Reading Data from Kafka and Writing Data to Elasticsearch

Description

This example analyzes offering purchase data and collects statistics on data results that meet specific conditions. The offering purchase data is stored in the Kafka source table, and then the analysis result is output to Elasticsearch .

For example, enter the following sample data:

{"order_id":"202103241000000001", "order_channel":"webShop", "order_time":"2021-03-24 10:00:00", "pay_amount":"100.00", "real_pay":"100.00", "pay_time":"2021-03-24 10:02:03", "user_id":"0001", "user_name":"Alice", "area_id":"330106"}

{"order_id":"202103241606060001", "order_channel":"appShop", "order_time":"2021-03-24 16:06:06", "pay_amount":"200.00", "real_pay":"180.00", "pay_time":"2021-03-24 16:10:06", "user_id":"0002", "user_name":"Jason", "area_id":"330106"}

DLI reads data from Kafka and writes the data to Elasticsearch. You can view the result in Kibana of the Elasticsearch cluster.

Prerequisites

1. You have created a DMS for Kafka instance.
   

   When you create a DMS Kafka instance, do not enable Kafka SASL_SSL.

2. You have created a CSS Elasticsearch cluster.

   In this example, the version of the created CSS cluster is 7.6.2, and security mode is disabled for the cluster.

Overall Process

Figure 1 shows the overall development process.

Figure 1 Job development flowchart

Step 1: Create an Elastic Resource Pool and Create Queues Within It

Step 2: Create a Kafka Topic

Step 3: Create an Elasticsearch Index

Step 4: Create an Enhanced Datasource Connection

Step 5: Run a Job

Step 6: Send Data and Query Results

Step 1: Create an Elastic Resource Pool and Create Queues Within It

The CIDR block of a new queue cannot overlap with the CIDR blocks of DMS Kafka and RDS for MySQL instances. Otherwise, datasource connections will fail to be created.

1. Log in to the DLI management console.
2. In the navigation pane on the left, choose Resources > Resource Pool.
3. On the displayed page, click Buy Resource Pool in the upper right corner.
4. On the displayed page, set the parameters.
   In this example, we will buy the resource pool in the CN East-Shanghai2 region. Table 1 describes the parameters.

   Table 1 Parameter descriptions

   Parameter	Description	Example Value
   Region	Select a region where you want to buy the elastic resource pool.	CN East-Shanghai2
   Project	Project uniquely preset by the system for each region	Default
   Name	Name of the elastic resource pool	dli_resource_pool
   Specifications	Specifications of the elastic resource pool	Standard
   CU Range	The maximum and minimum CUs allowed for the elastic resource pool	64-64
   CIDR Block	CIDR block the elastic resource pool belongs to. If you use an enhanced datasource connection, this CIDR block cannot overlap that of the data source. Once set, this CIDR block cannot be changed.	172.16.0.0/19
   Enterprise Project	Select an enterprise project for the elastic resource pool.	default

5. Click Buy.
6. Click Submit.
7. In the elastic resource pool list, locate the pool you just created and click Add Queue in the Operation column.
8. Set the basic parameters listed below.

   Table 2 Basic parameters for adding a queue

   Parameter	Description	Example Value
   Name	Name of the queue to add	dli_queue_01
   Type	Type of the queue To execute SQL jobs, select For SQL. To execute Flink or Spark jobs, select For general purpose.	For SQL jobs, select For SQL. For other scenarios, select For general purpose.
   Engine	SQL queue engine. The options are Spark and HetuEngine.	Spark
   Enterprise Project	Select an enterprise project.	default

9. Click Next and configure scaling policies for the queue.

   Click Create to add a scaling policy with varying priority, period, minimum CUs, and maximum CUs.

   Figure 2 shows the scaling policy configured in this example.

   Figure 2 Configuring a scaling policy when adding a queue
   

   Table 3 Scaling policy parameters

   Parameter	Description	Example Value
   Priority	Priority of the scaling policy in the current elastic resource pool. A larger value indicates a higher priority. In this example, only one scaling policy is configured, so its priority is set to 1 by default.	1
   Period	The first scaling policy is the default policy, and its Period parameter configuration cannot be deleted or modified. The period for the scaling policy is from 00 to 24.	00–24
   Min CU	Minimum number of CUs allowed by the scaling policy	16
   Max CU	Maximum number of CUs allowed by the scaling policy	64

10. Click OK.

Step 2: Create a Kafka Topic

1. On the Kafka management console, click an instance name on the DMS for Kafka page. Basic information of the Kafka instance is displayed.
2. Choose Topics in the navigation pane on the left. On the displayed page, click Create Topic. Configure the following parameters:
   • Topic Name: For this example, enter testkafkatopic.
   • Partitions: Set the value to 1.
   • Replicas: Set the value to 1.

