Use a Delta Lake cluster with Spark - Amazon EMR
Initialize a Spark session for Delta LakeWrite to a Delta Lake tableRead from a Delta Lake table
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Use a Delta Lake cluster with Spark

Starting with Amazon EMR version 6.9.0, you can use Delta Lake with your Spark cluster without the need for bootstrap actions. For Amazon EMR releases 6.8.0 and lower, you can use bootstrap actions to pre-install the necessary dependencies.

The following examples use the Amazon CLI to work with Delta Lake on an Amazon EMR Spark cluster.

To use Delta Lake on Amazon EMR with the Amazon Command Line Interface, first create a cluster. For information on how to specify the Delta Lake classification with Amazon Command Line Interface, see Supply a configuration using the Amazon Command Line Interface when you create a cluster or Supply a configuration with the Java SDK when you create a cluster.

  1. Create a file, configurations.json, with the following content:

    [{"Classification":"delta-defaults",  "Properties":{"delta.enabled":"true"} }]

  2. Create a cluster with the following configuration, replacing the example Amazon S3 bucket path and the subnet ID with your own.

    aws emr create-cluster 
     --release-label  emr-6.9.0  
     --applications Name=Spark  
     --configurations file://delta_configurations.json   
     --region us-east-1  
     --name My_Spark_Delta_Cluster  
     --log-uri  s3://DOC-EXAMPLE-BUCKET/  
     --instance-type m5.xlarge  
     --instance-count 2   
     --service-role EMR_DefaultRole_V2  
     --ec2-attributes  InstanceProfile=EMR_EC2_DefaultRole,SubnetId=subnet-1234567890abcdef0

    Alternatively, you can create an Amazon EMR cluster and Spark application with the following files as JAR dependencies in a Spark job:

    /usr/share/aws/delta/lib/delta-core.jar,
/usr/share/aws/delta/lib/delta-storage.jar,    
/usr/share/aws/delta/lib/delta-storage-s3-dynamodb.jar
    Note

    If you use Amazon EMR releases 6.9.0 or higher, use /usr/share/aws/delta/lib/delta-spark.jar instead of /usr/share/aws/delta/lib/delta-core.jar.

    For more information, see Submitting Applications.

    To include a jar dependency in the Spark job, you can add the following configuration properties to the Spark application:

    --conf “spark.jars=/usr/share/aws/delta/lib/delta-core.jar,
     /usr/share/aws/delta/lib/delta-storage.jar,
     /usr/share/aws/delta/lib/delta-storage-s3-dynamodb.jar"

    For more information about Spark job dependencies, see Dependency Management.

    If you use Amazon EMR releases 6.9.0 or higher, add the /usr/share/aws/delta/lib/delta-spark.jar configuration instead.

    --conf “spark.jars=/usr/share/aws/delta/lib/delta-spark.jar,
     /usr/share/aws/delta/lib/delta-storage.jar,
     /usr/share/aws/delta/lib/delta-storage-s3-dynamodb.jar"

Initialize a Spark session for Delta Lake

The following examples show how to launch the interactive Spark shell, use Spark submit, or use Amazon EMR Notebooks to work with Delta Lake on Amazon EMR.

spark-shell

  1. Connect to the primary node using SSH. For more information, see Connect to the primary node using SSH in the Amazon EMR Management Guide.

  2. Enter the following command to launch the Spark shell. To use the PySpark shell, replace spark-shell with pyspark.

    spark-shell \
   --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" \
   --conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"

    If you run Amazon EMR releases 6.15.0 or higher, you must also use the following configurations to use fine-grained access control based on Lake Formation with Delta Lake.

    spark-shell \  
  --conf spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension,com.amazonaws.emr.recordserver.connector.spark.sql.RecordServerSQLExtension \  
  --conf spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog \  
  --conf spark.sql.catalog.spark_catalog.lf.managed=true
spark-submit

  1. Connect to the primary node using SSH. For more information, see Connect to the primary node using SSH in the Amazon EMR Management Guide.

