Google Cloud Functions Sink (Legacy) Connector for Confluent Cloud¶
Tip
Confluent recommends using the Google Cloud Functions Gen 2 Sink connector, which offers support for both Gen 2 and Gen 1 functions while delivering improved performance. For more information, see Google Cloud Functions Gen 2 Sink Connector for Confluent Cloud.
The fully-managed Google Cloud Functions Sink connector for Confluent Cloud integrates Apache Kafka® with Google Cloud Functions. For basic information about functions, see the Google Cloud Functions Documentation.
The connector consumes records from Kafka topics and executes a Google Cloud
Function. Each request sent to Google Cloud Functions can contain up to the
max.batch.size
number of records.
Note
This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Google Functions Sink Connector for Confluent Platform.
Features¶
The Google Cloud Functions Sink connector provides the following features:
- Results from Google Cloud Functions are stored in the following topics:
success-<connector-id>
error-<connector-id>
- Input data formats supported are Bytes, AVRO, JSON_SR (JSON Schema), JSON (Schemaless) and PROTOBUF. If no schema is defined, values are encoded as plain strings. For example,
"name": "Kimberley Human"
is encoded asname=Kimberley Human
.
For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.
Limitations¶
Be sure to review the following information.
- For connector limitations, see Google Functions Sink Connector limitations.
- If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
- If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.
Quick Start¶
Use this quick start to get up and running with the Confluent Cloud Google Cloud Functions Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to a target Google Cloud Function.
- Prerequisites
- Authorized access to a Confluent Cloud cluster on Google Cloud.
- Access to a Google Cloud function. For basic information about functions, see the Google Cloud Functions Documentation.
- A Google Cloud service account. You download service account credentials as a JSON file. This credentials file is uploaded when you set up the connector configuration properties
- The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
- Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
- The target Google Cloud function and the Kafka cluster should be in the same region.
Kafka cluster credentials. The following lists the different ways you can provide credentials.
- Enter an existing service account resource ID.
- Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.
- Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.
The following
connector-service
account role must be enabled in your project:The Trigger type must be set to
HTTP
. Select Require authentication.
Using the Confluent Cloud Console¶
Step 1: Launch your Confluent Cloud cluster¶
See the Quick Start for Confluent Cloud for installation instructions.
Step 2: Add a connector¶
In the left navigation menu, click Connectors. If you already have connectors in your cluster, click + Add connector.
Step 4: Enter the connector details¶
Note
- Ensure you have all your prerequisites completed.
- An asterisk ( * ) designates a required entry.
At the Add Google Cloud Functions Sink Connector screen, complete the following:
If you’ve already populated your Kafka topics, select the topics you want to connect from the Topics list.
To create a new topic, click +Add new topic.
- Select the way you want to provide Kafka Cluster credentials. You can
choose one of the following options:
- Global Access: Allows your connector to access everything you have access to. With global access, connector access will be linked to your account. This option is not recommended for production.
- Granular access: Limits the access for your connector. You will be able to manage connector access through a service account. This option is recommended for production.
- Use an existing API key: Allows you to enter an API key and secret part you have stored. You can enter an API key and secret (or generate these in the Cloud Console).
- Click Continue.
- In the Cloud Functions project ID field, enter your Google Cloud project ID where the functions is deployed.
- Enter the Google Cloud function to invoke in the Google Cloud function name field. For basic information about Google Cloud Functions, see Your First Function.
- Upload your Google Cloud credentials JSON file created as part of the prerequisites.
- Click Continue.
Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR, PROTOBUF, JSON, or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format.
Show advanced configurations
Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.
Batch size: The maximum number of Kafka records to combine in a single function invocation. To disable batching of records, set this value to 1.
Max pending requests: The maximum number of pending requests that can be made to Google Cloud Functions concurrently.
Request timeout (ms): Sets the input Kafka record key format. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING. A valid schema must be available in Schema Registry to use a schema-based message format.
Retry timeout (ms): The maximum time, in milliseconds, that the connector attempts to request Google Cloud Functions before timing out (socket timeout).
Behavior on error: The connector’s behavior if the called GCP function returns an error.
For Transforms and Predicates, see the Single Message Transforms (SMT) documentation for details.
See Configuration Properties for all property values and definitions.
Click Continue.
Based on the number of topic partitions you select, you will be provided with a recommended number of tasks.
