文件名称：Scaling.Big.Data.with.Hadoop.and.Solr.2nd.Edition.1783553391
文件大小：5.37MB
文件格式：PDF
更新时间：2018-05-24 10:17:08
Big Data Hadoop Solr
Title: Scaling Big Data with Hadoop and Solr, 2nd Edition Author: Hrishikesh Vijay Karambelkar Length: 156 pages Edition: 1 Language: English Publisher: Packt Publishing Publication Date: 2015-03-31 ISBN-10: 1783553391 ISBN-13: 9781783553396 Understand, design, build, and optimize your big data search engine with Hadoop and Apache Solr About This Book Explore different approaches to making Solr work on big data ecosystems besides Apache Hadoop Improve search performance while working with big data A practical guide that covers interesting, real-life use cases for big data search along with sample code Who This Book Is For This book is aimed at developers, designers, and architects who would like to build big data enterprise search solutions for their customers or organizations. No prior knowledge of Apache Hadoop and Apache Solr/Lucene technologies is required. In Detail Together, Apache Hadoop and Apache Solr help organizations resolve the problem of information extraction from big data by providing excellent distributed faceted search capabilities. This book will help you learn everything you need to know to build a distributed enterprise search platform as well as optimize this search to a greater extent, resulting in the maximum utilization of available resources. Starting with the basics of Apache Hadoop and Solr, the book covers advanced topics of optimizing search with some interesting real-world use cases and sample Java code. This is a step-by-step guide that will teach you how to build a high performance enterprise search while scaling data with Hadoop and Solr in an effortless manner. Table of Contents Chapter 1. Processing Big Data Using Hadoop and MapReduce Chapter 2. Understanding Apache Solr Chapter 3. Enabling Distributed Search using Apache Solr Chapter 4. Big Data Search Using Hadoop and Its Ecosystem Chapter 5. Scaling Search Performance