Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Batch ingestion with Amazon SageMaker Feature Store Spark

Amazon SageMaker Feature Store Spark is a Spark connector that connects the Spark library to Feature Store. Feature Store Spark simplifies data ingestion from Spark DataFrames to feature groups. Feature Store supports batch data ingestion with Spark, using your existing ETL pipeline, on Amazon EMR, GIS, an Amazon Glue job, an Amazon SageMaker Processing job, or a SageMaker notebook.

Methods for installing and implementing batch data ingestion are provided for Python and Scala developers. Python developers can use the open-source sagemaker-feature-store-pyspark Python library for local development, installation on Amazon EMR, and for Jupyter Notebooks by following the instructions in the Amazon SageMaker Feature Store Spark GitHub repository. Scala developers can use the Feature Store Spark connector available in the Amazon SageMaker Feature Store Spark SDK Maven central repository.

You can use the Spark connector to ingest data in the following ways, depending on if the online store, offline store, or both are enabled.

For information about using the different ingestion methods, see Example implementations.

Feature Store Spark installation

Scala users

The Feature Store Spark SDK is available in the Amazon SageMaker Feature Store Spark SDK Maven central repository for Scala users.

Requirements

Declare the dependency in POM.xml

The Feature Store Spark connector has a dependency on the iceberg-spark-runtime library. You must therefore add corresponding version of the iceberg-spark-runtime library to the dependency if you're ingesting data into a feature group that you've auto-created with the Iceberg table format. For example, if you're using Spark 3.1, you must declare the following in your project’s POM.xml:

 <dependency>
 <groupId>software.amazon.sagemaker.featurestore</groupId>
 <artifactId>sagemaker-feature-store-spark-sdk_2.12</artifactId>
 <version>1.0.0</version>
 </dependency>
 
 <dependency>
   <groupId>org.apache.iceberg</groupId>
   <artifactId>iceberg-spark-runtime-3.1_2.12</artifactId>
   <version>0.14.0</version>
</dependency>
    

Python users

The Feature Store Spark SDK is available in the open-source Amazon SageMaker Feature Store Spark GitHub repository.

Requirements

We recommend setting the $SPARK_HOME to the directory where you have Spark installed. During installation, Feature Store uploads the required JAR to SPARK_HOME, so that the dependencies load automatically. Spark starting a JVM is required to make this PySpark library work.

Local installation

To find more info about the installation, enable verbose mode by appending --verbose to the following installation command.

pip3 install sagemaker-feature-store-pyspark-3.1 --no-binary :all:
    

Installation on Amazon EMR

Create an Amazon EMR cluster with the release version 6.1.0 or later. Enable SSH to help you troubleshoot any issues.

You can do one of the following to install the library:

export SPARK_HOME=/usr/lib/spark
sudo -E pip3 install sagemaker-feature-store-pyspark-3.1 --no-binary :all: --verbose      
    

Installation on a SageMaker notebook instance

Install a version of PySpark that's compatible with the Spark connector using the following commands:

!pip3 install pyspark==3.1.1 
!pip3 install sagemaker-feature-store-pyspark-3.1 --no-binary :all:  

If you're performing batch ingestion to the offline store, the dependencies aren't within the notebook instance environment.

from pyspark.sql import SparkSession
import feature_store_pyspark

extra_jars = ",".join(feature_store_pyspark.classpath_jars())

spark = SparkSession.builder \
    .config("spark.jars", extra_jars) \
    .config("spark.jars.packages", "org.apache.hadoop:hadoop-aws:3.2.1,org.apache.hadoop:hadoop-common:3.2.1") \
    .getOrCreate()
    

Installation on notebooks with GIS

Use the following information to help you install the PySpark connector in an Amazon Glue Interactive Session (GIS).

Amazon SageMaker Feature Store Spark requires a specific Spark connector JAR during the initialization of the session to be uploaded to your Amazon S3 bucket. For more information on uploading the required JAR to your S3 bucket, see Retrieving the JAR for Feature Store Spark.

After you’ve uploaded the JAR, you must provide the GIS sessions with the JAR using the following command.

