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Function
Prerequisites
Caveats
Syntax
Source Table Parameter Description
Result Table Parameters
Metadata
Key and Value Formats
Topic and Partition Discovery
Example 1: Reading DMS Kafka Metadata in CSV Format and Outputting It to a Kafka Sink (Applicable for Kafka Clusters Without SASL_SSL Enabled)
Example 2: Using DMS Kafka in JSON Format as the Source Table and Outputting It to a Kafka Sink (Applicable for Kafka Clusters Without SASL_SSL Enabled)
Example 3: Using DMS Kafka as the Source Table and Print as the Result Table (Applicable for Kafka Clusters with SASL_SSL Enabled)
Example 4: Using Kafka (MRS Cluster) as the Source Table and Print as the Result Table (Applicable for Kafka with SASL_SSL Enabled and MRS Using Kerberos Authentication)
Example 5: Using Kafka (MRS Cluster) as the Source Table and Print as the Result Table (Applicable for Kafka with SASL_SSL Enabled and MRS Using SASL_PLAINTEXT with Kerberos Authentication)
Example 6: Using Kafka (MRS Cluster) as the Source Table and Print as the Result Table (Applicable for Kafka with SSL Enabled and MRS Without Kerberos Authentication Enabled)
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Help Center/ Data Lake Insight/ Flink SQL Syntax Reference/ Flink OpenSource SQL 1.15 Syntax Reference/ Connectors/ Kafka

Kafka

Updated on 2025-01-10 GMT+08:00

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Function

The Kafka connector allows for reading data from and writing data into Kafka topics.

Apache Kafka is a fast, scalable, and fault-tolerant distributed message publishing and subscription system. It delivers high throughput and built-in partitions and provides data replicas and fault tolerance. Apache Kafka is applicable to scenarios of handling massive messages.

**Table 1** Supported types
Type	Description
Supported Table Types	Source table and result table
Supported Data Formats	CSV JSON Apache Avro Confluent Avro Debezium CDC Canal CDC Maxwell CDC OGG CDC Raw

Prerequisites

You have created a Kafka cluster.
An enhanced datasource connection has been created for DLI to connect to Kafka clusters, 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.
- For details about how to configure security group rules, see Security Group Overview.

Caveats

For details, see Apache Kafka SQL Connector.
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 Flink OpenSource SQL Jobs Using DEW to Manage Access Credentials.
Fields in the with parameter can only be enclosed in single quotes.
For details about how to use data types when creating tables, see Format.
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 Flink OpenSource SQL Jobs Using DEW to Manage Access Credentials.

Syntax

    
     
       
       create table kafkaSource(
  attr_name attr_type 
  (',' attr_name attr_type)* 
  (','PRIMARY KEY (attr_name, ...) NOT ENFORCED)
  (',' WATERMARK FOR rowtime_column_name AS watermark-strategy_expression)
)
with (
  'connector' = 'kafka',
  'topic' = '',
  'properties.bootstrap.servers' = '',
  'properties.group.id' = '',
  'scan.startup.mode' = '',
  'format' = ''
);

      

    
   

