Explore a full-text search storage engine that improves query performance: Integration of MySQL and Elasticsearch

Exploring full-text search storage engines to improve query performance: Integration of MySQL and Elasticsearch

Introduction:
With the rapid development of the Internet and the explosive growth of information, full-text search has become more and more popular in many application fields. is becoming more and more important. Although traditional relational databases such as MySQL can store and query data, their full-text search capabilities are limited. In order to improve the efficiency of full-text search, we can use open source search engines like Elasticsearch. This article will introduce the integration of MySQL and Elasticsearch to achieve a more efficient full-text search function.

Background:
For a typical application scenario, such as a blog website, we usually have a table containing article content, and the article content needs to be searched in full text. The traditional method is to use MySQL's LIKE statement to perform fuzzy queries. For small-scale applications, the performance problem may not be obvious. But when the data set becomes larger and larger, the query efficiency of traditional relational databases drops significantly. At this time, we need to use a more efficient solution to handle full-text search.

Solution:
Elasticsearch is a real-time distributed search and analysis engine written based on Lucene, which provides high-performance and powerful full-text search capabilities. For storage and relational database queries, MySQL is a mature and widely used solution. Combining the two can achieve a solution that can both store data and perform full-text search efficiently. Below we will introduce in detail how to integrate MySQL and Elasticsearch.

Step One: Install and Configure Elasticsearch
First, we need to install Elasticsearch. Download and install the latest version of Elasticsearch from the official website. After the installation is complete, open the elasticsearch.yml file in the config directory, set cluster.name to a unique name, and set network.host to the local IP address.

Step 2: Create index and mapping
In Elasticsearch, we need to create an index to store data and define mapping to specify the field type of the data. The process of creating indexes and mappings can be done using Elasticsearch's RESTful API. Here is an example:

PUT /my_index
{
"mappings": {

"article": {
  "properties": {
    "title": {
      "type": "text"
    },
    "content": {
      "type": "text"
    },
    "date": {
      "type": "date"
    }
  }
}

}
}

In this example, we create an index named my_index and define a type named article. In the article type, we define three fields: title, content, and date, and specify their data types.

Step 3: Synchronize data
Next, we need to synchronize the data in MySQL to Elasticsearch. To achieve this step, we can use the Elasticsearch plug-in elasticsearch-river-jdbc. Through this plug-in, we can establish a data source and import data from MySQL into the Elasticsearch index. Here is an example:

PUT /_river/my_river/_meta
{
"type": "jdbc",
"jdbc": {

"url": "jdbc:mysql://localhost:3306/mydb",
"user": "root",
"password": "password",
"sql": "SELECT id, title, content, date FROM articles",
"index": "my_index",
"type": "article"

}
}

In this example, we created a data source named my_river and specified the MySQL connection information and the SQL statement for the data to be imported.

Step 4: Perform full-text search
After the data synchronization is completed, we can use the full-text search function of Elasticsearch to query the data. Here is an example:

GET /my_index/article/_search
{
"query": {

"match": {
  "content": "Elasticsearch"
}

}
}

In this In the example, we searched for articles containing Elasticsearch keywords.

Conclusion:
By integrating MySQL and Elasticsearch, we can improve the performance and efficiency of full-text search. MySQL is responsible for storing and managing data, while Elasticsearch is responsible for efficient full-text search. Such solutions can be applied to various application scenarios, such as e-commerce websites, news websites and other applications that require efficient search. Through the above steps, we can easily integrate MySQL and Elasticsearch to achieve a more efficient full-text search storage engine.

Reference:

• Elasticsearch official document: https://www.elastic.co/guide/en/elasticsearch/guide/current/index.html
• Elasticsearch River JDBC plug-in: https://github.com/jprante/elasticsearch-river-jdbc

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