KafkaConnector

Connects to Apache Kafka, Confluent, RedPanda or other Kafka-compatible data stream services to send and receive messages.

Jump to Configuration

Typical Uses

Sending and receiving messages
Creating topics

How does it work?

The KafkaConnector uses your account credentials to perform various tasks:

Action	Task
Commit	Manually commit read offsets.
Get Metadata	Retrieve metadata into an attribute.
Receive	Receive messages from topics.
Send	Send messages to queues or topics.

Optional Input Port

This transformer has two modes, depending on whether a connector is attached to the Input port or not:

Input-driven: When input features are connected, the transformer runs once for each feature it receives in the Input port.
Run Once: When no input features are connected, the transformer runs one time.

When the Input port is in use, the Initiator output port is also enabled.

Heartbeat

When Action is Receive and the connection is still alive (Receive Mode is Stream or a batch is not yet complete), heartbeat features are output at a specified interval after a period of non-activity.

Heartbeat features have two attributes:

_heartbeat_id: Integer starting at 0. This is a unique identifier for each heartbeat feature output from this port.
_heartbeat_counter: Integer starting at 0 that resets whenever the connector receives a new message and a new period of inactivity begins. This counter represents the number of consecutive heartbeat features.

Usage Notes

The KafkaConnector connects to a list of bootstrap servers initially, but it may connect to further servers as they are discovered. It does not connect to the ZooKeeper instance directly.
The KafkaConnector allows for automatic or manual offset commits. See Committing offsets and reset policy.

Configuration

Input Ports

Output Ports

Output

Features with added attributes, as specified in parameters and according to Action:

Action	Output - Input-Driven	Output - Run Once
Commit	Input feature with commit details.	One new feature with commit details.
Get Metadata	Input feature with metadata details.	New feature with metadata details.
Receive	Multiple features, one for each message received, with details about the message.	Multiple features, one for each message received, with details about the message.
Send	Input feature with details about the sent message.	New feature with details about the sent message.

Parameters

Authentication

Credential Source

Select the type of credentials to use:

Anonymous: Access an Apache Kafka cluster without authenticating.
Web Connection (Recommended): Use an Apache Kafka web connection.
Embedded: Embed user name and password as transformer parameters.

Account

When Credential Source is Web Connection, select or create a Web Connection connecting to an Apache Kafka Web Service.

See Using Web Connections.

User Name and Password

When Credential Source is Embedded:

User Name	Provide a user name.
Password	Provide a password.

Security

Security Protocol

Select a protocol:

Plaintext - Unauthenticated, non-encrypted channel
SSL - Encrypted channel
SASL Plaintext - Authenticated, non-encrypted channel
SASL SSL - Authenticated, encrypted channel

SASL Mechanism

When Security Protocol is SASL Plaintext or SASL SSL, select an option:

Plain - username/password authentication
SCRAM-SHA-256 - SCRAM authentication with SHA-256 hash function
SCRAM-SHA-512 - SCRAM authentication with SHA-512 hash function

SSL CA Certificate

(Optional) When Security Protocol is SSL or SASL SSL, provide the path and file name of a certificate.

Brokers

Provide one or more bootstrap servers to connect to.

Host Name	Provide a host name.
Port	Provide the port.

See Kafka - Consumer config.

Action > Commit

Topic

Specify the topic for which to set the commit offset.

Commit Options

Receiving Connector Identifier	Set to the value of the _connector_id attribute produced by the upstream receiving KafkaConnector.
Offset	Specify the offset to commit, typically set to the _offset attribute of the received message.
Partition	Specify the partition to commit, typically set to the _partition attribute of the received message.
Advanced Consumer Options	Provide any additional configuration options, as JSON objects. Example: Copy `{ "socket.timeout.ms": 100, "check.crcs": true }` Any options set here will override settings in other parameters. See librdkafka - Configuration properties

Output Attributes

Attributes to Add

Select any available attributes to be included on the output features:

_offset	The offset of the message in the partition
_partition	The partition the message is stored in
_timestamp	The message timestamp from the cluster. Depending on server configuration, this timestamp may have slightly different meanings.
_topic	The topic the message received from or sent to
_value	The value of the message

Action > Get Metadata

Metadata is retrieved into the _metadata attribute, and is formatted as JSON. It includes cluster, broker, topic and partition metadata.

