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Updated on 2024-04-26 GMT+08:00

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Getting Started with Cloud Search Service

This section provides an example of how to use CSS, including creating indexes, importing data, and searching for data.

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 Cloud Search Service (CSS) to provide the product search function for users.

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

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

Preparations

You have registered with Huawei Cloud, performed real-name authentication, and ensure your account is not frozen or in arrears before using CSS.

If you already have a HUAWEI ID, skip this step. If you do not have one, perform the following operations to create an account:

Visit the Huawei Cloud official website.
In the upper right corner of the page, click Register and complete the registration as prompted.
Select the service agreement and click Enable.
Perform real-name authentication.
- If your account is an individual account, see Individual Real-Name Authentication.
- If your account is an enterprise account, see Enterprise Real-Name Authentication.

Step 1: Creating a Cluster

In this example, you need to create an Elasticsearch cluster named Sample-ESCluster. This cluster is used only for getting started with Elasticsearch. For this cluster, you are advised to select ess.spec-4u8g for Node Specifications, High I/O for Node Storage Type, and 40 GB for Node Storage Capacity.

Log in to the CSS management console.
Click Create Cluster in the upper right corner. The Create Cluster page is displayed.
The billing mode can be Pay-per-use or Yearly/Monthly. In this example, select Pay-per-use. You are billed by actual duration of use, with a billing cycle of one hour. For example, 58 minutes of usage will be rounded up to an hour and billed.

Specify Region and AZ.

**Table 1** Region and AZ parameters
Parameter	Description
Region	Select the region where the cluster is located from the drop-down list box.
AZ	Select AZs associated with the cluster region. A maximum of three AZs can be configured. In this example, select one AZ.

Configure basic cluster information.

Figure 1 Configuring cluster information

**Table 2** Basic configuration parameters
Parameter	Description
Version	Select a cluster version from the drop-down list box. In this example, select the version 7.10.2.
Name	User-defined cluster name. In this example, enter Sample-ESCluster.

Configure the cluster flavor.

Figure 2 Configuring the cluster flavors

**Table 3** Specification parameters
Parameter	Description
Nodes	Number of nodes in a cluster. Select a number from 1 to 32. In this example, select one node.
CPU Architecture	Currently, x86 and Kunpeng are supported. The supported type is determined by the actual regional environment.
Node Specifications	Specifications of nodes in a cluster. In this example, select ess.spec-4u8g.
Node Storage Type	Select a storage type. In this example, select High I/O.
Node Storage Capacity	Storage space. Its value varies with node specifications. The node storage capacity must be a multiple of 20. In this example, select 40 GB.
Master node	The master node manages all nodes in the cluster. If more than 20 nodes are required to store and analyze a large amount of data, you are advised to enable the master node to ensure cluster stability. In this scenario, the master node is not required.
Client node	The client node allows clients to access clusters and analyze data. If more than 20 nodes are required to store and analyze a large amount of data, you are advised to enable the client node to ensure cluster stability. In this example, the client node is not required.
Cold data node	The cold data node is used to store historical data, for which query responses can be returned in minutes. If you do not quire a quick query response, store historical data on cold data nodes to reduce costs. In this example, the cold data node is not required.

Set the enterprise project.
When creating a CSS cluster, you can bind an enterprise project to the cluster if you have enabled the enterprise project function. You can select an enterprise project created by the current user from the drop-down list on the right or click View Project Management to go to the Enterprise Project Management console and create a new project or view existing projects.

Click Next: Configure Network. Configure the cluster network.

Figure 3 Configuring networking

**Table 4** Network configuration parameters
Parameter	Description
VPC	A VPC is a secure, isolated, and logical network environment. Select the target VPC. Click View VPC to enter the VPC management console and view the created VPC names and IDs. If no VPCs are available, create one. NOTE: The VPC must contain CIDRs. Otherwise, cluster creation will fail. By default, a VPC will contain CIDRs.
Subnet	A subnet provides dedicated network resources that are isolated from other networks, improving network security. Select the target subnet. You can access the VPC management console to view the names and IDs of the existing subnets in the VPC.
Security Group	A security group implements access control for ECSs that have the same security protection requirements in a VPC. Click View Security Group to learn security group details.
Security Mode	After the security mode is enabled, communication will be encrypted and authentication required for the cluster. In this example, disable the security mode.

