Getting Started with Elasticsearch

Scenario Description

A women's clothing brand builds an e-commerce website. It uses traditional databases to provide a product search function for users. However, due to an increase in the number of users and business growth, the traditional databases have slow response and low accuracy. To improve user experience and user retention, the e-commerce website plans to use Elasticsearch to provide the product search function for users.

This section describes how to use Elasticsearch to provide the search function for users.

Assume that the e-commerce website provides the following data:

{
"products":[
{"productName":"Latest art shirts for women in 2017 autumn","size":"L"}
{"productName":"Latest art shirts for women in 2017 autumn","size":"M"}
{"productName":"Latest art shirts for women in 2017 autumn","size":"S"}
{"productName":"Latest jeans for women in spring 2018","size":"M"}
{"productName":"Latest jeans for women in spring 2018","size":"S"}
{"productName":"Latest casual pants for women in spring 2017","size":"L"}
{"productName":"Latest casual pants for women in spring 2017","size":"S"}
]
}

Step 1: Create a Cluster

Create a cluster using Elasticsearch as the search engine. In this example, suppose that you create a cluster named Es-xfx. This cluster is used only for getting started with Elasticsearch. For this cluster, you are advised to select css.medium.8 for Node Specifications, Common I/O for Node Storage Type, and 40 GB for Node Storage Capacity. For details, see Creating an Elasticsearch Cluster in Non-Security Mode.

After you create the cluster, switch to the cluster list to view the created cluster. If the Status of the cluster is Available, the cluster is created successfully.

Figure 1 Creating a cluster

Step 2: Import Data

CSS supports importing data to Elasticsearch using Logstash, Kibana, or APIs. Kibana lets you visualize your Elasticsearch data. The following procedure illustrates how to import data to Elasticsearch using Kibana.

1. On the Clusters page of the CSS management console, locate the row containing the target cluster and click Access Kibana in the Operation column.
2. In the left navigation pane of Kibana, choose Dev Tools.

   The text box on the left is the input box. The triangle icon in the upper right corner of the input box is the command execution button. The text box on the right area is the result output box.

   Figure 2 Console page
   
   

   The Kibana UI varies depending on the Kibana version.

3. On the Console page, run the following command to create index named my_store:
   (Versions earlier than 7.x)

   PUT /my_store
   {
     "settings": {
       "number_of_shards": 1
     },
     "mappings": {
       "products": {
         "properties": {
           "productName": {
             "type": "text",
             "analyzer": "ik_smart"
           },
           "size": {
             "type": "keyword"
           }
         }
       }
     }
   }

   (Versions later than 7.x)

   PUT /my_store
   {
     "settings": {
       "number_of_shards": 1
     },
     "mappings": {
             "properties": {
           "productName": {
             "type": "text",
             "analyzer": "ik_smart"
           },
           "size": {
             "type": "keyword"
           }
         }
       }
     }

   The command output is similar to the following:

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

4. On the Console page, run the following command to import data to index named my_store:

   (Versions earlier than 7.x)

   POST /my_store/products/_bulk
   {"index":{}}
   {"productName":"Latest art shirts for women in 2017 autumn","size":"L"}
   {"index":{}}
   {"productName":"Latest art shirts for women in 2017 autumn","size":"M"}
   {"index":{}}
   {"productName":"Latest art shirts for women in 2017 autumn","size":"S"}
   {"index":{}}
   {"productName":"Latest jeans for women in spring 2018","size":"M"}
   {"index":{}}
   {"productName":"Latest jeans for women in spring 2018","size":"S"}
   {"index":{}}
   {"productName":"Latest casual pants for women in spring 2017","size":"L"}
   {"index":{}}
   {"productName":"Latest casual pants for women in spring 2017","size":"S"}
   

   (Versions later than 7.x)

   POST /my_store/_doc/_bulk
   {"index":{}}
   {"productName":"Latest art shirts for women in 2017 autumn","size":"L"}
   {"index":{}}
   {"productName":"Latest art shirts for women in 2017 autumn","size":"M"}
   {"index":{}}
   {"productName":"Latest art shirts for women in 2017 autumn","size":"S"}
   {"index":{}}
   {"productName":"Latest jeans for women in spring 2018","size":"M"}
   {"index":{}}
   {"productName":"Latest jeans for women in spring 2018","size":"S"}
   {"index":{}}
   {"productName":"Latest casual pants for women in spring 2017","size":"L"}
   {"index":{}}{"productName":"Latest casual pants for women in spring 2017","size":"S"}

   If the value of the errors field in the command output is false, the data is imported successfully.

Step 3: Search for Data

• Full-text search

  If you access the e-commerce website and want to search for commodities whose names include "spring jeans", enter "spring jeans" to begin your search. The following text provides the command to be executed on Kibana and the command output.