Source Table Parameter Description

**Table 2** Source table parameters
Parameter	Mandatory	Default Value	Data Type	Description
connector	Yes	None	String	Specify what connector to use, for Kafka use kafka.
topic	No	None	String	Topic name(s) to read data from when the table is used as source. It also supports topic list for source by separating topic by semicolon like topic-1;topic-2. Note, only one of topic-pattern and topic can be specified for sources. When the table is used as sink, the topic name is the topic to write data to. Note topic list is not supported for sinks.
topic-pattern	No	None	String	The regular expression for a pattern of topic names to read from. All topics with names that match the specified regular expression will be subscribed by the consumer when the job starts running. Note, only one of topic-pattern and topic can be specified for sources. For more information, see Topic and Partition Discovery.
properties.bootstrap.servers	Yes	None	String	Comma separated list of Kafka brokers.
properties.group.id	optional for source, not applicable for sink	None	String	The ID of the consumer group for Kafka source. If group ID is not specified, an automatically generated ID KafkaSource-{tableIdentifier} will be used.
properties.*	No	None	String	This can set and pass arbitrary Kafka configurations. Suffix names must match the configuration key defined in Apache Kafka. Flink will remove the properties. key prefix and pass the transformed key and values to the underlying KafkaClient. For example, you can disable automatic topic creation via 'properties.allow.auto.create.topics' = 'false'. But there are some configurations that do not support to set, because Flink will override them, e.g. key.deserializer and value.deserializer.
format	Yes	None	String	The format used to deserialize and serialize the value part of Kafka messages. Either this parameter or the value.format parameter is required. For details about the message key and body of Kafka messages, see Key and Value Formats. Refer to Format for more details and format parameters.
key.format	No	None	String	The format used to deserialize and serialize the key part of Kafka messages. If a key format is defined, the key.fields parameter is required as well. Otherwise the Kafka records will have an empty key. Refer to Format for more details and format parameters.
key.fields	No	[]	List<String>	Defines an explicit list of physical columns from the table schema that configure the data type for the key format. By default, this list is empty and thus a key is undefined. The list should look like field1;field2.
key.fields-prefix	No	None	String	Defines a custom prefix for all fields of the key format to avoid name clashes with fields of the value format. By default, the prefix is empty. If a custom prefix is defined, both the table schema and key.fields will work with prefixed names. When constructing the data type of the key format, the prefix will be removed and the non-prefixed names will be used within the key format. Note that this parameter requires that value.fields-include must be set to EXCEPT_KEY.
value.format	No	None	String	The format used to deserialize and serialize the value part of Kafka messages. Either this parameter or the format parameter is required. If two parameters are configured, a conflict occurs. Refer to Format for more details and format parameters.
value.fields-include	No	ALL	Enum Possible values: [ALL, EXCEPT_KEY]	Defines a strategy how to deal with key columns in the data type of the value format. By default, ALL physical columns of the table schema will be included in the value format which means that key columns appear in the data type for both the key and value format.
scan.startup.mode	No	group-offsets	String	Startup mode for Kafka consumer. Valid values are: earliest-offset: start from the earliest offset possible. latest-offset: start from the latest offset. group-offsets: start from committed offsets in ZooKeeper/Kafka brokers of a specific consumer group. timestamp: start from user-supplied timestamp for each partition. specific-offsets: start from user-supplied specific offsets for each partition, and the position is specified by scan.startup.specific-offsets.
scan.startup.specific-offsets	No	None	String	Specify offsets for each partition in case of specific-offsets startup mode, e.g. partition:0,offset:42;partition:1,offset:300.
scan.startup.timestamp-millis	No	None	Long	Start from the specified epoch timestamp (milliseconds) used in case of timestamp startup mode.
scan.topic-partition-discovery.interval	No	None	Duration	Interval for consumer to discover dynamically created Kafka topics and partitions periodically.

