Chapter 13. Applying transformations to modify messages exchanged with Apache Kafka

Debezium provides several single message transformations (SMTs) that you can use to modify event records before Kafka Connect saves the records to Kafka topics. By default, when you configure one of these SMTs for a Debezium connector, Kafka Connect applies that transformation to every record that the connector emits. However, there might be instances in which you want to apply a transformation selectively, so that it modifies only that subset of change event messages that share a common characteristic.

For example, for a Debezium connector, you might want to run the transformation only on event messages from a specific table or that include a specific header key. In environments that run Apache Kafka 2.6 or greater, you can append a predicate statement to a transformation to instruct Kafka Connect to apply the SMT only to certain records. In the predicate, you specify a condition that Kafka Connect uses to evaluate each message that it processes. When a Debezium connector emits a change event message, Kafka Connect checks the message against the configured predicate condition. If the condition is true for the event message, Kafka Connect applies the transformation, and then writes the message to a Kafka topic. Messages that do not match the condition are sent to Kafka unmodified.

The situation is similar for predicates that you define for a sink connector SMT. The connector reads messages from a Kafka topic and Kafka Connect evaluates the messages against the predicate condition. If a message matches the condition, Kafka Connect applies the transformation and then passes the messages to the sink connector.

After you define a predicate, you can reuse it and apply it to multiple transforms. Predicates also include a negate option that you can use to invert a predicate so that the predicate condition is applied only to records that do not match the condition that is defined in the predicate statement. You can use the negate option to pair the predicate with other transforms that are based on negating the condition.

By default, Kafka Connect applies each single message transformation in the Debezium connector configuration to every change event record that it receives from Debezium. Beginning with Apache Kafka 2.6, you can define an SMT predicate for a transformation in the connector configuration that controls how Kafka Connect applies the transformation. The predicate statement defines the conditions under which Kafka Connect applies the transformation to event records emitted by Debezium. Kafka Connect evaluates the predicate statement and then applies the SMT selectively to the subset of records that match the condition that is defined in the predicate. Configuring Kafka Connect predicates is similar to configuring transforms. You specify a predicate alias, associate the alias with a transform, and then define the type and configuration for the predicate.

Because the TopicNameMatch predicate evaluates to true only for messages from the outbox table (outbox.event.*), the transformation is not applied to messages that originate from other tables in the database.

transforms=outbox
transforms.outbox.predicate=IsOutboxTable
transforms.outbox.type=io.debezium.transforms.outbox.EventRouter
predicates=IsOutboxTable
predicates.IsOutboxTable.type=org.apache.kafka.connect.transforms.predicates.TopicNameMatches
predicates.IsOutboxTable.pattern=outbox.event.*

You can control whether Debezium emits tombstone events, and how long Kafka retains them. Depending on your data pipeline, you might want to set the tombstones.on.delete property for a connector so that Debezium does not emit tombstone events.

Whether you enable Debezium to emit tombstones depends on how topics are consumed in your environment and by the characteristics of the sink consumer. Some sink connectors rely on tombstone events to remove records from downstream data stores. In cases where sink connectors rely on tombstone records to indicate when to delete records in downstream data stores, configure Debezium to emit them.

When you configure Debezium to generate tombstones, further configuration is required to ensure that sink connectors receive the tombstone events. The retention policy for a topic must be set so that the connector has time to read event messages before Kafka removes them during log compaction. The length of time that a topic retains tombstones before compaction is controlled by the delete.retention.ms property for the topic.

By default, the tombstones.on.delete property for a connector is set to true so that the connector generates a tombstone after each delete event. If you set the property to false to prevent Debezium from saving tombstone records to Kafka topics, the absence of tombstone records might lead to unintended consequences. Kafka relies on tombstone during log compaction to remove records that are related to a deleted key.

If you need to support sink connectors or downstream Kafka consumers that cannot process records with null values, rather than preventing Debezium from emitting tombstones, consider configuring an SMT for the connector with a predicate that uses the RecordIsTombstone predicate type to remove tombstone messages before consumers read them.

The default behavior is that a Debezium connector sends each change event record to a topic whose name is formed from the name of the database and the name of the table in which the change was made. In other words, a topic receives records for one physical table. When you want a topic to receive records for more than one physical table, you must configure the Debezium connector to re-route the records to that topic.

To route change event records for multiple physical tables to the same topic, configure the topic routing transformation in the Kafka Connect configuration for the Debezium connector. Configuration of the topic routing SMT requires you to specify regular expressions that determine:

The connector configuration in the following example sets several options for the topic routing SMT:

transforms=Reroute
transforms.Reroute.type=io.debezium.transforms.ByLogicalTableRouter
transforms.Reroute.topic.regex=(.*)customers_shard(.*)
transforms.Reroute.topic.replacement=$1customers_all_shards

A Debezium change event key uses the table columns that make up the table’s primary key. To route records for multiple physical tables to one topic, the event key must be unique across all of those tables. However, it is possible for each physical table to have a primary key that is unique within only that table. For example, a row in the myserver.mydb.customers_shard1 table might have the same key value as a row in the myserver.mydb.customers_shard2 table.

To ensure that each event key is unique across the tables whose change event records go to the same topic, the topic routing transformation inserts a field into change event keys. By default, the name of the inserted field is __dbz__physicalTableIdentifier. The value of the inserted field is the default destination topic name.

If you want to, you can configure the topic routing transformation to insert a different field into the key. To do this, specify the key.field.name option and set it to a field name that does not clash with existing primary key field names. For example:

transforms=Reroute
transforms.Reroute.type=io.debezium.transforms.ByLogicalTableRouter
transforms.Reroute.topic.regex=(.*)customers_shard(.*)
transforms.Reroute.topic.replacement=$1customers_all_shards
transforms.Reroute.key.field.name=shard_id

This example adds the shard_id field to the key structure in routed records.

If you want to adjust the value of the key’s new field, configure both of these options:

For example:

transforms.Reroute.key.field.regex=(.*)customers_shard(.*)
transforms.Reroute.key.field.replacement=$2

With this configuration, suppose that the default destination topic names are:

myserver.mydb.customers_shard1
myserver.mydb.customers_shard2
myserver.mydb.customers_shard3

The transformation uses the values in the second captured group, the shard numbers, as the value of the key’s new field. In this example, the inserted key field’s values would be 1, 2, or 3.

If your tables contain globally unique keys and you do not need to change the key structure, you can set the key.enforce.uniqueness option to false:

...
transforms.Reroute.key.enforce.uniqueness=false
...

In addition to the change event messages that a Debezium connector emits when a database change occurs, the connector also emits other types of messages, including heartbeat messages, and metadata messages about schema changes and transactions. Because the structure of these other messages differs from the structure of the change event messages that the SMT is designed to process, it’s best to configure the connector to selectively apply the SMT, so that it processes only the intended data change messages.

