a development web server, use the -d switch
|
||||
# superset runserver -d
|
||||
|
||||
|
||||
After installation, you should be able to point your browser to the right
|
||||
hostname:port `http://localhost:8088 <http://localhost:8088>`_, login using
|
||||
the credential you entered while creating the admin account, and navigate to
|
||||
`Menu -> Admin -> Refresh Metadata`. This action should bring in all of
|
||||
your datasources for Superset to be aware of, and they should show up in
|
||||
`Menu -> Datasources`, from where you can start playing with your data!
|
||||
|
||||
Please note that *gunicorn*, Superset default application server, does not
|
||||
work on Windows so you need to use the development web server.
|
||||
The development web server though is not intended to be used on production systems
|
||||
so better use a supported platform that can run *gunicorn*.
|
||||
|
||||
Configuration behind a load balancer
|
||||
------------------------------------
|
||||
|
||||
If you are running superset behind a load balancer or reverse proxy (e.g. NGINX
|
||||
or ELB on AWS), you may need to utilise a healthcheck endpoint so that your
|
||||
load balancer knows if your superset instance is running. This is provided
|
||||
at ``/health`` which will return a 200 response containing "OK" if the
|
||||
webserver is running.
|
||||
|
||||
If the load balancer is inserting X-Forwarded-For/X-Forwarded-Proto headers, you
|
||||
should set `ENABLE_PROXY_FIX = True` in the superset config file to extract and use
|
||||
the headers.
|
||||
|
||||
|
||||
Configuration
|
||||
-------------
|
||||
|
||||
To configure your application, you need to create a file (module)
|
||||
``superset_config.py`` and make sure it is in your PYTHONPATH. Here are some
|
||||
of the parameters you can copy / paste in that configuration module: ::
|
||||
|
||||
#---------------------------------------------------------
|
||||
# Superset specific config
|
||||
#---------------------------------------------------------
|
||||
ROW_LIMIT = 5000
|
||||
SUPERSET_WORKERS = 4
|
||||
|
||||
SUPERSET_WEBSERVER_PORT = 8088
|
||||
#---------------------------------------------------------
|
||||
|
||||
#---------------------------------------------------------
|
||||
# Flask App Builder configuration
|
||||
#---------------------------------------------------------
|
||||
# Your App secret key
|
||||
SECRET_KEY = '\2\1thisismyscretkey\1\2\e\y\y\h'
|
||||
|
||||
# The SQLAlchemy connection string to your database backend
|
||||
# This connection defines the path to the database that stores your
|
||||
# superset metadata (slices, connections, tables, dashboards, ...).
|
||||
# Note that the connection information to connect to the datasources
|
||||
# you want to explore are managed directly in the web UI
|
||||
SQLALCHEMY_DATABASE_URI = 'sqlite:////path/to/superset.db'
|
||||
|
||||
# Flask-WTF flag for CSRF
|
||||
CSRF_ENABLED = True
|
||||
|
||||
# Set this API key to enable Mapbox visualizations
|
||||
MAPBOX_API_KEY = ''
|
||||
|
||||
This file also allows you to define configuration parameters used by
|
||||
Flask App Builder, the web framework used by Superset. Please consult
|
||||
the `Flask App Builder Documentation
|
||||
<http://flask-appbuilder.readthedocs.org/en/latest/config.html>`_
|
||||
for more information on how to configure Superset.
|
||||
|
||||
Please make sure to change:
|
||||
|
||||
* *SQLALCHEMY_DATABASE_URI*, by default it is stored at *~/.superset/superset.db*
|
||||
* *SECRET_KEY*, to a long random string
|
||||
|
||||
Database dependencies
|
||||
---------------------
|
||||
|
||||
Superset does not ship bundled with connectivity to databases, except
|
||||
for Sqlite, which is part of the Python standard library.
|
||||
You'll need to install the required packages for the database you
|
||||
want to use as your metadata database as well as the packages needed to
|
||||
connect to the databases you want to access through Superset.
|
||||
|
||||
Here's a list of some of the recommended packages.
|
||||
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| database | pypi package | SQLAlchemy URI prefix |
|
||||
+===============+=====================================+=================================================+
|
||||
| MySQL | ``pip install mysqlclient`` | ``mysql://`` |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| Postgres | ``pip install psycopg2`` | ``postgresql+psycopg2://`` |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| Presto | ``pip install pyhive`` | ``presto://`` |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| Oracle | ``pip install cx_Oracle`` | ``oracle://`` |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| sqlite | | ``sqlite://`` |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| Redshift | ``pip install sqlalchemy-redshift`` | ``postgresql+psycopg2://`` |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| MSSQL | ``pip install pymssql`` | ``mssql://`` |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| Impala | ``pip install impyla`` | ``impala://`` |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| SparkSQL | ``pip install pyhive`` | ``jdbc+hive://`` |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| Greenplum | ``pip install psycopg2`` | ``postgresql+psycopg2://`` |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| Athena | ``pip install "PyAthenaJDBC>1.0.9"``| ``awsathena+jdbc://`` |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| Vertica | ``pip install | ``vertica+vertica_python://`` |
|
||||
| | sqlalchemy-vertica-python`` | |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
| ClickHouse | ``pip install | ``clickhouse://`` |
|
||||
| | sqlalchemy-clickhouse`` | |
|
||||
+---------------+-------------------------------------+-------------------------------------------------+
|
||||
|
||||
Note that many other database are supported, the main criteria being the
|
||||
existence of a functional SqlAlchemy dialect and Python driver. Googling
|
||||
the keyword ``sqlalchemy`` in addition of a keyword that describes the
|
||||
database you want to connect to should get you to the right place.