Result Table Parameters

**Table 3** Result table parameters
Parameter	Mandatory	Default Value	Data Type	Description
connector	Yes	None	String	Specify what connector to use, for Kafka use kafka.
topic	No	None	String	Topic name(s) to read data from when the table is used as source. It also supports topic list for source by separating topic by semicolon like topic-1;topic-2. Note, only one of topic-pattern and topic can be specified for sources. When the table is used as sink, the topic name is the topic to write data to. Note topic list is not supported for sinks.
properties.bootstrap.servers	Yes	None	String	Comma separated list of Kafka brokers.
properties.*	No	None	String	This can set and pass arbitrary Kafka configurations. Suffix names must match the configuration key defined in Apache Kafka. Flink will remove the properties. key prefix and pass the transformed key and values to the underlying KafkaClient. For example, you can disable automatic topic creation via 'properties.allow.auto.create.topics' = 'false'. But there are some configurations that do not support to set, because Flink will override them, e.g. key.deserializer and value.deserializer.
format	Yes	None	String	The format used to deserialize and serialize the value part of Kafka messages. Note, either this parameter or the value.format parameter is required. For details about the message key and body of Kafka messages, see Key and Value Formats. Refer to Format for more details and format parameters.
key.format	No	None	String	The format used to deserialize and serialize the key part of Kafka messages. If a key format is defined, the key.fields parameter is required as well. Otherwise the Kafka records will have an empty key. Refer to Format for more details and format parameters.
key.fields	No	[]	List<String>	Defines an explicit list of physical columns from the table schema that configure the data type for the key format. By default, this list is empty and thus a key is undefined. The list should look like field1;field2.
key.fields-prefix	No	None	String	Defines a custom prefix for all fields of the key format to avoid name clashes with fields of the value format. By default, the prefix is empty. If a custom prefix is defined, both the table schema and key.fields will work with prefixed names. When constructing the data type of the key format, the prefix will be removed and the non-prefixed names will be used within the key format. Note that this parameter requires that value.fields-include must be set to EXCEPT_KEY.
value.format	No	None	String	The format used to deserialize and serialize the value part of Kafka messages. Either this parameter or the format parameter is required. If two parameters are configured, a conflict occurs. Refer to Format for more details and format parameters.
value.fields-include	No	ALL	Enum Possible values: [ALL, EXCEPT_KEY]	Defines a strategy how to deal with key columns in the data type of the value format. By default, ALL physical columns of the table schema will be included in the value format which means that key columns appear in the data type for both the key and value format.
sink.partitioner	No	'default'	String	Output partitioning from Flink's partitions into Kafka's partitions. Valid values are: default: use the kafka default partitioner to partition records. fixed: each Flink partition ends up in at most one Kafka partition. round-robin: a Flink partition is distributed to Kafka partitions sticky round-robin. It only works when record's keys are not specified. Custom FlinkKafkaPartitioner subclass: e.g. org.mycompany.MyPartitioner.
sink.semantic	No	at-least-once	String	Defines the delivery semantic for the Kafka sink. Valid enumerationns are at-least-once, exactly-once, and none.
sink.parallelism	No	None	Integer	Defines the parallelism of the Kafka sink operator. By default, the parallelism is determined by the framework: using the same parallelism as the upstream chained operator.

Metadata

You can define metadata in the source table to obtain the metadata of Kafka messages.

For example, if multiple topics are defined in the WITH parameter and metadata is defined in the Kafka source table, the data read by Flink is labeled with the topic from which the data is read.

**Table 4** Metadata
Key	Data Type	R/W	Description
topic	STRING NOT NULL	R	Topic name of the Kafka record.
partition	INT NOT NULL	R	Partition ID of the Kafka record.
headers	MAP<STRING, BYTES> NOT NULL	R/W	Headers of the Kafka record as a map of raw bytes.
leader-epoch	INT NULL	R	Leader epoch of the Kafka record if available.
offset	BIGINT NOT NULL	R	Offset of the Kafka record in the partition.
timestamp	TIMESTAMP(3) WITH LOCAL TIME ZONE NOT NULL	R/W	Timestamp of the Kafka record.
timestamp-type	STRING NOT NULL	R	Timestamp type of the Kafka record. NoTimestampType: No timestamp is defined in the message. CreateTime: time when the message is generated. LogAppendTime: time when the message is added to the Kafka broker.

Key and Value Formats

Both the key and value part of a Kafka record can be serialized to and deserialized from raw bytes using one of the given formats.

Value Format
Since a key is optional in Kafka records, the following statement reads and writes records with a configured value format but without a key format. The format parameter is a synonym for value.format. All format options are prefixed with the format identifier.
```
CREATE TABLE KafkaTable (
  `ts` TIMESTAMP(3) METADATA FROM 'timestamp',
  `user_id` BIGINT,
  `item_id` BIGINT,
  `behavior` STRING
) WITH (
  'connector' = 'kafka',
  ...

  'format' = 'json',
  'json.ignore-parse-errors' = 'true'
)
```
The value format will be configured with the following data type:
```
ROW<`user_id` BIGINT, `item_id` BIGINT, `behavior` STRING>
```

Key and Value Format

The following example shows how to specify and configure key and value formats. The format options are prefixed with either the key or value plus format identifier.

CREATE TABLE KafkaTable (
  `ts` TIMESTAMP(3) METADATA FROM 'timestamp',
  `user_id` BIGINT,
  `item_id` BIGINT,
  `behavior` STRING
) WITH (
  'connector' = 'kafka',
  ...

