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mail.apache.org (hermes.apache.org [140.211.11.3]) by mx1-lw-eu.apache.org (ASF Mail Server at mx1-lw-eu.apache.org) with SMTP id B487D5FCD7 for ; Tue, 27 Jun 2017 20:12:29 +0000 (UTC) Received: (qmail 31050 invoked by uid 99); 27 Jun 2017 20:12:28 -0000 Received: from git1-us-west.apache.org (HELO git1-us-west.apache.org) (140.211.11.23) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 27 Jun 2017 20:12:28 +0000 Received: by git1-us-west.apache.org (ASF Mail Server at git1-us-west.apache.org, from userid 33) id F30DCE080D; Tue, 27 Jun 2017 20:12:27 +0000 (UTC) Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 8bit From: maximebeauchemin@apache.org To: commits@superset.incubator.apache.org Date: Tue, 27 Jun 2017 20:12:28 -0000 Message-Id: <1650544a59a346239ae61e8d6ad53701@git.apache.org> In-Reply-To: <5a1392ffa83044ecb2cb06b4681b73cc@git.apache.org> References: <5a1392ffa83044ecb2cb06b4681b73cc@git.apache.org> X-Mailer: ASF-Git Admin Mailer Subject: [02/14] incubator-superset-site git commit: Initial commit - Superset docs as of 0.18.5 archived-at: Tue, 27 Jun 2017 20:12:48 -0000 http://git-wip-us.apache.org/repos/asf/incubator-superset-site/blob/886f04b0/searchindex.js ---------------------------------------------------------------------- diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 0000000..befed68 --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({envversion:49,filenames:["druid","faq","gallery","index","installation","security","sqllab","tutorial","videos","visualization"],objects:{"superset.jinja_context":{PrestoTemplateProcessor:[6,0,1,""],url_param:[6,1,1,""]}},objnames:{"0":["py","class","Python class"],"1":["py","function","Python 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:1,restrict:5,role:5,schema:4,secur:5,setup:4,slice:[7,8],some:5,sourc:[1,5],sql:[4,6,8],sql_lab:5,sqlalchemi:4,ssl:4,start:[1,4],subset:5,superset:[1,3,4,7],support:0,tabl:[1,3,7,8],templat:6,theme:8,time:1,tool:[4,9],tutori:7,type:1,upgrade:4,video:8,virtualenv:4,visibl:1,visual:[1,9],when:1,why:1,yet:0,you:9,your:[4,7]}}) \ No newline at end of file http://git-wip-us.apache.org/repos/asf/incubator-superset-site/blob/886f04b0/security.html ---------------------------------------------------------------------- diff --git a/security.html b/security.html new file mode 100644 index 0000000..792349d --- /dev/null +++ b/security.html @@ -0,0 +1,364 @@ + + + + + + + + + + + Security — Superset's documentation documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
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  • Security
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+ +
+

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.

+_images/create_role.png +

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. +
  3. 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.
  4. +
+

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.

+
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+ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file http://git-wip-us.apache.org/repos/asf/incubator-superset-site/blob/886f04b0/sqllab.html ---------------------------------------------------------------------- diff --git a/sqllab.html b/sqllab.html new file mode 100644 index 0000000..60c7531 --- /dev/null +++ b/sqllab.html @@ -0,0 +1,292 @@ + + + + + + + + + + + SQL Lab — Superset's documentation documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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  • SQL Lab
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+

SQL Lab

+

SQL Lab is a modern, feature-rich SQL IDE written in +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 +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 +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

+
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 can be also be applied where needed.

+
+
+class superset.jinja_context.PrestoTemplateProcessor(database=None, query=None, table=None, **kwargs)[source]
+

Presto Jinja context

+

The methods described here are namespaced under presto in the +jinja context as in SELECT '{{ presto.some_macro_call() }}'

+
+ +
+
+superset.jinja_context.url_param(param, default=None)[source]
+

Get a url paramater

+ +++ + + + +
Parameters:
    +
  • param (str) – the url parameter to lookup
  • +
  • default (str) – the value to return in the absence of the parameter
  • +
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+ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file http://git-wip-us.apache.org/repos/asf/incubator-superset-site/blob/886f04b0/tutorial.html ---------------------------------------------------------------------- diff --git a/tutorial.html b/tutorial.html new file mode 100644 index 0000000..1d61d8d --- /dev/null +++ b/tutorial.html @@ -0,0 +1,416 @@ + + + + + + + + + + + Tutorial for Superset Administrators — Superset's documentation documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
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  • Tutorial for Superset Administrators
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+

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 +into a fresh DB, or configure the +example weather data +we use here.

+

Under the Sources menu, select the Databases option:

+_images/tutorial_01_sources_database.png +

On the resulting page, click on the green plus sign, near the top left:

+_images/tutorial_02_add_database.png +

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:
  2. +
+_images/tutorial_03_database_name.png +
    +
  1. Provide the SQLAlchemy Connection URI and test the connection:
  2. +
+_images/tutorial_04_sqlalchemy_connection_string.png +

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 +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:

+_images/tutorial_05_connection_popup.png +

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:

+_images/tutorial_06_list_of_tables.png +

If the connection looks good, save the configuration by clicking the Save +button at the bottom of the page:

+_images/tutorial_07_save_button.png +
+
+

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:

+_images/tutorial_08_sources_tables.png +

On the resulting page, click on the green plus sign, near the top left:

+_images/tutorial_09_add_new_table.png +

You only need a few pieces of information to add a new table to Superset:

+
    +
  • The name of the table
  • +
+_images/tutorial_10_table_name.png +
    +
  • The target database from the Database drop-down menu (i.e. the one +you just added above)
  • +
+_images/tutorial_11_choose_db.png +
    +
  • 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:

+_images/tutorial_07_save_button.png +

When redirected back to the list of tables, you should see a message indicating +that your table was created:

+_images/tutorial_12_table_creation_success_msg.png +

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:

+_images/tutorial_13_edit_table_config.png +

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:

+_images/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:

+_images/tutorial_15_click_table_name.png +

By default, you’ll be presented with a Table View:

+_images/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”:

+_images/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:

+_images/tutorial_18_choose_metric.png +

Select the COUNT(*) metric, then click the green Query button near the top +of the explore:

+_images/tutorial_19_click_query.png +

You’ll see your results in the table:

+_images/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:

+_images/tutorial_21_group_by.png +

and run the query:

+_images/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:

+_images/tutorial_23_group_by_more_dimensions.png +

And replace COUNT(*) with max__measurement_flag:

+_images/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:

+_images/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:

+_images/tutorial_26_row_limit.png +

We click Query and get the following results:

+_images/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”:

+_images/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:

+_images/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:

+_images/tutorial_30_bar_chart_results.png +
+
+

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:

+_images/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:

+_images/tutorial_31_save_slice_to_dashboard.png +

Click Save, which will direct you back to your original query. We see that +our slice and dashboard were successfully created:

+_images/tutorial_32_save_slice_confirmation.png +

Let’s check out our new dashboard. We click on the Dashboards menu:

+_images/tutorial_33_dashboard.png +

and find the dashboard we just created:

+_images/tutorial_34_weather_dashboard.png +

Things seemed to have worked - our slice is here!

+_images/tutorial_35_slice_on_dashboard.png +

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:

+_images/tutorial_36_adjust_dimensions.gif +

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.

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