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Home > Data > Data Processing > Hadoop Real-World Solutions Cookbook- Second Edition
Hadoop Real-World Solutions Cookbook- Second Edition
Hadoop Real-World Solutions Cookbook- Second Edition

Hadoop Real-World Solutions Cookbook- Second Edition: Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout , Second Edition

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Hadoop Real-World Solutions Cookbook- Second Edition

Chapter 2. Exploring HDFS

In this chapter, we'll take a look at the following recipes:

  • Loading data from a local machine to HDFS
  • Exporting HDFS data to a local machine
  • Changing the replication factor of an existing file in HDFS
  • Setting the HDFS block size for all the files in a cluster
  • Setting the HDFS block size for a specific file in a cluster
  • Enabling transparent encryption for HDFS
  • Importing data from another Hadoop cluster
  • Recycling deleted data from trash to HDFS
  • Saving compressed data in HDFS

Introduction

In the previous chapter, we discussed the installation and configuration details of a Hadoop cluster. In this chapter, we are going to explore the details of HDFS. As we know, Hadoop has two important components:

  • Storage: This includes HDFS
  • Processing: This includes Map Reduce

HDFS takes care of the storage part of Hadoop. So, let's explore the internals of HDFS through various recipes.

Loading data from a local machine to HDFS

In this recipe, we are going to load data from a local machine's disk to HDFS.

Getting ready

To perform this recipe, you should have an already Hadoop running cluster.

How to do it...

Performing this recipe is as simple as copying data from one folder to another. There are a couple of ways to copy data from the local machine to HDFS.

  • Using the copyFromLocal command
    • To copy the file on HDFS, let's first create a directory on HDFS and then copy the file. Here are the commands to do this:
      hadoop fs -mkdir /mydir1
hadoop fs -copyFromLocal /usr/local/hadoop/LICENSE.txt /mydir1
  • Using the put command
    • We will first create the directory, and then put the local file in HDFS:
      hadoop fs -mkdir /mydir2
hadoop fs -put /usr/local/hadoop/LICENSE.txt /mydir2

You can validate that the files have been copied to the correct folders by listing the files:

hadoop fs -ls /mydir1
hadoop fs -ls /mydir2

How it works...

When you use HDFS copyFromLocal or the put command, the...

Exporting HDFS data to a local machine

In this recipe, we are going to export/copy data from HDFS to the local machine.

Getting ready

To perform this recipe, you should already have a running Hadoop cluster.

How to do it...

Performing this recipe is as simple as copying data from one folder to the other. There are a couple of ways in which you can export data from HDFS to the local machine.

  • Using the copyToLocal command, you'll get this code:
    hadoop fs -copyToLocal /mydir1/LICENSE.txt /home/ubuntu
  • Using the get command, you'll get this code:
    hadoop fs -get/mydir1/LICENSE.txt /home/ubuntu

How it works...

When you use HDFS copyToLocal or the get command, the following things occur:

  1. First of all, the client contacts NameNode because it needs a specific file in HDFS.
  2. NameNode then checks whether such a file exists in its FSImage. If the file is not present, the error code is returned to the client.
  3. If the file exists, NameNode checks the metadata for blocks and replica placements in DataNodes...

Changing the replication factor of an existing file in HDFS

In this recipe, we are going to take a look at how to change the replication factor of a file in HDFS. The default replication factor is 3.

Getting ready

To perform this recipe, you should already have a running Hadoop cluster.

How to do it...

Sometimes. there might be a need to increase or decrease the replication factor of a specific file in HDFS. In this case, we'll use the setrep command.

This is how you can use the command:

hadoop fs -setrep [-R] [-w] <noOfReplicas><path> ...

In this command, a path can either be a file or directory; if its a directory, then it recursively sets the replication factor for all replicas.

  • The w option flags the command and should wait until the replication is complete
  • The r option is accepted for backward compatibility

First, let's check the replication factor of the file we copied to HDFS in the previous recipe:

hadoop fs -ls /mydir1/LICENSE.txt
-rw-r--r--   3 ubuntu supergroup  ...

Setting the HDFS block size for all the files in a cluster

In this recipe, we are going to take a look at how to set a block size at the cluster level.

Getting ready

To perform this recipe, you should already have a running Hadoop cluster.

How to do it...

The HDFS block size is configurable for all files in the cluster or for a single file as well. To change the block size at the cluster level itself, we need to modify the hdfs-site.xml file.

By default, the HDFS block size is 128MB. In case we want to modify this, we need to update this property, as shown in the following code. This property changes the default block size to 64MB:

<property>
<name>dfs.block.size</name>
    <value>67108864</value>
    <description>HDFS Block size</description>
</property>

If you have a multi-node Hadoop cluster, you should update this file in the nodes, that is, NameNode and DataNode. Make sure you save these changes and restart the HDFS daemons:

/usr/local/hadoop...