  'key.format' = 'json',
  'key.json.ignore-parse-errors' = 'true',
  'key.fields' = 'user_id;item_id',

  'value.format' = 'json',
  'value.json.fail-on-missing-field' = 'false',
  'value.fields-include' = 'ALL'
)

The key format includes the fields listed in key.fields (using ; as the delimiter) in the same order. Thus, it will be configured with the following data type:

ROW<`user_id` BIGINT, `item_id` BIGINT>

Since the value format is configured with 'value.fields-include' = 'ALL', key fields will also end up in the value format's data type:

ROW<`user_id` BIGINT, `item_id` BIGINT, `behavior` STRING>

Overlapping Format Fields
The connector cannot split the table's columns into key and value fields based on schema information if both key and value formats contain fields of the same name. The key.fields-prefix parameter allows to give key columns a unique name in the table schema while keeping the original names when configuring the key format.

The following example shows a key and value format that both contain a version field:
```
CREATE TABLE KafkaTable (
  `k_version` INT,
  `k_user_id` BIGINT,
  `k_item_id` BIGINT,
  `version` INT,
  `behavior` STRING
) WITH (
  'connector' = 'kafka',
  ...

  'key.format' = 'json',
  'key.fields-prefix' = 'k_',
  'key.fields' = 'k_version;k_user_id;k_item_id',

  'value.format' = 'json',
  'value.fields-include' = 'EXCEPT_KEY'
)
```
The value format must be configured in EXCEPT_KEY mode. The formats will be configured with the following data types:
```
Key format:
ROW<`version` INT, `user_id` BIGINT, `item_id` BIGINT>

Value format:
ROW<`version` INT, `behavior` STRING>
```

Topic and Partition Discovery

The config parameters topic and topic-pattern specify the topics or topic pattern to consume for source. The config parameter topic can accept topic list using semicolon separator like topic-1;topic-2. The config parameter topic-pattern will use regular expression to discover the matched topic. For example, if the topic-pattern is test-topic-[0-9], then all topics with names that match the specified regular expression (starting with test-topic- and ending with a single digit)) will be subscribed by the consumer when the job starts running.

To allow the consumer to discover dynamically created topics after the job started running, set a non-negative value for scan.topic-partition-discovery.interval. This allows the consumer to discover partitions of new topics with names that also match the specified pattern.

NOTE:

Note that topic list and topic pattern only work in sources. In sinks, Flink currently only supports a single topic.

Example 1: Reading DMS Kafka Metadata in CSV Format and Outputting It to a Kafka Sink (Applicable for Kafka Clusters Without SASL_SSL Enabled)

Create an enhanced datasource connection in the VPC and subnet where Kafka locates, and bind the connection to the required Flink elastic resource pool. For details, see .
Set Kafka security groups and add inbound rules to allow access from the Flink queue. Test the connectivity using the Kafka address by referring to . If the connection passes the test, it is bound to the queue.

Create a Flink OpenSource SQL job. Enter the following job script and submit the job.

When you create a job, set Flink Version to 1.15 in 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(
  `topic` String metadata virtual,
  `partition` int metadata virtual,
  `headers` MAP<STRING, BYTES> metadata virtual,
  `leader-epoch` INT metadata virtual,
  `offset` bigint metadata virtual,
  `timestamp-type` string metadata virtual,
  `event_time` TIMESTAMP(3) metadata FROM 'timestamp',
  `message` string
) WITH (
  'connector' = 'kafka',
  'topic' = 'SourceKafkaTopic',
  'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
  'properties.group.id' = 'GroupId',
  'scan.startup.mode' = 'latest-offset',
  'format' = 'csv',
  'csv.field-delimiter' = '\u0001',  
  'csv.quote-character' = ''''
);

CREATE TABLE kafkaSink (
  `topic` String,
  `partition` int,
  `headers` MAP<STRING, BYTES>,
  `leader-epoch` INT,
  `offset` bigint,
  `timestampType` string,
  `event_time` TIMESTAMP(3),
  `message` string -- Indicates that data written by users is read from Kafka.
) WITH (
  'connector' = 'kafka',
  'topic' = 'SinkKafkaTopic',
  'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
  'format' = 'json'
);
insert into kafkaSink select * from kafkaSource;

Send the following data to the topic of the source table in Kafka. The Kafka topic is kafkaSource.

For details, see Configuring Kafka Clients in Java.

{"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"}

{"order_id":"202103251202020001", "order_channel":"miniAppShop", "order_time":"2021-03-25 12:02:02", "pay_amount":"60.00", "real_pay":"60.00", "pay_time":"2021-03-25 12:03:00", "user_id":"0002", "user_name":"Bob", "area_id":"330110"}

Read the topic of the Kafka result table. The Kafka topic is kafkaSink.