%extra_jars s3:/<YOUR_BUCKET>/spark-connector-jars/sagemaker-feature-store-spark-sdk.jar

To install Feature Store Spark in the Amazon Glue runtime, use the %additional_python_modules magic command within the GIS notebook. Amazon Glue runs pip to the modules that you’ve specified under %additional_python_modules.

%additional_python_modules sagemaker-feature-store-pyspark-3.1

Before you start the Amazon Glue session, you must use both of the preceding magic commands.

Installation on an Amazon Glue job

To install the Spark connector on a Amazon Glue job, use the --extra-jars argument to provide the necessary JARs and --additional-python-modules to install the Spark Connector as job parameters when you create the Amazon Glue job as shown in the following example. For more information on uploading the required JAR to your S3 bucket, see Retrieving the JAR for Feature Store Spark.

glue_client = boto3.client('glue', region_name=region)
response = glue_client.create_job(
    Name=pipeline_id,
    Description='Feature Store Compute Job',
    Role=glue_role_arn,
    ExecutionProperty={'MaxConcurrentRuns': max_concurrent_run},
    Command={
        'Name': 'glueetl',
        'ScriptLocation': script_location_uri,
        'PythonVersion': '3'
    },
    DefaultArguments={
        '--TempDir': temp_dir_location_uri,
        '--additional-python-modules': 'sagemaker-feature-store-pyspark-3.1',
        '--extra-jars': "s3:/<YOUR_BUCKET>/spark-connector-jars/sagemaker-feature-store-spark-sdk.jar",
        ...
    },
    MaxRetries=3,
    NumberOfWorkers=149,
    Timeout=2880,
    GlueVersion='3.0',
    WorkerType='G.2X'
)
    

Installation on an Amazon SageMaker Processing job

To use Feature Store Spark with Amazon SageMaker Processing jobs, bring your own image. For more information about bringing your image, see Bring your own SageMaker image. Add the installation step to a Dockerfile. After you've pushed the Docker image to an Amazon ECR repository, you can use the PySparkProcessor to create the processing job. For more information about creating a processing job with the PySpark processor, see Data Processing with Apache Spark.

The following is an example of adding an installation step to the Dockerfile.

FROM <ACCOUNT_ID>.dkr.ecr.<AWS_REGION>.amazonaws.com/sagemaker-spark-processing:3.1-cpu-py38-v1.0

RUN /usr/bin/python3 -m pip install sagemaker-feature-store-pyspark-3.1 --no-binary :all: --verbose
      
    

Retrieving the JAR for Feature Store Spark

To retrieve the Feature Store Spark dependency JAR, you must install the Spark connector from the Python Package Index (PyPI) repository using pip in any Python environment with network access. A SageMaker Jupyter Notebook is an example of a Python environment with network access.

The following command installs the Spark connector.

!pip install sagemaker-feature-store-pyspark-3.1      
    

After you've installed Feature Store Spark, you can retrieve the JAR location and upload the JAR to Amazon S3.

The feature-store-pyspark-dependency-jars command provides the location of the necessary dependency JAR that Feature Store Spark added. You can use the command to retrieve the JAR and upload it to Amazon S3.

jar_location = !feature-store-pyspark-dependency-jars
jar_location = jar_location[0]

s3_client = boto3.client("s3")
s3_client.upload_file(jar_location, "<YOUR_BUCKET>","spark-connector-jars/sagemaker-feature-store-spark-sdk.jar")
      

Example implementations

Example Python script

FeatureStoreBatchIngestion.py

from pyspark.sql import SparkSession
from feature_store_pyspark.FeatureStoreManager import FeatureStoreManager
import feature_store_pyspark

spark = SparkSession.builder \
                    .getOrCreate()

# Construct test DataFrame
columns = ["RecordIdentifier", "EventTime"]
data = [("1","2021-03-02T12:20:12Z"), ("2", "2021-03-02T12:20:13Z"), ("3", "2021-03-02T12:20:14Z")]

df = spark.createDataFrame(data).toDF(*columns)

# Initialize FeatureStoreManager with a role arn if your feature group is created by another account
feature_store_manager= FeatureStoreManager("arn:aws:iam::111122223333:role/role-arn")
 
