Getting started with Apache Spark and CrateDB: A step by step tutorial

Apache Spark is an open-source distributed computing framework designed for high-speed, versatile big-data processing. It offers support for various data processing tasks, such as batch processing, real-time streaming, machine learning, and graph analytics. It is a popular choice for organizations looking to analyze large datasets efficiently.

Using Apache Spark with CrateDB can be a powerful combination for processing and analyzing large datasets. In this tutorial, we’ll walk through the process of setting up PySpark (Python API for Spark) to work with CrateDB, including data loading, processing, and writing results back to CrateDB.


  1. Running instance of CrateDB
  2. Python 3.x

Load data to CrateDB

The following commands create the sensor_data table in CrateDB and generate test data:

CREATE TABLE IF NOT EXISTS "doc"."sensor_data" (
   "id" INTEGER,
   "value" INTEGER,
   "machine" TEXT
INSERT INTO sensor_data (id, value, machine)
    CASE floor(random() * 3)
        WHEN 0 THEN 'machine 1'
        WHEN 1 THEN 'machine 2'
        WHEN 2 THEN 'machine 3'
        ELSE 'machine 4'
   END AS machine
  generate_series(1, 100) id;

Setting up Apache Spark

This tutorial will work with a single-node Apache Spark installation running on a Mac M1 machine. To set up Apache Spark on your machine use the following steps:

  1. Install Java and Scala, as the Apache Spark requires both to run:

brew install openjdk@11 brew install scala

Before verifying your Java installation, set the JAVA_HOME environment variable by adding the following line to your shell profile:

export JAVA_HOME="/usr/local/opt/openjdk@11"

  1. To install the latest version of Apache Spark (which includes PySpark) run:

brew install apache-spark

  1. Verify the installation of apache-spark and pyspark:

spark-shell --version
pyspark --version

  1. Finally, as CrateDB communicates with Spark via JDBC, download the Postgres JDBC driver in your working directory. In this tutorial, we use the postgresql-42.6.0.jar driver.

Connect Apache Spark and CrateDB

Load Data from CrateDB

You can load data from CrateDB into a PySpark DataFrame using the following code:

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("cratedb_test").config("spark.jars", "postgresql-42.6.0.jar").getOrCreate()
df ="jdbc")
               .option("url", "jdbc:postgresql://host:port/crate?sslmode=require")
               .option("driver", "org.postgresql.Driver")
               .option("user", "user")
               .option("password", "password")
               .option("dbtable", "doc.sensor_data").load()
  1. First, you need to start a PySpark session and configure it to use the PostgreSQL JDBC driver by adding the JAR file to the Spark session’s classpath.
  2. Once you have configured your PySpark session, you can use the API to load data from CrateDB into a DataFrame. You’ll need to provide the JDBC URL and specify the table or query from which you want to retrieve data. Make sure to specify the correct URL, table name, and authentification details.
  3. In this example, we load all data from the sensor_data table. You can also use a SQL query instead of a table name you need to perform more complex data retrieval operations.

Perform data manipulation and analysis

Once you have loaded the data into a PySpark DataFrame, you can perform various data manipulation and analysis tasks. For instance, you can filter, aggregate, and transform your data:

filtered_data = df.filter(df["value"] > 25)
grouped_df = df.groupBy("machine").agg({"value": "avg"})

Write Results Back to CrateDB

After processing your data, you can write the results back to CrateDB:

grouped_df.write.format("jdbc").option("url", "jdbc:postgresql://host:port/crate?sslmode=require")
                .option("driver", "org.postgresql.Driver")
                .option("user", "user")
                .option("password", "password")
                .option("dbtable", "doc.aggregated_sensor_data")

This code writes the resulting DataFrame back to CrateDB in a table named aggregated_sensor_data. Make sure to replace the connection URL and table name as needed.

After you’re done with your Spark job, stop the Spark session:


Now, you can run your code as any other Python application. After the execution is complete, you can check the content of the aggregated_sensor_data table:

SELECT * FROM aggregated_sensor_data;

The result shows grouped data by the “machine” column and calculated average values.

Wrap up

That’s it! You’ve learned how to set up Apache Spark with CrateDB, load data from CrateDB into a Spark DataFrame, perform data manipulations, and write the results back to CrateDB. You can build more complex data pipelines and analysis workflows using this foundation.

If you are interested in learning more about CrateDB, start your cluster on CrateDB Cloud today, including a forever free CRFREE plan. For further questions about CrateDB and its use cases, check our documentation and growing community.

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