   Retain default values for other parameters.

Step 3: Create an Elasticsearch Index

1. Log in to the CSS management console and choose Clusters > Elasticsearch from the navigation pane on the left.
2. On the Clusters page, click Access Kibana in the Operation column of the created CSS cluster.
3. On the displayed page, choose Dev Tools in the navigation pane on the left. The Console page is displayed.
4. On the displayed page, run the following command to create index shoporders:

   PUT /shoporders
   {
     "settings": {
       "number_of_shards": 1
     },
   	"mappings": {
   	  "properties": {
   	    "order_id": {
   	      "type": "text"
   	    },
   	    "order_channel": {
   	      "type": "text"
   	    },
   	    "order_time": {
   	      "type": "text"
   	    },
   	    "pay_amount": {
   	      "type": "double"
   	    },
   	    "real_pay": {
   	      "type": "double"
   	    },
   	    "pay_time": {
   	      "type": "text"
   	    },
   	    "user_id": {
   	      "type": "text"
   	    },
   	    "user_name": {
   	      "type": "text"
   	    },
   	    "area_id": {
   	      "type": "text"
   	    }
   	  }
   	}
   }

Step 4: Create an Enhanced Datasource Connection

• Connecting DLI to Kafka
  1. On the Kafka management console, click an instance name on the DMS for Kafka page. Basic information of the Kafka instance is displayed.
  2. In the Connection pane, obtain the Instance Address (Private Network). In the Network pane, obtain the VPC and subnet of the instance.
  3. Click the security group name in the Network pane. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
  4. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
  5. In the displayed dialog box, set the following parameters: For details, see the following section:
     • Connection Name: Enter a name for the enhanced datasource connection. For this example, enter dli_kafka.
     • Resource Pool: Select the elastic resource pool created in Step 1: Create an Elastic Resource Pool and Create Queues Within It.
     • VPC: Select the VPC of the Kafka instance.
     • Subnet: Select the subnet of Kafka instance.
     • Set other parameters as you need.

     Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

  6. Choose Resources > Queue Management from the navigation pane, locate the queue you created in Step 1: Create an Elastic Resource Pool and Create Queues Within It. In the Operation column, click More > Test Address Connectivity.
  7. In the displayed dialog box, enter Kafka instance address (private network):port in the Address box and click Test to check whether the instance is reachable.
• Connecting DLI to CSS
  1. On the CSS management console, choose Clusters > Elasticsearch. On the displayed page, click the name of the created CSS cluster to view basic information.
  2. On the Cluster Information page, obtain the Private Network Address, VPC, AND Subnet.
  3. Click the security group name. On the displayed page, click the Inbound Rules tab and add a rule to allow access from DLI queues. For example, if the CIDR block of the queue is 10.0.0.0/16, set Priority to 1, Action to Allow, Protocol to TCP, Type to IPv4, Source to 10.0.0.0/16, and click OK.
  4. Check whether the Kafka instance and Elasticsearch instance are in the same VPC and subnet.
     1. If they are, go to 7. You do not need to create an enhanced datasource connection again.
     2. If they are not, go to 5. Create an enhanced datasource connection to connect DLI to the subnet where the Elasticsearch instance locates.
  5. Log in to the DLI management console. In the navigation pane on the left, choose Datasource Connections. On the displayed page, click Create in the Enhanced tab.
  6. In the displayed dialog box, set the following parameters: For details, see the following section:
     • Connection Name: Enter a name for the enhanced datasource connection. For this example, enter dli_css.
     • Resource Pool: Select the elastic resource pool created in Step 1: Create an Elastic Resource Pool and Create Queues Within It.
     • VPC: Select the VPC of the Elasticsearch instance.
     • Subnet: Select the subnet of Elasticsearch instance.
     • Set other parameters as you need.

     Click OK. Click the name of the created datasource connection to view its status. You can perform subsequent steps only after the connection status changes to Active.

  7. Choose Resources > Queue Management from the navigation pane, locate the queue you created in Step 1: Create an Elastic Resource Pool and Create Queues Within It. In the Operation column, click More > Test Address Connectivity.
  8. In the displayed dialog box, enter floating IP address:database port of the Elasticsearch instance you have obtained in 2 in the Address box and click Test to check whether the database is reachable.

