Introduction Installation Retrieving the JAR for the Spark Connector Example Implementations

Batch Ingestion Spark Connector Setup

Introduction

Amazon SageMaker Feature Store supports batch data ingestion with Spark, using your existing ETL pipeline. It can be a pipeline on Amazon EMR, GIS, a AWS Glue Job, a Amazon SageMaker Processing job, or a SageMaker Notebook.

Methods for installing and implementing batch data ingestion are provided for Python and Scala. Python developers can use the sagemaker-feature-store-pyspark Python library for local development and installation on Amazon EMR. They can also run it from their Jupyter Notebooks. Scala developers can use the Feature Store Spark connector available in Maven.

You can use the Spark connector to ingest data in the following ways:

Ingest by default – Ingest your dataframe into the online store. When you use the connector to update the online store, the Spark connector uses the PutRecord operation to make the update. Within 15 minutes, Feature Store replicates the data from the online store to the offline store. The online store contains the latest value for the record. For more information about how the online and offline stores work, see Feature Store Concepts.
Offline store direct ingestion – Use the Spark connector to ingest your dataframe directly into the offline store. Ingesting the dataframe directly into the offline store doesn't update the online store.

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

Installation

Scala Users

Requirements

Spark >= 3.0.0 and <= 3.3.0
iceberg-spark-runtime >= 0.14.0
Scala >= 2.12.x
Amazon EMR >= 6.1.0 (only if you are using Amazon EMR)

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. The Feature Store Spark connector is available in the Maven central repository. 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

Requirements

Spark >= 3.0.0 and <= 3.3.0
Amazon EMR >= 6.1.0 (only if you are using Amazon EMR)
Kernel = conda_python3

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:

Create a custom step within Amazon EMR.
SSH into your cluster and install the library from there.

You can either create a custom step within Amazon EMR to start installing the library or SSH into your cluster to install the library directly in console.

Note

The following information uses Spark version 3.1, but you can specify any version that meets the requirements.



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

Note

If you want to install the dependent JARs automatically to SPARK_HOME, do not use the bootstrap step.

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

Important

You must use AWS Glue Version 2.0 or later.

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

The Amazon SageMaker Feature Store Spark Connector 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 the Spark Connector.

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 the Spark Connector in the AWS Glue runtime, use the %additional_python_modules magic command within the GIS notebook. AWS 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 AWS Glue session, you must use both of the preceding magic commands.

Installation on an AWS Glue Job

Important

You must use AWS Glue Version 2.0 or later.

To install the Spark connect on a AWS 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 AWS 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 the Spark Connector.


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 a Amazon SageMaker Processing Job

To use the Feature Store Spark Connector 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 the Spark Connector

To retrieve the Spark connector 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 the Amazon SageMaker Feature Store PySpark connector, 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 the Spark connector 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, it’s recommended to 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 like this:



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 the Spark connector as a normal dependency. No extra instruction is needed to run the application on all platforms.

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Document Conventions

Data Sources and Ingestion

Add Features to a Feature Group