Amazon EMR: Five Ways to Improve the Way You Use Hadoop

Amazon EMR (Elastic MapReduce) allows developers to avoid some of the burdens of setting up and administrating Hadoop tasks. Learn how to optimize it.

Apache Hadoop is an open source framework designed to distribute the storage and processing of massive data sets across virtually limitless servers. Amazon EMR (Elastic MapReduce) is a particularly popular service from Amazon that is used by developers trying to avoid the burden of set up and administration, and concentrate on working with their data.

Over the years, Amazon EMR has undergone many transformations. AWS is constantly working to improve their product, pushing new updates for EMR with every new Hadoop release. Amazon EMR now integrates with versatile Hadoop Ecosystem applications, offering an improved core architecture and an even more simplified interface.

For those who have stumbled upon this blog having little or no knowledge of Amazon EMR, but are familiar with Hadoop, here is a very quick overview:

  • EMR is Hadoop-as-a-Service from Amazon Web Services (AWS).
  • EMR supports Hadoop 2.6.0, Hive 1.0.0, Mahout 0.10.0, Pig 0.14.0, Hue 3.7.1, and Spark 1.4.1
  • The MapR distributions supported by EMR are – MapR 4.0.2 (MapR3/MapR5/MapR7 with Hadoop 2.4.0, Hive 0.13.1, and Pig 0.12.0).
  • Cost effective and integrated with other AWS Services.
  • Flexible resource utilization model.
  • No capacity planning, Hardware-on-Demand.
  • Easy to use with a flexible hourly usage model for clusters.
  • Integrated with other AWS services like S3, CloudFormation, Redshift, SQS, DynamoDB, and Cloudwatch.

The current EMR release (EMR-4.1.0) is based on the Apache Bigtop project. Describing Bigtop is well beyond the scope of this post, but you can read about it here. Instead, we are going to talk about five exciting – and unique – Amazon EMR features.

Amazon EMR Components

The current Amazon EMR release adds elements necessary to bring EMR up to date. The components are either community contributed editions or developed in-house at AWS. For example, Hadoop itself is a community edition, while the Amazon DynamoDB connector (emr-ddb-3.0.0) comes exclusively with EMR. Here is Amazon’s components guide.

You can also install recommended third-party software packages on your cluster using Bootstrap Actions. Third party libraries can be packaged directly into your Mapper or Reducer executable. Alternatively, you could upload statically compiled executables using the Hadoop distributed cache mechanism.

EMR Hadoop Nodes

Unlike standard distributions, there are three types of EMR Hadoop Nodes.

  • Master Node: Master Node runs NameNode, Resource Manager in YARN.
  • Slave Node-Core: Slave Node Core runs HDFS and Node Manager.
  • Slave Node-Task: Slave Node Task runs a Node Manager, but not HDFS.

Amazon EMR - nodes
Slave nodes in EMR are of particular interest. In EMR, Core nodes and Task nodes constitute a cluster’s slave nodes. Core nodes include Task Trackers and Data Nodes, with Data Nodes running the HDFS distributed file system. Since they store HDFS data, Data Nodes cannot be removed from a running cluster. Task Nodes, on the other hand, only act as Task Trackers and have no HDFS restrictions. Task nodes can, therefore, be scaled up and down according to the changing processing needs of a specific job. That’s how EMR supports dynamic clustering.

With HDFS out of picture within Task Slave Nodes, node failures or the addition of new nodes are far simpler to deal with, as there is no need for HDFS rebalancing.

EMR File System (EMRFS)

EMRFS is an extension of HDFS, which allows an Amazon EMR cluster to store and access data from Amazon S3. Amazon S3 is a great place to store huge data because of its low cost, durability, and availability. But one potential problem with S3 is its eventual consistency model. With eventual consistency, you might not get the updated objects as soon as they are added to your bucket. This might be a concern during certain multi-step ETL processing.

To address the issue, EMR provides something called consistent-view. By creating a DynamoDB database to track the data in S3, the consistent view provides read-after-write consistency and improved performance. The consistent view can be added to and enabled in an EMR cluster. Be aware that there is a small cost overhead for consistent view’s DynamoDB usage.

Transient and Long Running Clusters

With EMR, you can choose between running a non-committed cluster, called a transient cluster, or a long-running cluster for larger workloads. With a transient cluster, after the processing job is done, the cluster will be automatically terminated. That ensures your AWS bill properly reflects your actual use. Transient clusters are particularly suitable for periodic jobs.

