# Kafka Connect ExtractTopic SMT Usage Reference for Confluent Platform The `ExtractTopic` SMT (`io.confluent.connect.transforms.ExtractTopic`) extracts data from a Apache Kafka® record key, value, or header and uses it as the topic name. ## Description The `ExtractTopic` SMT extracts data from a Apache Kafka® record and uses it as the topic name. You can either use the entire key or value (which should be a string), or use a field from a map or struct. Use the specific transformation type designed for the record key (`io.confluent.connect.transforms.ExtractTopic$Key`) or value (`io.confluent.connect.transforms.ExtractTopic$Value`). You can also extract the entire value from a message header value (string) by using the specific type (`io.confluent.connect.transforms.ExtractTopic$Header`). ## Installation Confluent develops this transformation and does not ship it by default with Apache Kafka® or Confluent Platform. You can install this transformation using the [confluent connect plugin install](https://docs.confluent.io/confluent-cli/current/command-reference/connect/plugin/confluent_connect_plugin_install.html) command: ```properties confluent connect plugin install confluentinc/connect-transforms:latest ``` ## Properties | Name | Description | Type | Default | Valid values | Importance | |------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------|-----------|----------------------|--------------| | `field` | The field name or header key to use as the topic name. Required when extracting from a header using `ExtractTopic$Header`.
For `ExtractTopic$Key` or `ExtractTopic$Value`, leaving this blank configures the SMT to use the entire key or value as a string for the topic name. | string | “” | | medium | | `field.format` | The format used to parse the field path. The SMT supports two formats: `JSON_PATH` and `PLAIN`.

If set to `JSON_PATH`, the transformer interprets the field with a JSON path interpreter, which supports nested field extraction.

If set to `PLAIN` (or left blank), the transformer evaluates the field configuration as a non-nested field name. When using `ExtractTopic$Header`, only the default `PLAIN` format can be used, which extracts the header value as a string. | string | `PLAIN` | `JSON_PATH`, `PLAIN` | medium | | `skip.missing.or.null` | Determines how to handle missing fields and null values.

By default, the SMT throws an exception if a field defined in the `field` configuration is missing or null, or if no field is specified but the message’s key or value is null.

Set to `true` to silently ignore these conditions and pass the record unaltered. | boolean | `false` | | low | ## Examples The following configuration snippet shows how to use and configure the `ExtractTopic` SMT. ```json "transforms": "KeyExample,ValueFieldExample,KeyFieldExample,FieldJsonPathExample,HeaderExample" ``` ### Use the key as the topic name Use the key of the message as the topic name. ```json "transforms.KeyExample.type": "io.confluent.connect.transforms.ExtractTopic$Key" ``` ### Extract a field from the value Extract a required field named `f2` from the value, and use it as the topic name. ```json "transforms.ValueFieldExample.type": "io.confluent.connect.transforms.ExtractTopic$Value", "transforms.ValueFieldExample.field": "f2" ``` ### Extract a field from the key Extract a field named `f3` from the key, and use it as the topic name. If the field is null or missing, leave the topic name as-is. ```json "transforms.KeyFieldExample.type": "io.confluent.connect.transforms.ExtractTopic$Key", "transforms.KeyFieldExample.field": "f3", "transforms.KeyFieldExample.skip.missing.or.null": "true" ``` ### Extract a nested field with JSON Path Extract the value of a nested field — for example, the field `f3` inside the `f1` field — and use it as the topic name. The field path uses JSON Path syntax (for example, `$["f1"]["f3"]`). If the field is null or missing, leave the topic name as-is. ```json "transforms.FieldJsonPathExample.type": "io.confluent.connect.transforms.ExtractTopic$Value", "transforms.FieldJsonPathExample.field": "$["f1"]["f3"]", "transforms.FieldJsonPathExample.field.format": "JSON_PATH", "transforms.FieldJsonPathExample.skip.missing.or.null": "true" ``` ### Extract a header value as the topic name Extract the value of a message header (as a string) with key `h1` (required) and use it as the topic name. ```json "transforms.HeaderExample.type": "io.confluent.connect.transforms.ExtractTopic$Header", "transforms.HeaderExample.field": "h1" ``` ## Predicates Configure transformations with predicates to ensure they process only records that satisfy a particular condition. You can also use predicates in a transformation chain with the [Kafka Connect Filter (Kafka) SMT Usage Reference for Confluent Platform](filter-ak.md#ak-filter) to conditionally filter specific records. For more information, see [Predicates](filter-ak.md#predicates).