|
||||
|
||||
(AWS) Athena
|
||||
------------
|
||||
|
||||
This currently relies on an unreleased future version of `PyAthenaJDBC <https://github.com/laughingman7743/PyAthenaJDBC>`_. If you're adventurous or simply impatient, you can install directly from git: ::
|
||||
|
||||
pip install git+https://github.com/laughingman7743/PyAthenaJDBC@support_sqlalchemy
|
||||
|
||||
The connection string for Athena looks like this ::
|
||||
|
||||
awsathena+jdbc://{aws_access_key_id}:{aws_secret_access_key}@athena.{region_name}.amazonaws.com/{schema_name}?s3_staging_dir={s3_staging_dir}&...
|
||||
|
||||
Where you need to escape/encode at least the s3_staging_dir, i.e., ::
|
||||
|
||||
s3://... -> s3%3A//...
|
||||
|
||||
|
||||
Caching
|
||||
-------
|
||||
|
||||
Superset uses `Flask-Cache <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``.
|
||||
|
||||
|
||||
Deeper SQLAlchemy integration
|
||||
-----------------------------
|
||||
|
||||
It is possible to tweak the database connection information using the
|
||||
parameters exposed by SQLAlchemy. In the ``Database`` edit view, you will
|
||||
find an ``extra`` field as a ``JSON`` blob.
|
||||
|
||||
.. image:: _static/img/tutorial/add_db.png
|
||||
:scale: 30 %
|
||||
|
||||
This JSON string contains extra configuration elements. The ``engine_params``
|
||||
object gets unpacked into the
|
||||
`sqlalchemy.create_engine <http://docs.sqlalchemy.org/en/latest/core/engines.html#sqlalchemy.create_engine>`_ call,
|
||||
while the ``metadata_params`` get unpacked into the
|
||||
`sqlalchemy.MetaData <http://docs.sqlalchemy.org/en/rel_1_0/core/metadata.html#sqlalchemy.schema.MetaData>`_ call. Refer to the SQLAlchemy docs for more information.
|
||||
|
||||
|
||||
Schemas (Postgres & Redshift)
|
||||
-----------------------------
|
||||
|
||||
Postgres and Redshift, as well as other database,
|
||||
use the concept of **schema** as a logical entity
|
||||
on top of the **database**. For Superset to connect to a specific schema,
|
||||
there's a **schema** parameter you can set in the table form.
|
||||
|
||||
|
||||
SSL Access to databases
|
||||
-----------------------
|
||||
This example worked with a MySQL database that requires SSL. The configuration
|
||||
may differ with other backends. This is what was put in the ``extra``
|
||||
parameter ::
|
||||
|
||||
{
|
||||
"metadata_params": {},
|
||||
"engine_params": {
|
||||
"connect_args":{
|
||||
"sslmode":"require",
|
||||
"sslrootcert": "/path/to/my/pem"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
Druid
|
||||
-----
|
||||
|
||||
* From the UI, enter the information about your clusters in the
|
||||
``Admin->Clusters`` menu by hitting the + sign.
|
||||
|
||||
* Once the Druid cluster connection information is entered, hit the
|
||||
``Admin->Refresh Metadata`` menu item to populate
|
||||
|
||||
* Navigate to your datasources
|
||||
|
||||
Note that you can run the ``superset refresh_druid`` command to refresh the
|
||||
metadata from your Druid cluster(s)
|
||||
|
||||
|
||||
CORS
|
||||
-----
|
||||
|
||||
The extra CORS Dependency must be installed:
|
||||
|
||||
superset[cors]
|
||||
|
||||
|
||||
The following keys in `superset_config.py` can be specified to configure CORS:
|
||||
|
||||
|
||||
* ``ENABLE_CORS``: Must be set to True in order to enable CORS
|
||||
* ``CORS_OPTIONS``: options passed to Flask-CORS (`documentation <http://flask-cors.corydolphin.com/en/latest/api.html#extension>`)
|
||||
|
||||
|
||||
MIDDLEWARE
|
||||
----------
|
||||
|
||||
Superset allows you to add your own middleware. To add your own middleware, update the ``ADDITIONAL_MIDDLEWARE`` key in
|
||||
your `superset_config.py`. ``ADDITIONAL_MIDDLEWARE`` should be a list of your additional middleware classes.
