Use the analysis-aliws plug-in

Search for a document

1. Log on to the Kibana console of your Elasticsearch cluster.
   For more information, see Log on to the Kibana console.
2. In the left-side navigation pane, click Dev Tools.
3. On the Console tab of the page that appears, run one of the following commands to create an index:
   • Command for Elasticsearch clusters earlier than V7.0

     PUT /index
     {
         "mappings": {
             "fulltext": {
                 "properties": {
                     "content": {
                         "type": "text",
                         "analyzer": "aliws"
                     }
                 }
             }
         }
     }

   • Command for Elasticsearch clusters of V7.0 or later

     PUT /index
     {
       "mappings": {
         "properties": {
             "content": {
                 "type": "text",
                 "analyzer": "aliws"
               }
           }
       }
     }

   In this example, an index named index is created. In versions earlier than V7.0, the type of the index is fulltext. In V7.0 or later, the type of the index is _doc. The index contains the content property. The type of the property is text. In addition, the aliws analyzer is added to the index.

   If the running of the command is successful, the following result is returned:

   {
     "acknowledged": true,
     "shards_acknowledged": true,
     "index": "index"
   }

4. Run the following command to add a document:
   Notice The following command is suitable only for Elasticsearch clusters earlier than V7.0. For Elasticsearch clusters of V7.0 or later, you must change fulltext to _doc.

   POST /index/fulltext/1
   {
     "content": "I like go to school."
   }

   The preceding command adds a document named 1 and sets the value of the content field in the document to I like go to school..

   If the running of the command is successful, the following result is returned:

   {
     "_index": "index",
     "_type": "fulltext",
     "_id": "1",
     "_version": 1,
     "result": "created",
     "_shards": {
       "total": 2,
       "successful": 2,
       "failed": 0
     },
     "_seq_no": 0,
     "_primary_term": 1
   }

5. Run the following command to search for the document:
   Notice The following command is suitable only for Elasticsearch clusters earlier than V7.0. For Elasticsearch clusters of V7.0 or later, you must change fulltext to _doc.

   GET /index/fulltext/_search
   {
     "query": {
       "match": {
         "content": "school"
       }
     }
   }

   The preceding command uses the aliws analyzer to analyze all fulltext-type documents, and returns the document that has school contained in the content field.

   If the running of the command is successful, the following result is returned:

   {
     "took": 5,
     "timed_out": false,
     "_shards": {
       "total": 5,
       "successful": 5,
       "skipped": 0,
       "failed": 0
     },
     "hits": {
       "total": 1,
       "max_score": 0.2876821,
       "hits": [
         {
           "_index": "index",
           "_type": "fulltext",
           "_id": "2",
           "_score": 0.2876821,
           "_source": {
             "content": "I like go to school."
           }
         }
       ]
     }
   }

Configure a dictionary

The analysis-aliws plug-in allows you to upload a tailored dictionary file to it to replace the default dictionary file. After you upload a tailored dictionary file, all nodes in your Elasticsearch cluster automatically load the file. In this case, the system does not restart the cluster.

1. Log on to the Alibaba Cloud Elasticsearch console.
2. In the left-side navigation pane, click Elasticsearch Clusters.
3. In the top navigation bar, select a resource group and a region. On the Clusters page, click the ID of the desired cluster.
4. In the left-side navigation pane, click Plug-ins.
5. On the Built-in Plug-ins tab, find the analysis-aliws plug-in and click Dictionary Configuration in the Actions column.
6. In the Plug-ins panel, click Configure.
7. Select the method to upload the dictionary file. Then, upload the dictionary file based on the following instructions:
   

   The dictionary file must meet the following requirements:

   • Name: aliws_ext_dict.txt.
   • Encoding format: UTF-8.
   • Content: Each row contains one word and ends with \n (line feed in UNIX or Linux). No whitespace characters are used before and after this word. If the dictionary file that you want to upload is generated in Windows, you must use the dos2unix tool to convert the file before the upload.

   You can select the TXT File or Add OSS File method.

   • TXT File: If you select this method, click Upload TXT File and select a file that you want to upload from your on-premises machine.
   • Add OSS File: If you select this method, specify Bucket Name and File Name, and click Add.

     Make sure that the bucket you specify resides in the same region as your Elasticsearch cluster. If the content of the dictionary that is stored in OSS changes, you must manually upload the dictionary file again.

8. Click Save.
   The system does not restart your cluster but performs a rolling update to make the uploaded dictionary file take effect. The update requires about 10 minutes.

Test the analyzer

Run the following command to test the aliws analyzer:

GET _analyze
{
  "text": "I like go to school.",
  "analyzer": "aliws"
}

{
  "tokens" : [
    {
      "token" : "i",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "like",
      "start_offset" : 2,
      "end_offset" : 6,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "go",
      "start_offset" : 7,
      "end_offset" : 9,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "school",
      "start_offset" : 13,
      "end_offset" : 19,
      "type" : "word",
      "position" : 8
    }
  ]
}

Test the tokenizer

Run the following command to test the aliws_tokenizer tokenizer:

GET _analyze
{
  "text": "I like go to school.",
  "tokenizer": "aliws_tokenizer"
}

{
  "tokens" : [
    {
      "token" : "I",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : " ",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "like",
      "start_offset" : 2,
      "end_offset" : 6,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : " ",
      "start_offset" : 6,
      "end_offset" : 7,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "go",
      "start_offset" : 7,
      "end_offset" : 9,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : " ",
      "start_offset" : 9,
      "end_offset" : 10,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "to",
      "start_offset" : 10,
      "end_offset" : 12,
      "type" : "word",
      "position" : 6
    },
    {
      "token" : " ",
      "start_offset" : 12,
      "end_offset" : 13,
      "type" : "word",
      "position" : 7
    },
    {
      "token" : "school",
      "start_offset" : 13,
      "end_offset" : 19,
      "type" : "word",
      "position" : 8
    },
    {
      "token" : ".",
      "start_offset" : 19,
      "end_offset" : 20,
      "type" : "word",
      "position" : 9
    }
  ]
}