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Learning Hadoop 2
Learning Hadoop 2

Learning Hadoop 2: Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2

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Profile Icon GABRIELE MODENA
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S$74.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8 (4 Ratings)
Paperback Feb 2015 382 pages 1st Edition
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Arrow left icon
Profile Icon GABRIELE MODENA
Arrow right icon
S$74.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8 (4 Ratings)
Paperback Feb 2015 382 pages 1st Edition
eBook
S$41.98 S$59.99
Paperback
S$74.99
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Free Trial
eBook
S$41.98 S$59.99
Paperback
S$74.99
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Learning Hadoop 2

Chapter 2. Storage

After the overview of Hadoop in the previous chapter, we will now start looking at its various component parts in more detail. We will start at the conceptual bottom of the stack in this chapter: the means and mechanisms for storing data within Hadoop. In particular, we will discuss the following topics:

  • Describe the architecture of the Hadoop Distributed File System (HDFS)
  • Show what enhancements to HDFS have been made in Hadoop 2
  • Explore how to access HDFS using command-line tools and the Java API
  • Give a brief description of ZooKeeper—another (sort of) filesystem within Hadoop
  • Survey considerations for storing data in Hadoop and the available file formats

In Chapter 3, Processing – MapReduce and Beyond, we will describe how Hadoop provides the framework to allow data to be processed.

The inner workings of HDFS

In Chapter 1, Introduction, we gave a very high-level overview of HDFS; we will now explore it in a little more detail. As mentioned in that chapter, HDFS can be viewed as a filesystem, though one with very specific performance characteristics and semantics. It's implemented with two main server processes: the NameNode and the DataNodes, configured in a master/slave setup. If you view the NameNode as holding all the filesystem metadata and the DataNodes as holding the actual filesystem data (blocks), then this is a good starting point. Every file placed onto HDFS will be split into multiple blocks that might reside on numerous DataNodes, and it's the NameNode that understands how these blocks can be combined to construct the files.

Cluster startup

Let's explore the various responsibilities of these nodes and the communication between them by assuming we have an HDFS cluster that was previously shut down and then examining the startup behavior.

NameNode...

Command-line access to the HDFS filesystem

Within the Hadoop distribution, there is a command-line utility called hdfs, which is the primary way to interact with the filesystem from the command line. Run this without any arguments to see the various subcommands available. There are many, though; several are used to do things like starting or stopping various HDFS components. The general form of the hdfs command is:

hdfs <sub-command> <command> [arguments]

The two main subcommands we will use in this book are:

  • dfs: This is used for general filesystem access and manipulation, including reading/writing and accessing files and directories
  • dfsadmin: This is used for administration and maintenance of the filesystem. We will not cover this command in detail, though. Have a look at the -report command, which gives a listing of the state of the filesystem and all DataNodes:
    $ hdfs dfsadmin -report

Note

Note that the dfs and dfsadmin commands can also be used with the main Hadoop command-line...

Protecting the filesystem metadata

Because the fsimage file is so critical to the filesystem, its loss is a catastrophic failure. In Hadoop 1, where the NameNode was a single point of failure, the best practice was to configure the NameNode to synchronously write the fsimage and edits files to both local storage plus at least one other location on a remote filesystem (often NFS). In the event of NameNode failure, a replacement NameNode could be started using this up-to-date copy of the filesystem metadata. The process would require non-trivial manual intervention, however, and would result in a period of complete cluster unavailability.

Secondary NameNode not to the rescue

The most unfortunately named component in all of Hadoop 1 was the Secondary NameNode, which, not unreasonably, many people expect to be some sort of backup or standby NameNode. It is not; instead, the Secondary NameNode was responsible only for periodically reading the latest version of the fsimage and edits file and creating...

Apache ZooKeeper – a different type of filesystem

Within Hadoop, we will mostly talk about HDFS when discussing filesystems and data storage. But, inside almost all Hadoop 2 installations, there is another service that looks somewhat like a filesystem, but which provides significant capability crucial to the proper functioning of distributed systems. This service is Apache ZooKeeper (http://zookeeper.apache.org) and, as it is a key part of the implementation of HDFS HA, we will introduce it in this chapter. It is, however, also used by multiple other Hadoop components and related projects, so we will touch on it several more times throughout the book.

ZooKeeper started out as a subcomponent of HBase and was used to enable several operational capabilities of the service. When any complex distributed system is built, there are a series of activities that are almost always required and which are always difficult to get right. These activities include things such as handling shared locks...

Automatic NameNode failover

Now that we have introduced ZooKeeper, we can show how it is used to enable automatic NameNode failover.