A long-running cluster, on the other hand, is meant for persistent job execution. Imagine that you need to upload a huge amount of data for EMR processing. It can sometimes be inefficient to load it in smaller packages. With long-running clusters, you can query the cluster continuously or even periodically as it will be running even if there are no jobs in the queue.

Making a long-running cluster is easy. As the administrator, you will need to choose NO for auto-termination in Advanced Options -> Steps. That’s it!

Using S3Distcp to Move data between HDFS and S3

S3DistCp is an extension of the DistCp tool that lets you move large amounts of data between HDFS and S3 in a distributed manner. S3DistCp is more scalable and efficient for parallel copying large numbers of objects across buckets and between AWS accounts. S3DistCp copies data using distributed map-reduce jobs. However, the main benefit S3DistCp provides over DistCp, is by having a reducer run multiple HTTP upload threads to upload the files in parallel.

You can add S3DistCp as a step to EMR job in the AWS CLI:

aws emr add-steps --cluster-id j-1234MYCLUSTERXXXXX --steps Type=CUSTOM_JAR,Name="S3DistCp step",Jar=/home/hadoop/lib/emr-s3distcp-1.0.jar,\
Args=["--s3Endpoint,s3-eu-west-1.amazonaws.com","--src,s3://mybucket/logs/j-j-1234MYCLUSTERXXXXX/node/","--dest,hdfs:///output","--srcPattern,.*[a-zA-Z,]+"]

Or using EMR console like this:
Amazon EMR - s3distcp
To copy files from S3 to HDFS, you can run this command in the AWS CLI:

aws emr add-steps --cluster-id j-1234MYCLUSTERXXXXX --steps Type=CUSTOM_JAR,Name="S3DistCp step",Jar=/home/hadoop/lib/emr-s3distcp-1.0.jar,\
Args=["--src,s3://mybucket/logs/j-1234MYCLUSTERXXXXX/node/","--dest,hdfs:///output","--srcPattern,.*daemons.*-hadoop-.*"]

Conclusion

Amazon EMR allows organizations to launch Hadoop clusters and jobs almost instantaneously. With the backing of AWS’s tested infrastructure and services and their seamless integration between EMR and services like DynamoDB, Redshift, SQS, and Kinesis, users have many opportunities to explore.

Would you like to learn more? See how AOL significantly optimized its Amazon EMR infrastructure. Also, Cloud Academy offers a hands-on lab guiding you through the process of deploying S3-based data to an Amazon EMR cluster.

Do you have your own Hadoop or EMR experiences you’d like to share? Feel free to comment below.

Avatar

Written by

Chandan Patra

Cloud Computing and Big Data professional with 10 years of experience in pre-sales, architecture, design, build and troubleshooting with best engineering practices. Specialities: Cloud Computing - AWS, DevOps(Chef), Hadoop Ecosystem, Storm & Kafka, ELK Stack, NoSQL, Java, Spring, Hibernate, Web Service


Related Posts

Patrick Navarro
Patrick Navarro
— January 22, 2020

Top 5 AWS Salary Report Findings

At the speed the cloud tech space is developing, it can be hard to keep track of everything that’s happening within the AWS ecosystem. Advances in technology prompt smarter functionality and innovative new products, which in turn give rise to new job roles that have a ripple effect on t...

Read more
  • AWS
  • salary
Alisha Reyes
Alisha Reyes
— January 6, 2020

New on Cloud Academy: Red Hat, Agile, OWASP Labs, Amazon SageMaker Lab, Linux Command Line Lab, SQL, Git Labs, Scrum Master, Azure Architects Lab, and Much More

Happy New Year! We hope you're ready to kick your training in overdrive in 2020 because we have a ton of new content for you. Not only do we have a bunch of new courses, hands-on labs, and lab challenges on AWS, Azure, and Google Cloud, but we also have three new courses on Red Hat, th...

Read more
  • agile
  • AWS
  • Azure
  • Google Cloud Platform
  • Linux
  • OWASP
  • programming
  • red hat
  • scrum
Alisha Reyes
Alisha Reyes
— December 24, 2019

Cloud Academy’s Blog Digest: Azure Best Practices, 6 Reasons You Should Get AWS Certified, Google Cloud Certification Prep, and more

Happy Holidays from Cloud Academy We hope you have a wonderful holiday season filled with family, friends, and plenty of food. Here at Cloud Academy, we are thankful for our amazing customer like you.  Since this time of year can be stressful, we’re sharing a few of our latest article...

