Realtime Compute for Apache Flink:Kafka

Background information

Prerequisites

Limits

Limits on using CREATE TABLE AS (CTAS)

• Only Realtime Compute for Apache Flink VVR 4.0.12 or later supports using Kafka as a data source for the CREATE TABLE AS statement.

• Only the JSON format is supported for type inference and schema changes. Other data formats are not supported.

• Only type inference and schema changes for value fields in Kafka are supported. If you need to synchronize key fields in Kafka, you must specify them in the DDL statement. For more information, see Example 3.

Limits on YAML deployments for data ingestion

• Only Realtime Compute for Apache Flink VVR 8.0.10 or later supports using Kafka as a data source for YAML deployments for data ingestion.

• Only Debezium JSON and Canal JSON formats are supported. Other data formats are not supported.

• For a data source, only Realtime Compute for Apache Flink VVR 8.0.11 or later supports distributing data in the same table across multiple partitions.

Considerations

In Realtime Compute for Apache Flink VVR 8.0.11 or earlier, due to design defects in the Flink and Kafka communities, transactional writes are not recommended. When the Kafka connector is configured with 'sink.delivery-guarantee' = 'exactly-once', transactional writes are used. The following issues exist in transactional writes:

• The current sink implementation of the Kafka connector generates a transaction ID for each checkpoint of a Flink job to commit the transaction of data writes. If the checkpoint interval of the Flink job is set to a small value, the Coordinator component of the downstream Kafka cluster may run out of memory due to too many transaction IDs, which affects the stability of the entire downstream Kafka cluster.

• The current sink implementation of the Kafka connector creates a producer instance for each transaction. If many Kafka write transactions are committed at the same time, the TaskManager component of the Flink job may run out of memory due to too many transaction ID instances, which affects the stability of the current Flink job.

• If multiple Flink jobs use the same transaction ID prefix (sink.transactional-id-prefix), the transaction IDs generated by different Flink jobs based on the transaction ID prefix and checkpoint ID may affect each other. If an exception occurs in a Flink job, the Log Start Offset (LSO) of partitions in the Kafka cluster may be blocked, which affects data consumption in each partition of the Kafka cluster.

If you need exactly-once semantics, you can write data to a primary key table using Upsert Kafka to ensure idempotence with primary keys.

Troubleshoot network connectivity

If your Flink job fails to start and the error Timed out waiting for a node assignment occurs, the issue is usually caused by network connectivity issues between Flink and Kafka.

If the Kafka broker is not properly configured, the endpoint that the client receives in the first step is incorrect. Even if the IP address and port number specified by the properties.bootstrap.servers parameter can be connected, data cannot be read or written as expected. This issue typically occurs when a forwarding mechanism, such as a proxy, port forwarding, or a private line, is used to establish the connection between Flink and Kafka.

SQL

The Kafka connector can be used in SQL drafts to read data from and write data into Kafka topics.

Syntax structure

CREATE TABLE KafkaTable (
  `user_id` BIGINT,
  `item_id` BIGINT,
  `behavior` STRING,
  `ts` TIMESTAMP_LTZ(3) METADATA FROM 'timestamp' VIRTUAL
) WITH (
  'connector' = 'kafka',
  'topic' = 'user_behavior',
  'properties.bootstrap.servers' = 'localhost:9092',
  'properties.group.id' = 'testGroup',
  'scan.startup.mode' = 'earliest-offset',
  'format' = 'csv'
)

Metadata columns

CREATE TABLE kafka_source (
  -- Read the topic to which the message belongs as the value of the record_topic field
  `record_topic` STRING NOT NULL METADATA FROM 'topic' VIRTUAL,
  -- Read the timestamp in ConsumerRecord as the value of the ts field
  `ts` TIMESTAMP_LTZ(3) METADATA FROM 'timestamp' VIRTUAL,
  -- Read the offset of the message as the value of the record_offset field
  `record_offset` BIGINT NOT NULL METADATA FROM 'offset' VIRTUAL,
  ...
) WITH (
  'connector' = 'kafka',
  ...
);

CREATE TABLE kafka_sink (
  -- Write the timestamp in the ts field as the timestamp of ProducerRecord to Kafka
  `ts` TIMESTAMP_LTZ(3) METADATA FROM 'timestamp' VIRTUAL,
  ...
) WITH (
  'connector' = 'kafka',
  ...
);

