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    • Using Yandex Object Storage in Yandex Managed Service for Apache Spark™

In this article:

  • Required paid resources
  • Set up the infrastructure
  • Prepare a PySpark job
  • Check the result
  • Delete the resources you created
  1. Building a data platform
  2. Using Yandex Object Storage in Yandex Managed Service for Apache Spark™

Using Yandex Object Storage in Yandex Managed Service for Apache Spark™

Written by
Yandex Cloud
Updated at September 19, 2025
  • Required paid resources
  • Set up the infrastructure
  • Prepare a PySpark job
  • Check the result
  • Delete the resources you created

In Yandex Object Storage buckets, you can store both files required to run jobs in a Yandex Managed Service for Apache Spark™ cluster and the results of those jobs.

To use Object Storage in Managed Service for Apache Spark™:

  1. Set up your infrastructure.
  2. Prepare a PySpark job.
  3. Check the result.

If you no longer need the resources you created, delete them.

Required paid resourcesRequired paid resources

The support cost for this solution includes:

  • Object Storage bucket fee for using storage and performing data operations (see Object Storage pricing).
  • Cloud Logging fee for the amount of data written and the time of its retention (see Cloud Logging pricing).

Set up the infrastructureSet up the infrastructure

Management console
  1. Create a service account named spark-agent for the Apache Spark™ cluster and assign it the managed-spark.integrationProvider role to enable the Apache Spark™ cluster to interact with other resources.

  2. Create buckets:

    • <bucket_for_PySpark_job_source_code>.
    • <bucket_for_PySpark_job_output_data>.
  3. Grant permissions to the spark-agent service account for the created buckets:

    • <bucket_for_PySpark_job_source_code>: READ permission.
    • <bucket_for_PySpark_job_output_data>: READ and WRITE permission.
  4. Create a cloud network named spark-network.

    This will automatically create three subnets in different availability zones.

  5. Create a Managed Service for Apache Spark™ cluster with the following parameters:

    • Service account: spark-agent
    • Network: spark-network
    • Subnet: spark-network-ru-central1-a

Prepare a PySpark jobPrepare a PySpark job

For a PySpark job, we will use a Python script that is stored in the Object Storage bucket and creates a table named table_1 in database_1. Prepare a script file:

Management console
  1. Create a local file named job_save_table.py and paste the following script to it:

    job_save_table.py
    import random
    import sys
    from pyspark.sql import SparkSession
    
    
    def prepare_table(spark, database, table):
        create_database_sql = "create database if not exists {database}"
        create_table_sql = """
        create table if not exists {database}.{table} (
            id int,
            value double
        )
        """
        truncate_table_sql = "truncate table {database}.{table}"
    
        spark.sql(create_database_sql.format(database=database))
        spark.sql(create_table_sql.format(database=database, table=table))
        spark.sql(truncate_table_sql.format(database=database, table=table))
    
    
    def write_data(spark, database, table):
        data = [(i, random.random()) for i in range(100_000)]
        # Creating a dataframe
        df = spark.createDataFrame(data, schema=['id', 'value'])
        table_full_name = "{database}.{table}".format(database=database, table=table)
        df.write.mode('overwrite').format('json').saveAsTable(table_full_name)
    
    
    def main():
        # Creating a Spark session
        spark = (
            SparkSession
            .builder
            .appName('job_save_table')
            .config('spark.executor.instances', 1)
            .config('spark.sql.warehouse.dir', sys.argv[1])
            .config('spark.sql.catalogImplementation', 'hive')
            .getOrCreate()
        )
        database, table = 'database_1', 'table_1'
        prepare_table(spark, database, table)
        write_data(spark, database, table)
    
    
    if __name__ == '__main__':
        if len(sys.argv) != 2:
            print("Usage: job-save-table s3a://<bucket>/<folder>", file=sys.stderr)
            sys.exit(-1)
        main()
    
    
  2. In <bucket_for_PySpark_job_source_code>, create a folder named scripts and upload the job_save_table.py file to it.

  3. Create a job with the following settings:

    • Job type: PySpark
    • Main python file: s3a://<bucket_for_PySpark_job_source_code>/scripts/job_save_table.py
    • Arguments: s3a://<bucket_for_PySpark_job_output_data>/warehouse

Check the resultCheck the result

Management console
  1. Navigate to the folder dashboard and select Managed Service for Apache Spark™.
  2. Click the name of your cluster and open the Jobs tab.
  3. Wait for the PySpark job you created to change its status to Done.
  4. Make sure the warehouse folder in <bucket_for_PySpark_job_output_data> now contains database_1. The data from the new DB is now stored in the Object Storage bucket in JSON format.

Delete the resources you createdDelete the resources you created

Some resources are not free of charge. Delete the resources you no longer need to avoid paying for them:

Management console
  1. Object Storage buckets.
  2. Apache Spark™ cluster.

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