You can use one of the following methods to configure the connector to apply the SMT selectively:

The following table describes topic routing SMT configuration options.

For security reasons, the content-based routing SMT is not included with the Debezium connector archives. Instead, it is provided in a separate artifact, debezium-scripting-2.3.4.Final.tar.gz.

If you deploy the Debezium connector by building a custom Kafka Connect container image from a Dockerfile, to use the filter SMT, you must explicitly add the SMT artifact to your Kafka Connect environment. When you use AMQ Streams to deploy the connector, it can download the required artifacts automatically based on configuration parameters that you specify in the Kafka Connect custom resource. IMPORTANT: After the routing SMT is present in a Kafka Connect instance, any user who is allowed to add a connector to the instance can run scripting expressions. To ensure that scripting expressions can be run only by authorized users, be sure to secure the Kafka Connect instance and its configuration interface before you add the routing SMT.

The following procedure applies if you build your Kafka Connect container image from a Dockerfile. If you use AMQ Streams to create the Kafka Connect image, follow the instructions in the deployment topic for your connector.

The Groovy language needs the following libraries on the classpath:

The JavaScript language needs the following libraries on the classpath:

To configure a Debezium connector to route change event records based on the event content, you configure the ContentBasedRouter SMT in the Kafka Connect configuration for the connector.

Configuration of the content-based routing SMT requires you to specify a regular expression that defines the filtering criteria. In the configuration, you create a regular expression that defines routing criteria. The expression defines a pattern for evaluating event records. It also specifies the name of a destination topic where events that match the pattern are routed. The pattern that you specify might designate an event type, such as a table insert, update, or delete operation. You might also define a pattern that matches a value in a specific column or row.

For example, to reroute all update (u) records to an updates topic, you might add the following configuration to your connector configuration:

...
transforms=route
transforms.route.type=io.debezium.transforms.ContentBasedRouter
transforms.route.language=jsr223.groovy
transforms.route.topic.expression=value.op == 'u' ? 'updates' : null
...

The preceding example specifies the use of the Groovy expression language.

Records that do not match the pattern are routed to the default topic.

Debezium binds certain variables into the evaluation context for the SMT. When you create expressions to specify conditions to control the routing destination, the SMT can look up and interpret the values of these variables to evaluate conditions in an expression.

The following table lists the variables that Debezium binds into the evaluation context for the content-based routing SMT:

An expression can invoke arbitrary methods on its variables. Expressions should resolve to a Boolean value that determines how the SMT dispositions the message. When the routing condition in an expression evaluates to true, the message is retained. When the routing condition evaluates to false, the message is removed.

Expressions should not result in any side-effects. That is, they should not modify any variables that they pass.

In addition to the change event messages that a Debezium connector emits when a database change occurs, the connector also emits other types of messages, including heartbeat messages, and metadata messages about schema changes and transactions. Because the structure of these other messages differs from the structure of the change event messages that the SMT is designed to process, it’s best to configure the connector to selectively apply the SMT, so that it processes only the intended data change messages. You can use one of the following methods to configure the connector to apply the SMT selectively:

The way that you express content-based routing conditions depends on the scripting language that you use. For example, as shown in the basic configuration example, when you use Groovy as the expression language, the following expression reroutes all update (u) records to the updates topic, while routing other records to the default topic:

value.op == 'u' ? 'updates' : null

Other languages use different methods to express the same condition.

value.get('op') == 'u' ? 'updates' : null

value.op == 'u' ? 'updates' : null

Debezium generates data change events that have a complex structure. Each event consists of the following parts:

The following example shows part of the structure of a typical Debezium UPDATE change event:

{
	"op": "u",
	"source": {
		...
	},
	"ts_ms" : "...",
	"before" : {
		"field1" : "oldvalue1",
		"field2" : "oldvalue2"
	},
	"after" : {
		"field1" : "newvalue1",
		"field2" : "newvalue2"
	}
}

The complex format of the message in the preceding example provides detailed information about changes that occur in the source database. However, the format might not be suitable for some downstream consumers. Sink connectors, or other parts of the Kafka ecosystem might expect the message to explicitly identify the fields that a database operation changes or leaves unchanged. The ExtractChangedRecordState SMT adds headers to the change event message to identify the fields that are modified by a database operation, and the fields that remain unchanged.

The event changes SMT extracts the before and after fields from a Debezium UPDATE change event in a Kafka record. The transformation examines the before and after event state structures to identify the fields that are altered by an operation, and those that remain unchanged. Depending on the connector configuration, the transformation then produces a modified event message that adds message headers to list the changed fields, the unchanged fields, or both. If the event represents an INSERT or DELETE, this single message transformation has no effect.

You can configure the event changes SMT for a Debezium connector, or for a sink connector that consumes messages emitted by a Debezium connector. Configure the event changes SMT for a sink connector if you want Apache Kafka to retain the entire original Debezium change events. The decision to apply the SMT to a source or sink connector depends on your particular use case.

Depending on your use case, you can configure the transformation to modify the original message by performing one or both of the following tasks:

You configure the Debezium event changes SMT for a Kafka Connect source or sink connector by adding the SMT configuration details to your connector’s configuration. To obtain the default behavior, which doesn’t add any headers, add the transformation to the connector configuration, as in the following example:

transforms=changes,...
transforms.changes.type=io.debezium.transforms.ExtractChangedRecordState

As with any Kafka Connect connector configuration, you can set transforms= to multiple, comma-separated, SMT aliases in the order in which you want Kafka Connect to apply the SMTs.

The connector configuration in the following example sets several options for the event changes SMT:

transforms=changes,...
transforms.changes.type=io.debezium.transforms.ExtractChangedRecordState
transforms.changes.header.changed.name=Changed
transforms.changes.header.unchanged.name=Unchanged

In addition to the change event messages that a Debezium connector emits when a database change occurs, the connector also emits other types of messages, including heartbeat messages, and metadata messages about schema changes and transactions. Because the structure of these other messages differs from the structure of the change event messages that the SMT is designed to process, it’s best to configure the connector to selectively apply the SMT, so that it processes only the intended data change messages.

For more information about how to apply the SMT selectively, see Configure an SMT predicate for the transformation.

The following table describes the options that you can specify to configure the event changes SMT.

For security reasons, the filter SMT is not included with the Debezium connector archives. Instead, it is provided in a separate artifact, debezium-scripting-2.3.4.Final.tar.gz.