With parameters

• General

  Parameter	Description	Data type	Required	Default value	Remarks
  connector	The type of the table.	String	Yes	None	Set the value to kafka.
  properties.bootstrap.servers	The addresses of Kafka brokers.	String	Yes	None	Format: host:port,host:port,host:port. Separate multiple host:port pairs with commas (,).
  properties.*	The parameters that are configured for the Kafka client.	String	No	None	The suffix must be defined in the Kafka official documentation for producer and consumer configurations. Flink removes the properties. prefix and passes the remaining configuration to the Kafka client. For example, you can disable automatic topic creation by using 'properties.allow.auto.create.topics'='false'. You cannot modify the configurations of the following parameters by adding the properties. prefix because the values of the parameters are overwritten after you use the Kafka connector: key.deserializer value.deserializer
  format	The format that is used to read or write the value fields of Kafka messages.	String	No	None	Supported formats csv json avro debezium-json canal-json maxwell-json avro-confluent raw Note For more information about the format parameter, see Format parameter.
  key.format	The format that is used to read or write the key fields of Kafka messages.	String	No	None	Supported formats csv json avro debezium-json canal-json maxwell-json avro-confluent raw Note The key.options parameter is required when you configure this parameter.
  key.fields	The key fields in the source table or sink table that correspond to the key fields of Kafka messages.	String	No	None	Separate multiple field names with semicolons (;). For example, field1;field2
  key.fields-prefix	The custom prefix for all key fields in Kafka messages. You can configure this parameter to prevent name conflicts with the value fields.	String	No	None	This parameter is used only to distinguish the column names of source tables and sink tables. The prefix is removed from the column names when the key fields of Kafka messages are parsed or generated. Note The value.fields-include parameter must be set to EXCEPT_KEY when you configure this parameter.
  value.format	The format that is used to read or write the value fields of Kafka messages.	String	No	None	This parameter is equivalent to the format parameter. Therefore, only one of the format and value.format parameters can be configured. If both parameters are configured, a conflict occurs.
  value.fields-include	Specifies whether to include corresponding message keys when parsing or generating Kafka message values.	String	No	ALL	Valid values: ALL: All fields are processed as value fields of Kafka messages. This is the default value. EXCEPT_KEY: All fields except for the fields specified by the key.fields parameter are processed as the Kafka message value.

• Source table

  Parameter	Description	Data type	Required	Default value	Remarks
  topic	The name of the topic from which you want to read data.	String	No	None	Separate multiple topic names with semicolons (;), such as topic-1;topic-2 Note The topic parameter cannot be used together with the topic-pattern parameter.
  topic-pattern	The regular expression that is used to match topics. When a job is running, all topics whose names match the specified regular expression are subscribed by the consumer.	String	No	None	Note Only Realtime Compute for Apache Flink VVR 3.0.0 or later supports this parameter. The topic parameter cannot be used together with the topic-pattern parameter.
  properties.group.id	The ID of the consumer group.	String	No	KafkaSource-{Name of the source table}	If the specified group ID is used for the first time, you must set the properties.auto.offset.reset parameter to earliest or latest to specify the start offset for the first consumption.
  scan.startup.mode	The start offset from which data is read from Kafka.	String	No	group-offsets	Valid values: earliest-offset: reads data from the earliest partition of Kafka. latest-offset: reads data from the latest partition of Kafka. group-offsets (default value): Start reading from the offset submitted by the specified properties.group.id. Timestamp: Start reading from the timestamp specified by scan.startup.timestamp-millis. specific-offsets: reads data from the offset that is specified by the scan.startup.specific-offsets parameter. Note This parameter takes effect when the deployment is started without states. When the deployment is restarted from a checkpoint or resumed from the specified state, the deployment preferentially starts to read data from the progress that is saved in the state data.
  scan.startup.specific-offsets	The start offset of each partition when the scan.startup.mode parameter is set to specific-offsets.	String	No	None	For example, partition:0,offset:42;partition:1,offset:300
  scan.startup.timestamp-millis	The timestamp of the start offset when the scan.startup.mode parameter is set to timestamp.	Long	No	None	Unit: milliseconds
  scan.topic-partition-discovery.interval	The time interval at which the Kafka source checks for new partitions.	Duration	No	5 minutes	The default partition discovery interval is 5 minutes. To disable the dynamic partition discovery feature, you must explicitly set this parameter to a non-positive value. After the dynamic partition discovery feature is enabled, the Kafka source can automatically discover new partitions and read data from the partitions. In topic-pattern mode, the Kafka source reads data from new partitions of existing topics and data from all partitions of new topics that match the regular expression. Note For Realtime Compute for Apache Flink that uses VVR 6.0.X, the dynamic partition discovery feature is disabled by default. For Realtime Compute for Apache Flink that uses VVR 8.0 or later, this feature is enabled by default. The default partition discovery interval is 5 minutes.
  scan.header-filter	Kafka data filtering based on whether the data contains a specific message header.	String	No	None	Separate a header key and the value with a colon (:). Separate multiple headers with logical operators such as AND (&) or OR (|). The logical operator NOT (!) is supported. For example, depart:toy|depart:book&!env:test indicates that Kafka data that contains depart=toy or depart=book and does not contain env=test is retained. Note Only Realtime Compute for Apache Flink that uses VVR 8.0.6 or later supports this parameter. Parenthesis operations are not supported. Logical operations are performed from left to right in sequence. The header value in the UTF-8 format is converted into a string and compared with the header value specified by the scan.header-filter parameter.
  scan.check.duplicated.group.id	Specifies whether to check duplicate consumer groups that are specified by the properties.group.id parameter.	Boolean	No	false	Valid values: true: Checks duplicate consumer groups before a deployment starts. If duplicate consumer groups exist, an error is reported to prevent conflicts with existing consumer groups. false: Does not check duplicate consumer groups before a deployment starts. Note Only Realtime Compute for Apache Flink that uses VVR 6.0.4 or later supports this parameter.

