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Home > Cloud & Networking > System Administration > Hadoop 2.x Administration Cookbook
Hadoop 2.x Administration Cookbook
Hadoop 2.x Administration Cookbook

Hadoop 2.x Administration Cookbook: Administer and maintain large Apache Hadoop clusters

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Hadoop 2.x Administration Cookbook

Chapter 2. Maintaining Hadoop Cluster HDFS

In this chapter, we will cover the following recipes:

  • Configuring HDFS block size
  • Setting up Namenode metadata location
  • Loading data into HDFS
  • Configuring HDFS replication
  • HDFS balancer
  • Quota configuration
  • HDFS health and FSCK
  • Configuring rack awareness
  • Recycle or trash bin configuration
  • Distcp usage
  • Controlling block report storm
  • Configuring Datanode heartbeat

Introduction

In this chapter, we will take a look at the storage layer, which is HDFS, and how it can be configured for storing data. It is important to ensure the good health of this distributed filesystem, and make sure that the data it contains is available, even in the case of failures. In this chapter, we will take a look at the replication, quota setup, and balanced distribution of data across nodes, as well as covering recipes on rack awareness and heartbeat for communication with the master.

The recipes in this chapter assume that you already have a running cluster and have completed the steps given in Chapter 1, Hadoop Architecture and Deployment.

Note

While the recipes in this chapter will give you an overview of a typical configuration, we encourage you to adapt this proposal according to your needs. The block size plays an important role in the performance and the amount of data that is worked on by a mapper. It is good practice to set up passphrase less access between nodes,...

Configuring HDFS block size

Getting ready

To step through the recipes in this chapter, make sure you have completed the recipes in Chapter 1, Hadoop Architecture and Deployment or at least understand the basic Hadoop cluster setup.

How to do it...

  1. ssh to the master node, which is Namenode, and navigate to the directory where Hadoop is installed. In the previous chapter, Hadoop was installed at /opt/cluster/hadoop:
    $ ssh root@10.0.0.4
  2. Change to the Hadoop user, or any other user that is running Hadoop, by using the following:
    $ sudo su - hadoop
  3. Edit the hdfs-site.xml file and modify the parameter to reflect the changes, as shown in the following screenshot:
    How to do it...
  4. dfs.blocksize is the parameter that decides on the value of the HDFS block size. The unit is bytes and the default value is 64 MB in Hadoop 1 and 128 MB in Hadoop 2. The block size can be configured according to the need.
  5. Once the changes are made to hdfs-site.xml, copy the file across all nodes in the cluster.
  6. Then restart the Namenode and datanode...

Setting up Namenode metadata location

The most critical component of Hadoop is Namenode, and it is important to safeguard the information it stores. It stores metadata, which is a combination of namespace and inode structure.

All the steps are to be performed as the hadoop user. It is expected that the user has gone through Chapter 1, Hadoop Architecture and Deployment and understands the uses and function of Namenode.

Getting ready

You are going to need a preinstalled Hadoop as discussed in Chapter 1, Hadoop Architecture and Deployment. In the following recipes, we will configure the parameters for a copy of Hadoop that is already installed.

How to do it...

  1. ssh to the Namenode, which in this case is nn1.cluster1.com.
  2. Navigate to the /opt/cluster/hadoop/etc/hadoop directory. This is the directory where we installed Hadoop in the first chapter. If the user has installed it at a different location, then navigate to this directory.
  3. Configure the dfs.namenode.name.dir parameter, which defines the...

Loading data in HDFS

It is important to make sure that the cluster is working fine and the user can perform file operations on the cluster.

Getting ready

Log in to any of the nodes in the cluster. It's best to use the edge node, as stated in Chapter 1, Hadoop Architecture and Deployment, and switch to user hadoop.

Create a simple text file named file1.txt using any of your favorite text editors, and write some content in it.

How to do it...

  1. Connect to the client1.cluster1.com edge node and switch to the hadoop user.
  2. Copy the file1.txt file to HDFS, as shown in the following screenshot:
    How to do it...
  3. The user can check for the status of a file, as shown in the following screenshot:
    How to do it...
  4. The user can make sure that the file exists and its type is correct, as shown in the following screenshot. The user can execute the commands to see the sub options:
    $ hadoop fs (and hit enter to see the options)

How it works...

