Distributed Data Processing using Apache Spark and SageMaker Processing
This notebook’s CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.
Apache Spark is a unified analytics engine for large-scale data processing. The Spark framework is often used within the context of machine learning workflows to run data transformation or feature engineering workloads at scale. Amazon SageMaker provides a set of prebuilt Docker images that include Apache Spark and other dependencies needed to run distributed data processing jobs on Amazon SageMaker. This example notebook demonstrates how to use the prebuilt Spark images on SageMaker Processing using the SageMaker Python SDK.
This notebook walks through the following scenarios to illustrate the functionality of the SageMaker Spark Container:
Running a basic PySpark application using the SageMaker Python SDK’s
PySparkProcessorclassViewing the Spark UI via the
start_history_server()function of aPySparkProcessorobjectAdding additional Python and jar file dependencies to jobs
Running a basic Java/Scala-based Spark job using the SageMaker Python SDK’s
SparkJarProcessorclassSpecifying additional Spark configuration
Runtime
This notebook takes approximately 22 minutes to run.
Contents
Setup
Install the latest SageMaker Python SDK
This notebook requires the latest v2.x version of the SageMaker Python SDK. First, ensure that the latest version is installed.
[1]:
!pip install -U "sagemaker>2.0"
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Restart your notebook kernel after upgrading the SDK
Example 1: Running a basic PySpark application
The first example is a basic Spark MLlib data processing script. This script will take a raw data set and do some transformations on it such as string indexing and one hot encoding.
Setup S3 bucket locations and roles
First, setup some locations in the default SageMaker bucket to store the raw input datasets and the Spark job output. Here, you’ll also define the role that will be used to run all SageMaker Processing jobs.
[2]:
import logging
import sagemaker
from time import gmtime, strftime
sagemaker_logger = logging.getLogger("sagemaker")
sagemaker_logger.setLevel(logging.INFO)
sagemaker_logger.addHandler(logging.StreamHandler())
sagemaker_session = sagemaker.Session()
bucket = sagemaker_session.default_bucket()
role = sagemaker.get_execution_role()
Next, you’ll download the example dataset from a SageMaker staging bucket.
[3]:
# Fetch the dataset from the SageMaker bucket
import boto3
s3 = boto3.client("s3")
s3.download_file(
f"sagemaker-sample-files", "datasets/tabular/uci_abalone/abalone.csv", "./data/abalone.csv"
)
Write the PySpark script
The source for a preprocessing script is in the cell below. The cell uses the %%writefile directive to save this file locally. This script does some basic feature engineering on a raw input dataset. In this example, the dataset is the Abalone Data Set and the code below performs string indexing, one hot encoding, vector assembly, and combines them into a pipeline to perform these transformations in order. The script then does an 80-20
split to produce training and validation datasets as output.
[4]:
%%writefile ./code/preprocess.py
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import csv
import os
import shutil
import sys
import time
import pyspark
from pyspark.sql import SparkSession
from pyspark.ml import Pipeline
from pyspark.ml.feature import (
OneHotEncoder,
StringIndexer,
VectorAssembler,
VectorIndexer,
)
from pyspark.sql.functions import *
from pyspark.sql.types import (
DoubleType,
StringType,
StructField,
StructType,
)
def csv_line(data):
r = ",".join(str(d) for d in data[1])
return str(data[0]) + "," + r
def main():
parser = argparse.ArgumentParser(description="app inputs and outputs")
parser.add_argument("--s3_input_bucket", type=str, help="s3 input bucket")
parser.add_argument("--s3_input_key_prefix", type=str, help="s3 input key prefix")
parser.add_argument("--s3_output_bucket", type=str, help="s3 output bucket")
parser.add_argument("--s3_output_key_prefix", type=str, help="s3 output key prefix")
args = parser.parse_args()
spark = SparkSession.builder.appName("PySparkApp").getOrCreate()
# This is needed to save RDDs which is the only way to write nested Dataframes into CSV format
spark.sparkContext._jsc.hadoopConfiguration().set(
"mapred.output.committer.class", "org.apache.hadoop.mapred.FileOutputCommitter"
)
# Defining the schema corresponding to the input data. The input data does not contain the headers
schema = StructType(
[
StructField("sex", StringType(), True),
StructField("length", DoubleType(), True),
StructField("diameter", DoubleType(), True),