• Sink table

  Parameter	Description	Data type	Required	Default value	Remarks
  topic	The name of the topic into which data is written.	String	Yes	None	None
  sink.partitioner	The mapping pattern between Flink partitions and Kafka partitions.	String	No	default	Valid values: default: The default Kafka partitioner is used to partition data. This is the default value. fixed: Each Flink partition corresponds to a fixed Kafka partition. round-robin: Data in a Flink partition is distributed to the Kafka partitions in round-robin sequence. Custom partition mapping pattern: You can create a subclass of FlinkKafkaPartitioner to configure a custom partition mapping pattern if the fixed and round-robin patterns do not meet your business requirements. For example, org.mycompany.MyPartitioner
  sink.delivery-guarantee	The delivery semantics for the Kafka sink table.	String	No	at-least-once	Valid values: none: The delivery semantics is not ensured. Data may be lost or duplicated. at-least-once: The at-least-once semantics is provided to ensure that no data is lost. However, duplicate data may exist. This is the default value. exactly-once: Kafka transactions are used to ensure the exactly-once semantics. This ensures that data is not lost or duplicated. Note The sink.transactional-id-prefix parameter is required when you use the exactly-once semantics.
  sink.transactional-id-prefix	The prefix of the Kafka transaction ID that is used in the exactly-once semantics.	String	No	None	This parameter takes effect only when the sink.delivery-guarantee parameter is set to exactly-once.
  sink.parallelism	The parallelism of operators in the Kafka sink table.	Integer	No	None	The parallelism of upstream operators, which is determined by the framework.

• Parameters for data synchronization using the CREATE TABLE AS statement

  Parameter	Description	Data type	Required	Default value	Remarks
  json.infer-schema.flatten-nested-columns.enable	Specifies whether to recursively expand nested columns in a JSON text.	Boolean	No	false	Valid values: true: Nested columns are recursively expanded. Realtime Compute for Apache Flink uses the path that indexes the value of the column that is expanded as the name of the column. For example, the name of the column col in JSON {"nested": {"col": true}} is nested.col after the column is expanded. false: Nested types are parsed as the STRING type. This is the default value.
  json.infer-schema.primitive-as-string	Specifies whether to infer all basic types as the STRING type.	Boolean	No	false	Valid values: true: All basic types are inferred as the STRING type. false: Data types are inferred based on the data type mappings.

  You can add the properties. prefix to all configuration items supported by Kafka consumers and producers and use the configuration items in the WITH clause. For example, to set the request.timeout.ms parameter of a Kafka consumer or producer to 60000 milliseconds, configure 'properties.request.timeout.ms'='60000' in the WITH clause. For more information about the configuration items of Kafka consumers and producers, see the Apache Kafka official documentation.

Security and authentication

CREATE TABLE KafkaTable (
  `user_id` BIGINT,
  `item_id` BIGINT,
  `behavior` STRING,
  `ts` TIMESTAMP_LTZ(3) METADATA FROM 'timestamp'
) WITH (
  'connector' = 'kafka',
  ...
  'properties.security.protocol' = 'SASL_PLAINTEXT',
  'properties.sasl.mechanism' = 'PLAIN',
  'properties.sasl.jaas.config' = 'org.apache.flink.kafka.shaded.org.apache.kafka.common.security.plain.PlainLoginModule required username="username" password="password";'
)

CREATE TABLE KafkaTable (
  `user_id` BIGINT,
  `item_id` BIGINT,
  `behavior` STRING,
  `ts` TIMESTAMP_LTZ(3) METADATA FROM 'timestamp'
) WITH (
  'connector' = 'kafka',
  ...
  'properties.security.protocol' = 'SASL_SSL',
  /* Configure Secure Sockets Layer (SSL). */
  /* Specify the path of the CA certificate truststore provided by the server. */
  'properties.ssl.truststore.location' = '/flink/usrlib/kafka.client.truststore.jks',
  'properties.ssl.truststore.password' = 'test1234',
  /* Specify the path of the private key file keystore if client authentication is required. */
  'properties.ssl.keystore.location' = '/flink/usrlib/kafka.client.keystore.jks',
  'properties.ssl.keystore.password' = 'test1234',
  /* The algorithm used by the client to verify the server address. A null value indicates that server address verification is disabled. */
  'properties.ssl.endpoint.identification.algorithm' = '',
  /* Configure SASL. */
  /* Configure SCRAM-SHA-256 as the SASL mechanism. */
  'properties.sasl.mechanism' = 'SCRAM-SHA-256',
  /* Configure JAAS. */
  'properties.sasl.jaas.config' = 'org.apache.flink.kafka.shaded.org.apache.kafka.common.security.scram.ScramLoginModule required username="username" password="password";'
)

The CA certificate and private key mentioned in the sample code can be uploaded to the platform using the File Management feature in the Realtime Compute console. For example, if the files are uploaded to the /flink/usrlib directory and the CA certificate file is named my-truststore.jks, you can specify 'properties.ssl.truststore.location' = '/flink/usrlib/my-truststore.jks' in the WITH clause to use the certificate.