If you deploy the Debezium connector by building a custom Kafka Connect container image from a Dockerfile, to use the filter SMT, you must explicitly download the SMT archive and deploy the files alongside the connector plug-in. When you use AMQ Streams to deploy the connector, it can download the required artifacts automatically based on configuration parameters that you specify in the Kafka Connect custom resource. IMPORTANT: After the filter SMT is present in a Kafka Connect instance, any user who is allowed to add a connector to the instance can run scripting expressions. To ensure that scripting expressions can be run only by authorized users, be sure to secure the Kafka Connect instance and its configuration interface before you add the filter SMT.

The following procedure applies if you build your Kafka Connect container image from a Dockerfile. If you use AMQ Streams to create the Kafka Connect image, follow the instructions in the deployment topic for your connector.

The Groovy language needs the following libraries on the classpath:

The JavaScript language needs the following libraries on the classpath:

You configure the filter transformation in the Debezium connector’s Kafka Connect configuration. In the configuration, you specify the events that you are interested in by defining filter conditions that are based on business rules. As the filter SMT processes the event stream, it evaluates each event against the configured filter conditions. Only events that meet the criteria of the filter conditions are passed to the broker.

To configure a Debezium connector to filter change event records, configure the Filter SMT in the Kafka Connect configuration for the Debezium connector. Configuration of the filter SMT requires you to specify a regular expression that defines the filtering criteria.

For example, you might add the following configuration in your connector configuration.

...
transforms=filter
transforms.filter.type=io.debezium.transforms.Filter
transforms.filter.language=jsr223.groovy
transforms.filter.condition=value.op == 'u' && value.before.id == 2
...

The preceding example specifies the use of the Groovy expression language. The regular expression value.op == 'u' && value.before.id == 2 removes all messages, except those that represent update (u) records with id values that are equal to 2.

Debezium binds certain variables into the evaluation context for the filter SMT. When you create expressions to specify filter conditions, you can use the variables that Debezium binds into the evaluation context. By binding variables, Debezium enables the SMT to look up and interpret their values as it evaluates the conditions in an expression.

The following table lists the variables that Debezium binds into the evaluation context for the filter SMT:

An expression can invoke arbitrary methods on its variables. Expressions should resolve to a Boolean value that determines how the SMT dispositions the message. When the filter condition in an expression evaluates to true, the message is retained. When the filter condition evaluates to false, the message is removed.

Expressions should not result in any side-effects. That is, they should not modify any variables that they pass.

In addition to the change event messages that a Debezium connector emits when a database change occurs, the connector also emits other types of messages, including heartbeat messages, and metadata messages about schema changes and transactions. Because the structure of these other messages differs from the structure of the change event messages that the SMT is designed to process, it’s best to configure the connector to selectively apply the SMT, so that it processes only the intended data change messages. You can use one of the following methods to configure the connector to apply the SMT selectively:

The way that you express filtering conditions depends on the scripting language that you use.

For example, as shown in the basic configuration example, when you use Groovy as the expression language, the following expression removes all messages, except for update records that have id values set to 2:

value.op == 'u' && value.before.id == 2

Other languages use different methods to express the same condition.

value.get('op') == 'u' && value.get('before').get('id') == 2

value.op == 'u' && value.before.id == 2

The following table lists the configuration options that you can use with the filter SMT.

To extract message headers in an event record into the record value, configure the HeaderToValue SMT in the Kafka Connect configuration for a connector. You can configure the transformation to either remove the original headers or to copy them. To remove header fields from the record, configure the SMT to use the move operations. To retain the header fields in the original record, configure the SMT to use the copy operation. For example, to remove the headers event_timestamp and key from an event message, add the following lines to your connector configuration:

transforms=moveHeadersToValue
transforms.moveHeadersToValue.type=io.debezium.transforms.HeaderToValue
transforms.moveHeadersToValue.headers=event_timestamp,key
transforms.moveHeadersToValue.fields=timestamp,source.id
transforms.moveHeadersToValue.operation=move

The following example shows the headers and values of an event record before and after the transformation is applied.

The following table lists the configuration options that you can use with the HeaderToValue SMT.

Debezium generates data change events that have a complex structure. Each event consists of three parts:

The following example shows part of the message structure for an UPDATE change event:

{
	"op": "u",
	"source": {
		...
	},
	"ts_ms" : "...",
	"before" : {
		"field1" : "oldvalue1",
		"field2" : "oldvalue2"
	},
	"after" : {
		"field1" : "newvalue1",
		"field2" : "newvalue2"
	}
}

For more information about the change event structure for a connector, see the documentation for the connector.

After the event flattening SMT processes the message in the previous example, it simplifies the message format, resulting in the message in the following example:

{
	"field1" : "newvalue1",
	"field2" : "newvalue2"
}

The event flattening SMT extracts the after field from a Debezium change event in a Kafka record. The SMT replaces the original change event with only its after field to create a simple Kafka record.

You can configure the event flattening SMT for a Debezium connector or for a sink connector that consumes messages emitted by a Debezium connector. The advantage of configuring event flattening for a sink connector is that records stored in Apache Kafka contain whole Debezium change events. The decision to apply the SMT to a source or sink connector depends on your particular use case.

You can configure the transformation to do any of the following:

A database DELETE operation causes Debezium to generate two Kafka records:

Instead of dropping the record that contains the before row data, you can configure the event flattening SMT to do one of the following:

Similarly, instead of dropping the tombstone record, you can configure the event flattening SMT to keep the tombstone record in the stream.

Configure the Debezium event flattening SMT in a Kafka Connect source or sink connector by adding the SMT configuration details to your connector’s configuration. For example, to obtain the default behavior of the transformation, add it to the connector configuration without specifying any options, as in the following example:

transforms=unwrap,...
transforms.unwrap.type=io.debezium.transforms.ExtractNewRecordState

As with any Kafka Connect connector configuration, you can set transforms= to multiple, comma-separated, SMT aliases in the order in which you want Kafka Connect to apply the SMTs.

The following .properties example sets several event flattening SMT options:

transforms=unwrap,...
transforms.unwrap.type=io.debezium.transforms.ExtractNewRecordState
transforms.unwrap.drop.tombstones=false
transforms.unwrap.delete.handling.mode=rewrite
transforms.unwrap.add.fields=table,lsn

You can configure the event flattening SMT to add original change event metadata to the simplified Kafka record. For example, you might want the simplified record’s header or value to contain any of the following:

To add metadata to the simplified Kafka record’s header, specify the add.headers option. To add metadata to the simplified Kafka record’s value, specify the add.fields option. Each of these options takes a comma separated list of change event field names. Do not specify spaces. When there are duplicate field names, to add metadata for one of those fields, specify the struct as well as the field. For example:

transforms=unwrap,...
transforms.unwrap.type=io.debezium.transforms.ExtractNewRecordState
transforms.unwrap.add.fields=op,table,lsn,source.ts_ms
transforms.unwrap.add.headers=db
transforms.unwrap.delete.handling.mode=rewrite

With that configuration, a simplified Kafka record would contain something like the following:

{
 ...
	"__op" : "c",
	"__table": "MY_TABLE",
	"__lsn": "123456789",
	"__source_ts_ms" : "123456789",
 ...
}

Also, simplified Kafka records would have a __db header.

