Running Apache Spark on Amazon Web Services, Services, how to run and examples

Nixon Data Running Apache Spark on Amazon Web Services, Services, how to run and examples

Running Apache Spark on Amazon Web Services, Services supported, Steps to run with examples

1. Overview

Apache Spark is an open-source, distributed computing system that can process large amounts of data quickly. It is a powerful tool for data processing, machine learning, and graph processing, and it can run on a variety of platforms, including local and cluster environments.

One popular platform for running Apache Spark is Amazon Web Services (AWS). AWS offers a variety of services that can be used to run Spark, including Elastic MapReduce (EMR), EC2, and S3.

EMR is a managed service that makes it easy to set up, run, and scale Spark clusters. With EMR, users can launch a Spark cluster in just a few minutes and have the ability to scale it up or down as needed. EMR also offers a variety of other tools for data processing, including Hadoop and Hive.

EC2 is a service that allows users to rent virtual machines on-demand. These virtual machines can be used to run Spark clusters, and users have the flexibility to configure the machines as needed. EC2 also offers a variety of storage options, including Elastic Block Store (EBS) and S3.

S3 is a simple storage service that allows users to store and retrieve large amounts of data in the cloud. It can be used to store data for Spark clusters, and it also offers features such as versioning, lifecycle management, and cross-region replication.

In addition to these services, AWS also offers a variety of other tools that can be used with Spark, including Amazon Kinesis for streaming data, Amazon Redshift for data warehousing, and Amazon Glue for data cataloging and ETL.

AWS also provides a SDK for spark called “AWS Glue ETL Library” that allows developers to write Glue ETL jobs in Python or Scala, which can then be executed as Spark applications on EMR.

In conclusion, AWS provides a variety of services that can be used to run Apache Spark, including EMR, EC2, and S3. These services make it easy to set up, run, and scale Spark clusters, and they offer a variety of other tools for data processing and storage. With the help of AWS, organizations can leverage the power of Apache Spark to process large amounts of data quickly and easily.

2. List of AWS Services that support Apache Spark

Here is a list of AWS services on which Apache Spark can be run:

  1. Amazon Elastic MapReduce (EMR): A managed service that makes it easy to set up, run, and scale Spark clusters.
  2. Amazon Elastic Compute Cloud (EC2): A service that allows users to rent virtual machines on-demand, which can be used to run Spark clusters.
  3. Amazon Simple Storage Service (S3): A simple storage service that can be used to store data for Spark clusters.
  4. Amazon Glue: A fully managed extract, transform, and load (ETL) service that can be used to catalog and prepare data for Spark.
  5. Amazon Kinesis: A service for streaming data that can be used with Spark Streaming.
  6. Amazon Redshift: A data warehouse service that can be used to store and analyze large amounts of data in conjunction with Spark.
  7. AWS Glue ETL Library: A SDK for spark that allows developers to write Glue ETL jobs in Python or Scala, which can then be executed as Spark applications on EMR.
  8. Amazon SageMaker: Machine learning service that can be used with Spark’s machine learning libraries to build and deploy machine learning models.

Note that these services can be used together to create a powerful and flexible data processing and analysis environment. Additionally, AWS also provides other tools like AWS Data Exchange, AWS Data Pipeline, and many more which can be integrated with Spark to build a more robust data pipeline.

3. Running Apache Spark on AWS EMR with example

Amazon Elastic MapReduce (EMR) is a managed service provided by Amazon Web Services (AWS) that makes it easy to set up, run, and scale Apache Hadoop and Apache Spark clusters. EMR allows users to launch a cluster in just a few minutes and have the ability to scale it up or down as needed, without having to worry about the underlying infrastructure.

EMR supports Apache Spark by providing a pre-configured and optimized environment for running Spark applications. EMR clusters include Spark and all its dependencies, so users can start running Spark applications right away. EMR also provides a web-based console, command line tools, and APIs for managing and monitoring Spark clusters.

EMR also integrates with other AWS services such as Amazon S3 and Amazon Kinesis, making it easy to store and process data using Spark. Users can also use EMR to launch Spark clusters on Amazon EC2 instances, and they can configure the instances as needed.

EMR also includes a feature called “EMR Notebooks” which allows users to create, edit and run Jupyter notebook and Apache Zeppelin notebooks using Spark. This feature is particularly useful for data exploration, data visualization and prototyping spark jobs.

Furthermore, AWS Glue ETL Library is also integrated with EMR, which allows developers to write Glue ETL jobs in Python or Scala, which can then be executed as Spark applications on EMR.

In summary, AWS EMR makes it easy to run Apache Spark by providing a managed and optimized environment for running Spark applications, integrating with other AWS services and providing tools for managing and monitoring Spark clusters. EMR Notebooks and Glue ETL Library integration makes it more useful for data exploration, data visualization and prototyping spark jobs.

3.1 Steps to Setup AWS EMR

Setting up an Amazon Elastic MapReduce (EMR) cluster to run Apache Spark is a relatively straightforward process. EMR is a managed service provided by Amazon Web Services (AWS) that makes it easy to set up, run, and scale Hadoop and Spark clusters, so users can start running Spark applications right away.