Start offsets for source tables

Startup modes

CREATE TEMPORARY TABLE kafka_source (
  ...
) WITH (
  'connector' = 'kafka',
  ...
  -- Consume data from the earliest offset.
  'scan.startup.mode' = 'earliest-offset',
  -- Consume data from the latest offset.
  'scan.startup.mode' = 'latest-offset',
  -- Consume data from the offset that is committed by the consumer group my-group.
  'properties.group.id' = 'my-group',
  'scan.startup.mode' = 'group-offsets',
  'properties.auto.offset.reset' = 'earliest', -- If my-group is used for the first time, the consumption starts from the earliest offset.
  'properties.auto.offset.reset' = 'latest', -- If my-group is used for the first time, the consumption starts from the latest offset.
  -- Consume data from the timestamp 1655395200000, in milliseconds.
  'scan.startup.mode' = 'timestamp',
  'scan.startup.timestamp-millis' = '1655395200000',
  -- Consume data from the specified offset.
  'scan.startup.mode' = 'specific-offsets',
  'scan.startup.specific-offsets' = 'partition:0,offset:42;partition:1,offset:300'
);

Priorities of start offsets

If the offset is invalid due to expiration or issues in the Kafka cluster at any of the preceding steps, the offset is reset based on the policy specified by the properties.auto.offset.reset parameter. If this parameter is not configured, an exception is returned and manual intervention is required.

Typically, the Kafka source table starts reading data from the offset committed by a consumer group with a new group ID. The source table queries the Kafka cluster for the committed offset of the group. Since the group ID is new, no valid offset is returned, and the offset is reset based on the policy specified by the properties.auto.offset.reset parameter. Therefore, you must configure the properties.auto.offset.reset parameter to specify the offset reset policy when consuming data with a new group ID.

Offset commit for source tables

The Kafka source table commits a consumer offset to the Kafka cluster only after a successful checkpoint. If the checkpoint interval is excessively large, the consumer offset is delayed in being committed to the Kafka cluster. When a checkpoint is created, the Kafka source table stores the current data reading progress in the state backend. The offset committed to the Kafka cluster is not used for fault recovery but only to monitor the data reading progress in Kafka. If the offset fails to be committed, data accuracy is not affected.

Custom partitioner for sink tables

If the built-in Kafka producer does not meet your business requirements, you can use a custom Kafka partitioner to write data to the relevant partitions. A custom partitioner must inherit from FlinkKafkaPartitioner. After development, compile the JAR package and upload it to the Realtime Compute console using the File Management feature. Once uploaded and referenced, set the sink.partitioner parameter in the WITH clause to the full class path of the partitioner, such as org.mycompany.MyPartitioner.

Selection of Kafka, Upsert Kafka, or Kafka JSON catalog

Kafka is a message queue system designed exclusively for data insertion, lacking the capability to update or delete data. Consequently, Kafka is unable to handle Change Data Capture (CDC) from upstream systems or the retraction mechanisms of operators like aggregate and join in streaming SQL computations. To write data involving changes or retractions to Kafka, utilize an Upsert Kafka sink table, which manages change data in a specialized way.

If you want to synchronize change data from one or more upstream database tables to Kafka in batches, you can use a Kafka JSON catalog. If data in Kafka is in JSON format, a Kafka JSON catalog allows you to avoid configuring the schema and parameters in the WITH clause. For more information, see Manage Kafka JSON Catalog.

As a CTAS data source

• Type inference

  By default, Flink displays only the first layer of data in the JSON text during type inference. Flink infers the SQL data types based on the JSON data types and values and the data type mappings. The following table lists the mappings between JSON and Flink SQL data types.

  JSON type	Flink SQL type
  BOOLEAN	BOOLEAN
  STRING	DATE, TIMESTAMP, TIMESTAMP_LTZ, TIME, or STRING
  INT or LONG	BIGINT
  BIGINT	DECIMAL or STRING Note The precision of the DECIMAL type in Flink is limited. If the value of an integer exceeds the maximum precision of the DECIMAL type, Flink determines the data type of the value to be STRING to prevent loss of precision.
  FLOAT, DOUBLE, or BIG DECIMAL	DOUBLE
  ARRAY	STRING
  OBJECT	STRING

  Example

  • JSON text

    {
      "id": 101,
      "name": "VVP",
      "properties": {
        "owner": "Alibaba Cloud",
        "engine": "Flink"
      }
        "type": ["Big data"]
    }

  • The following table is written into the downstream system:

    id	name	properties	type
    101	VVP	{ "owner": "Alibaba Cloud", "engine": "Flink" }	["Big data"]

• Auxiliary type inference

  If type inference based on the above rules does not meet your needs, you can explicitly declare the data type of a specific column in the source table's DDL statement. If you use this method, Flink preferentially parses the field you want to use as the data type of the column declared in the DDL statement. In the following example, Flink parses the price field as the DECIMAL type instead of converting the field to the DOUBLE type based on the data type mappings.