# Load the feature definitions from input schema. The feature definitions can be used to create a feature group
feature_definitions = feature_store_manager.load_feature_definitions_from_schema(df)

feature_group_arn = "arn:aws:sagemaker:<AWS_REGION>:<ACCOUNT_ID>:feature-group/<YOUR_FEATURE_GROUP_NAME>"

# Ingest by default. The connector will leverage PutRecord API to ingest your data in stream
# https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_feature_store_PutRecord.html
feature_store_manager.ingest_data(input_data_frame=df, feature_group_arn=feature_group_arn)

# To select the target stores for ingestion, you can specify the target store as the paramter
# If OnlineStore is selected, the connector will leverage PutRecord API to ingest your data in stream
feature_store_manager.ingest_data(input_data_frame=df, feature_group_arn=feature_group_arn, target_stores=["OfflineStore", "OnlineStore"])

# If only OfflineStore is selected, the connector will batch write the data to offline store directly
feature_store_manager.ingest_data(input_data_frame=df, feature_group_arn=feature_group_arn, target_stores=["OfflineStore"])

# To retrieve the records failed to be ingested by spark connector
failed_records_df = feature_store_manager.get_failed_stream_ingestion_data_frame()
    

Submit a Spark job with example Python script

The PySpark version requires an extra dependent JAR to be imported, so extra steps are needed to run the Spark application.

If you did not specify SPARK_HOME during installation, then you have to load required JARs in JVM when running spark-submit. feature-store-pyspark-dependency-jars is a Python script installed by the Spark library to automatically fetch the path to all JARs for you.

spark-submit --jars `feature-store-pyspark-dependency-jars` FeatureStoreBatchIngestion.py
    

If you are running this application on Amazon EMR, we recommended that you run the application in client mode, so that you do not need to distribute the dependent JARs to other task nodes. Add one more step in Amazon EMR cluster with Spark argument similar to the following:

spark-submit --deploy-mode client --master yarn s3:/<PATH_TO_SCRIPT>/FeatureStoreBatchIngestion.py
    

Example Scala script

FeatureStoreBatchIngestion.scala

import software.amazon.sagemaker.featurestore.sparksdk.FeatureStoreManager
import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.apache.spark.sql.{Row, SparkSession}

object TestSparkApp {
  def main(args: Array[String]): Unit = {

    val spark = SparkSession.builder().getOrCreate()

    // Construct test DataFrame
    val data = List(
      Row("1", "2021-07-01T12:20:12Z"),
      Row("2", "2021-07-02T12:20:13Z"),
      Row("3", "2021-07-03T12:20:14Z")
    )
    
    val schema = StructType(
      List(StructField("RecordIdentifier", StringType), StructField("EventTime", StringType))
    )

    val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schema)
    
    // Initialize FeatureStoreManager with a role arn if your feature group is created by another account
    val featureStoreManager = new FeatureStoreManager("arn:aws:iam::111122223333:role/role-arn")
    
    // Load the feature definitions from input schema. The feature definitions can be used to create a feature group
    val featureDefinitions = featureStoreManager.loadFeatureDefinitionsFromSchema(df)

    val featureGroupArn = "arn:aws:sagemaker:<AWS_REGION>:<ACCOUNT_ID>:feature-group/<YOUR_FEATURE_GROUP_NAME>"
   
    // Ingest by default. The connector will leverage PutRecord API to ingest your data in stream
    // https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_feature_store_PutRecord.html
    featureStoreManager.ingestData(df, featureGroupArn)
    
    // To select the target stores for ingestion, you can specify the target store as the paramter
    // If OnlineStore is selected, the connector will leverage PutRecord API to ingest your data in stream
    featureStoreManager.ingestData(df, featureGroupArn, List("OfflineStore", "OnlineStore"))
    
    // If only OfflineStore is selected, the connector will batch write the data to offline store directly
    featureStoreManager.ingestData(df, featureGroupArn, ["OfflineStore"])
    
    // To retrieve the records failed to be ingested by spark connector
    val failedRecordsDf = featureStoreManager.getFailedStreamIngestionDataFrame()
  }
}
    

Submit a Spark job

Scala

You should be able to use Feature Store Spark as a normal dependency. No extra instruction is needed to run the application on all platforms.