# Run a Processing Job with Apache Spark
<a name="use-spark-processing-container"></a>

Apache Spark is a unified analytics engine for large-scale data processing. Amazon SageMaker AI provides prebuilt Docker images that include Apache Spark and other dependencies needed to run distributed data processing jobs. The following provides an example on how to run a Amazon SageMaker Processing job using Apache Spark.

With the [Amazon SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk#installing-the-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](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html#data-processing-with-spark) in the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/).

A code repository that contains the source code and Dockerfiles for the Spark images is available on [GitHub](https://github.com/aws/sagemaker-spark-container). 

 You can use the [https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.spark.processing.PySparkProcessor](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.spark.processing.PySparkProcessor) or [https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.spark.processing.SparkJarProcessor](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.spark.processing.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 `preprocess.py`. 

```
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,
)

spark_processor.run(
    submit_app="preprocess.py",
    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](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing.html). 

 If you are not using the [Amazon SageMaker AI Python SDK](https://sagemaker.readthedocs.io/) 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](https://github.com/aws/sagemaker-spark-container/blob/master/available_images.md) document. 

 To learn more about using the SageMaker Python SDK with Processing containers, see [Amazon SageMaker AI Python SDK](https://sagemaker.readthedocs.io/en/stable/).