  CREATE TABLE evolvingKafkaSource (
    price DECIMAL(18, 2)
  ) WITH (
    'connector' = 'kafka',
    'properties.bootstrap.servers' = 'localhost:9092',
    'topic' = 'evolving_kafka_demo',
    'scan.startup.mode' = 'earliest-offset',
    'format' = 'json'
  );

  However, if the data type you specify in the DDL statement differs from the actual data type, you can use one of the following methods to handle the issue:

  • If a column's declared data type has a wider range than the actual data type, the field is parsed according to the declared data type. For example, if a column declared as DOUBLE is used to hold BIGINT data, the column's data type is parsed as DOUBLE.

  • If the actual data type has a wider range than or is incompatible with the declared data type, an error is returned because CREATE TABLE AS does not support type changes. To resolve this, you must restart your deployment and declare a correct data type.

    The following figure shows the tree structure of data type ranges and compatibility.

    Note

    • In the above figure, data types closer to the root node have wider ranges. Data types on different branches are incompatible.

    • Auxiliary type inference is not supported for complex data types such as ROW, ARRAY, MAP, and MULTISET.

    • By default, Flink parses complex data types as STRING.

Examples

CREATE TEMPORARY TABLE kafka_source (
  id INT,
  name STRING,
  age INT
) WITH (
  'connector' = 'kafka',
  'topic' = 'source',
  'properties.bootstrap.servers' = '<yourKafkaBrokers>',
  'properties.group.id' = '<yourKafkaConsumerGroupId>',
  'format' = 'csv'
);

CREATE TEMPORARY TABLE kafka_sink (
  id INT,
  name STRING,
  age INT
) WITH (
  'connector' = 'kafka',
  'topic' = 'sink',
  'properties.bootstrap.servers' = '<yourKafkaBrokers>',
  'properties.group.id' = '<yourKafkaConsumerGroupId>',
  'format' = 'csv'
);

INSERT INTO kafka_sink SELECT id, name, age FROM kafka_source;

CREATE TEMPORARY TABLE kafkaTable (
  `offset` INT NOT NULL METADATA,
  `part` BIGINT NOT NULL METADATA FROM 'partition',
  PRIMARY KEY (`part`, `offset`) NOT ENFORCED
) WITH (
  'connector' = 'kafka',
  'properties.bootstrap.servers' = '<yourKafkaBrokers>',
  'topic' = 'kafka_evolution_demo',
  'scan.startup.mode' = 'earliest-offset',
  'format' = 'json',
  'json.infer-schema.flatten-nested-columns.enable' = 'true'
    -- Optional. Expand all nested columns.
);

CREATE TABLE IF NOT EXISTS hologres.kafka.`sync_kafka`
WITH (
  'connector' = 'hologres'
) AS TABLE vvp.`default`.kafkaTable;

CREATE TEMPORARY TABLE kafkaTable (
  `key_id` INT NOT NULL,
  `val_name` VARCHAR(200)
) WITH (
  'connector' = 'kafka',
  'properties.bootstrap.servers' = '<yourKafkaBrokers>',
  'topic' = 'kafka_evolution_demo',
  'scan.startup.mode' = 'earliest-offset',
  'key.format' = 'json',
  'value.format' = 'json',
  'key.fields' = 'key_id',
  'key.fields-prefix' = 'key_',
  'value.fields-prefix' = 'val_',
  'value.fields-include' = 'EXCEPT_KEY'
);

CREATE TABLE IF NOT EXISTS hologres.kafka.`sync_kafka`(
WITH (
  'connector' = 'hologres'
) AS TABLE vvp.`default`.kafkaTable;

CREATE TEMPORARY TABLE kafkaTable (
  `distinct_id` INT NOT NULL,
  `properties` STRING,
  `timestamp` TIMESTAMP_LTZ METADATA,
  `date` AS CAST(`timestamp` AS DATE)
) WITH (
  'connector' = 'kafka',
  'properties.bootstrap.servers' = '<yourKafkaBrokers>',
  'topic' = 'kafka_evolution_demo',
  'scan.startup.mode' = 'earliest-offset',
  'key.format' = 'json',
  'value.format' = 'json',
  'key.fields' = 'key_id',
  'key.fields-prefix' = 'key_'
);

CREATE TABLE IF NOT EXISTS hologres.kafka.`sync_kafka` WITH (
   'connector' = 'hologres'
) AS TABLE vvp.`default`.kafkaTable
ADD COLUMN
  `order_id` AS COALESCE(JSON_VALUE(`properties`, '$.order_id'), 'default');
-- Use COALESCE to handle null values.

DataStream API

• Create a Kafka source

  The Kafka source provides a builder class for constructing the instance of KafkaSource. The following sample code shows how to create a Kafka source to consume data from the earliest offset of the input-topic. The name of the consumer group is my-group. The Kafka message body is deserialized into a string.