In the simplified Kafka record, the SMT prefixes the metadata field names with a double underscore. When you specify a struct, the SMT also inserts an underscore between the struct name and the field name.

To add metadata to a simplified Kafka record that is for a DELETE operation, you must also configure delete.handling.mode=rewrite.

In addition to the change event messages that a Debezium connector emits when a database change occurs, the connector also emits other types of messages, including heartbeat messages, and metadata messages about schema changes and transactions. Because the structure of these other messages differs from the structure of the change event messages that the SMT is designed to process, it’s best to configure the connector to selectively apply the SMT, so that it processes only the intended data change messages.

For more information about how to apply the SMT selectively, see Configure an SMT predicate for the transformation.

The following table describes the options that you can specify to configure the event flattening SMT.

The Debezium MongoDB connector generates change events that have a complex structure. Each event message includes the following parts:

The following fragment shows the basic structure of a create change event that the connector emits after a MongoDB insert operation:

{
  "op": "c",
  "after": "{\"field1\":\"newvalue1\",\"field2\":\"newvalue1\"}",
  "source": { ... }
}

The complex format of the after field in the preceding example provides detailed information about changes that occur in the source database. However, some consumers cannot process messages that contain nested values. To convert the complex nested fields of the original message into a simpler, more universally compatible structure, use the event flattening SMT for MongoDB. The SMT flattens the structure of nested fields in a message, as shown in the following example:

{
  "field1" : "newvalue1",
  "field2" : "newvalue2"
}

For more information about the default structure of messages produced by the Debezium MongoDB connector, see the connector documentation.

The event flattening SMT for MongoDB extracts the after field from create or update change event messages emitted by the Debezium MongoDB connector. After the SMT processes the original change event message, it generates a simplified version that contains only the contents of the after field.

Depending on your use case, you can apply the ExtractNewDocumentState SMT to the Debezium MongoDB connector, or to a sink connector that consumes messages that the Debezium connector produces. If you apply the SMT to the Debezium MongoDB connector, the SMT modifies messages that the connector emits before they are sent to Apache Kafka. To ensure that Kafka retains the complete Debezium change event message in its original format, apply the SMT to a sink connector.

When you use the event flattening SMT to process a message emitted from a MongoDB connector, the SMT converts the structure of the records in the original message into properly typed Kafka Connect records that can be consumed by a typical sink connector. For example, the SMT converts the JSON strings that represent the after information in the original message into schema structures that any consumer can process.

Optionally, you can configure the event flattening SMT for MongoDB to modify messages in other ways during processing. For more information, see the configuration topic.

Configure the event flattening (ExtractNewDocumentState) SMT for MongoDB for sink connectors that consume the messages emitted by the Debezium MongoDB connector.

The following topics provide details:

transforms=unwrap,...
transforms.unwrap.type=io.debezium.connector.mongodb.transforms.ExtractNewDocumentState

transforms=unwrap,...
transforms.unwrap.type=io.debezium.connector.mongodb.transforms.ExtractNewDocumentState
transforms.unwrap.drop.tombstones=false
transforms.unwrap.delete.handling.mode=drop
transforms.unwrap.add.headers=op

By default, the event flattening SMT converts MongoDB arrays into arrays that are compatible with Apache Kafka Connect, or Apache Avro schemas. While MongoDB arrays can contain multiple types of elements, all elements in a Kafka array must be of the same type.

To ensure that the SMT encodes arrays in a way that meets the needs of your environment, you can specify the array.encoding configuration option. The following example shows the configuration for setting the array encoding:

transforms=unwrap,...
transforms.unwrap.type=io.debezium.connector.mongodb.transforms.ExtractNewDocumentState
transforms.unwrap.array.encoding=<array|document>

Depending on the configuration, the SMT processes each instance of an array in the source message by using one of the following encoding methods:

The following example shows how the Debezium MongoDB connector represents a database document that contains an array that includes heterogeneous data types:

{
    "_id": 1,
    "a1": [
        {
            "a": 1,
            "b": "none"
        },
        {
            "a": "c",
            "d": "something"
        }
    ]
}

{
    "_id": 1,
    "a1": {
        "_0": {
            "a": 1,
            "b": "none"
        },
        "_1": {
            "a": "c",
            "d": "something"
        }
    }
}

The document encoding option enables the SMT to process arbitrary arrays that are comprised of heterogeneous elements. However, before you use this option, always verify that the sink connector and other downstream consumers are capable of processing arrays that contain multiple data types.

When a database operation involves an embedded document, the Debezium MongoDB connector emits a Kafka event record that has a structure that reflects the hierarchical structure of the original document. That is, the event message represents nested documents as a set of nested field structure. In environments where downstream connectors cannot process messages that contain nested structures, you can configure the event flattening SMT to flatten hierarchical structures in the message. A flat message structure is better suited to table-like storage.

To configure the SMT to flatten nested structures, set the flatten.struct configuration option to true. In the converted message, field names are constructed to be consistent with the document source. The SMT renames each flattened field by concatenating the name of the parent document field with the name of the nested document field. A delimiter that is defined by the flatten.struct.delimiter option separates the components of the name. The default value of struct.delimiter is an underscore character (_).

The following example shows the configuration for specifying whether the SMT flattens nested structures:

transforms=unwrap,...
transforms.unwrap.type=io.debezium.connector.mongodb.transforms.ExtractNewDocumentState
transforms.unwrap.flatten.struct=<true|false>
transforms.unwrap.flatten.struct.delimiter=<string>

The following example shows an event message that is emitted by the MongoDB connector. The message includes a field for a document a that contains fields for two nested documents, b and c:

{
    "_id": 1,
    "a": {
            "b": 1,
            "c": "none"
    },
    "d": 100
}

The message in the following example shows the output after the SMT for MongoDB flattens the nested structures in the preceding message:

{
    "_id": 1,
    "a_b": 1,
    "a_c": "none",
    "d": 100
}

In the resulting message, the b and c fields that were nested in the original message are flattened and renamed. The renamed fields are formed by concatenating the name of the parent document a with the names of the nested documents: a_b and a_c. The components of the new field names are separated by an underscore character, as defined by the setting of the struct.delimiter configuration property,

In MongoDB, the $unset operator and the $rename operator both remove fields from a document. Because MongoDB collections are schemaless, after an update removes fields from a document, it’s not possible to infer the name of the missing field from the updated document. To support sink connectors or other consumers that might require information about removed fields, Debezium emits update messages that include a removedFields element that lists the names of the deleted fields.

