Elasticsearch Result Table

Function

DLI outputs Flink job output data to Elasticsearch of Cloud Search Service (CSS). Elasticsearch is a popular enterprise-class Lucene-powered search server and provides the distributed multi-user capabilities. It delivers multiple functions, including full-text retrieval, structured search, analytics, aggregation, and highlighting. With Elasticsearch, you can achieve stable, reliable, real-time search. Elasticsearch applies to diversified scenarios, such as log analysis and site search.

CSS is a fully managed, distributed search service. It is fully compatible with open-source Elasticsearch and provides DLI with structured and unstructured data search, statistics, and report capabilities.

For more information about CSS, see Cloud Search Service User Guide.

Prerequisites

• When creating a Flink OpenSource SQL job, you need to set Flink Version to 1.12 on the Running Parameters tab of the job editing page, select Save Job Log, and set the OBS bucket for saving job logs.
• You have created a cluster on CSS. For details about how to create a cluster, see Creating a Cluster in the Cloud Search Service User Guide
• An enhanced datasource connection has been created for DLI to connect to CSS, so that jobs can run on the dedicated queue of DLI and you can set the security group rules as required.
  • For details about how to set up an enhanced datasource connection, see Enhanced Datasource Connections in the Data Lake Insight User Guide.
  • For details about how to configure security group rules, see Security Group Overview in the Virtual Private Cloud User Guide.
• In Flink cross-source development scenarios, there is a risk of password leakage if datasource authentication information is directly configured. You are advised to use the datasource authentication provided by DLI.

  For details about datasource authentication, see Introduction to Datasource Authentication.

Precautions

• Currently, only CSS 7.X and later versions are supported. Version 7.6.2 is recommended.
• ICMP must be enabled for the security group inbound rules of the CSS cluster.
• For details about how to use data types, see section Format.
• Before submitting a Flink job, you are advised to select Save Job Log and set the OBS bucket for saving job logs. This helps you view logs and locate faults when the job fails to be submitted or runs abnormally.
• The Elasticsearch sink can work in either upsert mode or append mode, depending on whether a primary key is defined.
  • If a primary key is defined, the Elasticsearch sink works in upsert mode, which can consume queries containing UPDATE and DELETE messages.
  • If a primary key is not defined, the Elasticsearch sink works in append mode which can only consume queries containing INSERT messages.

  In the Elasticsearch result table, the primary key is used to calculate the Elasticsearch document ID. The document ID is a string of up to 512 bytes. It cannot have spaces. The Elasticsearch result table generates a document ID string for every row by concatenating all primary key fields in the order defined in the DDL using a key delimiter specified by document-id.key-delimiter. Certain types are not allowed as a primary key field as they do not have a good string representation, for example, BYTES, ROW, ARRAY, and MAP. If no primary key is specified, Elasticsearch will generate a document ID automatically.

• The Elasticsearch result table supports both static index and dynamic index.
  • If you want to have a static index, the index option value should be a plain string, such as myusers, all the records will be consistently written into the myusers index.
  • If you want to have a dynamic index, you can use {field_name} to reference a field value in the record to dynamically generate a target index. You can also use {field_name|date_format_string} to convert a field value of the TIMESTAMP, DATE, or TIME type into the format specified by date_format_string. date_format_string is compatible with Java's DateTimeFormatter. For example, if the option value is myusers-{log_ts|yyyy-MM-dd}, then a record with log_ts field value 2020-03-27 12:25:55 will be written into the myusers-2020-03-27 index.

Syntax

create table esSink (
  attr_name attr_type 
  (',' attr_name attr_type)* 
  (','PRIMARY KEY (attr_name, ...) NOT ENFORCED)
)
with (
  'connector' = 'elasticsearch-7',
  'hosts' = '',
  'index' = ''
);

Parameters

Example

In this example, data is read from the Kafka data source and written to the Elasticsearch result table. The procedure is as follows:

1. Create an enhanced datasource connection in the VPC and subnet where Elasticsearch and Kafka locate, and bind the connection to the required Flink elastic resource pool. For details, see Enhanced Datasource Connections.
2. Set Elasticsearch and Kafka security groups and add inbound rules to allow access from the Flink queue. Test the connectivity using the Elasticsearch and Kafka address by referring to Testing Address Connectivity. If the connection is successful, the datasource is bound to the queue. Otherwise, the binding fails.
3. Log in to Kibana of the Elasticsearch cluster, select Dev Tools, enter and execute the following statement to create an index whose value is orders:

   PUT /orders
   {
     "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"
   	    }
   	  }
   	}
   }

4. Create a Flink OpenSource SQL job. Enter the following job script and submit the job.
   When you create a job, set Flink Version to 1.12 on the Running Parameters tab. Select Save Job Log, and specify the OBS bucket for saving job logs. Change the values of the parameters in bold as needed in the following script.

   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',
     'topic' = 'KafkaTopic',
     'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
     'properties.group.id' = 'GroupId',
     'scan.startup.mode' = 'latest-offset',
     "format" = "json"
   );
   
   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' = 'ElasticsearchAddress:ElasticsearchPort',
     'index' = 'orders'
   );
   
   insert into elasticsearchSink select * from kafkaSource;

5. Connect to the Kafka cluster and insert the following test data into Kafka:

   {"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":"0001", "user_name":"Alice", "area_id":"330106"}

6. Enter the following statement in Kibana of the Elasticsearch cluster and view the result:

   GET orders/_search

   {
     "took" : 1,
     "timed_out" : false,
     "_shards" : {
       "total" : 1,
       "successful" : 1,
       "skipped" : 0,
       "failed" : 0
     },
     "hits" : {
       "total" : {
         "value" : 2,
         "relation" : "eq"
       },
       "max_score" : 1.0,
       "hits" : [
         {
           "_index" : "orders",
           "_type" : "_doc",
           "_id" : "ae7wpH4B1dV9conjpXeB",
           "_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" : "orders",
           "_type" : "_doc",
           "_id" : "au7xpH4B1dV9conjn3er",
           "_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" : "0001",
             "user_name" : "Alice",
             "area_id" : "330106"
           }
         }
       ]
     }
   }