


Easily integrate the new version of Elasticsearch7.9 Chinese search to Laravel7 project
The following tutorial column of Laravel will introduce to you how to easily integrate the new version of Elasticsearch7.9 Chinese search into the Laravel7 project. I hope it will be helpful to friends in need!
Only five steps:
#1. Start the integrated ik Chinese word segmentation plug-in Elasticsearch7.9 Docker Image
2, Laravel7 configuration Scout3, configure Model model4, Import data5. Search##Course Recommendation→:"Elasticsearch Full Text Search Practical Combat" (Practical Video)
From the course"Ten million-level data concurrency solution (theoretical practice)"
Demo address
www.ar414. com/search?query=php�%...
Search scope- Article content
- Title
- Tags
- Number of keywords appearing
- Number of keywords appearing
- High Highlight display
- Word segmentation display
- Result paging
##Foreword
Mainly because I just want to make a blog Search and organize it into articles by the wayLaravel Elasticsearch Many seniors have written tutorials and cases, but with the upgrade of Elasticsearch and laravel versions, many of the previous articles are not applicable to the new version. It is recommended that everyone use For any open source project, you should go through the documentation, mainly the version documentation currently in use, supplemented by tutorialsElasticsearch 7.9
- Laravel 7
- elasticsearch-analysis-ik v7.9
Reference
ik Chinese word segmentation plug-in- elasticsearch official document
Use integrated ik Chinese word segmentation plug-in Elasticsearch
Pull docker$ docker pull ar414/elasticsearch-7.9-ik-plugin
Copy after login
$ docker pull ar414/elasticsearch-7.9-ik-plugin
Create log And the data storage directory
is mapped locally into the docker container to prevent data loss when docker restarts$ mkdir -p /data/elasticsearch/data $ mkdir -p /data/elasticsearch/log $ chmod -R 777 /data/elasticsearch/data $ chmod -R 777 /data/elasticsearch/logCopy after login
Rundocker run -d -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" -v /data/elasticsearch/data:/var/lib/elasticsearch -v /data/elasticsearch/log:/var/log/elasticsearch ar414/elasticsearch-7.9-ik-plugin
Copy after login
docker run -d -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" -v /data/elasticsearch/data:/var/lib/elasticsearch -v /data/elasticsearch/log:/var/log/elasticsearch ar414/elasticsearch-7.9-ik-plugin
Verification$ curl http://localhost:9200{
"name" : "01ac21393985", "cluster_name" : "docker-cluster", "cluster_uuid" : "h8L336qcRb2i1aydOv04Og", "version" : {
"number" : "7.9.0", "build_flavor" : "default", "build_type" : "docker", "build_hash" : "a479a2a7fce0389512d6a9361301708b92dff667", "build_date" : "2020-08-11T21:36:48.204330Z", "build_snapshot" : false, "lucene_version" : "8.6.0", "minimum_wire_compatibility_version" : "6.8.0", "minimum_index_compatibility_version" : "6.0.0-beta1"
}, "tagline" : "You Know, for Search"}
Copy after login
$ curl http://localhost:9200{ "name" : "01ac21393985", "cluster_name" : "docker-cluster", "cluster_uuid" : "h8L336qcRb2i1aydOv04Og", "version" : { "number" : "7.9.0", "build_flavor" : "default", "build_type" : "docker", "build_hash" : "a479a2a7fce0389512d6a9361301708b92dff667", "build_date" : "2020-08-11T21:36:48.204330Z", "build_snapshot" : false, "lucene_version" : "8.6.0", "minimum_wire_compatibility_version" : "6.8.0", "minimum_index_compatibility_version" : "6.0.0-beta1" }, "tagline" : "You Know, for Search"}
Test Chinese word segmentationcurl -X POST "http://localhost:9200/_analyze?pretty" -H 'Content-Type: application/json' -d'
{
"analyzer": "ik_max_word",
"text": "laravel天下无敌"
}
'{
"tokens" : [
{
"token" : "laravel", "start_offset" : 0, "end_offset" : 7, "type" : "ENGLISH", "position" : 0 }, {
