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  • Book Overview & Buying Monitoring Hadoop
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Monitoring Hadoop

Monitoring Hadoop

By : Aman Singh
4.3 (6)
Buy this Book
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Monitoring Hadoop

Monitoring Hadoop

4.3 (6)
By: Aman Singh
Buy this Book

Overview of this book

This book is useful for Hadoop administrators who need to learn how to monitor and diagnose their clusters. Also, the book will prove useful for new users of the technology, as the language used is simple and easy to grasp.
Table of Contents (9 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction to Monitoring
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1. Introduction to Monitoring
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The need for monitoring
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The monitoring tools available in the market
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System logging
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Summary
2
2. Hadoop Daemons and Services
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2. Hadoop Daemons and Services
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Hadoop daemons
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YARN framework
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Summary
3
3. Hadoop Logging
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3. Hadoop Logging
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The need for logging events
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System logging
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Logging levels
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Logging in Hadoop
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Summary
4
4. HDFS Checks
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4. HDFS Checks
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HDFS overview
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Nagios master configuration
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The Nagios client configuration
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Summary
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5. MapReduce Checks
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5. MapReduce Checks
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MapReduce overview
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MapReduce control commands
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MapReduce health checks
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Nagios master configuration
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Nagios client configuration
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Summary
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6. Hadoop Metrics and Visualization Using Ganglia
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6. Hadoop Metrics and Visualization Using Ganglia
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Hadoop metrics
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Metrics contexts
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Metrics system design
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Metrics configuration
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Configuring Metrics2
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Exploring the metrics contexts
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Hadoop Ganglia integration
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Hadoop configuration
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Ganglia graphs
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Metrics APIs
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Summary
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7. Hive, HBase, and Monitoring Best Practices
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7. Hive, HBase, and Monitoring Best Practices
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Hive monitoring
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Hive metrics
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HBase Nagios monitoring
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HBase metrics
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Monitoring best practices
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The Filter class
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Nagios and Ganglia best practices
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Summary
8
Index
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Index

Chapter 3. Hadoop Logging

In any enterprise, how ever big or small it could be, logging is very important for security and improvement of services. With more and more systems going online, not just logging data but also reviewing the logs is very important.

Systems today are generating millions of lines of data, which could be simple web events, some database query logs, network logs, or any audit logs. Dealing with such large logs manually is neither practical nor viable in terms of time; there must be centralized logging and analysis tools to quickly extract the useful information.

In this chapter, we begin our journey by exploring the concepts of logging at both the system level and Hadoop level.

We'll cover the following topics in this chapter:

  • Different log levels
  • Logs written by various Hadoop daemons
  • Understanding audit logs and how they could be useful
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