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Home > Cloud & Networking > System Administration > YARN Essentials
YARN Essentials
YARN Essentials

YARN Essentials: A comprehensive, hands-on guide to install, administer, and configure settings in YARN

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YARN Essentials

Chapter 1. Need for YARN

YARN stands for Yet Another Resource Negotiator. YARN is a generic resource platform to manage resources in a typical cluster. YARN was introduced with Hadoop 2.0, which is an open source distributed processing framework from the Apache Software Foundation.

In 2012, YARN became one of the subprojects of the larger Apache Hadoop project. YARN is also coined by the name of MapReduce 2.0. This is since Apache Hadoop MapReduce has been re-architectured from the ground up to Apache Hadoop YARN.

Think of YARN as a generic computing fabric to support MapReduce and other application paradigms within the same Hadoop cluster; earlier, this was limited to batch processing using MapReduce. This really changed the game to recast Apache Hadoop as a much more powerful data processing system. With the advent of YARN, Hadoop now looks very different compared to the way it was only a year ago.

YARN enables multiple applications to run simultaneously on the same shared cluster and allows applications to negotiate resources based on need. Therefore, resource allocation/management is central to YARN.

YARN has been thoroughly tested at Yahoo! since September 2012. It has been in production across 30,000 nodes and 325 PB of data since January 2013.

Recently, Apache Hadoop YARN won the Best Paper Award at ACM Symposium on Cloud Computing (SoCC) in 2013!

The redesign idea

Initially, Hadoop was written solely as a MapReduce engine. Since it runs on a cluster, its cluster management components were also tightly coupled with the MapReduce programming paradigm.

The concepts of MapReduce and its programming paradigm were so deeply ingrained in Hadoop that one could not use it for anything else except MapReduce. MapReduce therefore became the base for Hadoop, and as a result, the only thing that could be run on Hadoop was a MapReduce job, batch processing. In Hadoop 1.x, there was a single JobTracker service that was overloaded with many things such as cluster resource management, scheduling jobs, managing computational resources, restarting failed tasks, monitoring TaskTrackers, and so on.

There was definitely a need to separate the MapReduce (specific programming model) part and the resource management infrastructure in Hadoop. YARN was the first attempt to perform this separation.

Limitations of the classical MapReduce or Hadoop 1.x

The main limitations of Hadoop 1.x can be categorized into the following areas:

  • Limited scalability:
    • Large Hadoop clusters reported some serious limitations on scalability. This is caused mainly by a single JobTracker service, which ultimately results in a serious deterioration of the overall cluster performance because of attempts to re-replicate data and overload live nodes, thus causing a network flood.
    • According to Yahoo!, the practical limits of such a design are reached with a cluster of ~5,000 nodes and 40,000 tasks running concurrently. Therefore, it is recommended that you create smaller and less powerful clusters for such a design.
  • Low cluster resource utilization:
    • The resources in Hadoop 1.x on each slave node (data node), are divided in terms of a fixed number of map and reduce slots.
    • Consider the scenario where a MapReduce job has already taken up all the available map slots and now wants more new map tasks to run. In this case, it cannot run new map tasks, even though all the reduce slots are still empty. This notion of a fixed number of slots has a serious drawback and results in poor cluster utilization.
  • Lack of support for alternative frameworks/paradigms:
    • The main focus of Hadoop right from the beginning was to perform computation on large datasets using parallel processing.
    • Therefore, the only programming model it supported was MapReduce.
    • With the current industry needs in terms of new use cases in the world of big data, many new and alternative programming models (such Apache Giraph, Apache Spark, Storm, Tez, and so on) are coming into the picture each day. There is definitely an increasing demand to support multiple programming paradigms besides MapReduce, to support the varied use cases that the big data world is facing.

YARN as the modern operating system of Hadoop

The MapReduce programming model is, no doubt, great for many applications, but not for everything in the world of computation. There are use cases that are best suited for MapReduce, but not all.

MapReduce is essentially batch-oriented, but support for real-time and near real-time processing are the emerging requirements in the field of big data.

YARN took cluster resource management capabilities from the MapReduce system so that new engines could use these generic cluster resource management capabilities. This lightened up the MapReduce system to focus on the data processing part, which it is good at and will ideally continue to be so.

YARN therefore turns into a data operating system for Hadoop 2.0, as it enables multiple applications to coexist in the same shared cluster. Refer to the following figure:

YARN as the modern operating system of Hadoop

YARN as a modern OS for Hadoop

What are the design goals for YARN

This section talks about the core design goals of YARN:

