Function

Create a source stream to obtain data from Kafka as input data for jobs.

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.

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.

Precautions

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.
For details about how to use data types when creating tables, see Format.

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' = ''
);

Parameters

**Table 1** Parameter description
Parameter	Mandatory	Default Value	Data Type	Description
connector	Yes	None	String	Connector to be used. Set this parameter to kafka.
topic	Yes	None	String	Topic name of the Kafka record. Note: Only one of topic and topic-pattern can be specified. If there are multiple topics, separate them with semicolons (;), for example, topic-1;topic-2.
topic-pattern	No	None	String	Regular expression for a pattern of topic names to read from. Only one of topic and topic-pattern can be specified. For example: 'topic.' '(topic-c\|topic-d)' '(topic-a\|topic-b\|topic-\\d)' '(topic-a\|topic-b\|topic-[0-9]*)'
properties.bootstrap.servers	Yes	None	String	Comma separated list of Kafka brokers.
properties.group.id	Yes	None	String	ID of the consumer group for the Kafka source.
properties.*	No	None	String	This parameter can set and pass arbitrary Kafka configurations. Note: The suffix to properties. must match the configuration key in Apache Kafka. For example, you can disable automatic topic creation via 'properties.allow.auto.create.topics' = 'false'. Some configurations are not supported, for example, 'key.deserializer' and 'value.deserializer'.
format	Yes	None	String	Format used to deserialize and serialize the value part of Kafka messages. Note: Either this parameter or the value.format parameter is required. Refer to Format for more details and format parameters.
key.format	No	None	String	Format used to deserialize and serialize the key part of Kafka messages. Note: 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 the columns in the table as the list of keys. This parameter must be configured in pair with key.format. This parameter is left empty by default. Therefore, no key is defined. The format is 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.
value.format	Yes	None	String	Format used to deserialize and serialize the value part of Kafka messages. Note: 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]	Whether to contain the key field when parsing the message body. Possible values are: ALL (default): All defined fields are included in the value of Kafka messages. EXCEPT_KEY: All the fields except those defined by key.fields are included in the value of Kafka messages.
scan.startup.mode	No	group-offsets	String	Start position for Kafka to read data. Possible values are: earliest-offset: Data is read from the earliest Kafka offset. latest-offset: Data is read from the latest Kafka offset. group-offsets (default): Data is read based on the consumer group. timestamp: Data is read from a user-supplied timestamp. When setting this option, you also need to specify scan.startup.timestamp-millis in WITH. specific-offsets: Data is read from user-supplied specific offsets for each partition. When setting this option, you also need to specify scan.startup.specific-offsets in WITH.
scan.startup.specific-offsets	No	None	String	This parameter takes effect only when scan.startup.mode is set to specific-offsets. It specifies the offsets for each partition, for example, partition:0,offset:42;partition:1,offset:300.
scan.startup.timestamp-millis	No	None	Long	Startup timestamp. This parameter takes effect when scan.startup.mode is set to timestamp.
scan.topic-partition-discovery.interval	No	None	Duration	Interval for a consumer to periodically discover dynamically created Kafka topics and partitions.

Metadata Column

You can define metadata columns in the source table to obtain the metadata of Kafka messages. For example, if multiple topics are defined in the WITH parameter and the metadata column is defined in the Kafka source table, the data read by Flink is labeled with the topic from which the data is read.

**Table 2** Metadata column
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 Kafka messages.
leader-epoch	INT NULL	R	Leader epoch of the Kafka record. For details, see example 1.
offset	BIGINT NOT NULL	R	Offset of the Kafka record.
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. The options are as follows: 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. For details, see example 1.

Example (SASL_SSL Disabled for the Kafka Cluster)

Example 1: Read data from the Kafka metadata column and write it to the Print sink.

Create an enhanced datasource connection in the VPC and subnet where Kafka locates, and bind the connection to the required Flink elastic resource pool.
Set Kafka security groups and add inbound rules to allow access from the Flink queue. Test the connectivity using the Kafka address. If the connection is successful, the datasource is bound to the queue. Otherwise, the binding fails.