StructField("height", DoubleType(), True),
StructField("whole_weight", DoubleType(), True),
StructField("shucked_weight", DoubleType(), True),
StructField("viscera_weight", DoubleType(), True),
StructField("shell_weight", DoubleType(), True),
StructField("rings", DoubleType(), True),
]
)
# Downloading the data from S3 into a Dataframe
total_df = spark.read.csv(
("s3://" + os.path.join(args.s3_input_bucket, args.s3_input_key_prefix, "abalone.csv")),
header=False,
schema=schema,
)
# StringIndexer on the sex column which has categorical value
sex_indexer = StringIndexer(inputCol="sex", outputCol="indexed_sex")
# one-hot-encoding is being performed on the string-indexed sex column (indexed_sex)
sex_encoder = OneHotEncoder(inputCol="indexed_sex", outputCol="sex_vec")
# vector-assembler will bring all the features to a 1D vector for us to save easily into CSV format
assembler = VectorAssembler(
inputCols=[
"sex_vec",
"length",
"diameter",
"height",
"whole_weight",
"shucked_weight",
"viscera_weight",
"shell_weight",
],
outputCol="features",
)
# The pipeline is comprised of the steps added above
pipeline = Pipeline(stages=[sex_indexer, sex_encoder, assembler])
# This step trains the feature transformers
model = pipeline.fit(total_df)
# This step transforms the dataset with information obtained from the previous fit
transformed_total_df = model.transform(total_df)
# Split the overall dataset into 80-20 training and validation
(train_df, validation_df) = transformed_total_df.randomSplit([0.8, 0.2])
# Convert the train dataframe to RDD to save in CSV format and upload to S3
train_rdd = train_df.rdd.map(lambda x: (x.rings, x.features))
train_lines = train_rdd.map(csv_line)
train_lines.saveAsTextFile(
"s3://" + os.path.join(args.s3_output_bucket, args.s3_output_key_prefix, "train")
)
# Convert the validation dataframe to RDD to save in CSV format and upload to S3
validation_rdd = validation_df.rdd.map(lambda x: (x.rings, x.features))
validation_lines = validation_rdd.map(csv_line)
validation_lines.saveAsTextFile(
"s3://" + os.path.join(args.s3_output_bucket, args.s3_output_key_prefix, "validation")
)
if __name__ == "__main__":
main()
Overwriting ./code/preprocess.py
Run the SageMaker Processing Job
Next, you’ll use the PySparkProcessor class to define a Spark job and run it using SageMaker Processing. A few things to note in the definition of the PySparkProcessor:
This is a multi-node job with two m5.xlarge instances (which is specified via the
instance_countandinstance_typeparameters)Spark framework version 3.1 is specified via the
framework_versionparameterThe PySpark script defined above is passed via via the
submit_appparameterCommand-line arguments to the PySpark script (such as the S3 input and output locations) are passed via the
argumentsparameterSpark event logs will be offloaded to the S3 location specified in
spark_event_logs_s3_uriand can be used to view the Spark UI while the job is in progress or after it completes
[5]:
from sagemaker.spark.processing import PySparkProcessor
# Upload the raw input dataset to a unique S3 location
timestamp_prefix = strftime("%Y-%m-%d-%H-%M-%S", gmtime())
prefix = "sagemaker/spark-preprocess-demo/{}".format(timestamp_prefix)
input_prefix_abalone = "{}/input/raw/abalone".format(prefix)
input_preprocessed_prefix_abalone = "{}/input/preprocessed/abalone".format(prefix)
sagemaker_session.upload_data(
path="./data/abalone.csv", bucket=bucket, key_prefix=input_prefix_abalone
)
# Run the processing job
spark_processor = PySparkProcessor(
base_job_name="sm-spark",
framework_version="3.1",
role=role,
instance_count=2,
instance_type="ml.m5.xlarge",
max_runtime_in_seconds=1200,
)
spark_processor.run(
submit_app="./code/preprocess.py",
arguments=[
"--s3_input_bucket",
bucket,
"--s3_input_key_prefix",
input_prefix_abalone,
"--s3_output_bucket",
bucket,
"--s3_output_key_prefix",
input_preprocessed_prefix_abalone,
],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
logs=False,
)
Creating processing-job with name sm-spark-2022-04-18-18-18-08-917
Job Name: sm-spark-2022-04-18-18-18-08-917
Inputs: [{'InputName': 'code', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sm-spark-2022-04-18-18-18-08-917/input/code/preprocess.py', 'LocalPath': '/opt/ml/processing/input/code', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]
Outputs: [{'OutputName': 'output-1', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sagemaker/spark-preprocess-demo/2022-04-18-18-18-08/spark_event_logs', 'LocalPath': '/opt/ml/processing/spark-events/', 'S3UploadMode': 'Continuous'}}]
.....................................................................!