For details, see Configuring Kafka Clients in Java.

{"topic":"kafkaSource","partition":1,"headers":{},"leader-epoch":0,"offset":4,"timestampType":"LogAppendTime","event_time":"2023-11-16 11:16:30.369","message":"{\"order_id\":\"202103251202020001\", \"order_channel\":\"miniAppShop\", \"order_time\":\"2021-03-25 12:02:02\", \"pay_amount\":\"60.00\", \"real_pay\":\"60.00\", \"pay_time\":\"2021-03-25 12:03:00\", \"user_id\":\"0002\", \"user_name\":\"Bob\", \"area_id\":\"330110\"}"}

{"topic":"kafkaSource","partition":0,"headers":{},"leader-epoch":0,"offset":6,"timestampType":"LogAppendTime","event_time":"2023-11-16 11:16:30.367","message":"{\"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\"}"}

{"topic":"kafkaSource","partition":2,"headers":{},"leader-epoch":0,"offset":5,"timestampType":"LogAppendTime","event_time":"2023-11-16 11:16:30.368","message":"{\"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\"}"}

Example 2: Using DMS Kafka in JSON Format as the Source Table and Outputting It to a Kafka Sink (Applicable for Kafka Clusters Without SASL_SSL Enabled)

Use the Kafka source table and Kafka result table to read JSON data from Kafka and output it to the log file.

Create an enhanced datasource connection in the VPC and subnet where Kafka locates, and bind the connection to the required Flink elastic resource pool. For details, see .
Set Kafka security groups and add inbound rules to allow access from the Flink queue. Test the connectivity using the Kafka address by referring to . If the connection passes the test, it is bound to the queue.

Create a Flink OpenSource SQL job. Enter the following job script and submit the job.

CREATE TABLE kafkaSource(
  order_id string,
  order_channel string,
  order_time timestamp(3),
  pay_amount double,
  real_pay double,
  pay_time string,
  user_id string,
  user_name string,  
  area_id string
) WITH (
  'connector' = 'kafka',
  'topic' = 'KafkaSourceTopic',
  'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
  'properties.group.id' = 'GroupId',
  'scan.startup.mode' = 'latest-offset',
  'format' = 'json'
);

CREATE TABLE kafkaSink (
  order_id string,
  order_channel string,
  order_time timestamp(3),
  pay_amount double,
  real_pay double,
  pay_time string,
  user_id string,
  user_name string,  
  area_id string
) WITH (
  'connector' = 'kafka',
  'topic' = 'KafkaSinkTopic',
  'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
  'format' = 'json'
);
insert into kafkaSink select * from kafkaSource;

Send the following data to the topic of the source table in 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"}

{"order_id":"202103251202020001", "order_channel":"miniAppShop", "order_time":"2021-03-25 12:02:02", "pay_amount":"60.00", "real_pay":"60.00", "pay_time":"2021-03-25 12:03:00", "user_id":"0002", "user_name":"Bob", "area_id":"330110"}

Read the topic of the Kafka result table. The data results are 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":"0001", "user_name":"Alice", "area_id":"330106"}

{"order_id":"202103251202020001", "order_channel":"miniAppShop", "order_time":"2021-03-25 12:02:02", "pay_amount":"60.00", "real_pay":"60.00", "pay_time":"2021-03-25 12:03:00", "user_id":"0002", "user_name":"Bob", "area_id":"330110"}

Example 3: Using DMS Kafka as the Source Table and Print as the Result Table (Applicable for Kafka Clusters with SASL_SSL Enabled)

Create a Kafka cluster for DMS, enable SASL_SSL, download the SSL certificate, and upload the downloaded certificate client.jks to an OBS bucket.

The properties.sasl.jaas.config field contains account passwords encrypted using DEW.

