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Home > Data > Database Administration > HBase High Performance Cookbook
HBase High Performance Cookbook
HBase High Performance Cookbook

HBase High Performance Cookbook: Solutions for optimization, scaling and performance tuning

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eBook Jan 2017 350 pages 1st Edition
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HBase High Performance Cookbook

Chapter 2. Loading Data from Various DBs

In this chapter, we will cover the following:

  • Extracting data from Oracle
  • Loading data using Oracle Big Data Connector
  • Bulk utilities
  • Using Hive and Apache Flume Streaming Data in Apache HBase
  • Using Sqoop

This will allow the actor to import data from different RDBMS/flat files.

Introduction

As we know, HBase is very effective in enabling real-time platforms to access read/write data randomly from the disk with commodity hardware, and there are many ways to do that, such as the following:

  • Put APIs
  • BulkLoad Tool
  • MapReduce jobs

Put APIs are the most straightforward way to place data into the HBase system, but they are only good for small sets of data and can be used for site-facing applications or for more real-time scenarios/use cases.

BulkLoad Tool runs the MapReduce job behind the scenes and loads data into HBase tables. These tools internally generate the HBase internal file format (HFile), which allows us to import the data into a live HBase cluster.

Note

In case of huge data or a very high write-intensive job, it's advisable to use the ImportTsv tool. Using MapReduce jobs in conjunction with HFileOutputFormat is acceptable; but as the data grows, it loses its performance, scalability, and maintainability, which are necessary for any software to be successful...

Extracting data from Oracle

HBase doesn't allow direct interaction or a pipeline for data import from Oracle and MySQL to HBase. The basic concept remains the same: to first extract the data into flat / text files (ImportTsv format), transform the data into HFiles, and then load them into HBase by telling the region server where to find them.

Getting ready

Let's start with getting public data from the following URL:

http://databank.worldbank.org/data/download/WDI_csv.zip

This will have the following files:

  • WDI_Data.csv
  • WDI_Country.csv (this is the file we will use)
  • WDI_Series.csv
  • WDI_CS_Notes.csv
  • WDI_ST_Notes.csv
  • WDI_Footnotes.csv
  • WDI_Description.csv

We will be using this as data and nothing else; this is freely available on the aforementioned World Bank site.

We will then create a table in Oracle Schema on your SQL prompt:

The names of the column used have an exact match with WDI_Country.csv:

CREATE TABLE WDI_COUNTRY 

(
"COUNTRY_CODE" VARCHAR2(100 BYTE),

"SHORT_NAME&quot...

Loading data using Oracle Big data connector

If there is a very large volume of data in the system, it is vital to have an extremely efficient data-processing engine between various touch points. Oracle Big Data connector is calibrated to do the following activities. We will only touch upon the loading part of it:

  • Connector for HDFS
  • Loader for Hadoop
  • Data Integrator Adaptor for Hadoop
  • R Advanced Analytics for Hadoop
  • XQuery for Hadoop

Getting Ready

Download Oracle connector from the following:

  1. Loading data using the Oracle Big data connector:

    http://www.oracle.com/technetwork/database/database-technologies/bdc/big-data-connectors/downloads/index.html

  2. Download for Linux x86-64
  3. Download Cloudera's Distribution (CDH3 or CDH4)
  4. JDK1.6.08 or later
  5. Hive 0.7.0, 0.8.1, or 0.9.0
  6. Oracle DB release 11.2.0.2 or 11.2.0.3, the same version of CDH3,CDH4

How to do it…

  1. Configure CDH or Apache Hadoop as shown in the preceding section.

    Tip

    Don't change anything in the HBase setup. Indicate clearly that jars...

Bulk utilities

The process for loading data using Bulk utilities is very similar:

  1. Extracting data from the source.
  2. Transforming the data into HFiles.
  3. Loading the files into HBase by guiding the region servers as to where to find them.

Getting ready...

