how to access a spark config from synapse pipeline

Question

how to access and use an environment variable from spark config in synapse pipeline activity

Answer 1

Hi Brunda Yelakala (INFOSYS LIMITED),

Thanks for reaching out to Microsoft Q&A.

You can read custom config values in a spark notebook as shown below.

Step1: Add your key-value pairs inside the config.txt file:

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spark.executorEnv.environmentName ppe  

Step2: You can upload a config file to your spark pool.

Step3: Which you are then able to access inside your notebooks:

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envName: str = spark.sparkContext.environment.get('environmentName', 'get')  

Please let us know if it helps

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Answer 2

Hi @Brunda Yelakala (INFOSYS LIMITED)
Thanks for the question and using MS Q&A platform.

To access a Spark configuration from a Synapse pipeline and use an environment variable, you can add the configuration in the "Advanced" section of the Spark Job Definition object in your pipeline. Here are the steps to do this:

1. Set Spark Configuration in Synapse Pipeline - In the Synapse Studio, add a Spark activity to your pipeline (such as Spark batch or streaming activity). Navigate to the Advanced settings section of the Spark activity. In the Spark Configuration section, you can define custom Spark settings or environment variables. These configurations can include various Spark properties such as memory settings or environment variables like spark.env.<variable_name>.These configurations will be used when the Spark job runs within the pipeline.
2. Access Environment Variables in Spark Job - Once the Spark activity executes in the pipeline, the environment variables set in the configuration are passed to the Spark job. These environment variables can then be accessed within the Spark job's execution context. Your Spark code can reference the environment variables, allowing it to dynamically adjust behavior based on the configured values.
3. Triggering the Pipeline - After setting up the Spark configurations and environment variables, you can trigger the pipeline. The configurations and environment variables will be passed to the Spark job when it is executed, and the job will run with these settings in place.

When you run your pipeline, the Spark Job Definition object will inherit the Spark Config options you specified in the "Advanced" section.

For more details, refer to Quickstart: Transform data using Apache Spark job definition.

Hope this helps. Do let us know if you have any further queries.

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