Metadata Options

Advanced Metadata Options

Provide any additional configuration options, as JSON objects.

Example:

Copy

{
 "socket.timeout.ms": 100,
 "check.crcs": true
}

Any options set here will override settings in other parameters.

See librdkafka - Configuration properties

Output Attributes

Attributes to Add

Select any available attributes to be included on the output features:

_metadata	JSON-formatted metadata.
_timestamp	The message timestamp from the cluster. Depending on server configuration, this timestamp may have slightly different meanings.
_topic	The topic the message received from or sent to
_value	The value of the message

Action > Receive

Topics

Specify the topics to receive from.

Receive Behavior

Mode

Select a message receiving method:

Stream: The connector continues to receive messages indefinitely. The translation must be terminated to stop receiving.
Batch: Only a given number of messages are received before the connection is closed. If the end of the partition is reached or there are no messages received after three retries, the connection is also closed.

Maximum per Second

(Optional) When Mode is Stream, specify the maximum number of messages that will be received per second.

If blank, no limit is applied.

Batch Size

When Mode is Batch, specify the maximum number of messages per batch.

Receive Options

Consumer Group ID

Specify the consumer group the connector belongs to.

See Kafka documentation.

Starting Offset

Select a starting offset position:

Earliest (default): Start receiving from the earliest uncommitted offset.
Latest: Start receiving from the latest uncommitted offset.
Custom: Specify a partition and offset.

Partition

When Starting Offset is Custom, specify the partition to assign the connector to.

Offset

When Starting Offset is Custom, specify the offset to start receiving from.

Auto Commit

Select a commit option:

Yes (default): Automatically commit the read offset periodically, regardless of whether FME processes the message successfully.
No: Do not automatically commit read offsets. A _connector_id attribute will be added, which can be used after the message is successfully processed to perform a manual commit with another KafkaConnector, Action > Commit.

Heartbeat

Interval

Specify the duration of a period of inactivity before a heartbeat feature is emitted, in Time Units.

Default is 1.

Time Unit

Select the Interval unit:

Seconds
Minutes
Hours (default)
Days

Advanced Consumer Options

Provide any additional configuration options, as JSON objects.

Example:

Copy

{
 "socket.timeout.ms": 100,
 "check.crcs": true
}

Any options set here will override settings in other parameters.

See librdkafka - Configuration properties

Message Options

Message Schema

Select the message schema type:

Simple: A single encoded string or binary value
Avro: A schema defined using Apache Avro

See Apache Avro - Specification.

Encoding

When Message Schema is Simple, select the message encoding.

The _key and _value output attributes contain the raw bytes received from the server by default.

If another encoding is selected, the message will be decoded to a string accordingly.

Schema Registry

When Message Schema is Avro, provide the URL for the schema registry.

See Schema Registry for Confluent Platform.

Output Attributes

Attributes to Add

Select any available attributes to be included on the output features:

_headers{}.name _headers{}.value	Structured list attributes containing the header keys and values of a received message. Note that headers for sent messages are not available.
_key	The key of the message
_len	The length of the message in bytes
_offset	The offset of the message in the partition
_partition	The partition the message is stored in
_timestamp	The message timestamp from the cluster. Depending on server configuration, this timestamp may have slightly different meanings.
_topic	The topic the message received from or sent to
_value	The value of the message

Action > Send

Topics

Specify the topics to send to.