|
||||
|
||||
For example, to use AUTH_REMOTE_USER from behind a proxy server like nginx, you have to add a simple middleware class to
|
||||
add the value of ``HTTP_X_PROXY_REMOTE_USER`` (or any other custom header from the proxy) to Gunicorn's ``REMOTE_USER``
|
||||
environment variable: ::
|
||||
|
||||
class RemoteUserMiddleware(object):
|
||||
def __init__(self, app):
|
||||
self.app = app
|
||||
def __call__(self, environ, start_response):
|
||||
user = environ.pop('HTTP_X_PROXY_REMOTE_USER', None)
|
||||
environ['REMOTE_USER'] = user
|
||||
return self.app(environ, start_response)
|
||||
|
||||
ADDITIONAL_MIDDLEWARE = [RemoteUserMiddleware, ]
|
||||
|
||||
*Adapted from http://flask.pocoo.org/snippets/69/*
|
||||
|
||||
|
||||
Upgrading
|
||||
---------
|
||||
|
||||
Upgrading should be as straightforward as running::
|
||||
|
||||
pip install superset --upgrade
|
||||
superset db upgrade
|
||||
superset init
|
||||
|
||||
SQL Lab
|
||||
-------
|
||||
SQL Lab is a powerful SQL IDE that works with all SQLAlchemy compatible
|
||||
databases. By default, queries are executed in the scope of a web
|
||||
request so they
|
||||
may eventually timeout as queries exceed the maximum duration of a web
|
||||
request in your environment, whether it'd be a reverse proxy or the Superset
|
||||
server itself.
|
||||
|
||||
On large analytic databases, it's common to run queries that
|
||||
execute for minutes or hours.
|
||||
To enable support for long running queries that
|
||||
execute beyond the typical web request's timeout (30-60 seconds), it is
|
||||
necessary to configure an asynchronous backend for Superset which consist of:
|
||||
|
||||
* one or many Superset worker (which is implemented as a Celery worker), and
|
||||
can be started with the ``superset worker`` command, run
|
||||
``superset worker --help`` to view the related options
|
||||
* a celery broker (message queue) for which we recommend using Redis
|
||||
or RabbitMQ
|
||||
* a results backend that defines where the worker will persist the query
|
||||
results
|
||||
|
||||
Configuring Celery requires defining a ``CELERY_CONFIG`` in your
|
||||
``superset_config.py``. Both the worker and web server processes should
|
||||
have the same configuration.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
class CeleryConfig(object):
|
||||
BROKER_URL = 'redis://localhost:6379/0'
|
||||
CELERY_IMPORTS = ('superset.sql_lab', )
|
||||
CELERY_RESULT_BACKEND = 'redis://localhost:6379/0'
|
||||
CELERY_ANNOTATIONS = {'tasks.add': {'rate_limit': '10/s'}}
|
||||
|
||||
CELERY_CONFIG = CeleryConfig
|
||||
|
||||
To setup a result backend, you need to pass an instance of a derivative
|
||||
of ``werkzeug.contrib.cache.BaseCache`` to the ``RESULTS_BACKEND``
|
||||
configuration key in your ``superset_config.py``. It's possible to use
|
||||
Memcached, Redis, S3 (https://pypi.python.org/pypi/s3werkzeugcache),
|
||||
memory or the file system (in a single server-type setup or for testing),
|
||||
or to write your own caching interface. Your ``superset_config.py`` may
|
||||
look something like:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# On S3
|
||||
from s3cache.s3cache import S3Cache
|
||||
S3_CACHE_BUCKET = 'foobar-superset'
|
||||
S3_CACHE_KEY_PREFIX = 'sql_lab_result'
|
||||
RESULTS_BACKEND = S3Cache(S3_CACHE_BUCKET, S3_CACHE_KEY_PREFIX)
|
||||
|
||||
# On Redis
|
||||
from werkzeug.contrib.cache import RedisCache
|
||||
RESULTS_BACKEND = RedisCache(
|
||||
host='localhost', port=6379, key_prefix='superset_results')
|
||||
|
||||
|
||||
Also note that SQL Lab supports Jinja templating in queries, and that it's
|
||||
possible to overload
|
||||
the default Jinja context in your environment by defining the
|
||||
``JINJA_CONTEXT_ADDONS`` in your superset configuration. Objects referenced
|
||||
in this dictionary are made available for users to use in their SQL.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
JINJA_CONTEXT_ADDONS = {
|
||||
'my_crazy_macro': lambda x: x*2,
|
||||
}
|
||||
|
||||
|
||||
Making your own build
|
||||
---------------------
|
||||
|
||||
For more advanced users, you may want to build Superset from sources. That
|
||||
would be the case if you fork the project to add features specific to
|
||||
your environment.::
|
||||
|
||||
# assuming $SUPERSET_HOME as the root of the repo
|
||||
cd $SUPERSET_HOME/superset/assets
|
||||
npm install
|
||||
npm run build
|
||||
cd $SUPERSET_HOME
|
||||
python setup.py install
|
||||
|
||||
|
||||
Blueprints
|
||||
----------
|
||||
|
||||
`Blueprints are Flask's reusable apps <http://flask.pocoo.org/docs/0.12/blueprints/>`_.
