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Elasticsearch Server: Second Edition
Elasticsearch Server: Second Edition

Elasticsearch Server: Second Edition: From creating your own index structure through to cluster monitoring and troubleshooting, this is the complete guide to implementing the ElasticSearch search engine on your own websites. Packed with real-life examples.

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Elasticsearch Server: Second Edition

Chapter 2. Indexing Your Data

In the previous chapter, we learned the basics about full text search and Elasticsearch. We also saw what Apache Lucene is. In addition to that, we saw how to install Elasticsearch, what the standard directory layout is, and what to pay attention to. We created an index, and we indexed and updated our data. Finally, we used the simple URI query to get data from Elasticsearch. By the end of this chapter, you will learn the following topics:

  • Elasticsearch indexing

  • Configuring your index structure mappings and knowing what field types we are allowed to use

  • Using batch indexing to speed up the indexing process

  • Extending your index structure with additional internal information

  • Understanding what segment merging is, how to configure it, and what throttling is

  • Understanding how routing works and how we can configure it to our needs

Elasticsearch indexing


We have our Elasticsearch cluster up and running, and we also know how to use the Elasticsearch REST API to index our data, delete it, and retrieve it. We also know how to use search to get our documents. If you are used to SQL databases, you might know that before you can start putting the data there, you need to create a structure, which will describe what your data looks like. Although Elasticsearch is a schema-less search engine and can figure out the data structure on the fly, we think that controlling the structure and thus defining it ourselves is a better way. In the following few pages, you'll see how to create new indices (and how to delete them). Before we look closer at the available API methods, let's see what the indexing process looks like.

Shards and replicas

As you recollect from the previous chapter, the Elasticsearch index is built of one or more shards and each of them contains part of your document set. Each of these shards can also have replicas...

Mappings configuration


If you are used to SQL databases, you may know that before you can start inserting the data in the database, you need to create a schema, which will describe what your data looks like. Although Elasticsearch is a schema-less search engine and can figure out the data structure on the fly, we think that controlling the structure and thus defining it ourselves is a better way. In the following few pages, you'll see how to create new indices (and how to delete them) and how to create mappings that suit your needs and match your data structure.

Note

Note that we didn't include all the information about the available types in this chapter and some features of Elasticsearch, such as nested type, parent-child handling, storing geographical points, and search, are described in the following chapters of this book.

Type determining mechanism

Before we start describing how to create mappings manually, we wanted to write about one thing. Elasticsearch can guess the document structure...

Batch indexing to speed up your indexing process


In the first chapter, we've seen how to index a particular document into Elasticsearch. Now, it's time to find out how to index many documents in a more convenient and efficient way than doing it one by one.

Preparing data for bulk indexing

Elasticsearch allows us to merge many requests into one packet. These packets can be sent as a single request. In this way, we can mix the following operations:

  • Adding or replacing the existing documents in the index (index)

  • Removing documents from the index (delete)

  • Adding new documents to the index when there is no other definition of the document in the index (create)

The format of the request was chosen for processing efficiency. It assumes that every line of the request contains a JSON object with the description of the operation followed by the second line with a JSON object itself. We can treat the first line as a kind of information line and the second as the data line. The exception to this rule is the...

Extending your index structure with additional internal information


Apart from the fields that are used to hold data, we can store additional information along with the documents. We already talked about different mapping options and what data type we can use. We would like to discuss in more detail some functionalities of Elasticsearch that are not used every day, but can make your life easier when it comes to data handling.

Note

Each of the field types discussed in the following sections should be defined on an appropriate type level—they are not index wide

Identifier fields

As you may recall, each document indexed in Elasticsearch has its own identifier and type. In Elasticsearch, there are two types of internal identifiers for the documents.

The first one is the _uid field, which is the unique identifier of the document in the index and is composed of the document's identifier and document type. This basically means that documents of different types that are indexed into the same index can...

Introduction to segment merging


In the Full-text searching section of Chapter 1, Getting Started with the Elasticsearch Cluster, we mentioned segments and their immutability. We wrote that the Lucene library, and thus Elasticsearch, writes data to certain structures that are written once and never changed. This allows for some simplification, but also introduces the need for additional work. One such example is deletion. Because a segment cannot be altered, information about deletions must be stored alongside and dynamically applied during search. This is done to eliminate deleted documents from the returned result set. The other example is the inability to modify documents (however, some modifications are possible, such as modifying numeric doc values). Of course, one can say that Elasticsearch supports document updates (please refer to the Manipulating data with the REST API section of Chapter 1, Getting Started with the Elasticsearch Cluster). However, under the hood, the old document...