  Command to be executed on Kibana:

  (Versions earlier than 7.x)

  GET /my_store/products/_search
  {
    "query": {"match": {
      "productName": "spring jeans"
    }}
  }

  (Versions later than 7.x)

  GET /my_store/_search
  {
    "query": {"match": {
      "productName": "spring jeans"
    }}
  }

  The command output is similar to the following:

  {
    "took": 80,
    "timed_out": false,
    "_shards": {
      "total": 1,
      "successful": 1,
      "skipped": 0,
      "failed": 0
    },
    "hits": {
      "total": 4,
      "max_score": 1.8069603,
      "hits": [
        {
          "_index": "my_store",
          "_type": "products",
          "_id": "yTG1QWUBRuneTTG2KJSq",
          "_score": 1.8069603,
          "_source": {
            "productName": "Latest jeans for women in spring 2018",
            "size": "M"
          }
        },
        {
          "_index": "my_store",
          "_type": "products",
          "_id": "yjG1QWUBRuneTTG2KJSq",
          "_score": 1.8069603,
          "_source": {
            "productName": "Latest jeans for women in spring 2018",
            "size": "S"
          }
        },
        {
          "_index": "my_store",
          "_type": "products",
          "_id": "yzG1QWUBRuneTTG2KJSq",
          "_score": 0.56677663,
          "_source": {
            "productName": "Latest casual pants for women in spring 2017",
            "size": "L"
          }
        },
        {
          "_index": "my_store",
          "_type": "products",
          "_id": "zDG1QWUBRuneTTG2KJSq",
          "_score": 0.56677663,
          "_source": {
            "productName": "Latest casual pants for women in spring 2017",
            "size": "S"
          }
        }
      ]
    }
  }

  • Elasticsearch supports full-text search. The preceding command searches for the information about all commodities whose names include "spring" or "jeans".
  • Unlike traditional databases, Elasticsearch can return results in milliseconds by using inverted indices.
  • Elasticsearch supports sorting by score. In the command output, information about the first two commodities contains both "spring" and "jeans", while that about the last two products contains only "spring". Therefore, the first two commodities rank prior to the last two due to high keyword match.

• Aggregation result display

  The e-commerce website provides the function of displaying aggregation results. For example, it classifies commodities corresponding to "spring" based on the size so that you can collect the number of products of different sizes. The following provides the command to be executed on Kibana and the command output.

  Command to be executed on Kibana:

  (Versions earlier than 7.x)

  GET /my_store/products/_search
  {
  "query": {
  "match": { "productName": "spring" }
  },
  "size": 0,
  "aggs": {
  "sizes": {
  "terms": { "field": "size" }
  }
  }
  }

  (Versions later than 7.x)

  GET /my_store/_search
  {
  "query": {
  "match": { "productName": "spring" }
  },
  "size": 0,
  "aggs": {
  "sizes": {
  "terms": { "field": "size" }
  }
  }
  }

  The command output is similar to the following:

  (Versions earlier than 7.x)

  {
    "took": 66,
    "timed_out": false,
    "_shards": {
      "total": 1,
      "successful": 1,
      "skipped": 0,
      "failed": 0
    },
    "hits": {
      "total": 4,
      "max_score": 0,
      "hits": []
    },
    "aggregations": {
      "sizes": {
        "doc_count_error_upper_bound": 0,
        "sum_other_doc_count": 0,
        "buckets": [
          {
            "key": "S",
            "doc_count": 2
          },
          {
            "key": "L",
            "doc_count": 1
          },
          {
            "key": "M",
            "doc_count": 1
          }
        ]
      }
    }
  }

  (Versions later than 7.x)

  {
    "took" : 27,
    "timed_out" : false,
    "_shards" : {
      "total" : 1,
      "successful" : 1,
      "skipped" : 0,
      "failed" : 0
    },
    "hits" : {
      "total" : {
        "value" : 3,
        "relation" : "eq"
      },
      "max_score" : null,
      "hits" : [ ]
    },
    "aggregations" : {
      "sizes" : {
        "doc_count_error_upper_bound" : 0,
        "sum_other_doc_count" : 0,
        "buckets" : [
          {
            "key" : "L",
            "doc_count" : 1
          },
          {
            "key" : "M",
            "doc_count" : 1
          },
          {
            "key" : "S",
            "doc_count" : 1
          }
        ]
      }
    }
  }

Step 4: Delete the Cluster

Once you understand the process and method of using Elasticsearch, you can perform the following steps to delete the cluster you created for the example and its data to avoid resource wastage.

After you delete a cluster, its data cannot be restored. Exercise caution when deleting a cluster.

1. Log in to the CSS management console. In the left navigation pane, click Clusters to switch to the Clusters page.
2. Locate the row that contains cluster Es-xfx and click More > Delete in the Operation column.
3. In the displayed dialog box, enter the name of the cluster to be deleted and click OK.