|
||||
Superset allows you to specify an array of Blueprints
|
||||
in your ``superset_config`` module. Here's
|
||||
an example on how this can work with a simple Blueprint. By doing
|
||||
so, you can expect Superset to serve a page that says "OK"
|
||||
at the ``/simple_page`` url. This can allow you to run other things such
|
||||
as custom data visualization applications alongside Superset, on the
|
||||
same server.
|
||||
|
||||
..code ::
|
||||
|
||||
from flask import Blueprint
|
||||
simple_page = Blueprint('simple_page', __name__,
|
||||
template_folder='templates')
|
||||
@simple_page.route('/', defaults={'page': 'index'})
|
||||
@simple_page.route('/<page>')
|
||||
def show(page):
|
||||
return "Ok"
|
||||
|
||||
BLUEPRINTS = [simple_page]
|
||||
@@ -1,161 +0,0 @@
|
||||
Security
|
||||
========
|
||||
Security in Superset is handled by Flask AppBuilder (FAB). FAB is a
|
||||
"Simple and rapid application development framework, built on top of Flask.".
|
||||
FAB provides authentication, user management, permissions and roles.
|
||||
|
||||
|
||||
Provided Roles
|
||||
--------------
|
||||
Superset ships with a set of roles that are handled by Superset itself.
|
||||
You can assume that these roles will stay up-to-date as Superset evolves.
|
||||
Even though it's possible for ``Admin`` usrs to do so, it is not recommended
|
||||
that you alter these roles in any way by removing
|
||||
or adding permissions to them as these roles will be re-synchronized to
|
||||
their original values as you run your next ``superset init`` command.
|
||||
|
||||
Since it's not recommended to alter the roles described here, it's right
|
||||
to assume that your security strategy should be to compose user access based
|
||||
on these base roles and roles that you create. For instance you could
|
||||
create a role ``Financial Analyst`` that would be made of set of permissions
|
||||
to a set of data sources (tables) and/or databases. Users would then be
|
||||
granted ``Gamma``, ``Financial Analyst``, and perhaps ``sql_lab``.
|
||||
|
||||
Admin
|
||||
"""""
|
||||
Admins have all possible rights, including granting or revoking rights from
|
||||
other users and altering other people's slices and dashboards.
|
||||
|
||||
Alpha
|
||||
"""""
|
||||
Alpha have access to all data sources, but they cannot grant or revoke access
|
||||
from other users. They are also limited to altering the objects that they
|
||||
own. Alpha users can add and alter data sources.
|
||||
|
||||
Gamma
|
||||
"""""
|
||||
Gamma have limited access. They can only consume data coming from data sources
|
||||
they have been giving access to through another complementary role.
|
||||
They only have access to view the slices and
|
||||
dashboards made from data sources that they have access to. Currently Gamma
|
||||
users are not able to alter or add data sources. We assume that they are
|
||||
mostly content consumers, though they can create slices and dashboards.
|
||||
|
||||
Also note that when Gamma users look at the dashboards and slices list view,
|
||||
they will only see the objects that they have access to.
|
||||
|
||||
sql_lab
|
||||
"""""""
|
||||
The ``sql_lab`` role grants access to SQL Lab. Note that while ``Admin``
|
||||
users have access to all databases by default, both ``Alpha`` and ``Gamma``
|
||||
users need to be given access on a per database basis.
|
||||
|
||||
Public
|
||||
""""""
|
||||
It's possible to allow logged out users to access some Superset features.
|
||||
|
||||
By setting ``PUBLIC_ROLE_LIKE_GAMMA = True`` in your ``superset_config.py``,
|
||||
you grant public role the same set of permissions as for the GAMMA role.
|
||||
This is useful if one wants to enable anonymous users to view
|
||||
dashboards. Explicit grant on specific datasets is still required, meaning
|
||||
that you need to edit the ``Public`` role and add the Public data sources
|
||||
to the role manually.
|
||||
|
||||
|
||||
Managing Gamma per data source access
|
||||
-------------------------------------
|
||||
Here's how to provide users access to only specific datasets. First make
|
||||
sure the users with limited access have [only] the Gamma role assigned to
|
||||
them. Second, create a new role (``Menu -> Security -> List Roles``) and
|
||||
click the ``+`` sign.
|
||||
|
||||
.. image:: _static/img/create_role.png
|
||||
:scale: 50 %
|
||||
|
||||
This new window allows you to give this new role a name, attribute it to users
|
||||
and select the tables in the ``Permissions`` dropdown. To select the data
|
||||
sources you want to associate with this role, simply click in the dropdown
|
||||
and use the typeahead to search for your table names.
|
||||
|
||||
You can then confirm with your Gamma users that they see the objects
|
||||
(dashboards and slices) associated with the tables related to their roles.
|
||||
|
||||
|
||||
Customizing
|
||||
-----------
|
||||
|
||||
The permissions exposed by FAB are very granular and allow for a great level
|
||||
of customization. FAB creates many permissions automagically for each model
|
||||
that is create (can_add, can_delete, can_show, can_edit, ...) as well as for
|
||||
each view. On top of that, Superset can expose more granular permissions like
|
||||
``all_datasource_access``.