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 orders (
  `topic` String metadata,
  `partition` int metadata,
  `headers` MAP<STRING, BYTES> metadata,
  `leaderEpoch` INT metadata from 'leader-epoch',
  `offset` bigint metadata,
  `timestamp` TIMESTAMP(3) metadata,
  `timestampType` string metadata from 'timestamp-type',
  `message` string
) WITH (
  'connector' = 'kafka',
  'topic' = 'KafkaTopic',
  '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 printSink (
  `topic` String,
  `partition` int,
  `headers` MAP<STRING, BYTES>,
  `leaderEpoch` INT,
  `offset` bigint,
  `timestamp` TIMESTAMP(3),
  `timestampType` string,
  `message` string -- Indicates that data written by users is read from Kafka.
) WITH (
  'connector' = 'print'
);

insert into printSink select * from orders;

If you need to read the value of each field instead of the entire message, use the following statements:

CREATE TABLE orders (
  `topic` String metadata,
  `partition` int metadata,
  `headers` MAP<STRING, BYTES> metadata,
  `leaderEpoch` INT metadata from 'leader-epoch',
  `offset` bigint metadata,
  `timestamp` TIMESTAMP(3) metadata,
  `timestampType` string metadata from 'timestamp-type',
  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' = '<yourTopic>',
  'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
  'properties.group.id' = 'GroupId',
  'scan.startup.mode' = 'latest-offset',
  "format" = "json"
);

CREATE TABLE printSink (
  `topic` String,
  `partition` int,
  `headers` MAP<STRING, BYTES>,
  `leaderEpoch` INT,
  `offset` bigint,
  `timestamp` TIMESTAMP(3),
  `timestampType` string,
  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' = 'print'
);

insert into printSink select * from orders;

Send the following data to the corresponding topics 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"}

Perform the following operations to view the output:

Log in to the DLI console. In the navigation pane, choose Job Management > Flink Jobs.
Click the name of the corresponding Flink job, choose Run Log, click OBS Bucket, and locate the folder of the log you want to view according to the date.
Go to the folder of the date, find the folder whose name contains taskmanager, download the taskmanager.out file, and view result logs.

The data result is as follows:

+I(fz-source-json,0,{},0,243,2021-12-27T09:23:32.253,CreateTime,{"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"})
+I(fz-source-json,0,{},0,244,2021-12-27T09:23:39.655,CreateTime,{"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"})
+I(fz-source-json,0,{},0,245,2021-12-27T09:23:48.405,CreateTime,{"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 2: Use the Kafka source table and Print 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.
Set Kafka security groups and add inbound rules to allow access from the Flink queue. Test the connectivity using the Kafka address. If the connection is successful, the datasource is bound to the queue. Otherwise, the binding fails.

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

CREATE TABLE orders (
  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' = '<yourTopic>',
  'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort',
  'properties.group.id' = 'GroupId',
  'scan.startup.mode' = 'latest-offset',
  "format" = "json"
);

CREATE TABLE printSink (
  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 printSink select * from orders;

Send the following test data to the corresponding topics 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"}

Perform the following operations to view the output:
1. Log in to the DLI console. In the navigation pane, choose Job Management > Flink Jobs.
2. Click the name of the corresponding Flink job, choose Run Log, click OBS Bucket, and locate the folder of the log you want to view according to the date.
3. Go to the folder of the date, find the folder whose name contains taskmanager, download the taskmanager.out file, and view result logs.
The data result is as follows:
```
+I(202103241000000001,webShop,2021-03-24T10:00,100.0,100.0,2021-03-2410:02:03,0001,Alice,330106)
+I(202103241606060001,appShop,2021-03-24T16:06:06,200.0,180.0,2021-03-2416:10:06,0001,Alice,330106)
+I(202103251202020001,miniAppShop,2021-03-25T12:02:02,60.0,60.0,2021-03-2512:03:00,0002,Bob,330110)
```

Example (SASL_SSL Enabled for the Kafka Cluster)

Example 1: Enable SASL_SSL authentication for the DMS cluster.