Steps 2 and 3 create a simple text file on the local filesystem and then copy it to HDFS to make sure...

Configuring HDFS replication

For redundancy, it is important to have multiple copies of data. In HDFS, this is achieved by placing copies of blocks on different nodes. By default, the replication factor is 3, which means that for each block written to HDFS, there will be three copies in total on the nodes in the cluster.

It is important to make sure that the cluster is working fine and the user can perform file operations on the cluster.

Getting ready

Log in to any of the nodes in the cluster. It is best to use the edge node, as stated in Chapter 1, and switch to the user hadoop.

Create a simple text file named file1.txt using any of your favorite text editors, and write some content in it.

How to do it...

  1. ssh to the Namenode, which in this case is nn1.cluster1.com, and switch to user hadoop.
  2. Navigate to the /opt/cluster/hadoop/etc/hadoop directory. This is the directory where we installed Hadoop in Chapter 1, Hadoop Architecture and Deployment. If the user has installed it at a different location...

HDFS balancer

In a long-running cluster, there might be unequal distribution of data across Datanodes. This could be due to failures of nodes or the addition of nodes to the cluster.

To make sure that the data is equally distributed across Datanodes, it is important to use Hadoop balancer to redistribute the blocks.

Getting ready

For this recipe, you will again use the same node on which we have already configured Namenode.

All operations will be done by user hadoop.

How to do it...

  1. Log in the nn1.cluster1.com node and change to user hadoop.
  2. Execute the balancer command as shown in the following screenshot:
    How to do it...
  3. By default, the balancer threshold is set to 10%, but we can change it, as shown in the following screenshot:
    How to do it...

How it works...

The balancer threshold defines the percentage of cluster disk space utilized, compared to the nodes in the cluster. For example, let's say we have 10 Datanodes in the cluster, with each having 100 GB of disk storage totaling to about 1 TB.

So, when we say the threshold...

Introduction

In this chapter, we will take a look at the storage layer, which is HDFS, and how it can be configured for storing data. It is important to ensure the good health of this distributed filesystem, and make sure that the data it contains is available, even in the case of failures. In this chapter, we will take a look at the replication, quota setup, and balanced distribution of data across nodes, as well as covering recipes on rack awareness and heartbeat for communication with the master.

The recipes in this chapter assume that you already have a running cluster and have completed the steps given in Chapter 1, Hadoop Architecture and Deployment.

Note

While the recipes in this chapter will give you an overview of a typical configuration, we encourage you to adapt this proposal according to your needs. The block size plays an important role in the performance and the amount of data that is worked on by a mapper. It is good practice to set up passphrase less access between nodes, so...

Configuring HDFS block size

Getting ready

To step through the recipes in this chapter, make sure you have completed the recipes in Chapter 1, Hadoop Architecture and Deployment or at least understand the basic Hadoop cluster setup.

How to do it...

  1. ssh to the master node, which is Namenode, and navigate to the directory where Hadoop is installed. In the previous chapter, Hadoop was installed at /opt/cluster/hadoop:

    $ ssh root@10.0.0.4

  2. Change to the Hadoop user, or any other user that is running Hadoop, by using the following:

    $ sudo su - hadoop

  3. Edit the hdfs-site.xml file and modify the parameter to reflect the changes, as shown in the following screenshot:

  4. dfs.blocksize is the parameter that decides on the value of the HDFS block size. The unit is bytes and the default value is 64 MB in Hadoop 1 and 128 MB in Hadoop 2. The block size can be configured according to the need.

  5. Once the changes are made to hdfs-site.xml, copy the file across all nodes in the cluster.

  6. Then restart the Namenode and...

Setting up Namenode metadata location

The most critical component of Hadoop is Namenode, and it is important to safeguard the information it stores. It stores metadata, which is a combination of namespace and inode structure.