"token" : "天下无敌", "start_offset" : 7, "end_offset" : 11, "type" : "CN_WORD", "position" : 1 }, {
"token" : "天下", "start_offset" : 7, "end_offset" : 9, "type" : "CN_WORD", "position" : 2 }, {
"token" : "无敌", "start_offset" : 9, "end_offset" : 11, "type" : "CN_WORD", "position" : 3 }
]}
Copy after login
curl -X POST "http://localhost:9200/_analyze?pretty" -H 'Content-Type: application/json' -d' { "analyzer": "ik_max_word", "text": "laravel天下无敌" } '{ "tokens" : [ { "token" : "laravel", "start_offset" : 0, "end_offset" : 7, "type" : "ENGLISH", "position" : 0 }, { "token" : "天下无敌", "start_offset" : 7, "end_offset" : 11, "type" : "CN_WORD", "position" : 1 }, { "token" : "天下", "start_offset" : 7, "end_offset" : 9, "type" : "CN_WORD", "position" : 2 }, { "token" : "无敌", "start_offset" : 9, "end_offset" : 11, "type" : "CN_WORD", "position" : 3 } ]}
Using Elasticsearch in Laravel project
Elasticsearch
The official SDK is provided. In the Laravel project, you can connect to Elasticsearch more elegantly
. Laravel itself provides a Scout full-text search solution. We only need to change the default The Algolia driver is replaced with ElasticSearch driver
.
Installation
laravel/scout- Easily integrate the new version of Elasticsearch7.9 Chinese search to Laravel7 project
$ composer require laravel/scout $ composer require Easily integrate the new version of Elasticsearch7.9 Chinese search to Laravel7 project
Copy after login
Configuration
- Generate Scout configuration file (config/scout.php)
-
$ php artisan vendor:publish --provider="Laravel\Scout\ScoutServiceProvider"Copied File [\vendor\laravel\scout\config\scout.php] To [\config\scout.php]Publishing complete.
Copy after loginSpecify Scout driver
SCOUT_DRIVER=Matchish\ScoutElasticSearch\Engines\ElasticSearchEngine
- .env
- file
Second type: Specify in
config/scout.php - Directly modify the default driver
'driver' => env('SCOUT_DRIVER', 'algolia')改为'driver' => env('SCOUT_DRIVER', 'Matchish\ScoutElasticSearch\Engines\ElasticSearchEngine')
Copy after login
- Specify the Elasticsearch service IP port
-
If you use docker deployment, use the IP of
docker0
.env, Linux checks through ifconfig
Configure in<div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:php;toolbar:false">ELASTICSEARCH_HOST=172.17.0.1:9200</pre><div class="contentsignin">Copy after login</div></div>
Registration service config/app.php
<div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:php;toolbar:false">'providers' => [ // Other Service Providers \Matchish\ScoutElasticSearch\ElasticSearchServiceProvider::class],</pre><div class="contentsignin">Copy after login</div></div>
Clear configuration cache-
$ php artisan config:clear
Copy after loginLaravel has been connected to Elasticsearch
实际业务中使用
需求
通过博客右上角的搜索框可以搜索到与关键词相关的文章,从以下几点匹配
- 文章内容
- 文章标题
- 文章标签
涉及到2张 Mysql表 以及字段
- article
- title
- tags
- article_content
- content
为文章配置 Elasticsearch 索引
-
创建索引配置文件(config/elasticsearch.php)
$ touch config/elasticsearch.php
Copy after login -
elasticsearch.php 配置字段映射
<?phpreturn [ 'indices' => [ 'mappings' => [ 'blog-articles' => [ "properties"=> [ "content"=> [ "type"=> "text", "analyzer"=> "ik_max_word", "search_analyzer"=> "ik_smart" ], "tags"=> [ "type"=> "text", "analyzer"=> "ik_max_word", "search_analyzer"=> "ik_smart" ], "title"=> [ "type"=> "text", "analyzer"=> "ik_max_word", "search_analyzer"=> "ik_smart" ] ] ] ] ],];
Copy after login
- analyzer:字段文本的分词器
- search_analyzer:搜索词的分词器
- 根据具体业务场景选择(颗粒小占用资源多,一般场景analyzer使用ik_max_word,search_analyzer使用ik_smart):
- ik_max_word:ik中文分词插件提供,对文本进行最大数量分词
laravel天下无敌
->laravel
,天下无敌
,天下
,无敌
- ik_smart: ik中文分词插件提供,对文本进行最小数量分词
laravel天下无敌
->laravel
,天下无敌
- ik_max_word:ik中文分词插件提供,对文本进行最大数量分词
配置文章模型
建议先看一遍 Laravel Scout 使用文档
-
引入Laravel Scout
namespace App\Models\Blog; use Laravel\Scout\Searchable; class Article extends BlogBaseModel { use Searchable; }
