Using spark-submit -D to set Environment Variable to Spark Job rimmalapudi Spark By {Examples}

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The -D parameter with spark-submit is used to set environment variables to a Spark job. Alternatively, you can also set these environment variables by using –config. These variables are commonly referred to as Spark configuration properties or Spark settings.

In this article, we shall discuss what is -D parameter or environment variable in a Spark job and different ways to pass them to Spark Job.

1. Introduction

When launching a Spark job, you can use the “-D” parameter to pass configuration properties as command-line arguments or set them as environment variables. The format for using the “-D” parameter is as follows:

spark-submit “-Dproperty=value” …

Here, “property” represents the name of the Spark configuration property, and “value” represents the desired value for that property. Multiple configuration properties can be specified by providing multiple “-D” parameters or environment variable assignments.

spark-submit “-Dproperty2=value” “-Dproperty2=value” …

These configuration properties help customize the behavior of the Spark application according to the specific requirements of your job. They can control various aspects such as memory allocation, parallelism, serialization, logging, and more. Some commonly used Spark configuration properties include:

spark.executor.memory: Sets the amount of memory per executor.

spark.executor.cores: Sets the number of cores per executor.

spark.driver.memory: Sets the amount of memory allocated to the driver.

spark.default.parallelism: Sets the default parallelism for RDD operations.

spark.serializer: Specifies the serializer used for data serialization.

By using the “-D” parameter or environment variables, you can easily modify these properties without modifying the source code of your Spark application. This flexibility allows you to experiment with different configurations and optimize the performance of your Spark jobs.

2. Different ways to Pass -D Parameter or Environment Variable to Spark Job

The -D parameter or environment variable in a Spark job enables you to set configuration properties at runtime, allowing you to customize various aspects of the Spark application’s behavior without modifying the code.

There are several ways to pass the “-D” parameter or environment variable to a Spark job. Let’s explore the different methods with detailed explanations:

2.1 Command-line argument with spark-submit:

When submitting the Spark job using the spark-submit command, you can pass the -D parameter as a command-line argument using the –conf option. The format is as follows:

spark-submit –conf “property=value” …

This method allows you to specify Spark configuration properties directly on the command line. For example, to set the executor memory to 4g and driver memory to 2g, you would use:

spark-submit –conf “spark.executor.memory=4g” –conf “spark.driver.memory=2g” …

In the example above,

Two configuration properties are set using the –conf option: spark.executor.memory is set to 4g, and spark.driver.memory is set to 2g. These properties control the memory allocation for the executor and driver, respectively.

When you run the spark-submit command, it will launch your Spark application (your-spark-app.jar) with the specified configuration properties. These properties will override any default settings or properties defined elsewhere.

Using command-line arguments with spark-submit provides a straightforward way to pass configuration properties to your Spark job without modifying the source code or other configuration files. It allows for quick customization and adaptability when submitting Spark jobs.

2.2 Environment variable with spark-submit:

Another approach is to set the -D parameter as an environment variable before running the spark-submit command. Each property should be specified in the format -Dproperty=value, and multiple properties can be separated by spaces. The format is as follows:

export SPARK_OPTS=”-Dproperty=value” spark-submit …

Here is how we can initialize SPARK_OPTS and run spark-submit,

You can set one or more environment variables using the SPARK_OPTS variable, where each variable assignment corresponds to a Spark configuration property. For example, to set the executor memory and driver memory, you would use:

export SPARK_OPTS=”-Dspark.executor.memory=4g -Dspark.driver.memory=2g”

2. Now Run the spark-submit command, which will inherit the environment variable you set in the previous step.

export SPARK_OPTS=”-Dspark.executor.memory=4g -Dspark.driver.memory=2g” spark-submit –class com.example.YourSparkApp –master yarn –deploy-mode cluster your-spark-app.jar

In the example above,

The environment variable SPARK_OPTS is set with two configuration properties: spark.executor.memory and spark.driver.memory. These properties specify the memory allocated to the executor and driver respectively.

When you run the spark-submit command, it will execute your Spark application (your-spark-app.jar) in the cluster mode using the YARN resource manager (specified by –master yarn). The application class com.example.YourSparkApp should be replaced with the appropriate class name for your Spark application.