|
||||
|
||||
We do not recommend altering the 3 base roles as there
|
||||
are a set of assumptions that Superset build upon. It is possible though for
|
||||
you to create your own roles, and union them to existing ones.
|
||||
|
||||
Permissions
|
||||
"""""""""""
|
||||
|
||||
Roles are composed of a set of permissions, and Superset has many categories
|
||||
of permissions. Here are the different categories of permissions:
|
||||
|
||||
- **Model & action**: models are entities like ``Dashboard``,
|
||||
``Slice``, or ``User``. Each model has a fixed set of permissions, like
|
||||
``can_edit``, ``can_show``, ``can_delete``, ``can_list``, ``can_add``, and
|
||||
so on. By adding ``can_delete on Dashboard`` to a role, and granting that
|
||||
role to a user, this user will be able to delete dashboards.
|
||||
- **Views**: views are individual web pages, like the ``explore`` view or the
|
||||
``SQL Lab`` view. When granted to a user, he/she will see that view in
|
||||
the its menu items, and be able to load that page.
|
||||
- **Data source**: For each data source, a permission is created. If the user
|
||||
does not have the ``all_datasource_access`` permission granted, the user
|
||||
will only be able to see Slices or explore the data sources that are granted
|
||||
to them
|
||||
- **Database**: Granting access to a database allows for the user to access
|
||||
all data sources within that database, and will enable the user to query
|
||||
that database in SQL Lab, provided that the SQL Lab specific permission
|
||||
have been granted to the user
|
||||
|
||||
|
||||
Restricting access to a subset of data sources
|
||||
""""""""""""""""""""""""""""""""""""""""""""""
|
||||
|
||||
The best way to go is probably to give user ``Gamma`` plus one or many other
|
||||
roles that would add access to specific data sources. We recommend that you
|
||||
create individual roles for each access profile. Say people in your finance
|
||||
department might have access to a set of databases and data sources, and
|
||||
these permissions can be consolidated in a single role. Users with this
|
||||
profile then need to be attributed ``Gamma`` as a foundation to the models
|
||||
and views they can access, and that ``Finance`` role that is a collection
|
||||
of permissions to data objects.
|
||||
|
||||
One user can have many roles, so a finance executive could be granted
|
||||
``Gamma``, ``Finance``, and perhaps another ``Executive`` role that gather
|
||||
a set of data sources that power dashboards only made available to executives.
|
||||
When looking at its dashboard list, this user will only see the
|
||||
list of dashboards it has access to, based on the roles and
|
||||
permissions that were attributed.
|
||||
|
||||
|
||||
Restricting the access to some metrics
|
||||
""""""""""""""""""""""""""""""""""""""
|
||||
|
||||
Sometimes some metrics are relatively sensitive (e.g. revenue).
|
||||
We may want to restrict those metrics to only a few roles.
|
||||
For example, assumed there is a metric ``[cluster1].[datasource1].[revenue]``
|
||||
and only Admin users are allowed to see it. Here’s how to restrict the access.
|
||||
|
||||
1. Edit the datasource (``Menu -> Source -> Druid datasources -> edit the
|
||||
record "datasource1"``) and go to the tab ``List Druid Metric``. Check
|
||||
the checkbox ``Is Restricted`` in the row of the metric ``revenue``.
|
||||
|
||||
2. Edit the role (``Menu -> Security -> List Roles -> edit the record
|
||||
“Admin”``), in the permissions field, type-and-search the permission
|
||||
``metric access on [cluster1].[datasource1].[revenue] (id: 1)``, then
|
||||
click the Save button on the bottom of the page.
|
||||
|
||||
Any users without the permission will see the error message
|
||||
*Access to the metrics denied: revenue (Status: 500)* in the slices.
|
||||
It also happens when the user wants to access a post-aggregation metric that
|
||||
is dependent on revenue.
|
||||
@@ -1,64 +0,0 @@
|
||||
SQL Lab
|
||||
=======
|
||||
|
||||
SQL Lab is a modern, feature-rich SQL IDE written in
|
||||
`React <https://facebook.github.io/react/>`_.
|
||||
|
||||
|
||||
Feature Overview
|
||||
----------------
|
||||
- Connects to just about any database backend
|
||||
- A multi-tab environment to work on multiple queries at a time
|
||||
- A smooth flow to visualize your query results using Superset's rich
|
||||
visualization capabilities
|
||||
- Browse database metadata: tables, columns, indexes, partitions
|
||||
- Support for long-running queries
|
||||
|
||||
- uses the `Celery distributed queue <http://www.python.org/>`_
|
||||
to dispatch query handling to workers
|
||||
- supports defining a "results backend" to persist query results
|
||||
|
||||
- A search engine to find queries executed in the past
|
||||
- Supports templating using the
|
||||
`Jinja templating language <http://jinja.pocoo.org/docs/dev/>`_
|
||||
which allows for using macros in your SQL code
|
||||
|
||||
Extra features
|
||||
--------------
|
||||
- Hit ``alt + enter`` as a keyboard shortcut to run your query
|
||||
|
||||
Templating with Jinja
|
||||
---------------------
|
||||
|
||||
.. code-block:: sql
|
||||
|
||||
SELECT *
|
||||
FROM some_table
|
||||
WHERE partition_key = '{{ presto.latest_partition('some_table') }}'
|
||||
|
||||
Templating unleashes the power and capabilities of a
|
||||
programming language within your SQL code.