Automatic NameNode failover introduces two new components to the system, a ZooKeeper quorum, and the ZooKeeper Failover Controller (ZKFC), which runs on each NameNode host. The ZKFC creates an ephemeral ZNode in ZooKeeper and holds this ZNode for as long as it detects the local NameNode to be alive and functioning correctly. It determines this by continuously sending simple health-check requests to the NameNode, and if the NameNode fails to respond correctly over a short period of time the ZKFC will assume the NameNode has failed. If a NameNode machine crashes or otherwise fails, the ZKFC session in ZooKeeper will be closed and the ephemeral ZNode will also be automatically removed.

The ZKFC processes are also monitoring the ZNodes of the other NameNodes in the cluster. If the ZKFC on the standby NameNode host sees the existing master ZNode disappear, it will...

The inner workings of HDFS


In Chapter 1, Introduction, we gave a very high-level overview of HDFS; we will now explore it in a little more detail. As mentioned in that chapter, HDFS can be viewed as a filesystem, though one with very specific performance characteristics and semantics. It's implemented with two main server processes: the NameNode and the DataNodes, configured in a master/slave setup. If you view the NameNode as holding all the filesystem metadata and the DataNodes as holding the actual filesystem data (blocks), then this is a good starting point. Every file placed onto HDFS will be split into multiple blocks that might reside on numerous DataNodes, and it's the NameNode that understands how these blocks can be combined to construct the files.

Cluster startup

Let's explore the various responsibilities of these nodes and the communication between them by assuming we have an HDFS cluster that was previously shut down and then examining the startup behavior.

NameNode startup

We'll...

Command-line access to the HDFS filesystem


Within the Hadoop distribution, there is a command-line utility called hdfs, which is the primary way to interact with the filesystem from the command line. Run this without any arguments to see the various subcommands available. There are many, though; several are used to do things like starting or stopping various HDFS components. The general form of the hdfs command is:

hdfs <sub-command> <command> [arguments]

The two main subcommands we will use in this book are:

  • dfs: This is used for general filesystem access and manipulation, including reading/writing and accessing files and directories

  • dfsadmin: This is used for administration and maintenance of the filesystem. We will not cover this command in detail, though. Have a look at the -report command, which gives a listing of the state of the filesystem and all DataNodes:

    $ hdfs dfsadmin -report

Note

Note that the dfs and dfsadmin commands can also be used with the main Hadoop command...

Protecting the filesystem metadata


Because the fsimage file is so critical to the filesystem, its loss is a catastrophic failure. In Hadoop 1, where the NameNode was a single point of failure, the best practice was to configure the NameNode to synchronously write the fsimage and edits files to both local storage plus at least one other location on a remote filesystem (often NFS). In the event of NameNode failure, a replacement NameNode could be started using this up-to-date copy of the filesystem metadata. The process would require non-trivial manual intervention, however, and would result in a period of complete cluster unavailability.

Secondary NameNode not to the rescue

The most unfortunately named component in all of Hadoop 1 was the Secondary NameNode, which, not unreasonably, many people expect to be some sort of backup or standby NameNode. It is not; instead, the Secondary NameNode was responsible only for periodically reading the latest version of the fsimage and edits file and creating...

Apache ZooKeeper – a different type of filesystem


Within Hadoop, we will mostly talk about HDFS when discussing filesystems and data storage. But, inside almost all Hadoop 2 installations, there is another service that looks somewhat like a filesystem, but which provides significant capability crucial to the proper functioning of distributed systems. This service is Apache ZooKeeper (http://zookeeper.apache.org) and, as it is a key part of the implementation of HDFS HA, we will introduce it in this chapter. It is, however, also used by multiple other Hadoop components and related projects, so we will touch on it several more times throughout the book.

ZooKeeper started out as a subcomponent of HBase and was used to enable several operational capabilities of the service. When any complex distributed system is built, there are a series of activities that are almost always required and which are always difficult to get right. These activities include things such as handling shared locks, detecting...

Automatic NameNode failover


Now that we have introduced ZooKeeper, we can show how it is used to enable automatic NameNode failover.

Automatic NameNode failover introduces two new components to the system, a ZooKeeper quorum, and the ZooKeeper Failover Controller (ZKFC), which runs on each NameNode host. The ZKFC creates an ephemeral ZNode in ZooKeeper and holds this ZNode for as long as it detects the local NameNode to be alive and functioning correctly. It determines this by continuously sending simple health-check requests to the NameNode, and if the NameNode fails to respond correctly over a short period of time the ZKFC will assume the NameNode has failed. If a NameNode machine crashes or otherwise fails, the ZKFC session in ZooKeeper will be closed and the ephemeral ZNode will also be automatically removed.

The ZKFC processes are also monitoring the ZNodes of the other NameNodes in the cluster. If the ZKFC on the standby NameNode host sees the existing master ZNode disappear, it will...

