Apache Superset is a modern, enterprise-ready business intelligence (BI) tool that enables data exploration, visualization, and dashboard creation. It is open-source, highly scalable, and designed to handle large datasets with ease. Whether you're a data engineer, analyst, or business user, Superset provides an intuitive interface to derive insights from your data. In this blog, we’ll dive into the fundamentals of Apache Superset, its architecture, and its key components including installation.
What is Apache Superset?
Apache Superset is a web-based application that allows users to create interactive dashboards, charts, and visualizations. It supports a wide range of data sources, including SQL-based databases (e.g., PostgreSQL, MySQL, BigQuery) and cloud data warehouses (e.g., Snowflake, Redshift). Superset is designed to be self-service, meaning even non-technical users can explore data and build visualizations without writing code.
One of Superset’s standout features is its ability to handle large datasets efficiently. It leverages a lightweight backend and a powerful SQL engine to query and visualize data in real-time. Additionally, Superset is highly extensible, allowing developers to customize its functionality through plugins and APIs.
Apache Superset Architecture.
Superset’s architecture is modular and designed for scalability. It consists of several key components that work together to deliver a seamless user experience.
Let’s break down the architecture:
1. Web Server
The web server is the front-facing component of Superset. It serves the user interface (UI) and handles user interactions. Superset’s UI is built using modern web technologies like React and Ant Design, providing a responsive and intuitive experience. The web server also manages authentication, authorization, and session management.
2. Metadata Database
Superset uses a metadata database to store information about dashboards, charts, datasets, and user permissions. By default, Superset uses SQLite for lightweight setups, but for production environments, it’s recommended to use a more robust database like PostgreSQL or MySQL. The metadata database ensures that all configurations and user-generated content are persisted.
3. SQL Lab
SQL Lab is Superset’s SQL IDE (Integrated Development Environment). It allows advanced users to write and execute SQL queries directly against their data sources. SQL Lab is particularly useful for data exploration and ad-hoc analysis. It supports querying multiple databases simultaneously and provides features like query history, result visualization, and query sharing.
4. Data Sources
Superset connects to a wide variety of data sources through SQLAlchemy, a Python SQL toolkit. Supported databases include relational databases (e.g., MySQL, PostgreSQL), columnar databases (e.g., Apache Druid), and cloud data warehouses (e.g., Google BigQuery, Snowflake). Superset also supports REST APIs and custom connectors for more specialized use cases.
5. Visualization Layer
The visualization layer is where Superset shines. It offers a rich library of chart types, including bar charts, line charts, pie charts, geospatial visualizations, and more. Users can create custom visualizations using the Explore interface or by writing custom queries in SQL Lab. Superset also supports custom visualization plugins, allowing developers to extend its capabilities.
6. Caching Layer
To improve performance, Superset includes a caching layer that stores the results of frequently accessed queries. It integrates with caching systems like Redis or Memcached to reduce load on the database and speed up dashboard rendering. The caching layer is configurable, allowing users to set expiration times and other parameters.
7. Asynchronous Query Execution
For long-running queries, Superset supports asynchronous execution using Celery, a distributed task queue. This ensures that the web server remains responsive even when processing complex queries. Asynchronous execution is particularly useful in production environments where large datasets and multiple users are involved.
8. Security and Authentication
Superset provides robust security features, including role-based access control (RBAC), integration with OAuth providers, and support for LDAP and OpenID Connect. Administrators can define granular permissions for users and groups, ensuring that sensitive data is protected.
Key Components of Apache Superset
Let’s take a closer look at some of the key components that make Superset a powerful BI tool:
1. Dashboards
Dashboards are the primary way users interact with data in Superset. A dashboard is a collection of visualizations (charts, tables, etc.) that can be arranged and customized to tell a data-driven story. Dashboards are highly interactive, allowing users to filter, drill down, and explore data in real-time.
2. Charts
Charts are the building blocks of dashboards. Superset offers a wide variety of chart types, from simple bar charts to complex geospatial visualizations. Each chart is backed by a dataset and can be customized using the Explore interface or SQL queries.
3. Datasets
A dataset in Superset represents a table or view in a database. Users can define datasets and configure them for use in charts and dashboards. Superset also supports virtual datasets, which are created by writing custom SQL queries.
4. Explore Interface
The Explore interface is a no-code tool for creating charts and visualizations. It provides a user-friendly way to select datasets, choose chart types, and configure visualization settings. The Explore interface is designed for non-technical users, making it easy to create insightful visualizations without writing SQL.
5. SQL Lab
As mentioned earlier, SQL Lab is a powerful SQL IDE for advanced users. It supports features like query autocomplete, syntax highlighting, and query sharing. SQL Lab is ideal for data exploration and ad-hoc analysis.
6. Plugins and Extensions
Superset’s extensibility is one of its greatest strengths. Developers can create custom visualization plugins, add new database connectors, or extend Superset’s functionality using its REST API. This makes Superset highly adaptable to specific use cases and requirements.
Why Choose Apache Superset?