  Java

  KafkaSource<String> source = KafkaSource.<String>builder()
      .setBootstrapServers(brokers)
      .setTopics("input-topic")
      .setGroupId("my-group")
      .setStartingOffsets(OffsetsInitializer.earliest())
      .setValueOnlyDeserializer(new SimpleStringSchema())
      .build();
  
  env.fromSource(source, WatermarkStrategy.noWatermarks(), "Kafka Source");

  XML

  The Kafka DataStream connector is available in the Maven central repository.

  <dependency>
      <groupId>com.alibaba.ververica</groupId>
      <artifactId>ververica-connector-kafka</artifactId>
      <version>${vvr-version}</version>
  </dependency>

  You must configure the following parameters when creating a Kafka source.

  Parameter	Description
  BootstrapServers	The addresses of Kafka brokers. You can call the setBootstrapServers(String) operation to configure the addresses.
  GroupId	The ID of the consumer group. You can call the setGroupId(String) method to configure the ID.
  Topics or Partition	The topics or names of the partitions to which you subscribe. You can configure a Kafka source to subscribe to topics or partitions by using one of the following subscription patterns: Topic list. After you configure a topic list, the Kafka source subscribes to all partitions of the specified topics. KafkaSource.builder().setTopics("topic-a","topic-b") Topic pattern. After you specify a regular expression, the Kafka source subscribes to all partitions of the topics that match the specified regular expression. KafkaSource.builder().setTopicPattern("topic.*") Partition list. After you configure a partition list, the Kafka source subscribes to the specified partitions. final HashSet<TopicPartition> partitionSet = new HashSet<>(Arrays.asList( new TopicPartition("topic-a", 0), // Partition 0 of topic "topic-a" new TopicPartition("topic-b", 5))); // Partition 5 of topic "topic-b" KafkaSource.builder().setPartitions(partitionSet)
  Deserializer	A deserializer that deserializes Kafka messages. You can call the setDeserializer(KafkaRecordDeserializationSchema) method to specify a deserializer. The KafkaRecordDeserializationSchema interface defines how a ConsumerRecord object is deserialized. If you want to deserialize only the value fields in the Kafka messages of the ConsumerRecord object, you can use one of the following methods: A Kafka source provides the setValueOnlyDeserializer(DeserializationSchema) method. The DeserializationSchema class defines how a Kafka message that is stored as a binary value is deserialized. Use a deserializer provided by Kafka. Multiple implementation classes are available. For example, you can use the StringDeserializer class to deserialize a message into a string. import org.apache.kafka.common.serialization.StringDeserializer; KafkaSource.<String>builder() .setDeserializer(KafkaRecordDeserializationSchema.valueOnly(StringDeserializer.class)); Note If you want to deserialize a ConsumerRecord object, you must create a class that implements the KafkaRecordDeserializationSchema interface.

  When using a Kafka DataStream connector, you must configure the following Kafka properties:

  • Start offset

    You can use an offset initializer to specify an offset for a Kafka source when it starts reading data. An offset initializer is an object based on the OffsetsInitializer interface. The KafkaSource class provides the following built-in offset initializers.

    Offset initializer	Code configuration
    Specifies that the Kafka source starts to consume messages from the earliest record of each partition.	KafkaSource.builder().setStartingOffsets(OffsetsInitializer.earliest())
    Specifies that the Kafka source starts to consume messages from the latest record of each partition.	KafkaSource.builder().setStartingOffsets(OffsetsInitializer.latest())
    Specifies that the Kafka source starts to consume messages from the first record of each partition. The first record has a timestamp that is greater than or equal to the specified timestamp. Unit: milliseconds.	KafkaSource.builder().setStartingOffsets(OffsetsInitializer.timestamp(1592323200000L))
    Specifies that the Kafka source starts to consume messages from the committed offset of each partition and a reset strategy is specified. If a partition does not have a committed offset, the reset strategy resets the offset and the Kafka source starts from the earliest record of the partition.	KafkaSource.builder().setStartingOffsets(OffsetsInitializer.committedOffsets(OffsetResetStrategy.EARLIEST))
    Specifies that the Kafka source starts to consume messages from the committed offset of each partition and no reset strategy is specified.	KafkaSource.builder().setStartingOffsets(OffsetsInitializer.committedOffsets())

    Note

    • If the built-in offset initializers do not meet your business requirements, you can create custom offset initializers.

    • If you do not specify an offset initializer, the OffsetsInitializer.earliest() offset initializer is used by default.

  • Streaming mode and batch mode

    A Kafka source can operate in streaming execution mode or batch execution mode. By default, a Kafka source operates in streaming execution mode, where the job continues to run until it fails or is canceled. If you want a Kafka source to operate in batch execution mode, call the setBounded(OffsetsInitializer) method to specify a stop offset. When all partitions reach their stop offsets, the Kafka source exits.

    Note

    Typically, a Kafka source operating in streaming execution mode does not have a stop offset. If you want to debug a Kafka source in streaming execution mode, call the setUnbounded(OffsetsInitializer) method to specify a stop offset. The methods to specify a stop offset vary based on whether you use streaming execution mode or batch execution mode.

  • Dynamic partition discovery

    If you want a running deployment to process data from new topics and from new partitions that match your subscription pattern without needing to restart the deployment, enable the dynamic partition discovery feature on the Kafka source.