Click Next: Configure Advanced Settings. Configure the automatic snapshot creation and other functions.
This cluster is used only for getting started. Cluster snapshots and advanced functions are not required.
Click Next: Confirm. Check the configuration and click Next to create a cluster.
Click Back to Cluster List to switch to the Clusters page. The cluster you created is listed on the displayed page and its status is Creating. If the cluster is successfully created, its status will change to Available.
Figure 4 Creating a cluster

Step 2: Importing Data

CSS supports importing data to Elasticsearch using Cloud Data Migration (CDM), Logstash, Kibana, or APIs. Kibana is an open-source data analysis and visualization platform. You can use Kibana to search for and view data stored in Elasticsearch indexes and display data in charts and maps. By default, the Elasticsearch cluster of CSS provides the access channel to Kibana. The following procedure illustrates how to import data to Elasticsearch using Kibana.

On the cluster management page, select the created Sample-ESCluster cluster and click Access Kibana in the Operation column to access the Kibana console.
In the Kibana navigation pane on the left, 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 5 Console page

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 7.x and later

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"
}

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 elegant shirts in autumn 2017","size":"L"}
{"index":{}}
{"productName":"Latest elegant shirts in autumn 2017","size":"M"}
{"index":{}}
{"productName":"Latest elegant shirts in autumn 2017","size":"S"}
{"index":{}}
{"productName":"Latest jeans in spring 2018","size":"M"}
{"index":{}}
{"productName":"Latest jeans in spring 2018","size":"S"}
{"index":{}}
{"productName":"Latest casual pants in spring 2017","size":"L"}
{"index":{}}
{"productName":"Latest casual pants in spring 2017","size":"S"}

Versions 7.x and later

POST /my_store/_doc/_bulk
{"index":{}}
{"productName":"Latest elegant shirts in autumn 2017","size":"L"}
{"index":{}}
{"productName":"Latest elegant shirts in autumn 2017","size":"M"}
{"index":{}}
{"productName":"Latest elegant shirts in autumn 2017","size":"S"}
{"index":{}}
{"productName":"Latest jeans in spring 2018","size":"M"}
{"index":{}}
{"productName":"Latest jeans in spring 2018","size":"S"}
{"index":{}}
{"productName":"Latest casual pants in spring 2017","size":"L"}
{"index":{}}
{"productName":"Latest casual pants 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: Searching 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 example shows the command to be executed on Kibana and the command output.

Run the following command:

Versions earlier than 7.x

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

Versions 7.x and later

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

The command output is similar to the following:

{
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 4,
      "relation" : "eq"
    },
    "max_score" : 1.7965372,
    "hits" : [
      {
        "_index" : "my_store",
        "_type" : "_doc",
        "_id" : "9xf6VHIBfClt6SDjw7H5",
        "_score" : 1.7965372,
        "_source" : {
          "productName": "Latest jeans in spring 2018",
          "size" : "M"
        }
      },
      {
        "_index" : "my_store",
        "_type" : "_doc",
        "_id" : "-Bf6VHIBfClt6SDjw7H5",
        "_score" : 1.7965372,
        "_source" : {
          "productName": "Latest jeans in spring 2018",
          "size" : "S"
        }
      },
      {
        "_index" : "my_store",
        "_type" : "_doc",
        "_id" : "-Rf6VHIBfClt6SDjw7H5",
        "_score" : 0.5945667,
        "_source" : {
          "productName": "Latest casual pants in spring 2017",
          "size" : "L"
        }
      },
      {
        "_index" : "my_store",
        "_type" : "_doc",
        "_id" : "-hf6VHIBfClt6SDjw7H5",
        "_score" : 0.5945667,
        "_source" : {
          "productName": "Latest casual pants in spring 2017",
          "size" : "S"
        }
      }
    ]
  }
}

Elasticsearch supports IK word segmentation. The preceding command segments "spring jeans" into "spring" and "jeans".
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 indexes.
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 contain 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 example shows the command to be executed on Kibana and the command output.

Run the following command:

Versions earlier than 7.x

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

Versions 7.x and later

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" : 31,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 4,
    "max_score" : 0.0,
    "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 7.x and later

{
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 4,
      "relation" : "eq"
    },
    "max_score" : null,
    "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
        }
      ]
    }
  }
}

(Optional) Step 4: Deleting Indexes

If an index is no longer used, run the following command to delete the index to avoid resource waste:

DELETE /my_store

The command output is similar to the following:

{
  "acknowledged" : true
}

(Optional) Step 5: Deleting the Cluster

You can delete the cluster if you do not need it.

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

Log in to the CSS management console. In the navigation pane on the left, choose Clusters > Elasticsearch.
Go to the cluster management page, locate the row that contains the target Sample-ESCluster cluster, and click More > Delete in the Operation column.
In the displayed dialog box, enter the name of the cluster to be deleted and click OK.