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

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' = 'xx',
  'properties.bootstrap.servers' = 'xx:9093,xx:9093,xx:9093',
  'properties.group.id' = 'GroupId',
  'scan.startup.mode' = 'latest-offset',
  'properties.connector.auth.open' = 'true',
  'properties.ssl.truststore.location' = 'obs://xx/xx.jks',  -- Location where the user uploads the certificate to
  'properties.sasl.mechanism' = 'PLAIN',  --  Value format: SASL_PLAINTEXT
  'properties.security.protocol' = 'SASL_SSL',
  'properties.sasl.jaas.config' = 'org.apache.kafka.common.security.plain.PlainLoginModule required username=\"xx\" password=\"xx\";', -- Account and password set when the Kafka cluster is created
  "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' = 'kafka',
  'topic' = 'xx',
  'properties.bootstrap.servers' = 'xx:9093,xx:9093,xx:9093',
  'properties.connector.auth.open' = 'true',
  'properties.ssl.truststore.location' = 'obs://xx/xx.jks',
  'properties.sasl.mechanism' = 'PLAIN',
  'properties.security.protocol' = 'SASL_SSL',
  'properties.sasl.jaas.config' = 'org.apache.kafka.common.security.plain.PlainLoginModule required username=\"xx\" password=\"xx\";',
  "format" = "json"
);
 
insert into ordersSink select * from ordersSource;

Example 2: Enable Kafka SASL_SSL authentication for the MRS cluster.

Enable Kerberos authentication for the MRS cluster.
Click the Components tab and click Kafka. In the displayed page, click the Service Configuration tab, locate the security.protocol, and set it to SASL_SSL.
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.
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.
In the following statements, set security.protocol to SASL_SSL.

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' = 'xx',
  'properties.bootstrap.servers' = 'xx:21009,xx:21009',
  'properties.group.id' = 'GroupId',
  'scan.startup.mode' = 'latest-offset',
  'properties.sasl.kerberos.service.name' = 'kafka',
  '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',  -- Password set for generating truststore.jks
  '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' = 'kafka',
  'topic' = 'xx',
  'properties.bootstrap.servers' = 'xx:21009,xx:21009',
  'properties.sasl.kerberos.service.name' = 'kafka',
  '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.ssl.truststore.location' = 'obs://xx/truststore.jks',
  'properties.ssl.truststore.password' = 'xx',
  'properties.security.protocol' = 'SASL_SSL',
  'properties.sasl.mechanism' = 'GSSAPI',
  "format" = "json"
);
 
insert into ordersSink select * from ordersSource;

Example 3: Enable Kerberos SASL_PAINTEXT authentication for the MRS cluster

Enable Kerberos authentication for the MRS cluster.
Click the Components tab and click Kafka. In the displayed page, click the Service Configuration tab, locate the security.protocol, and set it to SASL_PLAINTEXT.
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.
In the following statements, set security.protocol to SASL_PLAINTEXT.

CREATE TABLE ordersSources (
  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' = 'xx',
  'properties.bootstrap.servers' = 'xx:21007,xx:21007',
  'properties.group.id' = 'GroupId',
  'scan.startup.mode' = 'latest-offset',
  'properties.sasl.kerberos.service.name' = 'kafka',
  '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' = 'kafka',
  'topic' = 'xx',
  'properties.bootstrap.servers' = 'xx:21007,xx:21007',
  'properties.sasl.kerberos.service.name' = 'kafka',
  '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"
);
 
insert into ordersSink select * from ordersSource;

Example 4: Use SSL for the MRS cluster

Do not enable Kerberos authentication for the MRS cluster.
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.
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.
In the following statements, set security.protocol to SSL.

Set ssl.mode.enable to true.

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' = 'xx',
  'properties.bootstrap.servers' = 'xx:9093,xx:9093,xx:9093',
  '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',  -- Password set for generating truststore.jks
  'properties.security.protocol' = 'SSL',
  "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;

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. Configure the datasource connection or configure the security group of the Kafka cluster to allow access from the DLI queue.

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.

Kafka Source Table