Copy after login -
指定索引(刚刚配置文件中的elasticsearch.indices.mappings.blog-articles)
/** * 指定索引 * @return string */ public function searchableAs() { return 'blog-articles'; }
Copy after login -
设置导入索引的数据字段
/** * 设置导入索引的数据字段 * @return array */ public function toSearchableArray() { return [ 'content' => ArticleContent::query() ->where('article_id',$this->id) ->value('content'), 'tags' => implode(',',$this->tags), 'title' => $this->title ]; }
Copy after login -
指定 搜索索引中存储的唯一ID
/** * 指定 搜索索引中存储的唯一ID * @return mixed */ public function getScoutKey() { return $this->id; } /** * 指定 搜索索引中存储的唯一ID的键名 * @return string */ public function getScoutKeyName() { return 'id'; }
Copy after login
数据导入
其实是将数据表中的数据通过Elasticsearch导入到Lucene
Elasticsearch 是 Lucene 的封装,提供了 REST API 的操作接口
- 一键自动导入:
php artisan scout:import
- 导入指定模型:
php artisan scout:import ${model}
$ php artisan scout:import "App\Models\Blog\Article"Importing [App\Models\Blog\Article]Switching to the new index 5/5 [⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬⚬] 100%[OK] All [App\Models\Blog\Article] records have been imported.
导入失败,常见原因:
- Unresolvable dependency resolving [Parameter #0 [ integer $retries ]] in class Elasticsearch\Transport
- 解决: 修改配置后,没有清除配置缓存
- invalid_index_name_exception
- 解决: searchableAs配置错误,为索引创建别名后,指定别名
检查索引是否正确
$ curl -XGET http://localhost:9200/blog-articles/_mapping?pretty{ "blog-articles_1598362919" : { "mappings" : { "properties" : { "__class_name" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } }, "content" : { "type" : "text", "analyzer" : "ik_max_word", "search_analyzer" : "ik_smart" }, "tags" : { "type" : "text", "analyzer" : "ik_max_word", "search_analyzer" : "ik_smart" }, "title" : { "type" : "text", "analyzer" : "ik_max_word", "search_analyzer" : "ik_smart" } } } }}
测试
-
创建一个测试命令行
$ php artisan make:command ElasticTest
Copy after login 代码
<?phpnamespace App\Console\Commands;use App\Models\Blog\Article;use App\Models\Blog\ArticleContent;use Illuminate\Console\Command;use Illuminate\Support\Carbon;class ElasticTest extends Command{ /** * The name and signature of the console command. * * @var string */ protected $signature = 'elasticsearch {query}'; /** * The console command description. * * @var string */ protected $description = 'elasticsearch test'; /** * Create a new command instance. * * @return void */ public function __construct() { parent::__construct(); } /** * Execute the console command. * * @return mixed */ public function handle() { // $startTime = Carbon::now()->getPreciseTimestamp(3); $articles = Article::search($this->argument('query'))->get()->toArray(); $userTime = Carbon::now()->getPreciseTimestamp(3) - $startTime; echo "耗时(毫秒):{$userTime} \n"; //content在另外一张表中,方便观察测试 这里输出 if(!empty($articles)) { foreach($articles as &$article) { $article = ArticleContent::query()->where('article_id',$article['id'])->value('content'); } } var_dump($articles); }}
- 测试
$ php artisan elasticsearch 周杰伦
Copy after login
- 复杂查询
例如:自定义高亮显示//ONGR\ElasticsearchDSL\Highlight\Highlight ArticleModel::search($query,function($client,$body) { $higlight = new Highlight(); $higlight->addField('content',['type' => 'plain']); $higlight->addField('title'); $higlight->addField('tags'); $body->addHighlight($higlight); $body->setSource(['title','tags']); return $client->search(['index' => (new ArticleModel())->searchableAs(), 'body' => $body->toArray()]); })->raw();
Copy after login
复杂自定义查询回调中的$client和$body,可根据这两个包进行灵活操作
$client 官方 elasticsearch/elasticsearch package(https://packagist.org/packages/elasticsearch/elasticsearch)
$body ongr/elasticsearch-dsl package(https://packagist.org/packages/ongr/elasticsearch-dsl)
The above is the detailed content of Easily integrate the new version of Elasticsearch7.9 Chinese search to Laravel7 project. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



