Chapter 1, Processing Big Data Using Hadoop and MapReduce, introduces you with Apache Hadoop and its ecosystem, HDFS, and MapReduce. You will also learn how to write MapReduce programs, configure Hadoop cluster, the configuration files, and the administration of your cluster.

Chapter 2, Understanding Solr, introduces you to Apache Solr. It explains how you can configure the Solr instance, how to create indexes and load your data in the Solr repository, and how you can use Solr effectively for searching. It also discusses interesting features of Apache Solr.

Chapter 3, Making Big Data Work for Hadoop and Solr, brings the two worlds together; it drives you through different approaches for achieving Big Data work with architectures and their benefits and applicability.

Chapter 4, Using Big Data to Build Your Large Indexing, explains the NoSQL and concepts of distributed search. It then gets you into using different algorithms for Big Data search covering shards and indexing. It also talks about SolrCloud configuration and Lily.

Chapter 5, Improving Performance of Search while Scaling with Big Data, covers different levels of optimizations that you can perform on your Big Data search instance as the data keeps growing. It discusses different performance improvement techniques which can be implemented by the users for their deployment.

Appendix A, Use Cases for Big Data Search, describes some industry use cases and case studies for Big Data using Solr and Hadoop.

Appendix B, Creating Enterprise Search Using Apache Solr, shares a sample Solr schema which can be used by the users for experimenting with Apache Solr.

Appendix C, Sample MapReduce Programs to Build the Solr Indexes, provides a sample MapReduce program to build distributed Solr indexes for different approaches.

This book discusses different approaches, each approach needs a different set of software. To run Apache Hadoop/Solr instance, you need:

This book provides guidance for developers who wish to build high speed enterprise search platform using Hadoop and Solr. This book is primarily aimed at Java programmers, who wish to extend Hadoop platform to make it run as an enterprise search without prior knowledge of Apache Hadoop and Solr.

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text are shown as follows: "You will typically find the hadoop-example jar in /usr/share/hadoop, or in $HADOOP_HOME."

A block of code is set as follows:

public static class IndexReducer {
  protected void setup(Context context) throws IOException, InterruptedException {
      super.setup(context);
      SolrRecordWriter.addReducerContext(context);
  }
}

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

A programming task is divided into multiple identical subtasks, and when it is distributed among multiple machines for processing, it is called a map task. The results of these map tasks are combined together into one or many reduce tasks. Overall, this approach of computing tasks is called the MapReduce approach.

Any command-line input or output is written as follows:

java -Durl=http://node1:8983/solr/clusterCollection/update 
-jar post.jar ipod_video.xml

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "The admin UI will start showing the Cloud tab."

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