    Note

    By default, the dynamic partition discovery feature is enabled, with an interval of 5 minutes. To disable this feature, explicitly set the partition.discovery.interval.ms parameter to a non-positive value. The following sample code provides an example of how to configure the partition.discovery.interval.ms parameter.

    KafkaSource.builder()
        .setProperty("partition.discovery.interval.ms", "10000") // Check for new partitions every 10 seconds.

  • Event time and watermarks

    By default, a Kafka source uses the timestamp attached to a record as the event time for the record. You can define a watermark strategy based on the event time of each record and send the watermarks to downstream services.

    env.fromSource(kafkaSource, new CustomWatermarkStrategy(), "Kafka Source With Custom Watermark Strategy")

    For more information about custom watermark strategies, see Generating Watermarks.

  • Offset commit

    When a checkpoint is generated, a Kafka source commits the Kafka consumer offset of each partition to Kafka brokers. This ensures that the Kafka consumer offsets recorded on Kafka brokers are consistent with the state of the checkpoint. If checkpointing is disabled, a Kafka source relies on the Kafka consumer to commit the offsets to Kafka brokers. The automatic offset commit feature is configured using the enable.auto.commit and auto.commit.interval.ms parameters.

    Note

    Kafka sources do not use the committed offsets recorded on Kafka brokers for fault tolerance. When you commit offsets, Kafka brokers can monitor the progress of record consumption on each partition.

  • Additional properties

    In addition to the properties mentioned above, you can call the setProperties(Properties) and setProperty(String, String) methods to configure additional properties for the Kafka source and Kafka consumer. The following table describes the properties of a Kafka source.

    Property	Description
    client.id.prefix	Specifies the prefix for the client ID of the Kafka consumer.
    partition.discovery.interval.ms	Specifies the time interval at which the Kafka source checks for new partitions. Note If the Kafka source operates in batch execution mode, the property is automatically set to -1.
    register.consumer.metrics	Specifies whether to register metrics for the Kafka consumer in Realtime Compute for Apache Flink.
    Additional properties for the Kafka consumer	For more information about the configuration items of Kafka consumers, see Apache Kafka. Important The Kafka DataStream connector overwrites the values of the following properties: key.deserializer: The value of this property is set to ByteArrayDeserializer. value.deserializer: The value of this property is set to ByteArrayDeserializer. auto.offset.reset.strategy: The value of this property is set to OffsetsInitializer#getAutoOffsetResetStrategy().

    The following sample code shows how the Kafka consumer connects to the Kafka cluster using a JAAS configuration and the SASL/PLAIN authentication mechanism.

    KafkaSource.builder()
        .setProperty("sasl.mechanism", "PLAIN")
        .setProperty("sasl.jaas.config", "org.apache.kafka.common.security.plain.PlainLoginModule required username=\"username\" password=\"password\";")

  • Monitoring

    Kafka sources register metrics in Realtime Compute for Apache Flink for monitoring and diagnosis.

    • Metric scope

      All metrics of a Kafka source are registered under the KafkaSourceReader metric group, which is a subgroup of the operator metric group. Metrics for a specific partition are registered in the KafkaSourceReader.topic.<topic_name>.partition.<partition_id> metric group.

      For example, for a topic named my-topic and a partition named 1, the consumer offset of the partition is reported by the <some_parent_groups>.operator.KafkaSourceReader.topic.my-topic.partition.1.currentOffset metric. The number of successful commits of consumer offsets is measured by the <some_parent_groups>.operator.KafkaSourceReader.commitsSucceeded metric.

    • Metrics

      Metric name	Description	Scope
      currentOffset	Reports the Kafka consumer offset of a partition.	TopicPartition
      committedOffset	Reports the committed consumer offset of a partition.	TopicPartition
      commitsSucceeded	Reports the number of successful commits of consumer offsets.	KafkaSourceReader
      commitsFailed	Reports the number of failed commits of consumer offsets.	KafkaSourceReader

    • Metrics for a Kafka consumer

      The metrics for a Kafka consumer are registered in the KafkaSourceReader.KafkaConsumer metric group. For example, the records-consumed-total metric is registered at <some_parent_groups>.operator.KafkaSourceReader.KafkaConsumer.records-consumed-total.

      You can configure the register.consumer.metrics parameter to specify whether to register metrics for the Kafka consumer. By default, this parameter is set to true. For more information about the metrics for a Kafka consumer, see Apache Kafka.

• Create a Kafka sink

  A Kafka sink can write data from multiple streams to one or more Kafka topics.

  DataStream<String> stream = ...
  
  
  Properties properties = new Properties();
  properties.setProperty("bootstrap.servers", );
  KafkaSink<String> kafkaSink =
                  KafkaSink.<String>builder()
                          .setKafkaProducerConfig(kafkaProperties) // producer config
                          .setRecordSerializer(
                                  KafkaRecordSerializationSchema.builder()
                                          .setTopic("my-topic") // target topic
                                          .setKafkaValueSerializer(StringSerializer.class) // serialization schema
                                          .build())
                          .setDeliveryGuarantee(DeliveryGuarantee.EXACTLY_ONCE) // fault-tolerance
                          .build();
  
  stream.sinkTo(kafkaSink);

  You must configure the following parameters when creating a Kafka sink.