- To change the number of recommended tasks, enter the number of tasks for the connector to use in the Tasks field.
- Click Continue.
Verify the connection details.
Click Launch.
The status for the connector should go from Provisioning to Running.
Step 5: Check for records¶
Verify that records are being produced.
For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.
Tip
When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.
Using the Confluent CLI¶
Complete the following steps to set up and run the connector using the Confluent CLI.
Note
Make sure you have all your prerequisites completed.
Step 1: List the available connectors¶
Enter the following command to list available connectors:
confluent connect plugin list
Step 2: List the connector configuration properties¶
Enter the following command to show the connector configuration properties:
confluent connect plugin describe <connector-plugin-name>
The command output shows the required and optional configuration properties.
Step 3: Create the connector configuration file¶
Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties.
{
"connector.class": "GoogleCloudFunctionsSink",
"name": "GoogleCloudFunctionsSinkConnector_0",
"topics": "pageviews_proto",
"input.data.format": "PROTOBUF",
"kafka.auth.mode": "KAFKA_API_KEY",
"kafka.api.key": "****************",
"kafka.api.secret": "****************************************************************",
"function.name": "dev-test",
"project.id": "connect-2021",
"gcf.credentials.json": "*",
"tasks.max": "1"
}
Note the following property definitions:
"connector.class"
: Identifies the connector plugin name."name"
: Sets a name for your new connector."topics"
: Identifies the topic name or a comma-separated list of topic names."input.data.format"
: Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, or BYTES. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
"kafka.auth.mode"
: Identifies the connector authentication mode you want to use. There are two options:SERVICE_ACCOUNT
orKAFKA_API_KEY
(the default). To use an API key and secret, specify the configuration propertieskafka.api.key
andkafka.api.secret
, as shown in the example configuration (above). To use a service account, specify the Resource ID in the propertykafka.service.account.id=<service-account-resource-ID>
. To list the available service account resource IDs, use the following command:confluent iam service-account list
For example:
confluent iam service-account list Id | Resource ID | Name | Description +---------+-------------+-------------------+------------------- 123456 | sa-l1r23m | sa-1 | Service account 1 789101 | sa-l4d56p | sa-2 | Service account 2
"function.name"
: The name of your predefined Google Cloud function."project.id"
: Your GCP project ID."gcf.credentials.json"
: This contains the contents of the downloaded JSON file. See Formatting Google Cloud credentials for details about how to format and use the contents of the downloaded credentials file.
Optional:
"max.batch.size"
: The maximum number of records to combine when invoking a single Google Cloud function. Defaults to1
(batching disabled). Accepts values from1
to1000
."max.pending.requests"
: The maximum number of pending requests that can be made to Google Cloud functions concurrently. Defaults to1
."request.timeout"
: The maximum time in milliseconds that the connector will attempt a request to Google Cloud Functions before timing out (i.e., socket timeout). Defaults to300000
ms (5 minutes)."retry.timeout"
: The total amount of time, in milliseconds (ms), that the connector will exponentially backoff and retry failed requests (i.e., throttling). Response codes that are retried areHTTP 401 Unauthorized
andHTTP 500 Internal Server Error
. Defaults to300000
ms (5 minutes). Enter-1
to configure this property for indefinite retries.
Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI. See Unsupported transformations for a list of SMTs that are not supported with this connector.
See Configuration Properties for all property values and definitions.
Formatting Google Cloud credentials¶
The contents of the downloaded credentials file must be converted to string format before it can be used in the connector configuration.
Convert the JSON file contents into string format.
