Search and query using Solr in Beego

Beego is a fast Go language Web framework, and Solr is a search and query server based on Lucene. Using the two together can provide efficient search capabilities to web applications. This article will introduce how to use Solr for search and query in Beego.

Step One: Install Solr
Before you start using Solr, you need to install Solr first. Solr can download the latest version of binaries from the official website (https://lucene.apache.org/solr/). After downloading, decompress Solr to a local directory, enter the decompressed directory, and run the following command to start Solr:

./bin/solr start

After Solr is started, you can open it in the browser Access the Solr management interface (http://localhost:8983/solr/). If you can see the Solr management interface, Solr has been installed successfully.

Step 2: Create Solr index
Before running Solr, you need to define the index first. A Solr index is a collection of data with a specific structure used to store text data required for searches and queries. In order for Solr to provide search capabilities for our Beego application, we need to create an index that fits our data model. The definition of a Solr index consists of a set of special XML files called Solr configuration files. Creating a Solr configuration file can be done using the Solr instance's management tools or by manually editing the XML file.

The first step in creating a Solr index is to define a Schema.xml file. This file defines what field information the Solr index includes, which column should be used as the primary key, the type of fields, the operations of each field, etc. In order to create a Schema.xml with two fields, you can manually add the following content to the file:

< ;schema name="example-index" version="1.0">

<fields>
    <field name="id" type="string" indexed="true" stored="true" required="true" multiValued="false"/>
    <field name="title" type="string" indexed="true" stored="true" />
</fields>
<uniqueKey>id</uniqueKey>

In the above example, we defined a Schema containing two fields. xml: A primary key named "id" and a title named "title". The next step is to add the Index.xml file to the Solr configuration file. The Index.xml file is an XML file that specifies the Solr index structure to be built. Here is an example of an Index.xml file:

<schema name="example-index" />

< ;/index>

The above configures the Solr index's configuration file as a sample index, making sure to replace the sample name with the real index name. After completing the above two steps, the Solr index configuration file has been successfully created. To use this index in Solr, you need to upload the index to the Solr instance:

./bin/solr create -c example-index -d ./conf

This command will create a Create a new index named "example-index" and point Solr's configuration folder to the "conf" folder in the current directory.

Step 3: Use Solr in Beego
In order to use Solr in Beego, you need to install the Solr client library of the Go language. The command included in the library can be checked out from GitHub:

go get github.com/rtt/Go-Solr

In the Beego application, you first need to create a Solr connection. This can be done by creating a new Solr object:

import (

"github.com/rtt/Go-Solr"

)

func main() {

s, err := solr.Init("http://localhost:8983/solr")
if err != nil {
    panic(err)
}

}

Next, you can use Solr objects to perform search and query operations. Here is an example that demonstrates how to perform a text query in Solr:

import (

"fmt"
"github.com/rtt/Go-Solr"

)

func main() {

s, err := solr.Init("http://localhost:8983/solr")
if err != nil {
    panic(err)
}

query := solr.NewQuery()
query.Q("title:Solr")
res, err := s.Search("example-index", query)
if err != nil {
    fmt.Printf("Error searching: %s

", err)

} else {
    fmt.Printf("Found %d results:

", res.Results.NumFound)

    for _, doc := range res.Results.Docs {
        fmt.Printf("- id:%s title:%s

", doc["id"], doc["title"])

    }
}

}

In the above example, a Solr query object is first created and then used when searching. After searching, the ID and title of each matching document can be viewed by iterating through the result set.

Conclusion
Using Solr can provide powerful search and query capabilities for Beego applications. In this article, we introduced how to install Solr and create a Solr index. Then, we demonstrated how to use Solr of the Go language in Beego Client libraries to perform search and query operations. Combining these methods makes it easy to add efficient search capabilities to Beego applications.

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