  Parameter	Description
  Topic	The name of the topic to which data is written.
  Serialization schema	You must provide a KafkaRecordSerializationSchema to convert input data into a ProducerRecord object of Kafka when you create a Kafka sink. Realtime Compute for Apache Flink provides a schema builder that provides some common components, such as message key or message value serialization, topic selection, and message partitioning. You can also implement the corresponding interfaces for more advanced control. A Kafka sink calls the ProducerRecord<byte[], byte[]> serialize(T element, KafkaSinkContext context, Long timestamp) method for each incoming record to generate a ProducerRecord object that represents the serialized record. Then, the Kafka sink writes the ProducerRecord object to the required topic. The ProducerRecord class provides properties that you can configure to manage behavior when the Kafka producer writes a record to the required Kafka topic. You can configure the following properties of the ProducerRecord class: Set the name of the topic to which the record is written. Define the key for the record. Specify the name of the partition to which the record is written.
  Properties of the Kafka client	The bootstrap.servers property is required. This property specifies the addresses of the Kafka brokers. Separate multiple addresses with a comma (,).
  Fault tolerance semantics	After you enable the checkpointing feature, a Kafka sink can ensure exactly-once delivery. In addition to enabling the checkpointing feature, you can configure the DeliveryGuarantee parameter of a Kafka sink to specify one of the following fault tolerance semantics: DeliveryGuarantee.NONE: By default, the Kafka sink does not ensure data delivery. Data may be lost or duplicated. DeliveryGuarantee.AT_LEAST_ONCE: The Kafka sink ensures that data is not lost. However, data may be duplicated. DeliveryGuarantee.EXACTLY_ONCE: The Kafka sink ensures that data is not lost or duplicated. The Kafka transaction mechanism is used to ensure exactly-once delivery. Note For more information about the considerations for using the exactly-once semantics, see Usage notes of Semantic.EXACTLY_ONCE.

Usage notes of Semantic.EXACTLY_ONCE

• When using the transaction mechanism to write data into a Kafka topic, you must configure the isolation.level parameter for all Kafka consumers. Valid values:

  • read_committed: Only committed data is read.

  • read_uncommitted: Data that is not committed can be read. This is the default value.

• A Kafka sink operating in DeliveryGuarantee.EXACTLY_ONCE mode relies on transactions committed after a deployment is recovered from a checkpoint and before the checkpoint is taken. If the duration between the time a job fails and the time the job is restarted exceeds the specified transaction timeout period, data may be lost because Kafka automatically terminates transactions that time out. Therefore, it is recommended to configure a transaction timeout period longer than the estimated job downtime.

  By default, the transaction.max.timeout.ms parameter of Kafka brokers is set to 15 minutes. The transaction timeout you configure for a Kafka producer cannot exceed the transaction timeout setting for Kafka brokers. The Kafka sink sets the transaction.timeout.ms parameter in sink configuration to 1 hour by default. Therefore, you must extend the transaction timeout period for Kafka brokers before using the DeliveryGuarantee.EXACTLY_ONCE mode.

• DeliveryGuarantee.EXACTLY_ONCE mode uses a fixed-size pool for each Kafka sink. One producer from this pool is used per checkpoint. If the number of parallel checkpoints exceeds the pool size, the Kafka producer throws an exception causing the job to fail. You must configure a maximum pool size and the maximum number of concurrent checkpoints accordingly.

• When a deployment starts, the Kafka sink operating in DeliveryGuarantee.EXACTLY_ONCE mode attempts to terminate transactions that are not committed during checkpointing. These uncommitted transactions may block consumers from reading data. However, in the event of deployment failure before the first checkpoint, the information about previous pool sizes is lost after restarting the deployment. Therefore, it is unsafe to reduce the deployment parallelism before the first checkpoint is taken. If you want to reduce the deployment parallelism before the first checkpoint is taken, ensure that the value obtained by dividing the original parallelism by the new parallelism is greater than or equal to the value of FlinkKafkaProducer.SAFE_SCALE_DOWN_FACTOR.

• For a Kafka consumer with the isolation.level set to read_committed, any uncommitted transaction (neither terminated nor completed) will block all reads from the Kafka topic. If you perform the following steps:

  1. A user creates a transaction to write data to a topic.

  2. The user creates another transaction to write data to the topic.

  3. The user commits the second transaction.

  The Kafka consumer cannot read the data from the second transaction until the first transaction is committed or terminated. Therefore:

  • Data writes into a Kafka topic experience delays, approximately equal to the average checkpointing interval.

  • In the event of deployment failure, the topics written by the deployment block Kafka consumers from reading data until the deployment is restarted or the transactions time out.

FAQ

• How Flink Retrieves JSON Data from Kafka

• Connector

• Connector

• Why is there no output after windowing the data from a Kafka source table based on event time?

• Connecting to a Kafka Cluster Configured with Security Information

• Resolving Field Name Conflicts

• Space Management and Operations