Book Image

Hadoop Essentials

By : Shiva Achari
Book Image

Hadoop Essentials

By: Shiva Achari

Overview of this book

This book jumps into the world of Hadoop and its tools, to help you learn how to use them effectively to optimize and improve the way you handle Big Data. Starting with the fundamentals Hadoop YARN, MapReduce, HDFS, and other vital elements in the Hadoop ecosystem, you will soon learn many exciting topics such as MapReduce patterns, data management, and real-time data analysis using Hadoop. You will also explore a number of the leading data processing tools including Hive and Pig, and learn how to use Sqoop and Flume, two of the most powerful technologies used for data ingestion. With further guidance on data streaming and real-time analytics with Storm and Spark, Hadoop Essentials is a reliable and relevant resource for anyone who understands the difficulties - and opportunities - presented by Big Data today. With this guide, you'll develop your confidence with Hadoop, and be able to use the knowledge and skills you learn to successfully harness its unparalleled capabilities.
Table of Contents (15 chapters)
Hadoop Essentials
About the Author
About the Reviewers
Pillars of Hadoop – HDFS, MapReduce, and YARN


As we discussed about the file and data management in HBase, along with compaction, Splitting Regions also is an important process. The best performance in HBase is achieved when the data is distributed evenly across the Regions and RegionServers which can be achieved by Splitting the Region optimally. When a table is first created with default options, only one Region is allocated to the table as HBase will not have sufficient information to allocate the appropriate number of Regions. We have three types of Splitting triggers which are Pre-Splitting, Auto Splitting, and Forced Splitting.


To aid the splitting of a Region while creating a table, we can use Pre-Splitting to let HBase know initially the number of Regions to allocate to a table. For Pre-Splitting we should know the distribution of the data and if we Pre-Split the Regions and we have a data skew, then the distribution will be non-uniform and can limit the cluster performance. We also have to calculate the...