Validate Data Processing Results
Next, validate the output of our data preprocessing job by looking at the first 5 rows of the output dataset.
[6]:
print("Top 5 rows from s3://{}/{}/train/".format(bucket, input_preprocessed_prefix_abalone))
!aws s3 cp --quiet s3://$bucket/$input_preprocessed_prefix_abalone/train/part-00000 - | head -n5
Top 5 rows from s3://sagemaker-us-west-2-000000000000/sagemaker/spark-preprocess-demo/2022-04-18-18-18-08/input/preprocessed/abalone/train/
5.0,0.0,0.0,0.275,0.195,0.07,0.08,0.031,0.0215,0.025
7.0,0.0,0.0,0.305,0.225,0.07,0.1485,0.0585,0.0335,0.045
7.0,0.0,0.0,0.305,0.23,0.08,0.156,0.0675,0.0345,0.048
7.0,0.0,0.0,0.325,0.26,0.09,0.1915,0.085,0.036,0.062
9.0,0.0,0.0,0.33,0.26,0.08,0.2,0.0625,0.05,0.07
View the Spark UI
Next, you can view the Spark UI by running the history server locally in this notebook. (Note: this feature will only work in a local development environment with docker installed or on a Sagemaker Notebook Instance. This feature does not currently work in SageMaker Studio.)
[7]:
spark_processor.start_history_server()
Pulling spark history server image...
docker command: docker pull 314815235551.dkr.ecr.us-west-2.amazonaws.com/sagemaker-spark-processing:3.1-cpu
image pulled: 314815235551.dkr.ecr.us-west-2.amazonaws.com/sagemaker-spark-processing:3.1-cpu
History server terminated
Starting history server...
History server is up on https://TestingNotebook.notebook.us-west-2.sagemaker.aws/proxy/15050
After viewing the Spark UI, you can terminate the history server before proceeding.
[8]:
spark_processor.terminate_history_server()
History server is running, terminating history server
History server terminated
Example 2: Specify additional Python and jar file dependencies
The next example demonstrates a scenario where additional Python file dependencies are required by the PySpark script. You’ll use a sample PySpark script that requires additional user-defined functions (UDFs) defined in a local module.
[9]:
%%writefile ./code/hello_py_spark_app.py
import argparse
import time
# Import local module to test spark-submit--py-files dependencies
import hello_py_spark_udfs as udfs
from pyspark.sql import SparkSession, SQLContext
from pyspark.sql.functions import udf
from pyspark.sql.types import IntegerType
import time
if __name__ == "__main__":
print("Hello World, this is PySpark!")