Send Options

Create Missing Topics	Select an option for handling topics that do not exist: Yes (default): Create missing topics automatically. No: Do not create missing topics. If No, a feature will be output via the <Rejected> port for any messages sent to missing topics.
Number of Partitions	When Create Missing Topics is Yes, specify the number of partitions for new topics.
Replication Factor	When Create Missing Topics is Yes, specify the replication factor for new topics.
Advanced Producer Options	Provide any additional configuration options, as JSON objects. Example: Copy `{ "socket.timeout.ms": 100, "check.crcs": true }` Any options set here will override settings in other parameters. See librdkafka - Configuration properties

Message Options

Message Key	(Optional) Provide a message key, usually used for partitioning and log compaction.
Message Value	Provide the message as a string or binary value.
Message Schema	Select the message schema type: Simple: A single encoded string or binary value Avro: A schema defined using Apache Avro See Apache Avro - Specification.
Encoding	When Message Schema is Simple, select the message encoding. Numeric attributes will be converted to text before encoding. If fme-binary is selected, strings will be encoded as utf-8, binary attributes will be sent as-is, and other attribute types will not be supported.
Schema Registry	When Message Schema is Avro, provide the URL for the schema registry. See Schema Registry for Confluent Platform.
Key Schema	(Optional) When Message Schema is Avro, provide a JSON-based Avro schema to use for the message key.
Value Schema	When Message Schema is Avro, provide a JSON-based Avro schema to use for the message value.

Output Attributes

Attributes to Add

Select any available attributes to be included on the output features:

_headers{}.name _headers{}.value	Structured list attributes containing the header keys and values of a received message. Note that headers for sent messages are not available.
_key	The key of the message
_len	The length of the message in bytes
_offset	The offset of the message in the partition
_partition	The partition the message is stored in
_timestamp	The message timestamp from the cluster. Depending on server configuration, this timestamp may have slightly different meanings.
_topic	The topic the message received from or sent to
_value	The value of the message

Editing Transformer Parameters

Transformer parameters can be set by directly entering values, using expressions, or referencing other elements in the workspace such as attribute values or user parameters. Various editors and context menus are available to assist. To see what is available, click beside the applicable parameter.

How to Set Parameter Values

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters.

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

Content Types

String Functions	These functions manipulate and format strings.
Special Characters	A set of control characters is available in the Text Editor.
Math Functions	Math functions are available in both editors.
Date/Time Functions	Date and time functions are available in the Text Editor.
Math Operators	These operators are available in the Arithmetic Editor.
FME Feature Functions	These return primarily feature-specific values.
FME Parameters	FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters	Create your own editable parameters.

Dialog Options - Tables

Table Tools

Transformers with table-style parameters have additional tools for populating and manipulating values.

Row Reordering

Enabled once you have clicked on a row item. Choices include:

Add a row
Remove a row
Move current row up one
Move current row down one
Move current row to top
Move current row to bottom

Cut, Copy, and Paste

Enabled once you have clicked on a row item. Choices include:

Cut a row - delete and copy to clipboard
Copy a row to the clipboard
Paste a row from the clipboard

Cut, copy, and paste may be used within a transformer, or between transformers.

Filter

Start typing a string, and the matrix will only display rows matching those characters. Searches all columns. This only affects the display of attributes within the transformer - it does not alter which attributes are output.

Import

Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers.

Reset/Refresh

Generally resets the table to its initial state, and may provide additional options to remove invalid entries. Behavior varies between transformers.

Note: Not all tools are available in all transformers.

For more information, see Transformer Parameter Menu Options.

Reference

Processing Behavior	Feature-Based
Feature Holding	No
Dependencies	Apache Kafka
Aliases	ApacheKafkaConnector, ConfluentConnector, RedPandaConnector
History	Released FME 2019.0

FME Online Resources

The FME Community and Support Center Knowledge Base have a wealth of information, including active forums with 35,000+ members and thousands of articles.

Search for all results about the KafkaConnector on the FME Community.

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