Read more
  • AWS
  • azure best practices
  • blog digest
  • Cloud Academy
  • Google Cloud
Avatar
Guy Hummel
— December 12, 2019

Google Cloud Platform Certification: Preparation and Prerequisites

Google Cloud Platform (GCP) has evolved from being a niche player to a serious competitor to Amazon Web Services and Microsoft Azure. In 2019, research firm Gartner placed Google in the Leaders quadrant in its Magic Quadrant for Cloud Infrastructure as a Service for the second consecuti...

Read more
  • AWS
  • Azure
  • Google Cloud Platform
Alisha Reyes
Alisha Reyes
— December 10, 2019

New Lab Challenges: Push Your Skills to the Next Level

Build hands-on experience using real accounts on AWS, Azure, Google Cloud Platform, and more Meaningful cloud skills require more than book knowledge. Hands-on experience is required to translate knowledge into real-world results. We see this time and time again in studies about how pe...

Read more
  • AWS
  • Azure
  • Google Cloud
  • hands-on
  • labs
Alisha Reyes
Alisha Reyes
— December 5, 2019

New on Cloud Academy: AWS Solution Architect Lab Challenge, Azure Hands-on Labs, Foundation Certificate in Cyber Security, and Much More

Now that Thanksgiving is over and the craziness of Black Friday has died down, it's now time for the busiest season of the year. Whether you're a last-minute shopper or you already have your shopping done, the holidays bring so much more excitement than any other time of year. Since our...

Read more
  • AWS
  • AWS solution architect
  • AZ-203
  • Azure
  • cyber security
  • FCCS
  • Foundation Certificate in Cyber Security
  • Google Cloud Platform
  • Kubernetes
Avatar
Cloud Academy Team
— December 4, 2019

Understanding Enterprise Cloud Migration

What is enterprise cloud migration? Cloud migration is about moving your data, applications, and even infrastructure from your on-premises computers or infrastructure to a virtual pool of on-demand, shared resources that offer compute, storage, and network services at scale. Why d...

Read more
  • AWS
  • Azure
  • Data Migration
Wendy Dessler
Wendy Dessler
— November 27, 2019

6 Reasons Why You Should Get an AWS Certification This Year

In the past decade, the rise of cloud computing has been undeniable. Businesses of all sizes are moving their infrastructure and applications to the cloud. This is partly because the cloud allows businesses and their employees to access important information from just about anywhere. ...

Read more
  • AWS
  • Certifications
  • certified
Avatar
Andrea Colangelo
— November 26, 2019

AWS Regions and Availability Zones: The Simplest Explanation You Will Ever Find Around

The basics of AWS Regions and Availability Zones We’re going to treat this article as a sort of AWS 101 — it’ll be a quick primer on AWS Regions and Availability Zones that will be useful for understanding the basics of how AWS infrastructure is organized. We’ll define each section,...

Read more
  • AWS
Avatar
Dzenan Dzevlan
— November 20, 2019

Application Load Balancer vs. Classic Load Balancer

What is an Elastic Load Balancer? This post covers basics of what an Elastic Load Balancer is, and two of its examples: Application Load Balancers and Classic Load Balancers. For additional information — including a comparison that explains Network Load Balancers — check out our post o...

Read more
  • ALB
  • Application Load Balancer
  • AWS
  • Elastic Load Balancer
  • ELB
Albert Qian
Albert Qian
— November 13, 2019

Advantages and Disadvantages of Microservices Architecture

What are microservices? Let's start our discussion by setting a foundation of what microservices are. Microservices are a way of breaking large software projects into loosely coupled modules, which communicate with each other through simple Application Programming Interfaces (APIs). ...

Read more
  • AWS
  • Docker
  • Kubernetes
  • Microservices
Nisar Ahmad
Nisar Ahmad
— November 12, 2019

Kubernetes Services: AWS vs. Azure vs. Google Cloud

Kubernetes is a popular open-source container orchestration platform that allows us to deploy and manage multi-container applications at scale. Businesses are rapidly adopting this revolutionary technology to modernize their applications. Cloud service providers — such as Amazon Web Ser...

Read more
  • AWS
  • Azure
  • Google Cloud
  • Kubernetes