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HBase High Performance Cookbook

HBase High Performance Cookbook

By : Ruchir Choudhry
2.5 (2)
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HBase High Performance Cookbook

HBase High Performance Cookbook

2.5 (2)
By: Ruchir Choudhry

Overview of this book

Apache HBase is a non-relational NoSQL database management system that runs on top of HDFS. It is an open source, disturbed, versioned, column-oriented store and is written in Java to provide random real-time access to big Data. We’ll start off by ensuring you have a solid understanding the basics of HBase, followed by giving you a thorough explanation of architecting a HBase cluster as per our project specifications. Next, we will explore the scalable structure of tables and we will be able to communicate with the HBase client. After this, we’ll show you the intricacies of MapReduce and the art of performance tuning with HBase. Following this, we’ll explain the concepts pertaining to scaling with HBase. Finally, you will get an understanding of how to integrate HBase with other tools such as ElasticSearch. By the end of this book, you will have learned enough to exploit HBase for boost system performance.
Table of Contents (13 chapters)
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7
7. Large-Scale MapReduce
12
Index

HBase data model part 2

In the preceding section we discussed details about the core structure of the Hbase data model in this section we would like to go a step further deep to understand and do things, which will make your data model flexible, robust and scalable. And this is essential to know on an operation level too, as the cluster grows it becomes pivotal to take all consideration.

We should take the following steps to make it happen:

  • Timestamps
  • Versions
  • Sort order
  • Column metadata
  • Joins

Timestamps: Every put in HBase is marked with a unique timestamp and this acts as a unique reference for a provided version of a value.

HBase associates timestamp by default mapping it with the respective region server. This default characteristic has a lot of flexibility and you can even specify timestamp value while putting date to the cell of that region server.

In case of multi-datacenter timestamp provides a way to avoid conflicts. In some use cases you can also use it to provide consistency of versions...

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