Data Processing with Apache Spark - Amazon SageMaker

Data Processing with Apache Spark

Apache Spark is a unified analytics engine for large-scale data processing. Amazon SageMaker provides prebuilt Docker images that include Apache Spark and other dependencies needed to run distributed data processing jobs. With the Amazon SageMaker Python SDK, you can easily apply data transformations and extract features (feature engineering) using the Spark framework. For information about using the SageMaker Python SDK to run Spark processing jobs, see Data Processing with Spark in the Amazon SageMaker Python SDK.

A code repository that contains the source code and Dockerfiles for the Spark images is available on GitHub.

Running a Spark Processing Job

You can use the sagemaker.spark.PySparkProcessor or sagemaker.spark.SparkJarProcessor class to run your Spark application inside of a processing job. Note you can set MaxRuntimeInSeconds to a maximum runtime limit of 5 days. With respect to execution time, and number of instances used, simple spark workloads see a near linear relationship between the number of instances vs. time to completion.

The following code example shows how to run a processing job that invokes your PySpark script

from sagemaker.spark.processing import PySparkProcessor spark_processor = PySparkProcessor( base_job_name="spark-preprocessor", framework_version="2.4", role=role, instance_count=2, instance_type="ml.m5.xlarge", max_runtime_in_seconds=1200, ) submit_app="", arguments=['s3_input_bucket', bucket, 's3_input_key_prefix', input_prefix, 's3_output_bucket', bucket, 's3_output_key_prefix', output_prefix] )

For an in-depth look, see the Distributed Data Processing with Apache Spark and SageMaker Processing example notebook.

If you are not using the Amazon SageMaker Python SDK and one of its Processor classes to retrieve the pre-built images, you can retrieve these images yourself. The SageMaker prebuilt Docker images are stored in Amazon Elastic Container Registry (Amazon ECR). For a complete list of the available pre-built Docker images, see the available images document.

To learn more about using the SageMaker Python SDK with Processing containers, see Amazon SageMaker Python SDK.