Step-by-Step Guide to Integrating Apache Spark with Apache Superset

Apache Spark, known for its speed and scalability, is ideal for processing large datasets. By integrating Spark with Apache Superset, you can optimize performance, especially for handling larger datasets and creating fast, interactive visualizations. This guide walks you through the steps required to connect Spark to Apache Superset.

When using Apache Superset with large datasets, the following performance issues are often encountered:

• Slow Visualization Rendering: It takes significant time for charts and graphs to load.
• Slow Filtering and Interactivity: When users apply filters or interact with visuals, the response time is delayed.
• Slow Query Execution: The SQL queries took an extended amount of time to return results, and in some cases, they failed to return results even when working with smaller datasets.

Attempts to Improve Performance

• Used Local Data Sources: CSV files and other local data sources were used initially but did not scale well with larger datasets.
• Increased Web Server Timeout: This was adjusted in the superset_config.py file, but it only provided a slight improvement.
• Optimized Dashboard Visuals and SQL Queries: Efforts were made to reduce the number of visuals and optimize queries, but the gains were limited for larger datasets.

Solution: Integrating Apache Spark to Enhance Performance

To address these limitations, Apache Spark's distributed computing capabilities can significantly improve Apache Superset's query performance and responsiveness. This integration allows Apache Superset to efficiently process and visualize large datasets in real-time

Step 1: Verify Hadoop and Hive Installation

• Before connecting Spark to Apache Superset, ensure that Hadoop and Hive are correctly installed and operational. To ensure compatibility between Hive, Hadoop, and Spark, make sure that you are using versions that are compatible with each other. Refer to the official documentation for each component to verify version compatibility before proceeding with the integration.
• Check Hadoop Installation
• Run the following command to verify that Hadoop is installed and running:
  hadoop version
• If Hadoop is installed, this command will return the installed version and relevant details. Additionally, you can check that the HDFS (Hadoop Distributed File System) is operational by running:
  hdfs dfs -ls /
• This will list the directories in the Hadoop file system if everything is set up correctly.
• Verify Hive Installation
• To check that Hive is installed and working, execute:
  hive --version
• This will display the installed Hive version if Hive is correctly installed.
• Test Hive Connection to Hadoop
• You can further ensure that Hive is connected to Hadoop by running a simple Hive query:
  Hive
• Once inside the Hive CLI, try running a basic query:
  SHOW DATABASES;
• If Hive is connected and working correctly, this will display the available databases.

For detailed setup instructions, refer to this guide: Apache Hadoop Multi-Node Cluster Setup.

Note: You can skip the Kerberos configuration in this process.

Step 2: Prerequisites for Apache Superset Installation

• Before diving into the setup, ensure that your Ubuntu environment is prepared, Update Package Lists and Install Required Dependencies

  sudo apt update

  sudo apt install python3-pip

  pip3 install virtualenv

  sudo apt-get install python3-venv

  pip3 install Pillow

Step 3: Installing Apache Superset

• Create a Python Virtual Environment: Use virtual environments to isolateApache Superset dependencies from your system:

  python3 -m venv supersetdata

  source supersetdata/bin/activate

• Install Apache Superset: Inside your virtual environment, install Superset:

  pip install apache-superset

• Set Superset Config File and Flask App:

  SUPERSET_CONFIG_PATH=/home/gun/supersetdata/app/superset_config.py

  export FLASK_APP=superset

• Edit superset_config.py: Open the Superset config file and make the following updates:

  nano /supersetdata/app/superset_config.py

• Set a Strong Secret Key:

  SECRET_KEY = 'SUPER_SECRET_KEY_GOES_HERE_123'

• Set SQLAlchemy URI:

  SQLALCHEMY_DATABASE_URI = 'sqlite:////home/gun/supersetdata/app/superset.db?check_same_thread=false'

• Enable CSRF Protection:

  WTF_CSRF_ENABLED = True

• Initialize the Superset Database and Create an Admin User:

  superset db upgrade

  superset fab create-admin

• When prompted, provide the necessary details to create your admin user.

• Load Example Data and Initialize Roles:

  superset load_examples

  superset init

  

Step 4: Install PySpark and MySQL Connector

• Install PySpark: PySpark is essential for connecting Spark to Superset.

  pip install pyspark

• Install MySQL Connector for Hive:

  sudo apt install libmysql-java

• Verify Installation: Run:

  pyspark --version

Step 5: Integrate Spark and Hive with Apache Superset

• Open Superset in Your Browser: Start Superset and open it at http://localhost:8088/.
• Add Spark or Hive as a Database Source: Navigate to Sources > Databases and click the “+” to add a new database.
• For Spark, use a connection string like:

  spark://master-url:7077

• For Hive integration:
• Hive Connection URL
• Verify that the connection string in Superset is correct. It should follow this pattern:

  hive://username:password@hostname:port/default?auth=LDAP

  Replace the values accordingly based on your setup:

• username, password: Your Hive credentials.
• hostname, port: The Hive server's hostname and port.
• auth: If you’re using authentication like LDAP, Kerberos, or NONE.
• Test the Connection: Ensure Superset can communicate with Spark or Hive.
• Use Do as Command and Beeline: If you need to switch users or use Hive for querying:

  hadoop fs -D as=user_name -put localfile /hdfs/path

  beeline

• Note: The user connecting to Hive via Superset needs appropriate permissions to query Hive tables. Double-check user privileges on the Hive server to ensure that they can access the data

By following these steps, you've successfully integrated Spark with Apache Superset. This setup allows you to leverage Spark's big data processing capabilities and visualize the results through Superset’s rich UI, providing an end-to-end solution for big data analytics

What’s Next: Profiling Spark with ZettaProf

Now that you’ve integrated Spark with Superset for powerful visualizations, the next step is optimizing your Spark workloads for peak performance. In our upcoming blog, we’ll introduce ZettaProf, a comprehensive profiling tool designed to analyze and fine-tune your Spark deployments. Stay tuned for actionable insights to supercharge your big data analytics with Spark!