Elasticsearch实战指南：1.4版使用场景与案例解析

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"ElasticsearchBlueprints - 一本实践项目为基础的指南，教你如何利用Elasticsearch的动态和强大功能创建引人注目的搜索解决方案。基于Elasticsearch 1.4版本，作者Vineeth Mohan提供了详细的使用场景和案例。" 在深入探讨Elasticsearch Blueprint的内容之前，首先了解一下Elasticsearch。Elasticsearch是一个开源的全文搜索引擎，以其分布式、实时的搜索和分析能力而知名。它基于Lucene库构建，提供了RESTful接口，易于集成到各种应用程序中。在Elasticsearch 1.4版本中，它已经相当成熟且稳定，能够处理大规模数据的索引、搜索以及数据分析任务。本书的核心内容可能包括以下几个方面： 1. **安装与配置**：介绍如何在不同操作系统上安装Elasticsearch，包括基本的集群设置、节点配置以及网络和安全性设置。 2. **数据模型**：讲解Elasticsearch的数据结构，如文档、索引、类型和字段，以及如何定义映射（mapping）以优化搜索性能。 3. **索引与搜索**：详细阐述如何索引数据，包括批量导入、实时更新和删除。同时，书中会涵盖各种搜索查询，如全文搜索、短语匹配、布尔查询以及聚合查询等。 4. **分析器与分词**：讨论文本分析过程，选择和定制合适的分词器以满足语言特性和业务需求。 5. **实时性与性能**：解释Elasticsearch如何实现毫秒级的搜索响应时间，并提供优化策略，包括缓存管理、内存分配和集群负载均衡。 6. **分布式特性**：深入分析Elasticsearch的分布式架构，包括复制、分片和故障恢复机制，以确保高可用性和数据冗余。 7. **监控与运维**：介绍如何监控Elasticsearch的性能指标，以及使用工具进行日志管理和警报设置，以保证系统的稳定运行。 8. **案例研究**：通过实际项目案例展示如何在电子商务、社交媒体、日志分析等场景中应用Elasticsearch，解决特定的搜索和分析挑战。 9. **扩展与插件**：讨论Elasticsearch的生态系统，如Kibana用于可视化，Logstash用于日志收集和处理，以及Beats家族用于数据传输等。 10. **最佳实践**：总结使用Elasticsearch时应遵循的最佳实践，以避免常见问题并提升整体效率。本书的目标是帮助读者理解Elasticsearch的工作原理，并通过实际操作学习如何有效地利用其功能。通过阅读本书，你将能更好地掌握Elasticsearch，并将其应用于自己的项目中，创建出强大的搜索解决方案。

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.

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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Elasticsearch实战指南：1.4版使用场景与案例解析

elasticseach5.0官方实例

blueprint——CSS框架

Laravel开发-blueprint

flask blueprint 调用 app

TypeError: Flask.register_blueprint() missing 1 required positional argument: 'blueprint'

from flask import Blueprint login = Blueprint("login", __name__) from . import view

'Blueprint' object is not callable

flask应用框架，blueprint

Blueprint注册路由之后怎么把路由都显示在app.qy的文件里面

最新资源

from flask import Blueprint login = Blueprint("login", name) from . import view