Introduction

In the previous chapter, we discussed the installation and configuration details of a Hadoop cluster. In this chapter, we are going to explore the details of HDFS. As we know, Hadoop has two important components:

  • Storage: This includes HDFS

  • Processing: This includes Map Reduce

HDFS takes care of the storage part of Hadoop. So, let's explore the internals of HDFS through various recipes.

Loading data from a local machine to HDFS

In this recipe, we are going to load data from a local machine's disk to HDFS.

Getting ready

To perform this recipe, you should have an already Hadoop running cluster.

How to do it...

Performing this recipe is as simple as copying data from one folder to another. There are a couple of ways to copy data from the local machine to HDFS.

  • Using the copyFromLocal command

    • To copy the file on HDFS, let's first create a directory on HDFS and then copy the file. Here are the commands to do this:

      hadoop fs -mkdir /mydir1
hadoop fs -copyFromLocal /usr/local/hadoop/LICENSE.txt /mydir1

  • Using the put command

    • We will first create the directory, and then put the local file in HDFS:

      hadoop fs -mkdir /mydir2
hadoop fs -put /usr/local/hadoop/LICENSE.txt /mydir2

You can validate that the files have been copied to the correct folders by listing the files:

hadoop fs -ls /mydir1
hadoop fs -ls /mydir2

How it works...

When you use HDFS copyFromLocal or the put command, the following...

Exporting HDFS data to a local machine

In this recipe, we are going to export/copy data from HDFS to the local machine.

Getting ready

To perform this recipe, you should already have a running Hadoop cluster.

How to do it...

Performing this recipe is as simple as copying data from one folder to the other. There are a couple of ways in which you can export data from HDFS to the local machine.

  • Using the copyToLocal command, you'll get this code:

    hadoop fs -copyToLocal /mydir1/LICENSE.txt /home/ubuntu

  • Using the get command, you'll get this code:

    hadoop fs -get/mydir1/LICENSE.txt /home/ubuntu

How it works...

When you use HDFS copyToLocal or the get command, the following things occur:

  1. First of all, the client contacts NameNode because it needs a specific file in HDFS.

  2. NameNode then checks whether such a file exists in its FSImage. If the file is not present, the error code is returned to the client.

  3. If the file exists, NameNode checks the metadata for blocks and replica placements in DataNodes.

  4. NameNode...

Changing the replication factor of an existing file in HDFS

In this recipe, we are going to take a look at how to change the replication factor of a file in HDFS. The default replication factor is 3.

Getting ready

To perform this recipe, you should already have a running Hadoop cluster.

How to do it...

Sometimes. there might be a need to increase or decrease the replication factor of a specific file in HDFS. In this case, we'll use the setrep command.

This is how you can use the command:

hadoop fs -setrep [-R] [-w] <noOfReplicas><path> ...

In this command, a path can either be a file or directory; if its a directory, then it recursively sets the replication factor for all replicas.

  • The w option flags the command and should wait until the replication is complete

  • The r option is accepted for backward compatibility

First, let's check the replication factor of the file we copied to HDFS in the previous recipe:

hadoop fs -ls /mydir1/LICENSE.txt
-rw-r--r--   3 ubuntu supergroup      15429 2015...

Setting the HDFS block size for all the files in a cluster

In this recipe, we are going to take a look at how to set a block size at the cluster level.

Getting ready

To perform this recipe, you should already have a running Hadoop cluster.

How to do it...

The HDFS block size is configurable for all files in the cluster or for a single file as well. To change the block size at the cluster level itself, we need to modify the hdfs-site.xml file.

By default, the HDFS block size is 128MB. In case we want to modify this, we need to update this property, as shown in the following code. This property changes the default block size to 64MB:

<property>
<name>dfs.block.size</name>
    <value>67108864</value>
    <description>HDFS Block size</description>
</property>

If you have a multi-node Hadoop cluster, you should update this file in the nodes, that is, NameNode and DataNode. Make sure you save these changes and restart the HDFS daemons:

/usr/local/hadoop...
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Page 1 of 11

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Key benefits

  • Implement outstanding Machine Learning use cases on your own analytics models and processes.
  • Solutions to common problems when working with the Hadoop ecosystem.
  • Step-by-step implementation of end-to-end big data use cases.