parser = argparse.ArgumentParser(description="inputs and outputs")
parser.add_argument("--input", type=str, help="path to input data")
parser.add_argument("--output", required=False, type=str, help="path to output data")
args = parser.parse_args()
spark = SparkSession.builder.appName("SparkTestApp").getOrCreate()
sqlContext = SQLContext(spark.sparkContext)
# Load test data set
inputPath = args.input
outputPath = args.output
salesDF = spark.read.json(inputPath)
salesDF.printSchema()
salesDF.createOrReplaceTempView("sales")
# Define a UDF that doubles an integer column
# The UDF function is imported from local module to test spark-submit--py-files dependencies
double_udf_int = udf(udfs.double_x, IntegerType())
# Save transformed data set to disk
salesDF.select("date", "sale", double_udf_int("sale").alias("sale_double")).write.json(
outputPath
)
Overwriting ./code/hello_py_spark_app.py
[10]:
%%writefile ./code/hello_py_spark_udfs.py
def double_x(x):
return x + x
Overwriting ./code/hello_py_spark_udfs.py
Create a processing job with Python file dependencies
Then, you’ll create a processing job where the additional Python file dependencies are specified via the submit_py_files argument in the run() function. If your Spark application requires additional jar file dependencies, these can be specified via the submit_jars argument of the run() function.
[11]:
# Define job input/output URIs
timestamp_prefix = strftime("%Y-%m-%d-%H-%M-%S", gmtime())
prefix = "sagemaker/spark-preprocess-demo/{}".format(timestamp_prefix)
input_prefix_sales = "{}/input/sales".format(prefix)
output_prefix_sales = "{}/output/sales".format(prefix)
input_s3_uri = "s3://{}/{}".format(bucket, input_prefix_sales)
output_s3_uri = "s3://{}/{}".format(bucket, output_prefix_sales)
sagemaker_session.upload_data(
path="./data/data.jsonl", bucket=bucket, key_prefix=input_prefix_sales
)
spark_processor = PySparkProcessor(
base_job_name="sm-spark-udfs",
framework_version="3.1",
role=role,
instance_count=2,
instance_type="ml.m5.xlarge",
max_runtime_in_seconds=1200,
)
spark_processor.run(
submit_app="./code/hello_py_spark_app.py",
submit_py_files=["./code/hello_py_spark_udfs.py"],
arguments=["--input", input_s3_uri, "--output", output_s3_uri],
logs=False,
)
Copying dependency from local path ./code/hello_py_spark_udfs.py to tmpdir /tmp/tmpyxl2rp60
Uploading dependencies from tmpdir /tmp/tmpyxl2rp60 to S3 s3://sagemaker-us-west-2-000000000000/sm-spark-udfs-2022-04-18-18-25-48-865/input/py-files
Creating processing-job with name sm-spark-udfs-2022-04-18-18-25-48-865
Job Name: sm-spark-udfs-2022-04-18-18-25-48-865
Inputs: [{'InputName': 'py-files', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sm-spark-udfs-2022-04-18-18-25-48-865/input/py-files', 'LocalPath': '/opt/ml/processing/input/py-files', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'code', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sm-spark-udfs-2022-04-18-18-25-48-865/input/code/hello_py_spark_app.py', 'LocalPath': '/opt/ml/processing/input/code', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]
Outputs: []
................................................................!
Validate Data Processing Results
Next, validate the output of the Spark job by ensuring that the output URI contains the Spark _SUCCESS file along with the output json lines file.
[12]:
print("Output files in {}".format(output_s3_uri))
!aws s3 ls $output_s3_uri/
Output files in s3://sagemaker-us-west-2-000000000000/sagemaker/spark-preprocess-demo/2022-04-18-18-25-48/output/sales
2022-04-18 18:30:45 0 _SUCCESS
2022-04-18 18:30:44 51313 part-00000-8706a8c9-3dd3-4b24-8fcd-30ae62790f46-c000.json
Example 3: Run a Java/Scala Spark application
In the next example, you’ll take a Spark application jar (located in ./code/spark-test-app.jar) that is already built and run it using SageMaker Processing. Here, you’ll use the SparkJarProcessor class to define the job parameters.