Apache Superset stands out in the crowded BI landscape for several reasons:
Open Source: Superset is free to use and has a vibrant community of contributors.
Scalability: It can handle large datasets and multiple users with ease.
Flexibility: Superset supports a wide range of data sources and visualization types.
Ease of Use: Its intuitive interface makes it accessible to both technical and non-technical users.
Extensibility: Developers can customize and extend Superset to meet their needs.
Installation:
Now we are going to discusses how to install Apache Superset in a stand alone mode in ubuntu.
Note: Standalone mode is for testing purpose, for a production grade Apache Superset Platform install it on Kubernetes instead.
Important: Please make sure you have python 3.8.x is installed in your system, else you might end up having installation failure with compatibility issues.
Step 1: Run the following command to install required system packages:
sudo apt update && sudo apt install -y \python3 python3-pip python3-dev \build-essential libssl-dev libffi-dev \python3-venv libpq-dev libsasl2-dev \libmysqlclient-dev libldap2-dev libsqlite3-dev
Step 2: It's recommended to install Superset inside a virtual environment to avoid dependency issues.
python3 -m venv superset-venvsource superset-venv/bin/activateNote: Use "python3.8 -m venv superset-venv" if you have multiple version of python installed in the system.
Step 3: Upgrade Pip & Install Dependencies
pip install --upgrade pip setuptools wheel
Step 4: Install Apache Superset
Now, install Superset using
pip:pip install apache-superset
Step 5: Initialize Superset
Run the following commands to set up Superset and when prompted enter a username, password, and email for the admin account.
superset db upgradesuperset fab create-adminuserid: adminpassword: admin1234
Step 6: Load Example Data (Optional)
To test Superset with sample data:
superset load_examples
Step 7: Initialize Superset Metadata
superset init
Step 8: Start Superset Web Server
To start the Superset UI:
superset run -p 8089 --with-threads --reload --debugger
Navigate to http://localhost:8089 to test if Apache Superset is running.
Step 9: Possible Errors and Fixes:
If the installation fails with JSONEncoder that means incompatibility between Flask and Flask-WTF in your Apache Superset installation. Specifically,
JSONEncoder has been removed from flask.json in Flask version 2.3+, and older versions of Flask-WTF still reference it. To fix this issue, downgrade Flask to version 2.2.5, which retains JSONEncodersource ~/superset-venv/bin/activate
pip uninstall flask
pip install flask==2.2.5
If the error is "Could not locate a Flask application" means that Superset cannot find the Flask app. This usually happens when the environment is not set up correctly.
source ~/superset-venv/bin/activate
pip install flask flask-wtf flask-appbuilder
export FLASK_APP=superset
If the error indicates a package version conflict. Superset requires flask-wtf<1.1, but you have flask-wtf 1.2.1 installed, which is incompatible.
pip install "flask-wtf<1.1,>=1.0.1"
pip show flask-wtf
pip install --upgrade --force-reinstall apache-superset
pip uninstall flask-wtf -ypip install "flask-wtf<1.1,>=1.0.1"pip install --upgrade apache-superset
If Superset refusing to start due to insecure SECRET_KEY, which is insecure. You need to override it in the superset_config.py file.
openssl rand -base64 42
This will generate a secure random key. Example output:
Qj4u5Vf2oTp3P5zxy+w8Y6mFqLQYJ/U2PqYJ79KxYZc=
Set the SECRET_KEY in superset_config.py
nano ~/superset_config.py
SECRET_KEY = "Qj4u5Vf2oTp3P5zxy+w8Y6mFqLQYJ/U2PqYJ79KxYZc="
export SUPERSET_CONFIG_PATH=~/superset_config.py
superset run -p 8088 --with-threads --reload --debugger
If the error is missing the marshmallow_enum package. Try installing it inside your Superset virtual environment.
source /home/hdoop/superset-venv/bin/activate
pip install marshmallow_enum
superset run -p 8088 --with-threads --reload --debugger
Connect Apache Superset to Apache Hive 3.1.2, follow these steps:
Step 1: Install pyhive and thrift-sasl
source superset-venv/bin/activatepip install git+https://github.com/dropbox/PyHive.gitpip install thrift thrift-sasl sasl
Step 2: Configure Hive Connection in Superset
Login to Superset UI. Add a Database Connection. Go to "Data" → "Databases". Click "+ Database" (or "Add Database"). Enter Connection Details. Use the following SQLAlchemy URI format:
hive://<username>:<password>@<hive-host>:<hive-port>/<database>Example:hive://hdoop@localhost:10000/default
Steps for starting Apache Superset after System Restart
source superset-venv/bin/activateexport FLASK_APP=supersetexport SUPERSET_CONFIG_PATH=~/superset_config.py#superset db upgradesuperset initsuperset run -p 8089 --with-threads --reload --debuggerhttp://localhost:8089Connection String from Apache Superset:hive://<username>@<hive-host>:<hive-port>/<database>hive://hdoop@localhost:10000/default
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