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Francesco Tisiot
Francesco Tisiot

Posted on • Originally published at aiven.io on

Get started with Apache Superset and PostgreSQL

Data visualization can condense huge amounts of raw points into images that can drive business decisions. Apache Superset is emerging as a reliable open source tool for the dataviz world. Read on to find out how to use it to create data visualizations from an existing PostgreSQL table within an Aiven environment.

Image of a painter drawing a graph

"A picture is worth a thousand words" is a common saying in many languages. Similarly, in the data world, a visualization can condense huge amounts of raw points into an image that can be used to create insightful stories driving business decisions. Several tools exist in the dataviz ecosystem, with Apache Superset getting a lot of traction for its open source status and the advanced list of features it has for data exploration, visualization and sharing.

This blog post shows how to start creating amazing visualizations with Apache Superset building on an existing PostgreSQL table.

Run Apache Superset on Docker

Docker is a good friend here! It allows us to spin up - in a matter of minutes - complex software infrastructure without needing to understand and implement the setup manually. In cases like this when we're testing a new tool, it's a perfect fit!

We can start with cloning the Apache Superset GitHub repo, and navigating to the superset folder from our terminal:

git clone https://github.com/apache/superset.git
cd superset
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Then we can use docker-compose to create all the containers needed for Apache Superset:

docker-compose -f docker-compose-non-dev.yml up
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Boom! Our Apache Superset is ready with a nice set of pre-built content we can browse.

Now, let's go to localhost:8088 in our browser, and login with the safest duo ever (admin as user, and admin as password).

Apache Superset default screen

If you're curious, check out the Apache Superset docker page for more information.

Push the dataset to PostgreSQL

On this blog we've already talked about happiness, and how to push Kaggle's related dataset to PostgreSQL via Python. You can use the output mentioned there for the current example, or use your own. The end result in our example is a PostgreSQL instance named demo-pg, containing a table called happiness with content similar to the one below:

survey_yr | country | overall_rank |  score  |  gdp   | generosity | freedom  | social_support | life_exp | gov_trust      
---------------+---------+--------------+---------+--------+------------+----------+----------------+----------+----------
       19 | Finland |            1 |  7.7690 | 1.3400 |   0.1530   |  0.5960  |      1.587     |  0.9860  |  0.3930
       19 | Denmark |            2 |  7.60   | 1.3830 |   0.2520   |  0.5920  |      1.573     |  0.9960  |  0.4100
       19 | Norway  |            3 |  7.554  | 1.4880 |   0.2710   |  0.6030  |      1.582     |  1.0280  |  0.3410
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Connect Apache Superset to PostgreSQL

Apache Superset supports a wide range of source databases, and for PostgreSQL it uses the psycopg2 library. The PostgreSQL support comes out of the box with the default Docker setup.

All we need to do is go to the Apache Superset UI, and define a new datasource pointing to where our PostgreSQL database is:

  1. Select Data, and then Databases.
  2. Click the + DATABASE button.
  3. At the bottom of the modal window, choose Connect this database with a SQLAlchemy URI string instead.
  4. Insert demo-pg as DISPLAY NAME (or your own instance name).
  5. Use the PostgreSQL URI as SQLALCHEMY URI. You can find the PostgreSQL URI in Aiven's console, in the service detail view, under the Overview tab.
  6. Test that all the settings are correct by checking your connection, and making sure you get a nice Connection looks good! message.
  7. Click ADD to persist our demo-pg database definition.

demo-pg database entry

Now, it's time to include the happiness table.

  1. Back in the Apache Superset UI, switch to the Datasets tab.
  2. Create a table by filling the DATASOURCE with demo-pg, the SCHEMA with public and the table with - no surprises - happiness.
  3. Click ADD to persist the datasource definition.

Ta daaah!

happiness dataset entry

Visualize data

Now we are ready to use our artist skills, and create visualizations of our data: representing data this way makes it much easier to process.

  1. Back in the Apache Superset UI, let's navigate to Charts tab.
  2. Click +CHART.
  3. In the popup window, select the public.happiness dataset we created before, and Pivot Table v2 as visualization.

Let's use the dataset, and create a heatmap of the top 10 happy countries over the years. We can do that with the following configuration:

  • country for the ROWS
  • survey_yr for the COLUMNS
  • For the METRIC select a SIMPLE calculation based on the overall_rank column with AVG as aggregated function.
  • In the FILTERS section, add a SIMPLE filter based on the overall_rank metrics being <= than 10.
  • At the bottom, set the AGGREGATION FUNCTION as Average, this will drive the overall row/column aggregation in the pivot table.

Check out the Query parameters overview:

example of query panel with settings

Now, in the CUSTOMIZE tab set the following options:

  • For PIVOT TABLE TYPE select Table Heatmap
  • For the ROWS SORT BY select value ascending
  • Deselect the SHOW COLS TOTALS
  • Name the visualization Country ranking heatmap

We're ready to press the RUN button above CUSTOMIZE.

Aaaaand, we end up with a lovely shaded red heatmap showing the countries that have been included in the top 10 at least once in the previous 5 years, ordered by their overall average position.

Pivot table with heatmap containing the country overall ranking

To see where these countries are around the world, the same data could be used to create a map.
Try the following settings:

  • World Map as visualization type
  • country as COUNTRY COLUMN
  • Full Name as COUNTRY FIELD TYPE
  • Use a SIMPLE calculation based on the score column with AVG aggregation for the METRIC FOR COLOR parameter
  • Choose the COUNTRY COLOR SCHEME, the screenshot shows red/yellow/blue but the choice is yours.

The resulting map clearly indicates that lot of work needs to be done to raise happiness levels in the Global South.

Picture of a world heatmap

Add calculated fields in the dataset

Sometimes the source dataset doesn't contain all the required fields. This is the case in our example too, with the survey_yr field containing only the last two digits of the year (ie. 18, 19), hence not being recognized by Apache Superset as timestamp, which is stopping us from using any trend visualization.

Fear not! Apache Superset allows us to change the shape of our dataset, without any modification to the original table, by adding calculated columns.

  1. In the Apache Superset UI, switch to the Datasets panel.
  2. Click the Edit pencil icon under the Action section of the happiness dataset.
  3. Open the CALCULATED COLUMNS tab and create a new item, with the following settings:
    • 2000+survey_yr as SQL EXPRESSION
    • LABEL set to Year
    • DATETIME as DATA TYPE
    • %Y as DATETIME FORMAT
    • Is temporal checkbox selected.

Now the Year column is available, enabling us to create the trend visualizations like the linechart of the top 5 positions over time.

Picture of top 5 trendline

Challenge yourself, and try creating this chart yourself as an Apache Superset.
A word of caution: Finland is a pretty happy place - and we think so too!

Wrapping up

A dataset is only useful when stored properly, and made available for queries. It becomes meaningful when insights are discovered and communicated across the company. The combination of PostgreSQL and Apache Superset offers a best-in-class solution for data storage, discovery and visualization enabling companies to be effective and data-driven.

Some more info:

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