The following example shows part of an update message for an operation that results in the removal of the field a:

"payload": {
  "op": "u",
  "ts_ms": "...",
  "before": "{ ... }",
  "after": "{ ... }",
  "updateDescription": {
    "removedFields": ["a"],
    "updatedFields": null,
    "truncatedArrays": null
  }
}

In the preceding example, the before and after represent the state of the source document before and after the document was updated. These fields are present in the event message that a connector emits only if the capture.mode for the connector is set as described in the following list:

Assuming a connector that is configured to capture full documents, when the ExtractNewDocumentState SMT receives an update message for an $unset event, the SMT re-encodes the message by representing the removed field has a null value, as shown in the following example:

{
    "id": 1,
    "a": null
}

For connectors that are not configured to capture full documents, when the SMT receives an update event for an $unset operation, it produces the following output message:

{
   "a": null
}

After the SMT flattens an event message, the resulting message no longer indicates whether the operation that generated the event was of type create, update or initial snapshot read. Typically, you can identify delete operations by configuring the connectors to expose information about the tombstone or rewrite events that accompany a deletion. For more information about configuring the connector to expose information about tombstones and rewrites in event messages, see the drop.tombstones and delete.handling.mode properties.

To report the type of a database operation in an event message, the SMT can add an op field to one of the following elements:

For example, to add a header property that shows the type of the original operation, add the transform, and then add the add.headers property to the connector configuration, as in the following example:

transforms=unwrap,...
transforms.unwrap.type=io.debezium.connector.mongodb.transforms.ExtractNewDocumentState
transforms.unwrap.add.headers=op

Based on the preceding configuration, the SMT reports the event type by adding an op header to the message and assigning it a string value to identify the type of the operation. The assigned string value is based on the op field value in the original MongoDB change event message.

The event flattening SMT for MongoDB can add metadata fields from the original change event message to the simplified message. The added metadata fields are prefixed with a double underscore ("__"). Adding metadata to the event record makes it possible to include content such as the name of the collection in which a change event occurred, or to include connector-specific fields, such as a replica set name. Currently, the SMT can add fields from the following change event sub-structures only: source, transaction and updateDescription.

For more information about the MongoDB change event structure, see the MongoDB connector documentation.

For example, you might specify the following configuration to add the replica set name (rs) and the collection name for a change event to the final flattened event record:

transforms=unwrap,...
transforms.unwrap.type=io.debezium.connector.mongodb.transforms.ExtractNewDocumentState
transforms.unwrap.add.fields=rs,collection

The preceding configuration results in the following content being added to the flattened record:

{ "__rs" : "rs0", "__collection" : "my-collection", ... }

If you want the SMT to add metadata fields to delete events, set the value of the delete.handling.mode option to rewrite.

In addition to the change event messages that a Debezium connector emits when a database change occurs, the connector also emits other types of messages, including heartbeat messages, and metadata messages about schema changes and transactions. Because the structure of these other messages differs from the structure of the change event messages that the SMT is designed to process, it’s best to configure the connector to selectively apply the SMT, so that it processes only the intended data change messages.

For more information about how to apply the SMT selectively, see Configure an SMT predicate for the transformation.

The following table describes the configuration options for the MongoDB event flattening SMT.

version:VERSION, connector:CONNECTOR, source.ts_ms:EVENT_TIMESTAMP

version:VERSION, connector:CONNECTOR, source.ts_ms:EVENT_TIMESTAMP

To understand how the Debezium outbox event router SMT is configured, review the following example of a Debezium outbox message:

# Kafka Topic: outbox.event.order
# Kafka Message key: "1"
# Kafka Message Headers: "id=4d47e190-0402-4048-bc2c-89dd54343cdc"
# Kafka Message Timestamp: 1556890294484
{
  "{\"id\": 1, \"lineItems\": [{\"id\": 1, \"item\": \"Debezium in Action\", \"status\": \"ENTERED\", \"quantity\": 2, \"totalPrice\": 39.98}, {\"id\": 2, \"item\": \"Debezium for Dummies\", \"status\": \"ENTERED\", \"quantity\": 1, \"totalPrice\": 29.99}], \"orderDate\": \"2019-01-31T12:13:01\", \"customerId\": 123}"
}

A Debezium connector that is configured to apply the outbox event router SMT generates the above message by transforming a Debezium raw message like this:

# Kafka Message key: "406c07f3-26f0-4eea-a50c-109940064b8f"
# Kafka Message Headers: ""
# Kafka Message Timestamp: 1556890294484
{
  "before": null,
  "after": {
    "id": "406c07f3-26f0-4eea-a50c-109940064b8f",
    "aggregateid": "1",
    "aggregatetype": "Order",
    "payload": "{\"id\": 1, \"lineItems\": [{\"id\": 1, \"item\": \"Debezium in Action\", \"status\": \"ENTERED\", \"quantity\": 2, \"totalPrice\": 39.98}, {\"id\": 2, \"item\": \"Debezium for Dummies\", \"status\": \"ENTERED\", \"quantity\": 1, \"totalPrice\": 29.99}], \"orderDate\": \"2019-01-31T12:13:01\", \"customerId\": 123}",
    "timestamp": 1556890294344,
    "type": "OrderCreated"
  },
  "source": {
    "version": "2.3.4.Final",
    "connector": "postgresql",
    "name": "dbserver1-bare",
    "db": "orderdb",
    "ts_usec": 1556890294448870,
    "txId": 584,
    "lsn": 24064704,
    "schema": "inventory",
    "table": "outboxevent",
    "snapshot": false,
    "last_snapshot_record": null,
    "xmin": null
  },
  "op": "c",
  "ts_ms": 1556890294484
}

This example of a Debezium outbox message is based on the default outbox event router configuration, which assumes an outbox table structure and event routing based on aggregates. To customize behavior, the outbox event router SMT provides numerous configuration options.

To apply the default outbox event router SMT configuration, your outbox table is assumed to have the following columns:

Column        |          Type          | Modifiers
--------------+------------------------+-----------
id            | uuid                   | not null
aggregatetype | character varying(255) | not null
aggregateid   | character varying(255) | not null
type          | character varying(255) | not null
payload       | jsonb                  |

To configure a Debezium connector to support the outbox pattern, configure the outbox.EventRouter SMT. To obtain the default behavior of the SMT, add it to the connector configuration without specifying any options, as in the following example:

transforms=outbox,...
transforms.outbox.type=io.debezium.transforms.outbox.EventRouter

In addition to the change event messages that a Debezium connector emits when a database change occurs, the connector also emits other types of messages, including heartbeat messages, and metadata messages about schema changes and transactions. Because the structure of these other messages differs from the structure of the change event messages that the SMT is designed to process, it’s best to configure the connector to selectively apply the SMT, so that it processes only the intended data change messages. You can use one of the following methods to configure the connector to apply the SMT selectively:

The outbox event router SMT supports arbitrary payload formats. The payload column value in an outbox table is passed on transparently. An alternative to working with JSON is to use Avro. This can be beneficial for message format governance and for ensuring that outbox event schemas evolve in a backwards-compatible way.