  • Scalability:
    • Scalability is a key requirement for big data. Hadoop was primarily meant to work on a cluster of thousands of nodes with commodity hardware. Also, the cost of hardware is reducing year-on-year.
    • YARN is therefore designed to perform efficiently on this network of a myriad of nodes.
  • High cluster utilization:
    • In Hadoop 1.x, the cluster resources were divided in terms of fixed size slots for both map and reduce tasks. This means that there could be a scenario where map slots might be full while reduce slots are empty, or vice versa. This was definitely not an optimal utilization of resources, and it needed further optimization.
    • YARN fine-grained resources in terms of RAM, CPU, and disk (containers), leading to an optimal utilization of the available resources.
  • Locality awareness:
    • This is a key requirement for YARN when dealing with big data; moving computation is cheaper than moving data.
    • This helps to minimize network congestion and increase the overall throughput of the system.
  • Multitenancy:
    • With the core development of Hadoop at Yahoo, primarily to support large-scale computation, HDFS also acquired a permission model, quotas, and other features to improve its multitenant operation.
    • YARN was therefore designed to support multitenancy in its core architecture. Since cluster resource allocation/management is at the heart of YARN, sharing processing and storage capacity across clusters was central to the design.
    • YARN has the notion of pluggable schedulers and the Capacity Scheduler with YARN has been enhanced to provide a flexible resource model, elastic computing, application limits, and other necessary features that enable multiple tenants to securely share the cluster in an optimized way.
  • Support for programming model:
    • The MapReduce programming model is no doubt great for many applications, but not for everything in the world of computation.
    • As the world of big data is still in its inception phase, organizations are heavily investing in R&D to develop new and evolving frameworks to solve a variety of problems that big data brings.
  • A flexible resource model:
    • Besides mismatch with the emerging frameworks’ requirements, the fixed number of slots for resources had serious problems. It was straightforward for YARN to come up with a flexible and generic resource management model.
  • A secure and auditable operation:
    • As Hadoop continued to grow to manage more tenants with a myriad of use cases across different industries, the requirements for isolation became more demanding.
    • Also, the authorization model lacked strong and scalable authentication. This is because Hadoop was designed with parallel processing in mind, with no comprehensive security. Security was an afterthought.
    • YARN understands this and adds security-related requirements into its design.
  • Reliability/availability:
    • Although fault tolerance is in the core design, in reality maintaining a large Hadoop cluster is a tedious task.
    • All issues related to high availability, failures, failures on restart, and reliability were therefore a core requirement for YARN.
  • Backward compatibility:
    • Hadoop 1.x has been in the picture for a while, with many successful production deployments across many industries. This massive installation base of MapReduce applications and the ecosystem of related projects, such as Hive, Pig, and so on, would not tolerate a radical redesign. Therefore, the new architecture reused as much code from the existing framework as possible, and no major surgery was conducted on it. This made MRv2 able to ensure satisfactory compatibility with MRv1 applications.

Summary

In this chapter, you learned what YARN is and how it has turned out to be the modern operating system for Hadoop, making it a multiapplication platform.

In Chapter 2, YARN Architecture, we will be talking about the architecture details of YARN.

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Description

If you have a working knowledge of Hadoop 1.x but want to start afresh with YARN, this book is ideal for you. You will be able to install and administer a YARN cluster and also discover the configuration settings to fine-tune your cluster both in terms of performance and scalability. This book will help you develop, deploy, and run multiple applications/frameworks on the same shared YARN cluster.

Who is this book for?

If you have a working knowledge of Hadoop 1.x but want to start afresh with YARN, this book is ideal for you. You will be able to install and administer a YARN cluster and also discover the configuration settings to fine-tune your cluster both in terms of performance and scalability. This book will help you develop, deploy, and run multiple applications/frameworks on the same shared YARN cluster.

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Publication date : Feb 24, 2015
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Concepts :
System Administration
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Hadoop

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Table of Contents

11 Chapters
1. Need for YARN Chevron down icon Chevron up icon
1. Need for YARN
The redesign idea
Summary
2. YARN Architecture Chevron down icon Chevron up icon
2. YARN Architecture
Core components of YARN architecture
YARN scheduler policies
Recent developments in YARN architecture
Summary
3. YARN Installation Chevron down icon Chevron up icon
3. YARN Installation
Single-node installation
The fully-distributed mode
Operating Hadoop and YARN clusters
Web interfaces of the Ecosystem
Summary
4. YARN and Hadoop Ecosystems Chevron down icon Chevron up icon
4. YARN and Hadoop Ecosystems
The Hadoop 2 release
A short introduction to Hadoop 1.x and MRv1
MRv1 versus MRv2
Understanding where YARN fits into Hadoop
Old and new MapReduce APIs
Backward compatibility of MRv2 APIs
Practical examples of MRv1 and MRv2
Summary
5. YARN Administration Chevron down icon Chevron up icon
5. YARN Administration
Container allocation
Container configurations
YARN scheduling policies
YARN multitenancy application support
Administration of YARN
Summary
6. Developing and Running a Simple YARN Application Chevron down icon Chevron up icon
6. Developing and Running a Simple YARN Application
Running sample examples on YARN
Monitoring YARN applications with web GUI
YARN's MapReduce support
The YARN application workflow
Summary
7. YARN Frameworks Chevron down icon Chevron up icon
7. YARN Frameworks
Apache Samza
Storm-YARN
Apache Spark
Apache Tez
Apache Giraph
HOYA (HBase on YARN)
KOYA (Kafka on YARN)
Summary
8. Failures in YARN Chevron down icon Chevron up icon
8. Failures in YARN
ResourceManager failures
ApplicationMaster failures
NodeManager failures
Container failures
Hardware Failures
Summary
9. YARN – Alternative Solutions Chevron down icon Chevron up icon
9. YARN – Alternative Solutions
Mesos
Omega
Corona
Summary
10. YARN – Future and Support Chevron down icon Chevron up icon
10. YARN – Future and Support
What YARN means to the big data industry
Journey – present and future
YARN-supported frameworks
Summary
Index Chevron down icon Chevron up icon
Index

