Elasticsearch

Function

DLI outputs the output data of the Flink job to an index in the Elasticsearch engine of the 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 details, see Elasticsearch SQL Connector.

Prerequisites

• Ensure that you have created a cluster on CSS using your account.

Caveats

• When you create a Flink OpenSource SQL job, set Flink Version to 1.15 in the Running Parameters tab. Select Save Job Log, and specify the OBS bucket for saving job logs.
• Storing authentication credentials such as usernames and passwords in code or plaintext poses significant security risks. It is recommended using DEW to manage credentials instead. Storing encrypted credentials in configuration files or environment variables and decrypting them when needed ensures security. For details, see .
• Fields in the with parameter can only be enclosed in single quotes.
• Only CSS 7.X or later clusters are currently supported.
• If security mode is enabled and HTTPS is enabled, you need to configure the username, password, and certificate location. Note that the hosts field value in this scenario starts with https.
• ICMP must be enabled for the security group inbound rule of the CSS cluster.
• Fields in the with parameter can only be enclosed in single quotes.
• For details about how to use data types, see section Format.

Syntax

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

Parameter Description

Key Handling

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/DELETE messages.
• If a primary key is not defined, the Elasticsearch sink works in append mode which can only consume queries containing INSERT only messages.

In the Elasticsearch connector, the primary key is used to calculate the Elasticsearch document ID, which is a string of up to 512 bytes. It cannot have whitespaces.

The Elasticsearch connector 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, e.g. BYTES, ROW, ARRAY, MAP, etc.

If no primary key is specified, Elasticsearch will generate a document ID automatically.

Dynamic Index

The Elasticsearch sink supports both static index and dynamic index.

• If you want to have a static index, the index option value should be a plain string, e.g. myusers, all the records will be consistently written into 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 use {field_name|date_format_string} to convert a field value of TIMESTAMP/DATE/TIME type into the format specified by the date_format_string. The 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 myusers-2020-03-27 index.
  • You can use {now()|date_format_string} to convert the current system time to the format specified by date_format_string. The corresponding time type of now() is TIMESTAMP_WITH_LTZ. When formatting the system time as a string, the time zone configured in the session through table.local-time-zone will be used. You can use NOW(), now(), CURRENT_TIMESTAMP, or current_timestamp.
    

    When using the dynamic index generated by the current system time, for changelog stream, there is no guarantee that the records with the same primary key can generate the same index name. Therefore, the dynamic index based on the system time can only support append only stream.

Example

In this example, data is read from the Kafka data source and written to the Elasticsearch result table (Elasticsearch 7.10.2). 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 .
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 addresses by referring to . If the connection passes the test, it is bound to the queue.
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.
   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" : 201,
     "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" : "fopyx4sBUuT2wThgYGcp",
           "_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"
           }
         },
         {
           "_index" : "orders",
           "_type" : "_doc",
           "_id" : "f4pyx4sBUuT2wThgYGcr",
           "_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"
           }
         }
       ]
     }
   }