LaravelEloquent Model Retrieval: Easily obtaining database data EloquentORM provides a concise and easy-to-understand way to operate the database. This article will introduce various Eloquent model search techniques in detail to help you obtain data from the database efficiently. 1. Get all records. Use the all() method to get all records in the database table: useApp\Models\Post;$posts=Post::all(); This will return a collection. You can access data using foreach loop or other collection methods: foreach($postsas$post){echo$post->

The future of PHP will be achieved by adapting to new technology trends and introducing innovative features: 1) Adapting to cloud computing, containerization and microservice architectures, supporting Docker and Kubernetes; 2) introducing JIT compilers and enumeration types to improve performance and data processing efficiency; 3) Continuously optimize performance and promote best practices.

PHP and Python each have their own advantages, and the choice should be based on project requirements. 1.PHP is suitable for web development, with simple syntax and high execution efficiency. 2. Python is suitable for data science and machine learning, with concise syntax and rich libraries.

PHP and Python each have their own advantages, and choose according to project requirements. 1.PHP is suitable for web development, especially for rapid development and maintenance of websites. 2. Python is suitable for data science, machine learning and artificial intelligence, with concise syntax and suitable for beginners.

PHP remains important in modern web development, especially in content management and e-commerce platforms. 1) PHP has a rich ecosystem and strong framework support, such as Laravel and Symfony. 2) Performance optimization can be achieved through OPcache and Nginx. 3) PHP8.0 introduces JIT compiler to improve performance. 4) Cloud-native applications are deployed through Docker and Kubernetes to improve flexibility and scalability.

Efficiently process 7 million records and create interactive maps with geospatial technology. This article explores how to efficiently process over 7 million records using Laravel and MySQL and convert them into interactive map visualizations. Initial challenge project requirements: Extract valuable insights using 7 million records in MySQL database. Many people first consider programming languages, but ignore the database itself: Can it meet the needs? Is data migration or structural adjustment required? Can MySQL withstand such a large data load? Preliminary analysis: Key filters and properties need to be identified. After analysis, it was found that only a few attributes were related to the solution. We verified the feasibility of the filter and set some restrictions to optimize the search. Map search based on city

How does Laravel play a role in backend logic? It simplifies and enhances backend development through routing systems, EloquentORM, authentication and authorization, event and listeners, and performance optimization. 1. The routing system allows the definition of URL structure and request processing logic. 2.EloquentORM simplifies database interaction. 3. The authentication and authorization system is convenient for user management. 4. The event and listener implement loosely coupled code structure. 5. Performance optimization improves application efficiency through caching and queueing.

The reasons why PHP is the preferred technology stack for many websites include its ease of use, strong community support, and widespread use. 1) Easy to learn and use, suitable for beginners. 2) Have a huge developer community and rich resources. 3) Widely used in WordPress, Drupal and other platforms. 4) Integrate tightly with web servers to simplify development deployment.