The following points have to be remembered when using Bulk utilities:

  • HBase/Hadoop cluster with MapReduce/Yarn should be running. You can run jps to check it.
  • Access rights (user/group) are needed to execute the program.
  • Table schema needs to be designed to the input structure.
  • Split points need to be taken into consideration.
  • The entire stack (compaction, split, block size, max file size, flush size, version compression, mem store size, block cache, garbage collections nproc, and so on) needs to be fine-tuned to make the best of it.

The WAL are not written; thus, data lost during the failure may not be recovered as there is no replication performed by reading the WAL.

How to do it…

There are multiple ways to do this work, such as writing your own...

Using Hive with Apache HBase

Hive is an ETL engine for HBase/Hadoop. It has an SQL-like query language, popularly known as Hive QA for SELECT(read) and INSERT(write). The main objective is to do ad hoc analysis on Petabyte-level data. Hive integration was originally introduced in HIVE-705.

Getting ready

How to do it…

The first step is to use HbaseStorageHandler to register...

Using Sqoop

Sqoop provides an excellent way to import data in parallel from existing RDBMs to HDFS. We get an exact set of table structures that are imported. This happens because of parallel processing. These files can have text delimited by ',' '|', and so on. After manipulating imported records by using MapReduce or Hive, the output result set can be exported back to RDBMS. The data imported can be done in real time or in the batch process (using a cron job).

Getting ready

Prerequisites:

HBase and Hadoop cluster must be up and running.

You can do a wget to http://mirrors.gigenet.com/apache/sqoop/1.4.6/sqoop-1.4.6.tar.gz

Untar it to /u/HbaseB using tar –zxvf sqoop-1.4.6.tar.gz

It will create a /u/HbaseB/sqoop-1.4.6 folder.

A Sqoop user is created in the target DB, which has read/write access and is not bound strictly with CPU and memory (RAM, Storage) limitation by the DBAs.

How to do it…

  1. Log in to MySQL by executing the following command:
    Mysql –h yourMySqlHostName...
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Key benefits

  • Architect a good HBase cluster for a very large distributed system
  • Get to grips with the concepts of performance tuning with HBase
  • A practical guide full of engaging recipes and attractive screenshots to enhance your system’s performance

Description

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.

Who is this book for?

This book is intended for developers and architects who want to know all about HBase at a hands-on level. This book is also for big data enthusiasts and database developers who have worked with other NoSQL databases and now want to explore HBase as another futuristic scalable database solution in the big data space.

What you will learn

  • Configure HBase from a high performance perspective
  • Grab data from various RDBMS/Flat files into the HBASE systems
  • Understand table design and perform CRUD operations
  • Find out how the communication between the client and server happens in HBase
  • Grasp when to use and avoid MapReduce and how to perform various tasks with it
  • Get to know the concepts of scaling with HBase through practical examples
  • Set up Hbase in the Cloud for a small scale environment
  • Integrate HBase with other tools including ElasticSearch

Product Details

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Publication date : Jan 31, 2017
Length: 350 pages
Edition : 1st
Language : English
ISBN-13 : 9781783983070
Category :
Data
Languages :
Java
Concepts :
Database Administration
Tools :
HBase

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Publication date : Jan 31, 2017
Length: 350 pages
Edition : 1st
Language : English
ISBN-13 : 9781783983070
Category :
Data
Languages :
Java
Concepts :
Database Administration
Tools :
HBase