All the steps are to be performed as the hadoop user. It is expected that the user has gone through Chapter 1, Hadoop Architecture and Deployment and understands the uses and function of Namenode.

Getting ready

You are going to need a preinstalled Hadoop as discussed in Chapter 1, Hadoop Architecture and Deployment. In the following recipes, we will configure the parameters for a copy of Hadoop that is already installed.

How to do it...

  1. ssh to the Namenode, which in this case is nn1.cluster1.com.

  2. Navigate to the /opt/cluster/hadoop/etc/hadoop directory. This is the directory where we installed Hadoop in the first chapter. If the user has installed it at a different location, then navigate to this directory.

  3. Configure the dfs.namenode.name.dir parameter, which defines the...

Loading data in HDFS

It is important to make sure that the cluster is working fine and the user can perform file operations on the cluster.

Getting ready

Log in to any of the nodes in the cluster. It's best to use the edge node, as stated in Chapter 1, Hadoop Architecture and Deployment, and switch to user hadoop.

Create a simple text file named file1.txt using any of your favorite text editors, and write some content in it.

How to do it...

  1. Connect to the client1.cluster1.com edge node and switch to the hadoop user.

  2. Copy the file1.txt file to HDFS, as shown in the following screenshot:

  3. The user can check for the status of a file, as shown in the following screenshot:

  4. The user can make sure that the file exists and its type is correct, as shown in the following screenshot. The user can execute the commands to see the sub options:

    $ hadoop fs (and hit enter to see the options)

How it works...

Steps 2 and 3 create a simple text file on the local filesystem and then copy it to HDFS to make sure that...

Configuring HDFS replication

For redundancy, it is important to have multiple copies of data. In HDFS, this is achieved by placing copies of blocks on different nodes. By default, the replication factor is 3, which means that for each block written to HDFS, there will be three copies in total on the nodes in the cluster.

It is important to make sure that the cluster is working fine and the user can perform file operations on the cluster.

Getting ready

Log in to any of the nodes in the cluster. It is best to use the edge node, as stated in Chapter 1, and switch to the user hadoop.

Create a simple text file named file1.txt using any of your favorite text editors, and write some content in it.

How to do it...

  1. ssh to the Namenode, which in this case is nn1.cluster1.com, and switch to user hadoop.

  2. Navigate to the /opt/cluster/hadoop/etc/hadoop directory. This is the directory where we installed Hadoop in Chapter 1, Hadoop Architecture and Deployment. If the user has installed it at a different location...

HDFS balancer

In a long-running cluster, there might be unequal distribution of data across Datanodes. This could be due to failures of nodes or the addition of nodes to the cluster.

To make sure that the data is equally distributed across Datanodes, it is important to use Hadoop balancer to redistribute the blocks.

Getting ready

For this recipe, you will again use the same node on which we have already configured Namenode.

All operations will be done by user hadoop.

How to do it...

  1. Log in the nn1.cluster1.com node and change to user hadoop.

  2. Execute the balancer command as shown in the following screenshot:

  3. By default, the balancer threshold is set to 10%, but we can change it, as shown in the following screenshot:

How it works...

The balancer threshold defines the percentage of cluster disk space utilized, compared to the nodes in the cluster. For example, let's say we have 10 Datanodes in the cluster, with each having 100 GB of disk storage totaling to about 1 TB.

So, when we say the threshold is...

Quota configuration

In a multitenancy cluster, it is important to control the utilization both in terms of HDFS space, memory, and CPU utilization. In this recipe, we will be looking at how we can restrict a user or a project from using more than the allotted HDFS space.

Getting ready

Make sure that there is a running cluster, and that the user is already well versed in the recipes that we have looked at so far.

How to do it...

  1. Connect to Namenode and change the user to hadoop.

  2. Create a directory named projects on HDFS, as shown in the following screenshot:

  3. By default, there is no quota configured on any directory.

  4. To see what options can be set on the projects directory, use the following command:

    $ hadoop fs -count -q /projects

  5. The two leftmost fields show the namespace and disk space quota, which currently is not set, as shown in the following screenshot:

  6. To set the namespace quota, which will define how many inodes can be allocated for this projects directory, enter the following code. Inodes...