|
||||
|
||||
Templates can also be used to write generic queries that are
|
||||
parameterized so they can be re-used easily.
|
||||
|
||||
|
||||
Available macros
|
||||
''''''''''''''''
|
||||
|
||||
We expose certain modules from Python's standard library in
|
||||
Superset's Jinja context:
|
||||
- ``time``: ``time``
|
||||
- ``datetime``: ``datetime.datetime``
|
||||
- ``uuid``: ``uuid``
|
||||
- ``random``: ``random``
|
||||
- ``relativedelta``: ``dateutil.relativedelta.relativedelta``
|
||||
- more to come!
|
||||
|
||||
`Jinja's builtin filters <http://jinja.pocoo.org/docs/dev/templates/>`_ can be also be applied where needed.
|
||||
|
||||
|
||||
.. autoclass:: superset.jinja_context.PrestoTemplateProcessor
|
||||
:members:
|
||||
|
||||
.. autofunction:: superset.jinja_context.url_param
|
||||
@@ -1,308 +0,0 @@
|
||||
Tutorial for Superset Administrators
|
||||
====================================
|
||||
|
||||
This tutorial targets a Superset administrator: someone configuring Superset
|
||||
for an organization on behalf of users. We'll show you how to connect Superset
|
||||
to a new database and configure a table in that database for analysis. You'll
|
||||
also explore the data you've exposed and add a visualization to a dashboard
|
||||
so that you get a feel for the end-to-end user experience.
|
||||
|
||||
Connecting to a new database
|
||||
----------------------------
|
||||
|
||||
We assume you already have a database configured and can connect to it from the
|
||||
instance on which you’re running Superset. If you’re just testing Superset and
|
||||
want to explore sample data, you can load some
|
||||
`sample PostgreSQL datasets <https://wiki.postgresql.org/wiki/Sample_Databases>`_
|
||||
into a fresh DB, or configure the
|
||||
`example weather data <https://github.com/dylburger/noaa-ghcn-weather-data>`_
|
||||
we use here.
|
||||
|
||||
Under the **Sources** menu, select the *Databases* option:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_01_sources_database.png
|
||||
:scale: 70%
|
||||
|
||||
On the resulting page, click on the green plus sign, near the top left:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_02_add_database.png
|
||||
:scale: 70%
|
||||
|
||||
You can configure a number of advanced options on this page, but for
|
||||
this walkthrough, you’ll only need to do **two things**:
|
||||
|
||||
1. Name your database connection:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_03_database_name.png
|
||||
:scale: 70%
|
||||
|
||||
2. Provide the SQLAlchemy Connection URI and test the connection:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_04_sqlalchemy_connection_string.png
|
||||
:scale: 70%
|
||||
|
||||
This example shows the connection string for our test weather database.
|
||||
As noted in the text below the URI, you should refer to the SQLAlchemy
|
||||
documentation on
|
||||
`creating new connection URIs <http://docs.sqlalchemy.org/en/rel_1_0/core/engines.html#database-urls>`_
|
||||
for your target database.
|
||||
|
||||
Click the **Test Connection** button to confirm things work end to end.
|
||||
Once Superset can successfully connect and authenticate, you should see
|
||||
a popup like this:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_05_connection_popup.png
|
||||
:scale: 50%
|
||||
|
||||
Moreover, you should also see the list of tables Superset can read from
|
||||
the schema you’re connected to, at the bottom of the page:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_06_list_of_tables.png
|
||||
:scale: 70%
|
||||
|
||||
If the connection looks good, save the configuration by clicking the **Save**
|
||||
button at the bottom of the page:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_07_save_button.png
|
||||
:scale: 70%
|
||||
|
||||
Adding a new table
|
||||
------------------
|
||||
|
||||
Now that you’ve configured a database, you’ll need to add specific tables
|
||||
to Superset that you’d like to query.
|
||||
|
||||
Under the **Sources** menu, select the *Tables* option:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_08_sources_tables.png
|
||||
:scale: 70%
|
||||
|
||||
On the resulting page, click on the green plus sign, near the top left:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_09_add_new_table.png
|
||||
:scale: 70%
|
||||
|
||||
You only need a few pieces of information to add a new table to Superset:
|
||||
|
||||
* The name of the table
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_10_table_name.png
|
||||
:scale: 70%
|
||||
|
||||
* The target database from the **Database** drop-down menu (i.e. the one
|
||||
you just added above)
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_11_choose_db.png
|
||||
:scale: 70%
|
||||
|
||||
* Optionally, the database schema. If the table exists in the “default” schema
|
||||
(e.g. the *public* schema in PostgreSQL or Redshift), you can leave the schema
|
||||
field blank.