Introduction to routing


By default, Elasticsearch will try to distribute your documents evenly among all the shards of the index. However, that's not always the desired situation. In order to retrieve the documents, Elasticsearch must query all the shards and merge the results. However, if you can divide your data on some basis (for example, the client identifier), you can use a powerful document and query distribution control mechanism—routing. In short, it allows us to choose a shard that will be used to index or search data.

Default indexing

During indexing operations, when you send a document for indexing, Elasticsearch looks at its identifier to choose the shard in which the document should be indexed. By default, Elasticsearch calculates the hash value of the document's identifier and on the basis of that, it puts the document in one of the available primary shards. Then, those documents are redistributed to the replicas. The following diagram shows a simple illustration of how indexing...

Summary


In this chapter, we learned how Elasticsearch indexing works. We learned to create our own mappings that define index structure and create indices using them. We learned what batch indexing is and how to use it, and how we can index our data efficiently. We also learned what additional information can be stored along with the documents. In addition to that, we've seen what segment merging is, how to configure it, and what throttling is. Finally, we used and configured routing.

In the next chapter, we will concentrate on searching. We will start with how to query Elasticsearch and what the basic queries we can use are. In addition to that, we will use filters and learn why they are important. We will see how we can validate our queries and how to use the highlighting functionality. Finally, we will use compound queries, we will get into querying internals, and we will sort our results.

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Table of Contents

8 Chapters
Getting Started with the Elasticsearch Cluster Chevron down icon Chevron up icon
Indexing Your Data Chevron down icon Chevron up icon
Searching Your Data Chevron down icon Chevron up icon
Extending Your Index Structure Chevron down icon Chevron up icon
Make Your Search Better Chevron down icon Chevron up icon
Beyond Full-text Searching Chevron down icon Chevron up icon
Elasticsearch Cluster in Detail Chevron down icon Chevron up icon
Administrating Your Cluster Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(13 Ratings)
5 star 53.8%
4 star 23.1%
3 star 23.1%
2 star 0%
1 star 0%
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Silverhawk May 04, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is an excellent read for both beginning and experienced ElasticSearch Users.It goes into detail about what ElasticSearch is, how it works and setting it up in a single or clustered environment.The book covers the different APIs available and explains some of the best practices for architecting and searching your data.It also explains some more complex topics such as extending your index structures to handle data that isn't flat, index aliasing and detecting the language of documents.The one thing I found missing was securing ElasticSearch clusters but it seems in general this is something that is a shortcoming with the ElasticSearch product as a whole so I can't take away from this author for not covering it.
Amazon Verified review Amazon
A. Pryor Jun 02, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A powerful guide to get you building search indexes fast! I'd suggest this to anyone interested in search from entry level engineers through experienced architects. I use this book as a reference for my team, it's incredible in-depth and has excellent API examples. It's up to date with all the new features in the Elasticsearch, and the section on aggregations is particularly interesting to me.It covers enough Lucene if you don't have any experience, but also dives into the more complex topics quickly with example code.Thorough demonstration of search API's of Elasticsearch. Everything from indexing, querying, relevance, highlights. Percolators and aggregators explained.Covers deploying and maintaining a cluster.Overall a great reference, well written, good purchase.
Amazon Verified review Amazon
William Tak Shing, Wong May 13, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is definitely an easy to read book for ElasticSearch. It's focus is on giving a complete elastcisearch reference for both beginning and intermediate ElasticSearch users. The author has a lot of references to the his other book Mastering Elasticsearch which I don't have a copy and can't comment. For advance users, I would recommend using the Elasticsearch.org reference.I have both the first and second edition of this book. For the second edition, it includes all the latest features for elasticsearch 1.0. I especially like the chapter 7 Elasticsearch Cluster in Details. It talks about the elasticsearch clustering in details and gives cluster tuning guidelines. This is extremely useful for any production deployment. It will be great if some real life examples are included in this section as well.If you are new to Elasticsearch, the best way to learn is to download a copy and try out with a few online tutorials. Once the basic concepts are there, reading this book will help tremendously.
Amazon Verified review Amazon
drizzt Jun 19, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It's probably the best book ever written about Elasticsearch.Chapter 3 (Searching Your Data) is so clear and complete even a total newbie can use it, and together with Chapters 4 and 5 you can really improve your Search stuff.The only BIG thing that could be better explained is the Percolator, I suggest you to read also the Elasticsearch post about the last improvements on it.
Amazon Verified review Amazon
George Apr 29, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very solid book
Amazon Verified review Amazon
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