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Table of Contents

12 Chapters
1. Configuring HBase Chevron down icon Chevron up icon
1. Configuring HBase
Introduction
Configuring and deploying HBase
Using the filesystem
Administering clusters
Managing clusters
2. Loading Data from Various DBs Chevron down icon Chevron up icon
2. Loading Data from Various DBs
Introduction
Extracting data from Oracle
Loading data using Oracle Big data connector
Bulk utilities
Using Hive with Apache HBase
Using Sqoop
3. Working with Large Distributed Systems Part I Chevron down icon Chevron up icon
3. Working with Large Distributed Systems Part I
Introduction
Scaling elastically or Auto Scaling with built-in fault tolerance
Auto Scaling HBase using AWS
Works on different VM/physical, cloud hardware
4. Working with Large Distributed Systems Part II Chevron down icon Chevron up icon
4. Working with Large Distributed Systems Part II
Introduction
Read path
Write Path
Snappy
LZO compression
LZ4 compressor
Replication
5. Working with Scalable Structure of tables Chevron down icon Chevron up icon
5. Working with Scalable Structure of tables
Introduction
HBase data model part 1
HBase data model part 2
How HBase truly scales on key and schema design
6. HBase Clients Chevron down icon Chevron up icon
6. HBase Clients
Introduction
HBase REST and Java Client
Working with Apache Thrift
Working with Apache Avro
Working with Protocol buffer
Working with Pig and using Shell
7. Large-Scale MapReduce Chevron down icon Chevron up icon
7. Large-Scale MapReduce
Introduction
8. HBase Performance Tuning Chevron down icon Chevron up icon
8. HBase Performance Tuning
Introduction
Working with infrastructure/operating systems
Working with Java virtual machines
Changing the configuration of components
Working with HDFS
9. Performing Advanced Tasks on HBase Chevron down icon Chevron up icon
9. Performing Advanced Tasks on HBase
Machine learning using Hbase
Real-time data analysis using Hbase and Mahout
Full text indexing using Hbase
10. Optimizing Hbase for Cloud Chevron down icon Chevron up icon
10. Optimizing Hbase for Cloud
Introduction
Configuring Hbase for the Cloud
Connecting to an Hbase cluster using the command line
Backing up and restoring Hbase
Terminating an HBase cluster
Accessing HBase data with hive
Viewing the Hbase user interface
Monitoring HBase with CloudWatch
Monitoring Hbase with Ganglia
11. Case Study Chevron down icon Chevron up icon
11. Case Study
Introduction
Configuring Lily Platform
Integrating elastic search with Hbase
Configuring
Index Chevron down icon Chevron up icon
Index

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GeneM Jul 12, 2017
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This book is in sad need of a proof reader and an editor. It has good topics. Too many times I have to imagine what he meant to write.
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Adam Aug 25, 2017
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Makes a complex subject impossible to follow. There may be some good content here but its hidden amongst bad grammar, run on sentences, typos, and even review comments which were not removed.E.g. " if an inconsistency between the checksum and the block contents is observed, This does not make sense!, the communication is sent to the HDFS master".
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About the author

Profile icon Ruchir Choudhry
Ruchir Choudhry
Ruchir Choudhry is a principle architect in one of the largest e-commerce companies and specializes in leading, articulating, technology vision, strategizing, and implementing very large-scale software engineering-driven technology changes, with a track record of over 16 years of success. He was responsible for leading strategy, architecture, engineering, and operations of multitenant e-commerce sites and platforms in US, UK, Brazil, and other major markets for Walmart. The sites helped Walmart enter new markets and/or grow its market share. The sites combined service millions of customers and take in orders with annual revenues exceeding $2.0 billion. His personal interest is in performance and scalability. Recently, he has become obsessed with technology as a vehicle to drive and prioritize optimization across organizations and in the world. He is a core team member in conceptualizing, designing, and reshaping a new platform that will serve the next generation of frontend engineering, based on the cutting edge technology in WalMart.com and NBC/GE/VF Image ware. He has led some of the most complex and technologically challenging R&D and innovative projects in VF Image Ware, Walmart.com and in GE/NBC (China and Vancouver Olympic websites), Hiper World Cyber Tech Limited (which created the first wireless-based payment gateway of India that worked on non-smart phones, which was presented at Berlin in 1999). He is the author of more than 8 white papers, which spans from biometric-based single sign on to Java Cards, performance tuning, and JVM tuning, among others. He was a presenter of JVM, performance optimization using Jboss, in Berlin and various other places. Ruchir Choudhry did his BE at Bapuji Institute of Technology, MBA in information technology at National institute of Engineering and Technology, and his MS Systems at BITS Pilani. He is currently working and consulting on HBase, Spark, and Cassandra. He can be reached at ruchirchoudhry@gmail.com
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