CREATE TABLE ordersSource (
  order_id string,
  order_channel string,
  order_time timestamp(3),
  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' = 'KafkaAddress1:9093,KafkaAddress2:9093',
  'properties.group.id' = 'GroupId',
  'scan.startup.mode' = 'latest-offset',
  'properties.connector.auth.open' = 'true',
  'properties.ssl.truststore.location' = 'obs://xx/client.jks',  -- Location where the user uploads the certificate to
  'properties.sasl.mechanism' = 'PLAIN', 
  'properties.security.protocol' = 'SASL_SSL',
  'properties.sasl.jaas.config' = 'xx',  -- Key in DEW secret management, whose value is like org.apache.kafka.common.security.plain.PlainLoginModule required username=xx password=xx;
  'format' = 'json',
  'dew.endpoint' = 'kms.xx.com', --Endpoint information for the DEW service being used
  'dew.csms.secretName' = 'xx', --Name of the DEW shared secret
  'dew.csms.decrypt.fields' = 'properties.sasl.jaas.config', --The properties.sasl.jaas.config field value must be decrypted and replaced using DEW secret management.
  'dew.csms.version' = 'v1'
);

CREATE TABLE ordersSink (
  order_id string,
  order_channel string,
  order_time timestamp(3),
  pay_amount double,
  real_pay double,
  pay_time string,
  user_id string,
  user_name string,
  area_id string
) WITH (
  'connector' = 'print'
);
 insert into ordersSink select * from ordersSource;

Example 4: Using Kafka (MRS Cluster) as the Source Table and Print as the Result Table (Applicable for Kafka with SASL_SSL Enabled and MRS Using Kerberos Authentication)

Enable Kerberos authentication for the MRS cluster.
Click the Components tab and click Kafka. On the displayed page, click the Service Configuration tab, locate the ssl.mode.enable, set it to true, and restart Kafka.
Download the user credential. Log in to the FusionInsight Manager of the MRS cluster and choose System > Permission > User. Locate the row that contains the target user, click More, and select Download Authentication Credential.
Obtain the truststore.jks file using the authentication credential and store the credential and truststore.jks file in OBS.
If "Message stream modified (41)" is displayed, the JDK version may be incorrect. Change the JDK version in the sample code to a version earlier than 8u_242 or delete the renew_lifetime = 0m configuration item from the krb5.conf configuration file.
Set the port to the sasl_ssl.port configured in the Kafka service configuration. The default value is 21009.
In the following statements, set security.protocol to SASL_SSL.
The properties.ssl.truststore.password field in the with parameter is encrypted using DEW.

CREATE TABLE ordersSource (
  order_id string,
  order_channel string,
  order_time timestamp(3),
  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',
  'properties.sasl.kerberos.service.name' = 'kafka', -- Value configured in the MRS cluster
  'properties.connector.auth.open' = 'true',
  'properties.connector.kerberos.principal' = 'xx', --Username
  'properties.connector.kerberos.krb5' = 'obs://xx/krb5.conf',
  'properties.connector.kerberos.keytab' = 'obs://xx/user.keytab',
  'properties.security.protocol' = 'SASL_SSL',
  'properties.ssl.truststore.location' = 'obs://xx/truststore.jks',
  'properties.ssl.truststore.password' = 'xx',  -- Key in the DEW secret
  'properties.sasl.mechanism' = 'GSSAPI',
  'format' = 'json',
  'dew.endpoint'='kms.xx.myhuaweicloud.com', --Endpoint information for the DEW service being used
  'dew.csms.secretName'='xx', --Name of the DEW shared secret
  'dew.csms.decrypt.fields'='properties.ssl.truststore.password', --The properties.ssl.truststore.password field value must be decrypted and replaced using DEW secret management.
  'dew.csms.version'='v1'
);

CREATE TABLE ordersSink (
  order_id string,
  order_channel string,
  order_time timestamp(3),
  pay_amount double,
  real_pay double,
  pay_time string,
  user_id string,
  user_name string,
  area_id string
) WITH (
  'connector' = 'print'
);
 insert into ordersSink select * from ordersSource;

Example 5: Using Kafka (MRS Cluster) as the Source Table and Print as the Result Table (Applicable for Kafka with SASL_SSL Enabled and MRS Using SASL_PLAINTEXT with Kerberos Authentication)

Enable Kerberos authentication for the MRS cluster.
Click the Components tab and click Kafka. On the displayed page, click the Service Configuration tab, locate the ssl.mode.enable, set it to true, and restart Kafka.
Log in to the FusionInsight Manager of the MRS cluster and download the user credential. Choose System > Permission > User. Locate the row that contains the target user, choose More > Download Authentication Credential. Upload the credential to OBS.
If error message "Message stream modified (41)" is displayed, the JDK version may be incorrect. Change the JDK version in the sample code to a version earlier than 8u_242 or delete the renew_lifetime = 0m configuration item from the krb5.conf configuration file.
Set the port to the sasl.port configured in the Kafka service configuration. The default value is 21007.
In the following statements, set security.protocol to SASL_PLAINTEXT.