The Spark job will start with the configuration properties specified in the SPARK_OPTS environment variable, which will override any default settings or properties defined elsewhere.

Using environment variables with spark-submit provides a convenient way to pass configuration properties to your Spark job without modifying the command-line arguments each time you submit the job. It allows for easier customization and adaptation to different environments or requirements.

2.3 Using Configuration File:

To pass -D parameters or environment variables to a Spark job using a configuration file, you can follow these steps:

1. Create a configuration file, typically named spark-defaults.conf. You can place this file in the Spark configuration directory (e.g., conf/ within your Spark installation directory) or in a directory specified by the SPARK_CONF_DIR environment variable.

2. Inside the configuration file, specify the desired configuration properties in the format property=value. Each property should be on a separate line.
Example:

spark.executor.memory 4g
spark.driver.memory 2g

In the example above, two properties are set: spark.executor.memory with a value of 4g and spark.driver.memory with a value of 2g. These properties determine the memory allocation for the executor and driver, respectively.

3. Run the spark-submit command, which will automatically read the configuration properties from the spark-defaults.conf file.

spark-submit –class com.example.YourSparkApp –master yarn –deploy-mode cluster your-spark-app.jar

In the above example,

the spark-submit command will execute your Spark application (your-spark-app.jar) in cluster mode using the YARN resource manager (–master yarn). The application class com.example.YourSparkApp should be replaced with the appropriate class name for your Spark application.

The Spark job will start with the configuration properties specified in the spark-defaults.conf file, overriding any default settings or properties defined elsewhere.

Using a configuration file allows you to define and manage the Spark configuration properties in a separate file, making it easier to maintain and modify the properties without modifying the spark-submit command each time. It provides a more organized and reusable approach to configure your Spark jobs.

2.4 Programmatically within Spark code:

To pass -D parameters or environment variables to a Spark job programmatically within your Spark code, you can use the SparkConf object to set the desired configuration properties. Here’s how you can do it:

//. Import the SparkConf class in your Spark application code.
import org.apache.spark.SparkConf

//2. Create an instance of SparkConf.
val conf = new SparkConf()

//3.Use the set() method of the SparkConf object to set the desired configuration properties.
conf.set(“spark.executor.memory”, “4g”)
conf.set(“spark.driver.memory”, “2g”)

//4.Pass the SparkConf object to the SparkSession or SparkContext constructor when creating the Spark session or context.
import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
.config(conf)
.appName(“YourSparkApp”)
.getOrCreate()

In the above example,

we pass the conf object to the config() method of SparkSession.builder() to configure the Spark session with the desired properties.

You can replace “YourSparkApp” it with the desired name for your Spark application.

By setting the configuration properties programmatically within your Spark code, you can dynamically adjust the properties based on your application logic.

This approach is useful when you need fine-grained control over the configuration properties and want to customize them based on runtime conditions or external factors.

Note that programmatically setting configuration properties within Spark code will override any default settings or properties specified through other methods such as command-line arguments or configuration files.

3. Conclusion

In conclusion, the “-D” parameter or environment variable in a Spark job is a flexible mechanism for configuring and customizing various aspects of the Spark application’s behavior. It allows you to set configuration properties at runtime without modifying the source code, providing greater flexibility and adaptability to different environments and requirements.

The -D parameter can be used with:

spark-submit as a command-line argument using the –conf option.

Set as an environment variable using the SPARK_OPTS variable.

Alternatively, you can use a configuration file (spark-defaults.conf).

Programmatically set configuration properties within your Spark code using the SparkConf object.

Overall, the “-D” parameter or environment variable in a Spark job provides a powerful and convenient way to fine-tune and manage configuration properties, making it easier to run and optimize Spark applications efficiently.

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 The -D parameter with spark-submit is used to set environment variables to a Spark job. Alternatively, you can also set these environment variables by using –config. These variables are commonly referred to as Spark configuration properties or Spark settings. In this article, we shall discuss what is -D parameter or environment variable in a Spark  Read More Apache Spark