Elasticsearch实战：构建高效搜索解决方案

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《Elasticsearch Blueprints》是一本实用的基于项目的学习指南，它深入浅出地介绍了如何利用Elasticsearch这款强大的分布式搜索引擎构建引人入胜的搜索解决方案。Elasticsearch与Apache Solr类似，但更注重大数据集、无模式架构和高可用性，其核心技术依赖于Apache Lucene库，提供了全面的全文搜索功能，包括自动完成、"morelikethis"搜索、多语言支持以及丰富的查询DSL。书中首先引导读者从创建一个类似Google的网页搜索服务开始，通过实践操作学习如何构建高效的搜索结果生成。随后，读者将了解如何利用Elasticsearch在电子商务场景中，帮助用户快速筛选和定位他们感兴趣的产品，通过考量如相关性、文档集合相关性、用户使用习惯、地理位置等因素来优化搜索结果的排序。接着，作者探讨了Elasticsearch处理复杂现实世界数据的能力，特别是在关系型内容管理方面。Elasticsearch作为强大的分析搜索平台，结合实时数据可视化技术，能够实现实时数据分析。此外，书中还讲解了如何通过各种分析器技巧提升搜索质量，扩大匹配范围，使得搜索结果更加精准。本书的最后一部分聚焦于Elasticsearch的地理功能，模拟真实世界的搜索场景，使搜索结果更具地理位置相关性。这使得读者能够在实际项目中充分利用Elasticsearch的这些特性，以满足不同规模的数据需求，无论是小型项目还是大型数据仓库，甚至处理PB级别的非结构化数据。版权信息表明，《Elasticsearch Blueprints》由Vineeth Mohan撰写，由Packt Publishing出版，所有内容未经许可不得复制或传播。尽管作者和出版社已尽力确保信息的准确性，但本书提供的信息是“按现状”销售，不附带任何保证，包括明示或暗示的保修。同时，关于书中提及的所有公司和产品的商标信息，Packt Publishing已尽其所能正确标注。这本书是一个实用的工具，旨在帮助读者掌握Elasticsearch的核心技术和应用，实现高效、智能的搜索解决方案。

Preface

[ ix ]

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[ 1 ]

Google-like Web Search

Text search problems are one of the key and common use cases for web-based

applications. Developers over the world have been keen to bring an open source

solution to this problem. Hence, the Lucene revolution happened. Lucene is the heart

of most of the search engines that you see today. It basically accepts the text that is to

be searched, stores it in an easy searchable form or data structure (inverted index),

and then accepts various types of search queries and returns a set of matching results.

After the rst search revolution, came the second one. Many server-based search

solutions, such as Apache SOLR, were built on top of Lucene and marked the second

phase of the search revolution. Here, a powerful wrapper was made to interface web

users that wanted to index and search text of Lucene. Many powerful tools, notably

SOLR, were developed at this stage of revolution. Some of these search frameworks

were able to provide document database features too. Then, the next phase of the

search revolution came, which is still on-going. The design goal of this phase is

provide scaling solutions for the existing stack. Elasticsearch is a search and analytic

engine that provides a powerful wrapper to Lucene along with an inbuilt document

database and provisions various scaling solutions. The document database is also

implemented using Lucene. Though competitors of Elasticsearch have some more

advanced feature sets, those tools lack the simplicity and the wide range of scalability

solutions Elasticsearch offers. Hence, we can see that Elasticsearch is the farthest point

to which the search revolution has reached and is the future of text search.

This chapter takes you along the course to build a simple scalable search server.

We will see how to create an index and add some documents to it and try out some

essential features such as highlighting and pagination of results. Also, we will

cover topics such as how to set an analyzer for our text and how to apply lters to

eliminate unwanted characters such as HTML tags, and so on.

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Chapter 1

[ 3 ]

4. The -d command is used to run Elasticsearch in the background rather than

the foreground. By running the application in the foreground, we can track

the changes taking place in it through the logs spitted in the console. The

default behavior is to run in the foreground.

One of the basic design goals of Elasticsearch is its high congurability clubbed with

its optimal default congurations that get you started seamlessly. So, all you have to

do is start Elasticsearch. You don't have to learn any complex conguration concepts

at least to get started. So our search server is up and running now.

To see the frontend of your Elasticsearch server, you can visit

http://localhost:9200/_plugin/head/.

Communicating with the Elasticsearch

server

cURL will be our tool of choice that we will use to communicate with Elasticsearch.

Elasticsearch follows a REST-like protocol for its exposed web API. Some of its

features are as follows:

• PUT: The HTTP method PUT is used to send configurations to Elasticsearch.

• POST: The HTTP method POST is used to create new documents or to perform

a search operation. While successful indexing of documents is done using

POST, Elasticsearch provides you with a unique ID that points to the index file.

• GET: The HTTP method GET is used to retrieve an already indexed document.

Each document has a unique ID called a doc ID (short form for document's

ID). When we index a document using POST, it provides a document ID,

which can be used to retrieve the original document.

• DELETE: The HTTP method DELETE is used to delete documents from the

Elasticsearch index. Deletion can be performed based on a search query or

directly using the document ID.

To specify the HTTP method in cURL, you can use the -X option, for example, CURL

-X POST http://localhost/. JSON is the data format used to communicate with

Elasticsearch. To specify the data in cURL, we can specify it in the following forms:

• A command line: You can use the -d option to specify the JSON to be sent in

the command line itself, for example:

curl –X POST 'http://localhost:9200/news/public/'

–d '{ "time" : "12-10-2010"}

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