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Key benefits

  • Become an expert Hadoop administrator and perform tasks to optimize your Hadoop Cluster
  • Import and export data into Hive and use Oozie to manage workflow.
  • Practical recipes will help you plan and secure your Hadoop cluster, and make it highly available

Description

Hadoop enables the distributed storage and processing of large datasets across clusters of computers. Learning how to administer Hadoop is crucial to exploit its unique features. With this book, you will be able to overcome common problems encountered in Hadoop administration. The book begins with laying the foundation by showing you the steps needed to set up a Hadoop cluster and its various nodes. You will get a better understanding of how to maintain Hadoop cluster, especially on the HDFS layer and using YARN and MapReduce. Further on, you will explore durability and high availability of a Hadoop cluster. You’ll get a better understanding of the schedulers in Hadoop and how to configure and use them for your tasks. You will also get hands-on experience with the backup and recovery options and the performance tuning aspects of Hadoop. Finally, you will get a better understanding of troubleshooting, diagnostics, and best practices in Hadoop administration. By the end of this book, you will have a proper understanding of working with Hadoop clusters and will also be able to secure, encrypt it, and configure auditing for your Hadoop clusters.

Who is this book for?

If you are a system administrator with a basic understanding of Hadoop and you want to get into Hadoop administration, this book is for you. It’s also ideal if you are a Hadoop administrator who wants a quick reference guide to all the Hadoop administration-related tasks and solutions to commonly occurring problems

What you will learn

  • • Set up the Hadoop architecture to run a Hadoop cluster smoothly
  • • Maintain a Hadoop cluster on HDFS, YARN, and MapReduce
  • • Understand high availability with Zookeeper and Journal Node
  • • Configure Flume for data ingestion and Oozie to run various workflows
  • • Tune the Hadoop cluster for optimal performance
  • • Schedule jobs on a Hadoop cluster using the Fair and Capacity scheduler
  • • Secure your cluster and troubleshoot it for various common pain points

Product Details

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Publication date : May 26, 2017
Length: 348 pages
Edition : 1st
Language : English
ISBN-13 : 9781787126879
Vendor :
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Category :
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Concepts :
System Administration
Tools :
Hadoop

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Publication date : May 26, 2017
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Edition : 1st
Language : English
ISBN-13 : 9781787126879
Vendor :
Apache
Category :
Cloud & Networking
Concepts :
System Administration
Tools :
Hadoop