Description

Big data is the current requirement. Most organizations produce huge amount of data every day. With the arrival of Hadoop-like tools, it has become easier for everyone to solve big data problems with great efficiency and at minimal cost. Grasping Machine Learning techniques will help you greatly in building predictive models and using this data to make the right decisions for your organization. Hadoop Real World Solutions Cookbook gives readers insights into learning and mastering big data via recipes. The book not only clarifies most big data tools in the market but also provides best practices for using them. The book provides recipes that are based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout and many more such ecosystem tools. This real-world-solution cookbook is packed with handy recipes you can apply to your own everyday issues. Each chapter provides in-depth recipes that can be referenced easily. This book provides detailed practices on the latest technologies such as YARN and Apache Spark. Readers will be able to consider themselves as big data experts on completion of this book. This guide is an invaluable tutorial if you are planning to implement a big data warehouse for your business.

Who is this book for?

Readers who have a basic knowledge of big data systems and want to advance their knowledge with hands-on recipes.

What you will learn

  • Installing and maintaining Hadoop 2.X cluster and its ecosystem.
  • Write advanced Map Reduce programs and understand design patterns.
  • Advanced Data Analysis using the Hive, Pig, and Map Reduce programs.
  • Import and export data from various sources using Sqoop and Flume.
  • Data storage in various file formats such as Text, Sequential, Parquet, ORC, and RC Files.
  • Machine learning principles with libraries such as Mahout
  • Batch and Stream data processing using Apache Spark

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 31, 2016
Length: 290 pages
Edition : 2nd
Language : English
ISBN-13 : 9781784398002
Vendor :
Apache
Category :
Data
Concepts :
Data Processing
Tools :
Hadoop

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Publication date : Mar 31, 2016
Length: 290 pages
Edition : 2nd
Language : English
ISBN-13 : 9781784398002
Vendor :
Apache
Category :
Data
Concepts :
Data Processing
Tools :
Hadoop