How a source application produces Avro formatted content for outbox message payloads is out of the scope of this documentation. One possibility is to leverage the KafkaAvroSerializer class to serialize GenericRecord instances. To ensure that the Kafka message value is the exact Avro binary data, apply the following configuration to the connector:

transforms=outbox,...
transforms.outbox.type=io.debezium.transforms.outbox.EventRouter
value.converter=io.debezium.converters.BinaryDataConverter

By default, the payload column value (the Avro data) is the only message value. Configuration of BinaryDataConverter as the value converter propagates the payload column value as-is into the Kafka message value.

The Debezium connectors may be configured to emit heartbeat, transaction metadata, or schema change events (support varies by connector). These events cannot be serialized by the BinaryDataConverter so additional configuration must be provided so the converter knows how to serialize these events. As an example, the following configuration illustrates using the Apache Kafka JsonConverter with no schemas:

transforms=outbox,...
transforms.outbox.type=io.debezium.transforms.outbox.EventRouter
value.converter=io.debezium.converters.BinaryDataConverter
value.converter.delegate.converter.type=org.apache.kafka.connect.json.JsonConverter
value.converter.delegate.converter.type.schemas.enable=false

The delegate Converter implementation is specified by the delegate.converter.type option. If any extra configuration options are needed by the converter, they can also be specified, such as the disablement of schemas shown above using schemas.enable=false.

Your outbox table might contain columns whose values you want to add to the emitted outbox messages. For example, consider an outbox table that has a value of purchase-order in the aggregatetype column and another column, eventType, whose possible values are order-created and order-shipped. Additional fields can be added with the syntax column:placement:alias.

The allowed values for placement are: - header - envelope - partition

To emit the eventType column value in the outbox message header, configure the SMT like this:

transforms=outbox,...
transforms.outbox.type=io.debezium.transforms.outbox.EventRouter
transforms.outbox.table.fields.additional.placement=eventType:header:type

The result will be a header on the Kafka message with type as its key, and the value of the eventType column as its value.

To emit the eventType column value in the outbox message envelope, configure the SMT like this:

transforms=outbox,...
transforms.outbox.type=io.debezium.transforms.outbox.EventRouter
transforms.outbox.table.fields.additional.placement=eventType:envelope:type

To control which partition the outbox message is produced on, configure the SMT like this:

transforms=outbox,...
transforms.outbox.type=io.debezium.transforms.outbox.EventRouter
transforms.outbox.table.fields.additional.placement=partitionColumn:partition

Note that for the partition placement, adding an alias will have no effect.

You may have noticed that the Debezium outbox message contains the payload represented as a String. So when this string, is actually JSON, it appears as escaped in the result Kafka message like shown below:

# Kafka Topic: outbox.event.order
# Kafka Message key: "1"
# Kafka Message Headers: "id=4d47e190-0402-4048-bc2c-89dd54343cdc"
# Kafka Message Timestamp: 1556890294484
{
  "{\"id\": 1, \"lineItems\": [{\"id\": 1, \"item\": \"Debezium in Action\", \"status\": \"ENTERED\", \"quantity\": 2, \"totalPrice\": 39.98}, {\"id\": 2, \"item\": \"Debezium for Dummies\", \"status\": \"ENTERED\", \"quantity\": 1, \"totalPrice\": 29.99}], \"orderDate\": \"2019-01-31T12:13:01\", \"customerId\": 123}"
}

The outbox event router allows you to expand this message content to "real" JSON with the companion schema being deduced from the JSON document itself. That way the result in Kafka message looks like:

# Kafka Topic: outbox.event.order
# Kafka Message key: "1"
# Kafka Message Headers: "id=4d47e190-0402-4048-bc2c-89dd54343cdc"
# Kafka Message Timestamp: 1556890294484
{
  "id": 1, "lineItems": [{"id": 1, "item": "Debezium in Action", "status": "ENTERED", "quantity": 2, "totalPrice": 39.98}, {"id": 2, "item": "Debezium for Dummies", "status": "ENTERED", "quantity": 1, "totalPrice": 29.99}], "orderDate": "2019-01-31T12:13:01", "customerId": 123
}

To enable this transformation, you have to set the table.expand.json.payload to true and use the JsonConverter like below:

transforms=outbox,...
transforms.outbox.type=io.debezium.transforms.outbox.EventRouter
transforms.outbox.table.expand.json.payload=true
value.converter=org.apache.kafka.connect.json.JsonConverter

The following table describes the options that you can specify for the outbox event router SMT. In the table, the Group column indicates a configuration option classification for Kafka.

To understand how to configure the Debezium MongoDB outbox event router SMT, consider the following example of a Debezium outbox message:

# Kafka Topic: outbox.event.order
# Kafka Message key: "b2730779e1f596e275826f08"
# Kafka Message Headers: "id=596e275826f08b2730779e1f"
# Kafka Message Timestamp: 1556890294484
{
  "{\"id\": {\"$oid\": \"da8d6de63b7745ff8f4457db\"}, \"lineItems\": [{\"id\": 1, \"item\": \"Debezium in Action\", \"status\": \"ENTERED\", \"quantity\": 2, \"totalPrice\": 39.98}, {\"id\": 2, \"item\": \"Debezium for Dummies\", \"status\": \"ENTERED\", \"quantity\": 1, \"totalPrice\": 29.99}], \"orderDate\": \"2019-01-31T12:13:01\", \"customerId\": 123}"
}

A Debezium connector that is configured to apply the MongoDB outbox event router SMT generates the preceding message by transforming a raw Debezium change event message as in the following example:

# Kafka Message key: { "id": "{\"$oid\": \"596e275826f08b2730779e1f\"}" }
# Kafka Message Headers: ""
# Kafka Message Timestamp: 1556890294484
{
  "patch": null,
  "after": "{\"_id\": {\"$oid\": \"596e275826f08b2730779e1f\"}, \"aggregateid\": {\"$oid\": \"b2730779e1f596e275826f08\"}, \"aggregatetype\": \"Order\", \"type\": \"OrderCreated\", \"payload\": {\"_id\": {\"$oid\": \"da8d6de63b7745ff8f4457db\"}, \"lineItems\": [{\"id\": 1, \"item\": \"Debezium in Action\", \"status\": \"ENTERED\", \"quantity\": 2, \"totalPrice\": 39.98}, {\"id\": 2, \"item\": \"Debezium for Dummies\", \"status\": \"ENTERED\", \"quantity\": 1, \"totalPrice\": 29.99}], \"orderDate\": \"2019-01-31T12:13:01\", \"customerId\": 123}}",
  "source": {
    "version": "2.3.4.Final",
    "connector": "mongodb",
    "name": "fulfillment",
    "ts_ms": 1558965508000,
    "snapshot": false,
    "db": "inventory",
    "rs": "rs0",
    "collection": "customers",
    "ord": 31,
    "h": 1546547425148721999
  },
  "op": "c",
  "ts_ms": 1556890294484
}

This example of a Debezium outbox message is based on the default outbox event router configuration, which assumes an outbox collection structure and event routing based on aggregates. To customize behavior, the outbox event router SMT provides numerous configuration options.