Step 5: Run a Job

1. On the DLI management console, choose Job Management > Flink Jobs. On the Flink Jobs page, click Create Job.
2. In the Create Job dialog box, set Type to Flink OpenSource SQL and Name to FlinkKafkaES. Click OK.
3. On the job editing page, set the following parameters and retain the default values of other parameters.
   • Queue: Select the queue created in Step 1: Create an Elastic Resource Pool and Create Queues Within It.
   • Flink Version: Select 1.12.
   • Save Job Log: Enable this function.
   • OBS Bucket: Select an OBS bucket for storing job logs and grant access permissions of the OBS bucket as prompted.
   • Enable Checkpointing: Enable this function.
   • Enter a SQL statement in the editing pane. The following is an example. Modify the parameters in bold as you need.
     

     In this example, the syntax version of Flink OpenSource SQL is 1.12. In this example, the data source is Kafka and the result data is written to Elasticsearch.

   • Create a Kafka source table and connect DLI to the Kafka data source.

     CREATE TABLE kafkaSource (
       order_id string,
       order_channel string,
       order_time string,
       pay_amount double,
       real_pay double,
       pay_time string,
       user_id string,
       user_name string,
       area_id string
     ) with (
       "connector" = "kafka",
       "properties.bootstrap.servers" = "10.128.0.120:9092,10.128.0.89:9092,10.128.0.83:9092",-- Internal network address and port number of the Kafka instance
       "properties.group.id" = "click",
       "topic" = "testkafkatopic",--Created Kafka topic
       "format" = "json",
       "scan.startup.mode" = "latest-offset"
     );

   • Create an Elasticsearch result table to display the data analyzed by DLI.

     CREATE TABLE elasticsearchSink (
       order_id string,
       order_channel string,
       order_time string,
       pay_amount double,
       real_pay double,
       pay_time string,
       user_id string,
       user_name string,
       area_id string
     ) WITH (
       'connector' = 'elasticsearch-7',
       'hosts' = '192.168.168.125:9200', --Private IP address and port of the CSS cluster
       'index' = 'shoporders' --Created Elasticsearch engine
     );
     --Write Kafka data to Elasticsearch indexes
     insert into
       elasticsearchSink
     select
       *
     from
       kafkaSource;

4. Click Check Semantic and ensure that the SQL statement passes the check. Click Save. Click Start, confirm the job parameters, and click Start Now to execute the job. Wait until the job status changes to Running.

Step 6: Send Data and Query Results

1. Kafaka sends data.

   Use the Kafka client to send data to topics created in Step 2: Create a Kafka Topic to simulate real-time data streams.

   The sample data is as follows:

   {"order_id":"202103241000000001", "order_channel":"webShop", "order_time":"2021-03-24 10:00:00", "pay_amount":"100.00", "real_pay":"100.00", "pay_time":"2021-03-24 10:02:03", "user_id":"0001", "user_name":"Alice", "area_id":"330106"}
   
   {"order_id":"202103241606060001", "order_channel":"appShop", "order_time":"2021-03-24 16:06:06", "pay_amount":"200.00", "real_pay":"180.00", "pay_time":"2021-03-24 16:10:06", "user_id":"0002", "user_name":"Jason", "area_id":"330106"}

2. View the data processing result on Elasticsearch.
   After the message is sent to Kafka, run the following statement in Kibana for the CSS cluster and check the result:

   GET shoporders/_search

   The query result is as follows:

   {
     "took" : 0,
     "timed_out" : false,
     "_shards" : {
       "total" : 1,
       "successful" : 1,
       "skipped" : 0,
       "failed" : 0
     },
     "hits" : {
       "total" : {
         "value" : 2,
         "relation" : "eq"
       },
       "max_score" : 1.0,
       "hits" : [
         {
           "_index" : "shoporders",
           "_type" : "_doc",
           "_id" : "6fswzIAByVjqg3_qAyM1",
           "_score" : 1.0,
           "_source" : {
             "order_id" : "202103241000000001",
             "order_channel" : "webShop",
             "order_time" : "2021-03-24 10:00:00",
             "pay_amount" : 100.0,
             "real_pay" : 100.0,
             "pay_time" : "2021-03-24 10:02:03",
             "user_id" : "0001",
             "user_name" : "Alice",
             "area_id" : "330106"
           }
         },
         {
           "_index" : "shoporders",
           "_type" : "_doc",
           "_id" : "6vs1zIAByVjqg3_qyyPp",
           "_score" : 1.0,
           "_source" : {
             "order_id" : "202103241606060001",
             "order_channel" : "appShop",
             "order_time" : "2021-03-24 16:06:06",
             "pay_amount" : 200.0,
             "real_pay" : 180.0,
             "pay_time" : "2021-03-24 16:10:06",
             "user_id" : "0002",
             "user_name" : "Jason",
             "area_id" : "330106"
           }
         }
       ]
     }
   }