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Table of Contents

11 Chapters
1. Getting Started with Hadoop 2.X Chevron down icon Chevron up icon
1. Getting Started with Hadoop 2.X
Introduction
Installing a single-node Hadoop Cluster
Installing a multi-node Hadoop cluster
Adding new nodes to existing Hadoop clusters
Executing the balancer command for uniform data distribution
Entering and exiting from the safe mode in a Hadoop cluster
Decommissioning DataNodes
Performing benchmarking on a Hadoop cluster
2. Exploring HDFS Chevron down icon Chevron up icon
2. Exploring HDFS
Introduction
Loading data from a local machine to HDFS
Exporting HDFS data to a local machine
Changing the replication factor of an existing file in HDFS
Setting the HDFS block size for all the files in a cluster
Setting the HDFS block size for a specific file in a cluster
Enabling transparent encryption for HDFS
Importing data from another Hadoop cluster
Recycling deleted data from trash to HDFS
Saving compressed data in HDFS
3. Mastering Map Reduce Programs Chevron down icon Chevron up icon
3. Mastering Map Reduce Programs
Introduction
Writing the Map Reduce program in Java to analyze web log data
Executing the Map Reduce program in a Hadoop cluster
Adding support for a new writable data type in Hadoop
Implementing a user-defined counter in a Map Reduce program
Map Reduce program to find the top X
Map Reduce program to find distinct values
Map Reduce program to partition data using a custom partitioner
Writing Map Reduce results to multiple output files
Performing Reduce side Joins using Map Reduce
Unit testing the Map Reduce code using MRUnit
4. Data Analysis Using Hive, Pig, and Hbase Chevron down icon Chevron up icon
4. Data Analysis Using Hive, Pig, and Hbase
Introduction
Storing and processing Hive data in a sequential file format
Storing and processing Hive data in the RC file format
Storing and processing Hive data in the ORC file format
Storing and processing Hive data in the Parquet file format
Performing FILTER By queries in Pig
Performing Group By queries in Pig
Performing Order By queries in Pig
Performing JOINS in Pig
Writing a user-defined function in Pig
Analyzing web log data using Pig
Performing the Hbase operation in CLI
Performing Hbase operations in Java
Executing the MapReduce programming with an Hbase Table
5. Advanced Data Analysis Using Hive Chevron down icon Chevron up icon
5. Advanced Data Analysis Using Hive
Introduction
Processing JSON data in Hive using JSON SerDe
Processing XML data in Hive using XML SerDe
Processing Hive data in the Avro format
Writing a user-defined function in Hive
Performing table joins in Hive
Executing map side joins in Hive
Performing context Ngram in Hive
Call Data Record Analytics using Hive
Twitter sentiment analysis using Hive
Implementing Change Data Capture using Hive
Multiple table inserting using Hive
6. Data Import/Export Using Sqoop and Flume Chevron down icon Chevron up icon
6. Data Import/Export Using Sqoop and Flume
Introduction
Importing data from RDMBS to HDFS using Sqoop
Exporting data from HDFS to RDBMS
Using query operator in Sqoop import
Importing data using Sqoop in compressed format
Performing Atomic export using Sqoop
Importing data into Hive tables using Sqoop
Importing data into HDFS from Mainframes
Incremental import using Sqoop
Creating and executing Sqoop job
Importing data from RDBMS to Hbase using Sqoop
Importing Twitter data into HDFS using Flume
Importing data from Kafka into HDFS using Flume
Importing web logs data into HDFS using Flume
7. Automation of Hadoop Tasks Using Oozie Chevron down icon Chevron up icon
7. Automation of Hadoop Tasks Using Oozie
Introduction
Implementing a Sqoop action job using Oozie
Implementing a Map Reduce action job using Oozie
Implementing a Java action job using Oozie
Implementing a Hive action job using Oozie
Implementing a Pig action job using Oozie
Implementing an e-mail action job using Oozie
Executing parallel jobs using Oozie (fork)
Scheduling a job in Oozie
8. Machine Learning and Predictive Analytics Using Mahout and R Chevron down icon Chevron up icon
8. Machine Learning and Predictive Analytics Using Mahout and R
Introduction
Setting up the Mahout development environment
Creating an item-based recommendation engine using Mahout
Creating a user-based recommendation engine using Mahout
Predictive analytics on Bank Data using Mahout
Text data clustering using K-Means using Mahout
Population Data Analytics using R
Twitter Sentiment Analytics using R
Performing Predictive Analytics using R
9. Integration with Apache Spark Chevron down icon Chevron up icon
9. Integration with Apache Spark
Introduction
Running Spark standalone
Running Spark on YARN
Performing Olympics Athletes analytics using the Spark Shell
Creating Twitter trending topics using Spark Streaming
Twitter trending topics using Spark streaming
Analyzing Parquet files using Spark
Analyzing JSON data using Spark
Processing graphs using Graph X
Conducting predictive analytics using Spark MLib
10. Hadoop Use Cases Chevron down icon Chevron up icon
10. Hadoop Use Cases
Introduction
Call Data Record analytics
Web log analytics
Sensitive data masking and encryption using Hadoop
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 4.3
(4 Ratings)
5 star 50%
4 star 25%
3 star 25%
2 star 0%
1 star 0%
Amazon Customer Mar 01, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent book with lot of practical examples
Amazon Verified review Amazon
Thakur Jaiveer Singh Jun 09, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good one
Amazon Verified review Amazon
Amazon Customer Jan 21, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Good
Amazon Verified review Amazon
Sudhir Chawla Aug 02, 2016
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
This is an interesting book on Hadoop, which explains the installation part along with how to setup load balancer. Further down it deep dives to explain about HDFS, Map reduce program. A very good aspect of this book is that it allows you to know about various analysis tools like Hive, Pig and Hbase, moverover it goes in more details to explain about data analysis using Hive.Further down are some Import and export tools to onboard data into HDFS.A good book for someone who has basic knowledge of Hadoop and want to enhance it further by using different tools. This book gives a good start to people with experience in Java and Unix environment.Some good points of the book are:- Examples given with very clear explanation.- The author tried to used practical exercises instead of just basics.- Tried to cover many tools and deep-dive into some of the key toolsDemerits :- This book is meant for advance level developers. This is already mentioned in the book to have basic knowledge but I believe it demands more than basic.
Amazon Verified review Amazon

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Deep Learning with TensorFlow and Keras – 3rd edition
Deep Learning with TensorFlow and Keras – 3rd edition
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Oct 2022 698 pages
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Machine Learning Engineering with Python
Machine Learning Engineering with Python
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Aug 2023 462 pages
Full star icon 4.6 (36)
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About the author

Profile icon Deshpande
Deshpande
Tanmay Deshpande is a Hadoop and big data evangelist. He currently works with Schlumberger as a Big Data Architect in Pune, India. He has interest in a wide range of technologies, such as Hadoop, Hive, Pig, NoSQL databases, Mahout, Sqoop, Java, cloud computing, and so on. He has vast experience in application development in various domains, such as oil and gas, finance, telecom, manufacturing, security, and retail. He enjoys solving machine-learning problems and spends his time reading anything that he can get his hands on. He has great interest in open source technologies and has been promoting them through his talks. Before Schlumberger, he worked with Symantec, Lumiata, and Infosys. Through his innovative thinking and dynamic leadership, he has successfully completed various projects. He regularly blogs on his website http://hadooptutorials.co.in. You can connect with him on LinkedIn at https://www.linkedin.com/in/deshpandetanmay/. He has also authored Mastering DynamoDB, published in August 2014, DynamoDB Cookbook, published in September 2015, Hadoop Real World Solutions Cookbook-Second Edition, published in March 2016, Hadoop: Data Processing and Modelling, published in August, 2016, and Hadoop Blueprints, published in September 2016, all by Packt Publishing.
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