CREATE TABLE ordersSource (
  order_id string,
  order_channel string,
  order_time timestamp(3),
  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',
  'properties.sasl.kerberos.service.name' = 'kafka', -- Configured in the MRS cluster
  'properties.connector.auth.open' = 'true',
  'properties.connector.kerberos.principal' = 'xx',
  'properties.connector.kerberos.krb5' = 'obs://xx/krb5.conf',
  'properties.connector.kerberos.keytab' = 'obs://xx/user.keytab',
  'properties.security.protocol' = 'SASL_PLAINTEXT',
  'properties.sasl.mechanism' = 'GSSAPI',
  'format' = 'json'
);

CREATE TABLE ordersSink (
  order_id string,
  order_channel string,
  order_time timestamp(3),
  pay_amount double,
  real_pay double,
  pay_time string,
  user_id string,
  user_name string,
  area_id string
) WITH (
  'connector' = 'print'
);
 insert into ordersSink select * from ordersSource;

Example 6: Using Kafka (MRS Cluster) as the Source Table and Print as the Result Table (Applicable for Kafka with SSL Enabled and MRS Without Kerberos Authentication Enabled)

Do not enable Kerberos authentication for the MRS cluster.
Download the user credential. Log in to the FusionInsight Manager of the MRS cluster and choose System > Permission > User. Locate the row that contains the target user, click More, and select Download Authentication Credential.
Obtain the truststore.jks file using the authentication credential and store the credential and truststore.jks file in OBS.
Set the port to the ssl.port configured in the Kafka service configuration. The default value is 9093.
Set security.protocol in the with parameter to SSL.
In the Kafka configuration of the MRS cluster, set ssl.mode.enable to true and restart Kafka.

The properties.ssl.truststore.password field in the with parameter is encrypted using DEW.

CREATE TABLE ordersSource (
  order_id string,
  order_channel string,
  order_time timestamp(3),
  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',
  'properties.connector.auth.open' = 'true',
  'properties.ssl.truststore.location' = 'obs://xx/truststore.jks',
  'properties.ssl.truststore.password' = 'xx',  -- Key for DEW secret management, whose value is the password set when generating truststore.jks
  'properties.security.protocol' = 'SSL',
  'format' = 'json',
  'dew.endpoint' = 'kms.xx.com', --Endpoint information for the DEW service being used
  'dew.csms.secretName' = 'xx', --Name of the DEW shared secret
  'dew.csms.decrypt.fields' = 'properties.ssl.truststore.password', --The properties.ssl.truststore.password field value must be decrypted and replaced using DEW secret management.
  'dew.csms.version' = 'v1'
);

CREATE TABLE ordersSink (
  order_id string,
  order_channel string,
  order_time timestamp(3),
  pay_amount double,
  real_pay double,
  pay_time string,
  user_id string,
  user_name string,
  area_id string
) WITH (
  'connector' = 'print'
);
 insert into ordersSink select * from ordersSource;

FAQ

Q: What should I do if the Flink job execution fails and the log contains the following error information?
```
org.apache.kafka.common.errors.TimeoutException: Timeout expired while fetching topic metadata
```
A: The datasource connection is not bound, the binding fails, or the security group of the Kafka cluster is not configured to allow access from the network segment of the DLI queue. Reconfigure the datasource connection or configure the security group of the Kafka cluster to allow access from the DLI queue.

For details, see Enhanced Datasource Connections.

Q: What should I do if the Flink job execution fails and the log contains the following error information?

Caused by: java.lang.RuntimeException: RealLine:45;Table 'default_catalog.default_database.printSink' declares persistable metadata columns, but the underlying DynamicTableSink doesn't implement the SupportsWritingMetadata interface. If the column should not be persisted, it can be declared with the VIRTUAL keyword.

A: The metadata type is defined in the sink table, but the Print connector does not support deletion of matadata from the sink table.

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