|
||||
|
||||
Click on the **Save** button to save the configuration:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_07_save_button.png
|
||||
:scale: 70%
|
||||
|
||||
When redirected back to the list of tables, you should see a message indicating
|
||||
that your table was created:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_12_table_creation_success_msg.png
|
||||
:scale: 70%
|
||||
|
||||
This message also directs you to edit the table configuration. We’ll edit a limited
|
||||
portion of the configuration now - just to get you started - and leave the rest for
|
||||
a more advanced tutorial.
|
||||
|
||||
Click on the edit button next to the table you’ve created:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_13_edit_table_config.png
|
||||
:scale: 70%
|
||||
|
||||
On the resulting page, click on the **List Table Column** tab. Here, you’ll define the
|
||||
way you can use specific columns of your table when exploring your data. We’ll run
|
||||
through these options to describe their purpose:
|
||||
|
||||
* If you want users to group metrics by a specific field, mark it as **Groupable**.
|
||||
* If you need to filter on a specific field, mark it as **Filterable**.
|
||||
* Is this field something you’d like to get the distinct count of? Check the **Count
|
||||
Distinct** box.
|
||||
* Is this a metric you want to sum, or get basic summary statistics for? The **Sum**,
|
||||
**Min**, and **Max** columns will help.
|
||||
* The **is temporal** field should be checked for any date or time fields. We’ll cover
|
||||
how this manifests itself in analyses in a moment.
|
||||
|
||||
Here’s how we’ve configured fields for the weather data. Even for measures like the
|
||||
weather measurements (precipitation, snowfall, etc.), it’s ideal to group and filter
|
||||
by these values:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_14_field_config.png
|
||||
|
||||
As with the configurations above, click the **Save** button to save these settings.
|
||||
|
||||
Exploring your data
|
||||
-------------------
|
||||
|
||||
To start exploring your data, simply click on the table name you just created in
|
||||
the list of available tables:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_15_click_table_name.png
|
||||
|
||||
By default, you’ll be presented with a Table View:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_16_datasource_chart_type.png
|
||||
|
||||
Let’s walk through a basic query to get the count of all records in our table.
|
||||
First, we’ll need to change the **Since** filter to capture the range of our data.
|
||||
You can use simple phrases to apply these filters, like "3 years ago":
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_17_choose_time_range.png
|
||||
|
||||
The upper limit for time, the **Until** filter, defaults to "now", which may or may
|
||||
not be what you want.
|
||||
|
||||
Look for the Metrics section under the **GROUP BY** header, and start typing "Count"
|
||||
- you’ll see a list of metrics matching what you type:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_18_choose_metric.png
|
||||
|
||||
Select the *COUNT(\*)* metric, then click the green **Query** button near the top
|
||||
of the explore:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_19_click_query.png
|
||||
|
||||
You’ll see your results in the table:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_20_count_star_result.png
|
||||
|
||||
Let’s group this by the *weather_description* field to get the count of records by
|
||||
the type of weather recorded by adding it to the *Group by* section:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_21_group_by.png
|
||||
|
||||
and run the query:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_22_group_by_result.png
|
||||
|
||||
Let’s find a more useful data point: the top 10 times and places that recorded the
|
||||
highest temperature in 2015.
|
||||
|
||||
We replace *weather_description* with *latitude*, *longitude* and *measurement_date* in the
|
||||
*Group by* section:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_23_group_by_more_dimensions.png
|
||||
|
||||
And replace *COUNT(\*)* with *max__measurement_flag*:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_24_max_metric.png
|
||||
|
||||
The *max__measurement_flag* metric was created when we checked the box under **Max** and
|
||||
next to the *measurement_flag* field, indicating that this field was numeric and that
|
||||
we wanted to find its maximum value when grouped by specific fields.
|
||||
|
||||
In our case, *measurement_flag* is the value of the measurement taken, which clearly
|
||||
depends on the type of measurement (the researchers recorded different values for
|
||||
precipitation and temperature). Therefore, we must filter our query only on records
|
||||
where the *weather_description* is equal to "Maximum temperature", which we do in
|
||||
the **Filters** section at the bottom of the explore:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_25_max_temp_filter.png
|
||||
|
||||
Finally, since we only care about the top 10 measurements, we limit our results to
|
||||
10 records using the *Row limit* option under the **Options** header:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_26_row_limit.png
|
||||
|
||||
We click **Query** and get the following results:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_27_top_10_max_temps.png
|
||||
|
||||
In this dataset, the maximum temperature is recorded in tenths of a degree Celsius.
|
||||
The top value of 1370, measured in the middle of Nevada, is equal to 137 C, or roughly
|
||||
278 degrees F. It’s unlikely this value was correctly recorded. We’ve already been able
|
||||
to investigate some outliers with Superset, but this just scratches the surface of what
|
||||
we can do.
|
||||
|
||||
You may want to do a couple more things with this measure:
|
||||
|
||||
* The default formatting shows values like 1.37k, which may be difficult for some
|
||||
users to read. It’s likely you may want to see the full, comma-separated value.