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
(5 Ratings)
5 star 20%
4 star 80%
3 star 0%
2 star 0%
1 star 0%
francois dlx Jun 08, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is the book you need to maintain and design your Hadoop architecture.It describe not only the architecture but also how it work. It describe the past for compatibility and the future to prepare enhancement.You found in it a lot of information to install and admin yarn modules.It also cover the failures.For design stage, it give samples and some informations on frameworks to link it with TEZ, KAFKA, STORM...To manage or develop a cluster, you must have this book.
Amazon Verified review Amazon
Sunilkumar Chandran Jun 05, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Yarn essentials books provided more detailed guidelines on YARM framework, the installation for single node and distributed systems in cluster. The explanation on the performance and scalability are very good for understanding and good one for any one to start big data career.The exaples are commendable.
Amazon Verified review Amazon
consultstevenj Jun 05, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Yarn Essentials does an excellent job of answering the big questions about YARN and it's place in the Hadoop stack. The initial chapters on need for Yarn and Architecture do a great job of explaining YARN's function and how it related to the change from Hadoop 1 to Hadoop 2. What every confusion is remaining gets addressed in YARN and the Hadoop Ecosystem. The balance of graphics, code examples and explaining makes this a good YARN introduction. The book covers both practical concern like install and failures , the frameworks that take advantage of YARN and what might be in store in the future for the YARN Apache project. I would say this is a solid addition to your Hadoop library.
Amazon Verified review Amazon
Kumaravel Kandasami Jun 04, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
YARN Essentials - As the book name suggests, is a clear simple to read book on YARN technology.In think at this point, it is the only book that is available in the market on YARN covering to this detail.I would recommend this book to teams planning to migrate from MR1 to MR2, or starting on YARN clusters.
Amazon Verified review Amazon
ssanupindi Jul 15, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I liked this book as it helped me to understand the features of Yarn for Hadoop 2.x version.Chapter 2 & 3 covers with Yarn Architecture and Installing Yarn (in sudo & distributed modes) and covers with important configuration files.The chapter 4 covers the differences of MRV1 & MRV2 and provides MRv1 and MRV2 Example code and helped to clearly understnad the differences in the implementation of MRV1 & MRV2.Next, Chapter5 which covers with Yarn Administration and this chapter is most interesting one in this entire book and it helps understand the container allocation and scheuling polices etc.And other chapters tells the other frameworks Yarn supports and the other interesting chapter in this book is the last chapter @ failures in Yarn which covers with various scenarios when a particualr component of Yarn fails.In overall, this book is worth for the money i paid.
Amazon Verified review Amazon

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About the authors

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Profile icon Fasale
Fasale
Amol Fasale has more than 4 years of industry experience actively working in the fields of big data and distributed computing; he is also an active blogger in and contributor to the open source community. Amol works as a senior data system engineer at MakeMyTrip.com, a very well-known travel and hospitality portal in India, responsible for real-time personalization of online user experience with Apache Kafka, Apache Storm, Apache Hadoop, and many more. Also, Amol has active hands-on experience in Java/J2EE, Spring Frameworks, Python, machine learning, Hadoop framework components, SQL, NoSQL, and graph databases. You can follow Amol on Twitter at @amolfasale or on LinkedIn. Amol is very active on social media. You can catch him online for any technical assistance; he would be happy to help. Amol has completed his bachelor's in engineering (electronics and telecommunication) from Pune University and postgraduate diploma in computers from CDAC.
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Profile icon Nirmal Kumar
Nirmal Kumar
Nirmal Kumar is a lead software engineer at iLabs, the R&D team at Impetus Infotech Pvt. Ltd. He has more than 8 years of experience in open source technologies such as Java, JEE, Spring, Hibernate, web services, Hadoop, Hive, Flume, Sqoop, Kafka, Storm, NoSQL databases such as HBase and Cassandra, and MPP databases such as Teradata. You can follow him on Twitter at @nirmal___kumar. He spends most of his time reading about and playing with different technologies. He has also undertaken many tech talks and training sessions on big data technologies. He has attained his master's degree in computer applications from Harcourt Butler Technological Institute (HBTI), Kanpur, India and is currently part of the big data R&D team in iLabs at Impetus Infotech Pvt. Ltd.
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Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks? Chevron down icon Chevron up icon
  • You can get the information you need immediately
  • You can easily take them with you on a laptop
  • You can download them an unlimited number of times
  • You can print them out
  • They are copy-paste enabled
  • They are searchable
  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.