  2. Enter the following command to launch the Spark session for Delta Lake.

    spark-submit  
—conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" 
—conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"

    If you run Amazon EMR releases 6.15.0 or higher, you must also use the following configurations to use fine-grained access control based on Lake Formation with Delta Lake.

    spark-submit \  `
--conf spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension,com.amazonaws.emr.recordserver.connector.spark.sql.RecordServerSQLExtension 
--conf spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog \  
--conf spark.sql.catalog.spark_catalog.lf.managed=true
EMR Studio notebooks

To initialize a Spark session using Amazon EMR Studio notebooks, configure your Spark session using %%configure magic command in your Amazon EMR notebook, as in the following example. For more information, see Use EMR Notebooks magics in the Amazon EMR Management Guide.

%%configure -f
{
  "conf": {
    "spark.sql.extensions":  "io.delta.sql.DeltaSparkSessionExtension",
     "spark.sql.catalog.spark_catalog":  "org.apache.spark.sql.delta.catalog.DeltaCatalog"
  }
}

If you run Amazon EMR releases 6.15.0 or higher, you must also use the following configurations to use fine-grained access control based on Lake Formation with Delta Lake.

%%configure -f   
{
  "conf": {
    "spark.sql.extensions": "io.delta.sql.DeltaSparkSessionExtension,com.amazonaws.emr.recordserver.connector.spark.sql.RecordServerSQLExtension",
    "spark.sql.catalog.spark_catalog": "org.apache.spark.sql.delta.catalog.DeltaCatalog",
    "spark.sql.catalog.spark_catalog.lf.managed": "true"
  }
}

Write to a Delta Lake table

The following example shows how to create a DataFrame and write it as a Delta Lake dataset. The example shows how to work with datasets with the Spark shell while connected to the primary node using SSH as the default hadoop user.

Note

To paste code samples into the Spark shell, type :paste at the prompt, paste the example, and then press CTRL + D.

PySpark

Spark includes a Python based shell, pyspark, that you can use to prototype Spark programs written in Python. Just as with spark-shell, invoke pyspark on the primary node.

## Create a DataFrame
data =  spark.createDataFrame([("100", "2015-01-01", "2015-01-01T13:51:39.340396Z"),
("101",  "2015-01-01", "2015-01-01T12:14:58.597216Z"),
("102", "2015-01-01", "2015-01-01T13:51:40.417052Z"),
("103",  "2015-01-01",  "2015-01-01T13:51:40.519832Z")],
["id", "creation_date",  "last_update_time"])

## Write a DataFrame as a Delta Lake dataset to the S3  location
spark.sql("""CREATE  TABLE IF NOT EXISTS delta_table (id string, creation_date string, 
last_update_time string)
USING delta location
's3://DOC-EXAMPLE-BUCKET/example-prefix/db/delta_table'""");

data.writeTo("delta_table").append()
Scala
import org.apache.spark.sql.SaveMode
import org.apache.spark.sql.functions._
 
// Create a DataFrame
val data = Seq(("100",  "2015-01-01",  "2015-01-01T13:51:39.340396Z"),
("101",  "2015-01-01",  "2015-01-01T12:14:58.597216Z"),
("102",  "2015-01-01", "2015-01-01T13:51:40.417052Z"),
("103",  "2015-01-01",  "2015-01-01T13:51:40.519832Z")).toDF("id", "creation_date",  "last_update_time")

// Write a DataFrame as a Delta Lake dataset to the S3  location
spark.sql("""CREATE  TABLE IF NOT EXISTS delta_table (id string,
creation_date string,
last_update_time string)
USING delta location
's3://DOC-EXAMPLE-BUCKET/example-prefix/db/delta_table'""");

data.write.format("delta").mode("append").saveAsTable("delta_table")
SQL
-- Create a Delta  Lake table with the S3 location
CREATE TABLE delta_table(id string,
creation_date string, 
last_update_time string)
USING delta LOCATION
's3://DOC-EXAMPLE-BUCKET/example-prefix/db/delta_table';

-- insert data into the table
INSERT INTO delta_table VALUES  ("100", "2015-01-01",  "2015-01-01T13:51:39.340396Z"),
("101",  "2015-01-01",  "2015-01-01T12:14:58.597216Z"),
("102",  "2015-01-01", "2015-01-01T13:51:40.417052Z"),
("103",  "2015-01-01", "2015-01-01T13:51:40.519832Z");

Read from a Delta Lake table

PySpark
ddf = spark.table("delta_table")
ddf.show()
Scala
val ddf =  spark.table("delta_table")
ddf.show()
SQL
SELECT * FROM delta_table;