|
||||
You can change the formatting of any measure by editing its config (*Edit Table
|
||||
Config > List Sql Metric > Edit Metric > D3Format*)
|
||||
* Moreover, you may want to see the temperature measurements in plain degrees C,
|
||||
not tenths of a degree. Or you may want to convert the temperature to degrees
|
||||
Fahrenheit. You can change the SQL that gets executed agains the database, baking
|
||||
the logic into the measure itself (*Edit Table Config > List Sql Metric > Edit
|
||||
Metric > SQL Expression*)
|
||||
|
||||
For now, though, let’s create a better visualization of these data and add it to
|
||||
a dashboard.
|
||||
|
||||
We change the Chart Type to "Distribution - Bar Chart":
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_28_bar_chart.png
|
||||
|
||||
Our filter on Maximum temperature measurements was retained, but the query and
|
||||
formatting options are dependent on the chart type, so you’ll have to set the
|
||||
values again:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_29_bar_chart_series_metrics.png
|
||||
|
||||
You should note the extensive formatting options for this chart: the ability to
|
||||
set axis labels, margins, ticks, etc. To make the data presentable to a broad
|
||||
audience, you’ll want to apply many of these to slices that end up in dashboards.
|
||||
For now, though, we run our query and get the following chart:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_30_bar_chart_results.png
|
||||
:scale: 70%
|
||||
|
||||
Creating a slice and dashboard
|
||||
------------------------------
|
||||
|
||||
This view might be interesting to researchers, so let’s save it. In Superset,
|
||||
a saved query is called a **Slice**.
|
||||
|
||||
To create a slice, click the **Save as** button near the top-left of the
|
||||
explore:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_19_click_query.png
|
||||
|
||||
A popup should appear, asking you to name the slice, and optionally add it to a
|
||||
dashboard. Since we haven’t yet created any dashboards, we can create one and
|
||||
immediately add our slice to it. Let’s do it:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_31_save_slice_to_dashboard.png
|
||||
:scale: 70%
|
||||
|
||||
Click Save, which will direct you back to your original query. We see that
|
||||
our slice and dashboard were successfully created:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_32_save_slice_confirmation.png
|
||||
:scale: 70%
|
||||
|
||||
Let’s check out our new dashboard. We click on the **Dashboards** menu:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_33_dashboard.png
|
||||
|
||||
and find the dashboard we just created:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_34_weather_dashboard.png
|
||||
|
||||
Things seemed to have worked - our slice is here!
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_35_slice_on_dashboard.png
|
||||
:scale: 70%
|
||||
|
||||
But it’s a bit smaller than we might like. Luckily, you can adjust the size
|
||||
of slices in a dashboard by clicking, holding and dragging the bottom-right
|
||||
corner to your desired dimensions:
|
||||
|
||||
.. image:: _static/img/tutorial/tutorial_36_adjust_dimensions.gif
|
||||
:scale: 120%
|
||||
|
||||
After adjusting the size, you’ll be asked to click on the icon near the
|
||||
top-right of the dashboard to save the new configuration.
|
||||
|
||||
Congrats! You’ve successfully linked, analyzed, and visualized data in Superset.
|
||||
There are a wealth of other table configuration and visualization options, so
|
||||
please start exploring and creating slices and dashboards of your own.
|
||||
@@ -1,54 +0,0 @@
|
||||
User Guide
|
||||
==========
|
||||
|
||||
The user guide is a collection of short videos showing different aspect
|
||||
of Caravel.
|
||||
|
||||
Quick Intro
|
||||
'''''''''''
|
||||
This video demonstrates how Caravel works at a high level, it shows how
|
||||
to navigate through datasets and dashboards that are already available.
|
||||
|
||||
.. youtube:: https://www.youtube.com/watch?v=3Txm_nj_R7M
|
||||
|
||||
Dashboard Creation
|
||||
''''''''''''''''''
|
||||
This video walk you through the creation of a simple dashboard as a
|
||||
collection of data slices.
|
||||
|
||||
- Coming soon!
|
||||
|
||||
Dashboard Filtering
|
||||
'''''''''''''''''''
|
||||
This video shows how to create dynamic filters on dashboards, how to
|
||||
immunize certain widgets from being affected by filters.
|
||||
|
||||
- Coming soon!
|
||||
|
||||
Customize CSS and dashboard themes
|
||||
''''''''''''''''''''''''''''''''''
|
||||
A quick walkthrough on how to apply existing CSS templates, alter them and
|
||||
create new ones.
|
||||
|
||||
- Coming soon!
|
||||
|
||||
Slice Annotations
|
||||
'''''''''''''''''
|
||||
A short video on how to annotate your charts, the markdown language and
|
||||
to toggle them on dashboards.
|
||||
|
||||
- Coming soon!
|
||||
|
||||
Adding a Table
|
||||
''''''''''''''
|
||||
This videos shows you how to expose a new table in Caravel, and how to
|
||||
define the semantics on how this can be accessed by others in the ``Explore``
|
||||
and ``Dashboard`` views.
|
||||
|
||||
- Coming soon!
|
||||
|
||||
Define SQL Expressions
|
||||
''''''''''''''''''''''
|
||||
A walkthrough on how to create your own derived dimensions and metrics.
|
||||
|
||||
- Coming soon!
|
||||
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Reference in New Issue
Block a user