To apply the default MongoDB outbox event router SMT configuration, your outbox collection is assumed to have the following fields:

{
  "_id": "objectId",
  "aggregatetype": "string",
  "aggregateid": "objectId",
  "type": "string",
  "payload": "object"
}

To configure a Debezium MongoDB connector to support the outbox pattern, configure the outbox.MongoEventRouter SMT. To obtain the default behavior of the SMT, add it to the connector configuration without specifying any options, as in the following example:

transforms=outbox,...
transforms.outbox.type=io.debezium.connector.mongodb.transforms.outbox.MongoEventRouter

In addition to the change event messages that a Debezium connector emits when a database change occurs, the connector also emits other types of messages, including heartbeat messages, and metadata messages about schema changes and transactions. Because the structure of these other messages differs from the structure of the change event messages that the SMT is designed to process, it’s best to configure the connector to selectively apply the SMT, so that it processes only the intended data change messages. You can use one of the following methods to configure the connector to apply the SMT selectively:

The MongoDB outbox event router SMT supports arbitrary payload formats. The payload field value in an outbox collection is passed on transparently. An alternative to working with JSON is to use Avro. This can be beneficial for message format governance and for ensuring that outbox event schemas evolve in a backwards-compatible way.

How a source application produces Avro formatted content for outbox message payloads is out of the scope of this documentation. One possibility is to leverage the KafkaAvroSerializer class to serialize GenericRecord instances. To ensure that the Kafka message value is the exact Avro binary data, apply the following configuration to the connector:

transforms=outbox,...
transforms.outbox.type=io.debezium.connector.mongodb.transforms.outbox.MongoEventRouter
value.converter=io.debezium.converters.ByteArrayConverter

By default, the payload field value (the Avro data) is the only message value. Configuration of ByteArrayConverter as the value converter propagates the payload field value as-is into the Kafka message value.

Note that this differs from the BinaryDataConverter suggested for other SMTs. This is due to the different approach MongoDB takes to storing byte arrays internally.

The Debezium connectors may be configured to emit heartbeat, transaction metadata, or schema change events (support varies by connector). These events cannot be serialized by the ByteArrayConverter so additional configuration must be provided so the converter knows how to serialize these events. As an example, the following configuration illustrates using the Apache Kafka JsonConverter with no schemas:

transforms=outbox,...
transforms.outbox.type=io.debezium.connector.mongodb.transforms.outbox.MongoEventRouter
value.converter=io.debezium.converters.ByteArrayConverter
value.converter.delegate.converter.type=org.apache.kafka.connect.json.JsonConverter
value.converter.delegate.converter.type.schemas.enable=false

The delegate Converter implementation is specified by the delegate.converter.type option. If any extra configuration options are needed by the converter, they can also be specified, such as the disablement of schemas shown above using schemas.enable=false.

Your outbox collection might contain fields whose values you want to add to the emitted outbox messages. For example, consider an outbox collection that has a value of purchase-order in the aggregatetype field and another field, eventType, whose possible values are order-created and order-shipped. Additional fields can be added with the syntax field:placement:alias.

The allowed values for placement are: - header - envelope - partition

To emit the eventType field value in the outbox message header, configure the SMT like this:

transforms=outbox,...
transforms.outbox.type=io.debezium.transforms.outbox.EventRouter
transforms.outbox.collection.fields.additional.placement=eventType:header:type

The result will be a header on the Kafka message with type as its key, and the value of the eventType field as its value.

To emit the eventType field value in the outbox message envelope, configure the SMT like this:

transforms=outbox,...
transforms.outbox.type=io.debezium.transforms.outbox.EventRouter
transforms.outbox.collection.fields.additional.placement=eventType:envelope:type

To control which partition the outbox message is produced on, configure the SMT like this:

transforms=outbox,...
transforms.outbox.type=io.debezium.transforms.outbox.EventRouter
transforms.outbox.collection.fields.additional.placement=partitionField:partition

Note that for the partition placement, adding an alias will have no effect.

By default, the payload of the Debezium outbox message is represented as a string. When the original source of the string is in JSON format, the resulting Kafka message uses escape sequences to represent the string, as shown in the following example:

# Kafka Topic: outbox.event.order
# Kafka Message key: "1"
# Kafka Message Headers: "id=596e275826f08b2730779e1f"
# Kafka Message Timestamp: 1556890294484
{
  "{\"id\": {\"$oid\": \"da8d6de63b7745ff8f4457db\"}, \"lineItems\": [{\"id\": 1, \"item\": \"Debezium in Action\", \"status\": \"ENTERED\", \"quantity\": 2, \"totalPrice\": 39.98}, {\"id\": 2, \"item\": \"Debezium for Dummies\", \"status\": \"ENTERED\", \"quantity\": 1, \"totalPrice\": 29.99}], \"orderDate\": \"2019-01-31T12:13:01\", \"customerId\": 123}"
}

You can configure the outbox event router to expand the message content, converting the escaped JSON back to its original, unescaped JSON format. In the converted string, the companion schema is deduced from the original JSON document. The following examples shows the expanded JSON in the resulting Kafka message:

# Kafka Topic: outbox.event.order
# Kafka Message key: "1"
# Kafka Message Headers: "id=596e275826f08b2730779e1f"
# Kafka Message Timestamp: 1556890294484
{
  "id": "da8d6de63b7745ff8f4457db", "lineItems": [{"id": 1, "item": "Debezium in Action", "status": "ENTERED", "quantity": 2, "totalPrice": 39.98}, {"id": 2, "item": "Debezium for Dummies", "status": "ENTERED", "quantity": 1, "totalPrice": 29.99}], "orderDate": "2019-01-31T12:13:01", "customerId": 123
}

To enable string conversion in the transformation, set the value of collection.expand.json.payload to true and use the StringConverter as shown in the following example:

transforms=outbox,...
transforms.outbox.type=io.debezium.connector.mongodb.transforms.outbox.MongoEventRouter
transforms.outbox.collection.expand.json.payload=true
value.converter=org.apache.kafka.connect.storage.StringConverter

The following table describes the options that you can specify for the outbox event router SMT. In the table, the Group column indicates a configuration option classification for Kafka.