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Table of Contents

13 Chapters
1. Hadoop Architecture and Deployment Chevron down icon Chevron up icon
1. Hadoop Architecture and Deployment
Introduction
Building and compiling Hadoop
Installation methods
Setting up host resolution
Installing a single-node cluster - HDFS components
Installing a single-node cluster - YARN components
Installing a multi-node cluster
Configuring the Hadoop Gateway node
Decommissioning nodes
Adding nodes to the cluster
2. Maintaining Hadoop Cluster HDFS Chevron down icon Chevron up icon
2. Maintaining Hadoop Cluster HDFS
Introduction
Configuring HDFS block size
Setting up Namenode metadata location
Loading data in HDFS
Configuring HDFS replication
HDFS balancer
Quota configuration
HDFS health and FSCK
Configuring rack awareness
Recycle or trash bin configuration
Distcp usage
Control block report storm
Configuring Datanode heartbeat
3. Maintaining Hadoop Cluster – YARN and MapReduce Chevron down icon Chevron up icon
3. Maintaining Hadoop Cluster – YARN and MapReduce
Introduction
Running a simple MapReduce program
Hadoop streaming
Configuring YARN history server
Job history web interface and metrics
Configuring ResourceManager components
YARN containers and resource allocations
ResourceManager Web UI and JMX metrics
Preserving ResourceManager states
4. High Availability Chevron down icon Chevron up icon
4. High Availability
Introduction
Namenode HA using shared storage
ZooKeeper configuration
Namenode HA using Journal node
Resourcemanager HA using ZooKeeper
Rolling upgrade with HA
Configure shared cache manager
Configure HDFS cache
HDFS snapshots
Configuring storage based policies
Configuring HA for Edge nodes
5. Schedulers Chevron down icon Chevron up icon
5. Schedulers
Introduction
Configuring users and groups
Fair Scheduler configuration
Fair Scheduler pools
Configuring job queues
Job queue ACLs
Configuring Capacity Scheduler
Queuing mappings in Capacity Scheduler
YARN and Mapred commands
YARN label-based scheduling
YARN SLS
6. Backup and Recovery Chevron down icon Chevron up icon
6. Backup and Recovery
Introduction
Initiating Namenode saveNamespace
Using HDFS Image Viewer
Fetching parameters which are in-effect
Configuring HDFS and YARN logs
Backing up and recovering Namenode
Configuring Secondary Namenode
Promoting Secondary Namenode to Primary
Namenode recovery
Namenode roll edits – online mode
Namenode roll edits – offline mode
Datanode recovery – disk full
Configuring NFS gateway to serve HDFS
Recovering deleted files
7. Data Ingestion and Workflow Chevron down icon Chevron up icon
7. Data Ingestion and Workflow
Introduction
Hive server modes and setup
Using MySQL for Hive metastore
Operating Hive with ZooKeeper
Loading data into Hive
Partitioning and Bucketing in Hive
Hive metastore database
Designing Hive with credential store
Configuring Flume
Configure Oozie and workflows
8. Performance Tuning Chevron down icon Chevron up icon
8. Performance Tuning
Tuning the operating system
Tuning the disk
Tuning the network
Tuning HDFS
Tuning Namenode
Tuning Datanode
Configuring YARN for performance
Configuring MapReduce for performance
Hive performance tuning
Benchmarking Hadoop cluster
9. HBase Administration Chevron down icon Chevron up icon
9. HBase Administration
Introduction
Setting up single node HBase cluster
Setting up multi-node HBase cluster
Inserting data into HBase
Integration with Hive
HBase administration commands
HBase backup and restore
Tuning HBase
HBase upgrade
Migrating data from MySQL to HBase using Sqoop
10. Cluster Planning Chevron down icon Chevron up icon
10. Cluster Planning
Introduction
Disk space calculations
Nodes needed in the cluster
Memory requirements
Sizing the cluster as per SLA
Network design
Estimating the cost of the Hadoop cluster
Hardware and software options
11. Troubleshooting, Diagnostics, and Best Practices Chevron down icon Chevron up icon
11. Troubleshooting, Diagnostics, and Best Practices
Introduction
Namenode troubleshooting
Datanode troubleshooting
Resourcemanager troubleshooting
Diagnose communication issues
Parse logs for errors
Hive troubleshooting
HBase troubleshooting
Hadoop best practices
12. Security Chevron down icon Chevron up icon
12. Security
Introduction
Encrypting disk using LUKS
Configuring Hadoop users
HDFS encryption at Rest
Configuring SSL in Hadoop
In-transit encryption
Enabling service level authorization
Securing ZooKeeper
Configuring auditing
Configuring Kerberos server
Configuring and enabling Kerberos for Hadoop
Index Chevron down icon Chevron up icon
Index

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Microsoft Azure Fundamentals Certification and Beyond
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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(2 Ratings)
5 star 50%
4 star 0%
3 star 0%
2 star 50%
1 star 0%
Jayesh Jun 02, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Writer has fair knowledge on subject.
Amazon Verified review Amazon
Zvonko Pino Varela Apr 29, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
It looks out of date. I followed the instructions step by step but I found a lot of mistakes, and many instructions didn't work. It's far better to install Apache Ambari and follow those directions.
Amazon Verified review Amazon

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About the author

Profile icon Aman Singh
Aman Singh
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Gurmukh Singh is a seasoned technology professional with 14+ years of industry experience in infrastructure design, distributed systems, performance optimization, and networks. He has worked in big data domain for the last 5 years and provides consultancy and training on various technologies. He has worked with companies such as HP, JP Morgan, and Yahoo. He has authored Monitoring Hadoop by Packt Publishing
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