Here are the steps to set up an EMR cluster and run Spark on it:

  1. Sign in to the AWS Management Console and navigate to the EMR service.
  2. Click on the “Create cluster” button to start creating a new cluster.
  3. On the “Create Cluster” page, select the “Go to advanced options” button.
  4. Select Spark as the application that you want to run on the cluster. You can also add other applications like Hadoop, Hive, Pig, etc.
  5. Choose the number of instances and instance types for the cluster. You can also choose to launch the cluster in a Virtual Private Cloud (VPC) if you have one.
  6. Select the storage options for the cluster. You can use Amazon S3 or Amazon EBS to store data.
  7. In the “Security and access” section, you can configure the security settings for the cluster. You can also create or use an existing security group.
  8. In the “Advanced Options” section, you can configure settings like bootstrap actions, custom applications, and software versions.
  9. Once you have configured all the settings, click on the “Create cluster” button.
  10. EMR will now launch the cluster and install the necessary software, including Spark. The cluster status will be displayed in the EMR console, and you can monitor the progress of the cluster creation.
  11. Once the cluster is running, you can use the EMR console, command line tools, or APIs to submit Spark applications to the cluster. You can also use EMR Notebooks to run Jupyter and Apache Zeppelin notebook on the cluster.
  12. To stop or terminate the cluster, you can use the EMR console or the AWS

3.2 Example for running Apache Spark job on AWS EMR that will read data from AWS S3 and write word count to AWS Kenesis

Amazon Elastic MapReduce (EMR) is a web service that makes it easy to process large amounts of data using the popular Apache Hadoop and Apache Spark frameworks. With EMR, you can set up, operate, and scale a cluster of virtual machines to process big data workloads. This allows you to focus on analyzing your data rather than managing the underlying infrastructure.

In this article, we will walk through an example of creating an EMR cluster that will read data from an S3 bucket and write the results to Amazon Kinesis. We will be using PySpark, the Python API for Spark, to perform a word count on a text file stored in an S3 bucket and stream the results to a Kinesis stream.

Step 1: Create an S3 Bucket

The first step is to create an S3 bucket to store the input data for our job. Log in to the AWS Management Console and navigate to the S3 service. Click on the “Create Bucket” button and enter a unique name for the bucket. Once the bucket is created, upload a text file to be used as input for the job.

Step 2: Create an IAM Role

EMR requires an IAM role that has permissions to access the S3 bucket and other resources. To create the role, navigate to the IAM service in the AWS Management Console and click on the “Roles” menu. Click on the “Create role” button and select “EMR” as the service that will use the role. Attach the “AmazonS3ReadOnlyAccess” and “AmazonKinesisFullAccess” policies to the role.

Step 3: Create a Kinesis Stream

Next, we will create a Kinesis stream to receive the output from our job. Navigate to the Kinesis service in the AWS Management Console and click on the “Create data stream” button. Enter a name for the stream and set the number of shards to 1.

Step 4: Create an EMR Cluster

Now we are ready to create our EMR cluster. Navigate to the EMR service in the AWS Management Console and click on the “Create cluster” button. On the “Create cluster” page, select “Advanced options” and enter the name of the IAM role created in step 2. Under “Software Configuration”, select the latest version of Hadoop and Spark.

Step 5: Submit a PySpark Job

Once the cluster is up and running, we can submit our PySpark job to process the data and stream the results to the Kinesis stream. We will use the AWS CLI to submit the job. First, we need to create a script that contains the PySpark code for our job. Here is an example of PySpark code that reads data from an S3 bucket, performs a word count, and writes the results to Amazon Kinesis:

from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession
from pyspark.streaming import StreamingContext
from pyspark.streaming.kinesis import KinesisUtils, InitialPositionInStream

# Set up a SparkConf and SparkContext
conf = SparkConf().setAppName("WordCountApp")
sc = SparkContext(conf=conf)

# Create a SparkSession
spark = SparkSession(sc)

# Set up a StreamingContext with a batch interval of 1 second
ssc = StreamingContext(sc, 1)

# Define the Kinesis stream to read from and the AWS credentials
kinesisStream = "myStream"
accessKey = "AKIAIOSFODNN7EXAMPLE"
secretKey = "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY"

# Create a DStream from the Kinesis stream
lines = KinesisUtils.createStream(
    ssc, "WordCountApp", kinesisStream, "https://kinesis.us-west-2.amazonaws.com",
    "us-west-2", accessKey, secretKey, InitialPositionInStream.LATEST, 2)

# Perform a word count on the DStream
counts = lines.flatMap(lambda line: line.split(" ")) \
              .map(lambda word: (word, 1)) \
              .reduceByKey(lambda a, b: a + b)

# Print the word counts
counts.pprint()

# Start the streaming context
ssc.start()

# Wait for the streaming to terminate
ssc.awaitTermination()

This example uses the KinesisUtils API to create a DStream from a Kinesis stream, then performs a word count on the DStream, prints the results, and starts the streaming context.

Please note that you will need to replace the kinesisStream, accessKey, secretKey and InitialPositionInStream with your own values and also configure the spark-submit command to include the necessary dependencies and configuration parameters to connect to the Kinesis stream and the AWS credentials.

You need to also have your S3 bucket and kenesis stream configured.