Quick tip: Using Apache Spark with SingleStore Notebooks

Abstract

SingleStore has been providing a cloud portal and a DBaaS offering for some time. Additionally, it has offered a Spark Connector for a while, but Apache Spark had to be run externally. The recent addition of notebooks to the cloud portal has significantly improved Data Science capabilities, including the ability to use Apache Spark. Spark can now be installed in the notebook environment in a few minutes. This article will show how.

The notebook file used in this article is available on GitHub.

Create a SingleStore Cloud account

A previous article showed the steps to create a free SingleStore Cloud account. We'll use the following settings:

• Workspace Group Name: Spark Demo Group
• Cloud Provider: AWS
• Region: US East 1 (N. Virginia)
• Workspace Name: spark-demo
• Size: S-00

Create a new notebook

From the left navigation pane in the cloud portal, we'll select Develop > Notebooks.

In the top right of the web page, we'll select New Notebook > New Notebook, as shown in Figure 1.

Figure 1. New Notebook.

We'll call the notebook spark_demo, select a Blank notebook template from the available options, and save it in the Personal location.

Fill out the notebook

Install Apache Spark

We can easily install Java and Spark, as follows:

!conda install -y --quiet -c conda-forge openjdk pyspark

Once the installation has been completed, we can check the version of Java, as follows:

!java version

Example output:

openjdk version "1.8.0_382"
OpenJDK Runtime Environment (Zulu 8.72.0.17-CA-linux64) (build 1.8.0_382-b05)
OpenJDK 64-Bit Server VM (Zulu 8.72.0.17-CA-linux64) (build 25.382-b05, mixed mode)

Next, let's check the version of PySpark:

import pyspark
print(pyspark.__version__)

Example output:

3.5.1

Finally, we'll check the version of Python:

import sys
print(sys.version)

Example output:

3.11.6 | packaged by conda-forge | (main, Oct  3 2023, 10:40:35) [GCC 12.3.0]

There is a useful Spark Python Supportability Matrix that shows the compatibility of Python with various Spark releases.

Test Apache Spark

Now, let's test the Apache Spark installation.

First, let's create a SparkSession:

from pyspark.sql import SparkSession

# Create a Spark session
spark = SparkSession.builder.appName("Spark Test").getOrCreate()

Next, let's create a DataFrame:

# Create a DataFrame
data = [("Peter", 27), ("Paul", 28), ("Mary", 29)]
df = spark.createDataFrame(data, ["Name", "Age"])

Now we'll show the DataFrame:

# Show the content of the DataFrame
df.show()

The output should be as follows:

+-----+---+
| Name|Age|
+-----+---+
|Peter| 27|
| Paul| 28|
| Mary| 29|
+-----+---+

Finally, we'll stop the SparkSession:

# Stop the Spark session
spark.stop()

Summary

In this short article, we've seen how to install and use Apache Spark in the SingleStore notebook environment. In future articles, we'll explore Spark's capabilities more extensively and demonstrate how to integrate it with the SingleStore Data Platform for reading and writing data using a database.