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Description

If you are a system or application developer interested in learning how to solve practical problems using the Hadoop framework, then this book is ideal for you. You are expected to be familiar with the Unix/Linux command-line interface and have some experience with the Java programming language. Familiarity with Hadoop would be a plus.

Who is this book for?

If you are a system or application developer interested in learning how to solve practical problems using the Hadoop framework, then this book is ideal for you. You are expected to be familiar with the Unix/Linux command-line interface and have some experience with the Java programming language. Familiarity with Hadoop would be a plus.

What you will learn

  • Write distributed applications using the MapReduce framework
  • Go beyond MapReduce and process data in real time with Samza and iteratively with Spark
  • Familiarize yourself with data mining approaches that work with very large datasets
  • Prototype applications on a VM and deploy them to a local cluster or to a cloud infrastructure (Amazon Web Services)
  • Conduct batch and real time data analysis using SQLlike tools
  • Build data processing flows using Apache Pig and see how it enables the easy incorporation of custom functionality
  • Define and orchestrate complex workflows and pipelines with Apache Oozie
  • Manage your data lifecycle and changes over time
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ISBN-13 : 9781783285518
Category :
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Table of Contents

12 Chapters
1. Introduction Chevron down icon Chevron up icon
2. Storage Chevron down icon Chevron up icon
3. Processing – MapReduce and Beyond Chevron down icon Chevron up icon
4. Real-time Computation with Samza Chevron down icon Chevron up icon
5. Iterative Computation with Spark Chevron down icon Chevron up icon
6. Data Analysis with Apache Pig Chevron down icon Chevron up icon
7. Hadoop and SQL Chevron down icon Chevron up icon
8. Data Lifecycle Management Chevron down icon Chevron up icon
9. Making Development Easier Chevron down icon Chevron up icon
10. Running a Hadoop Cluster Chevron down icon Chevron up icon
11. Where to Go Next Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8
(4 Ratings)
5 star 50%
4 star 25%
3 star 0%
2 star 0%
1 star 25%
Douglas Almquist Jan 04, 2016
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
This book has no publication date, no information about it, no reviews... nothing.Why would anyone buy it?
Amazon Verified review Amazon
daincredibleholg May 05, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I got the Learning Hadoop 2 eBook and was really positively surprised. The author gives a great overview and inside view of the Hadoop ecosystem.The book is targeted on software and system developers but I am pretty sure it will also help technical architects to understand the main concepts.The author found a great balance between giving an introduction, pointing to the main differences between Hadoop 1 and 2 and then going straight into a lot of coding examples which are all available on GitHub.Another nice thing about this book is that it gives a nice overview of frameworks and tools like Hadoop, Samza and Pig. So the main ecosystem should be covered with just one book and the reader can then decide which topic is worth to go deeper next.
Amazon Verified review Amazon
Alexander Helf May 04, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
When looking for a book about Hadoop one may find "Learning Hadoop 2". This the the successor of "Hadoop Beginner's Guide" from the same author and focuses on Hadoop version 2.Even without the knowledge of the previous Hadoop version you get a quick overview about the history and the core features.The middle of the book contains some technology chapters (streaming, programming, SQL) which use the same example to show the different aspects. With a basic Java knowhow the code is easy to understand (but I did not executed the code).The main focus of the book is the developing part but with the last chapters the reader get some idea how a Hadoop system is created and running.To me the most valuable part is that you get a guide what the different frameworks on top of Hadoop do!But as always there are parts which could have been better.The first thing is that the book (released 2015) uses version 2.2 from 2013. This may not be a problem but checking the examples with a newer version and updating the intro may be helpful.The second point is the "dual" approach with a local installation and the infos about running the code on the cloud. At first this looks very interesting but in the end I think this may be better placed as an appendix.ButConclusion: If you want to get an overview or a good introduction what "Hadoop" (and the related frameworks) means this may be the right book.
Amazon Verified review Amazon
PJG Apr 29, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a thorough guide to Hadoop 2, and has a lot of detail packed in to it's 382 pages. As part of Packt's "learning" series, I was pleasantly surprised by the amount of depth here: the book covers a lot of essential material - details of HDFS, a quick description of variouscloud-based services that offer Hadoop, sentiment analysis (going significantly the now quite tired canonical example of word counting in documents), YARN, Tez, ZooKeeper, Pig... It is really quite impressive and I think this would be helpful for anyone looking for a clear guide to Hadoop - not just people specifically interested in moving from Hadoop 1.The final chapters have some topics that are of particular interest, such as an extended discussion of managing the data lifecycle (with some code examples using Oozie and Avro), and Hadoop cluster basics, both of which are nice additions and differentiate the book somewhat from it's competitors (Hadoop books represent a crowded market these days it seems!). Recommended if you are seekingsomething that is practically-oriented to bridge the gap before "Hadoop: The Definitive Guide".
Amazon Verified review Amazon
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