You configure the partition routing transformation in the Debezium connector’s Kafka Connect configuration. The configuration specifies the following parameters:

By default, Debezium routes all change event records for a configured data collection to a single Apache Kafka topic. Connectors do not direct event records to specific partitions in the topic.

To configure a Debezium connector to route events to a specific partition, configure the PartitionRouting SMT in the Kafka Connect configuration for the Debezium connector.

For example, you might add the following configuration in your connector configuration.

...
topic.creation.default.partitions=2
topic.creation.default.replication.factor=1
...

topic.prefix=fulfillment
transforms=PartitionRouting
transforms.PartitionRouting.type=io.debezium.transforms.partitions.PartitionRouting
transforms.PartitionRouting.partition.payload.fields=change.name
transforms.PartitionRouting.partition.topic.num=2
transforms.PartitionRouting.predicate=allTopic
predicates=allTopic
predicates.allTopic.type=org.apache.kafka.connect.transforms.predicates.TopicNameMatches
predicates.allTopic.pattern=fulfillment.*
...

Based on the preceding configuration, whenever the SMT receives a message that is bound for a topic with a name that begin with the prefix, fulfillment, it redirects the message to a specific topic partition.

The SMT computes the target partition from a hash of the value of the name field in the message payload. By specifying the`allTopic` predicate, the configuration selectively applies the SMT. The change prefix is a special keyword that enables the SMT to automatically refer to elements in the payload that describe the before or after states of the data. If a specified field is not present in the event message, the SMT ignores it. If none of the fields exist in the message, then the transformation ignores the event message entirely, and delivers the original version of the message to the default destination topic. The number of partitions specified by the topic.num setting in the SMT configuration must match the number of partitions specified by the Kafka Connect configuration. For example, in the preceding configuration example, the value specified by the Kafka Connect property topic.creation.default.partitions matches the topic.num value in the SMT configuration.

Given this Products table

Based on the configuration, the SMT routes change events for the records that have the field name hammer to the same partition. That is, the items with id values 104, 105, and 106 are routed to the same partition.

Suppose that you want to route events from two data collections (t1, t2) to the same topic (for example, my_topic), and you want to partition events from data collection t1 by using field f1, and partition events from data collection t2 by using field f2.

You could apply the following configuration:

transforms=PartitionRouting
transforms.PartitionRouting.type=io.debezium.transforms.partitions.PartitionRouting
transforms.PartitionRouting.partition.payload.fields=change.f1,change.f2
transforms.PartitionRouting.partition.topic.num=2
transforms.PartitionRouting.predicate=myTopic

predicates=myTopic
predicates.myTopic.type=org.apache.kafka.connect.transforms.predicates.TopicNameMatches
predicates.myTopic.pattern=my_topic

The preceding configuration does not specify how to re-route events so that they are sent to a specific destination topic. For information about how to send events to topics other than their default destination topics, see the Topic Routing SMT., see the Topic Routing SMT.

The Debezium ComputePartition SMT is to be discontinued in a future release. The information in the following section describes how migrate from the ComputePartition SMT to the new PartitionRouting SMT.

Assuming that the configuration sets the same number of partitions for all topics, replace the following ComputePartition`configuration with the `PartitionRouting SMT. The following examples provide a comparison of the two configuration.

...
topic.creation.default.partitions=2
topic.creation.default.replication.factor=1
...
topic.prefix=fulfillment
transforms=ComputePartition
transforms.ComputePartition.type=io.debezium.transforms.partitions.ComputePartition
transforms.ComputePartition.partition.data-collections.field.mappings=inventory.products:name,inventory.orders:purchaser
transforms.ComputePartition.partition.data-collections.partition.num.mappings=inventory.products:2,inventory.orders:2
...

Replace the preceding ComputePartition with the following PartitionRouting configuration. Example: PartitionRouting configuration that replaces the earlier ComputePartition configuration

...
topic.creation.default.partitions=2
topic.creation.default.replication.factor=1
...

topic.prefix=fulfillment
transforms=PartitionRouting
transforms.PartitionRouting.type=io.debezium.transforms.partitions.PartitionRouting
transforms.PartitionRouting.partition.payload.fields=change.name,change.purchaser
transforms.PartitionRouting.partition.topic.num=2
transforms.PartitionRouting.predicate=allTopic
predicates=allTopic
predicates.allTopic.type=org.apache.kafka.connect.transforms.predicates.TopicNameMatches
predicates.allTopic.pattern=fulfillment.*
...

If the SMT emits events to topics that do not share the same number of partitions, you must specify unique partition.num.mappings values for each topic. For example, in the following example, the topic for the legacy products collection is configured with 3 partitions, and the topic for the orders data collection is configured with 2 partitions:

...
topic.prefix=fulfillment
transforms=ComputePartition
transforms.ComputePartition.type=io.debezium.transforms.partitions.ComputePartition
transforms.ComputePartition.partition.data-collections.field.mappings=inventory.products:name,inventory.orders:purchaser
transforms.ComputePartition.partition.data-collections.partition.num.mappings=inventory.products:3,inventory.orders:2
...

Replace the preceding ComputePartition configuration with the following PartitionRouting configuration: .PartitionRouting configuration that sets unique partition.topic.num values for different topics

...
topic.prefix=fulfillment

transforms=ProductsPartitionRouting,OrdersPartitionRouting
transforms.ProductsPartitionRouting.type=io.debezium.transforms.partitions.PartitionRouting
transforms.ProductsPartitionRouting.partition.payload.fields=change.name
transforms.ProductsPartitionRouting.partition.topic.num=3
transforms.ProductsPartitionRouting.predicate=products

transforms.OrdersPartitionRouting.type=io.debezium.transforms.partitions.PartitionRouting
transforms.OrdersPartitionRouting.partition.payload.fields=change.purchaser
transforms.OrdersPartitionRouting.partition.topic.num=2
transforms.OrdersPartitionRouting.predicate=products

predicates=products,orders
predicates.products.type=org.apache.kafka.connect.transforms.predicates.TopicNameMatches
predicates.products.pattern=fulfillment.inventory.products
predicates.orders.type=org.apache.kafka.connect.transforms.predicates.TopicNameMatches
predicates.orders.pattern=fulfillment.inventory.orders
...

The following table lists the configuration options that you can set for the partition routing SMT.