In the run() function you’ll specify:
The location of the Spark application jar file in the
submit_appargumentThe main class for the Spark application in the
submit_classargumentInput/output arguments for the Spark application
[13]:
from sagemaker.spark.processing import SparkJarProcessor
# Upload the raw input dataset to S3
timestamp_prefix = strftime("%Y-%m-%d-%H-%M-%S", gmtime())
prefix = "sagemaker/spark-preprocess-demo/{}".format(timestamp_prefix)
input_prefix_sales = "{}/input/sales".format(prefix)
output_prefix_sales = "{}/output/sales".format(prefix)
input_s3_uri = "s3://{}/{}".format(bucket, input_prefix_sales)
output_s3_uri = "s3://{}/{}".format(bucket, output_prefix_sales)
sagemaker_session.upload_data(
path="./data/data.jsonl", bucket=bucket, key_prefix=input_prefix_sales
)
spark_processor = SparkJarProcessor(
base_job_name="sm-spark-java",
framework_version="3.1",
role=role,
instance_count=2,
instance_type="ml.m5.xlarge",
max_runtime_in_seconds=1200,
)
spark_processor.run(
submit_app="./code/spark-test-app.jar",
submit_class="com.amazonaws.sagemaker.spark.test.HelloJavaSparkApp",
arguments=["--input", input_s3_uri, "--output", output_s3_uri],
logs=False,
)
Creating processing-job with name sm-spark-java-2022-04-18-18-31-12-003
Job Name: sm-spark-java-2022-04-18-18-31-12-003
Inputs: [{'InputName': 'code', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sm-spark-java-2022-04-18-18-31-12-003/input/code/spark-test-app.jar', 'LocalPath': '/opt/ml/processing/input/code', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]
Outputs: []
.................................................................!
Example 4: Specifying additional Spark configuration
Overriding Spark configuration is crucial for a number of tasks such as tuning your Spark application or configuring the Hive metastore. Using the SageMaker Python SDK, you can easily override Spark/Hive/Hadoop configuration.
The next example demonstrates this by overriding Spark executor memory/cores.
For more information on configuring your Spark application, see the EMR documentation on Configuring Applications
[14]:
# Upload the raw input dataset to a unique S3 location
timestamp_prefix = strftime("%Y-%m-%d-%H-%M-%S", gmtime())
prefix = "sagemaker/spark-preprocess-demo/{}".format(timestamp_prefix)
input_prefix_abalone = "{}/input/raw/abalone".format(prefix)
input_preprocessed_prefix_abalone = "{}/input/preprocessed/abalone".format(prefix)
sagemaker_session.upload_data(
path="./data/abalone.csv", bucket=bucket, key_prefix=input_prefix_abalone
)
spark_processor = PySparkProcessor(
base_job_name="sm-spark",
framework_version="3.1",
role=role,
instance_count=2,
instance_type="ml.m5.xlarge",
max_runtime_in_seconds=1200,
)
configuration = [
{
"Classification": "spark-defaults",
"Properties": {"spark.executor.memory": "2g", "spark.executor.cores": "1"},
}
]
spark_processor.run(
submit_app="./code/preprocess.py",
arguments=[
"--s3_input_bucket",
bucket,
"--s3_input_key_prefix",
input_prefix_abalone,
"--s3_output_bucket",
bucket,
"--s3_output_key_prefix",
input_preprocessed_prefix_abalone,
],
configuration=configuration,
logs=False,
)
Creating processing-job with name sm-spark-2022-04-18-18-36-38-904
Job Name: sm-spark-2022-04-18-18-36-38-904
Inputs: [{'InputName': 'conf', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sm-spark-2022-04-18-18-36-38-904/input/conf/configuration.json', 'LocalPath': '/opt/ml/processing/input/conf', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'code', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://sagemaker-us-west-2-000000000000/sm-spark-2022-04-18-18-36-38-904/input/code/preprocess.py', 'LocalPath': '/opt/ml/processing/input/code', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]
Outputs: []
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Notebook CI Test Results
This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.