Add the escape character
\
before all\n
entries in the Private Key section so that each section begins with\\n
(see the highlighted lines below). The example below has been formatted so that the\\n
entries are easier to see. Most of the credentials key has been omitted.Tip
A script is available that converts the credentials to a string and also adds additional
\
escape characters where needed. See Stringify Google Cloud Credentials.{ "connector.class": "GoogleCloudFunctionsSink", "name": "GoogleCloudFunctionsSinkConnector_0", "kafka.api.key": "<my-kafka-api-key>", "kafka.api.secret": "<my-kafka-api-secret>", "topics": "<topic-name>", "data.format": "AVRO", "function.name": "dev-test", "project.id": "connect-2021", "gcf.credentials.json": "{\"type\":\"service_account\",\"project_id\":\"connect- 1234567\",\"private_key_id\":\"omitted\", \"private_key\":\"-----BEGIN PRIVATE KEY----- \\nMIIEvAIBADANBgkqhkiG9w0BA \\n6MhBA9TIXB4dPiYYNOYwbfy0Lki8zGn7T6wovGS5pzsIh \\nOAQ8oRolFp\rdwc2cC5wyZ2+E+bhwn \\nPdCTW+oZoodY\\nOGB18cCKn5mJRzpiYsb5eGv2fN\/J \\n...rest of key omitted... \\n-----END PRIVATE KEY-----\\n\", \"client_email\":\"pub-sub@connect-123456789.iam.gserviceaccount.com\", \"client_id\":\"123456789\",\"auth_uri\":\"https:\/\/accounts.google.com\/o\/oauth2\/ auth\",\"token_uri\":\"https:\/\/oauth2.googleapis.com\/ token\",\"auth_provider_x509_cert_url\":\"https:\/\/ www.googleapis.com\/oauth2\/v1\/ certs\",\"client_x509_cert_url\":\"https:\/\/www.googleapis.com\/ robot\/v1\/metadata\/x509\/pub-sub%40connect- 123456789.iam.gserviceaccount.com\"}", "tasks.max": "1" }
Add all the converted string content to the
"gcf.credentials.json"
section of your configuration file as shown in the example above.
Step 4: Load the properties file and create the connector¶
Enter the following command to load the configuration and start the connector:
confluent connect cluster create --config-file <file-name>.json
For example:
confluent connect cluster create --config-file google-functions-sink-config.json
Example output:
Created connector GoogleCloudFunctionsSinkConnector_0 lcc-ix4dl
Step 5: Check the connector status¶
Enter the following command to check the connector status:
confluent connect cluster list
Example output:
ID | Name | Status | Type
+-----------+-------------------------------------+---------+------+
lcc-ix4dl | GoogleCloudFunctionsSinkConnector_0 | RUNNING | sink
Step 6: Check for records.¶
Verify that records are being produced.
For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.
Tip
When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.
Configuration Properties¶
Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.
Which topics do you want to get data from?¶
topics
Identifies the topic name or a comma-separated list of topic names.
- Type: list
- Importance: high
Schema Config¶
schema.context.name
Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.
- Type: string
- Default: default
- Importance: medium
Input messages¶
input.data.format
Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON or BYTES. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.
- Type: string
- Importance: high
How should we connect to your data?¶
name
Sets a name for your connector.
- Type: string
- Valid Values: A string at most 64 characters long
- Importance: high
Kafka Cluster credentials¶
kafka.auth.mode
Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode.
- Type: string
- Default: KAFKA_API_KEY
- Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
- Importance: high
kafka.api.key
Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.
- Type: password
- Importance: high
kafka.service.account.id
The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.
- Type: string
- Importance: high
kafka.api.secret
Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.
- Type: password
- Importance: high
How should we connect to your function?¶
function.name
The Google Cloud function to invoke.
- Type: string
- Importance: high
project.id
The Google Cloud Project ID where the function is deployed.
- Type: string
- Importance: high
GCP credentials¶
gcf.credentials.json
GCP service account JSON file with invoker permission for Functions.
- Type: password
- Importance: high
Cloud Function details¶
max.batch.size
The maximum number of Kafka records to combine in a single function invocation. To disable batching of records, set this value to 1.
- Type: int
- Default: 1
- Valid Values: [1,…]
- Importance: low
max.pending.requests
The maximum number of pending requests that can be made to Google Cloud Functions concurrently.
- Type: int
- Default: 1
- Valid Values: [1,…,128]
- Importance: low
request.timeout.ms
The maximum time, in milliseconds, that the connector attempts to request Google Cloud Functions before timing out (socket timeout).
- Type: int
- Default: 300000 (5 minutes)
- Valid Values: [0,…]
- Importance: low
retry.timeout.ms
The total amount of time, in milliseconds, that the connector will exponentially backoff and retry failed requests i.e on throttling. Response codes that are retried are HTTP 401 Unauthorized and HTTP 500 Internal Server Error. A value of -1 indicates indefinite retrying.
- Type: int
- Default: 300000 (5 minutes)
- Valid Values: [-1,…]
- Importance: low
How should we handle errors?¶
behavior.on.error
The connector’s behavior if the called GCP function returns an error. Valid options are ‘log’ and ‘fail’. ‘log’ logs the error message and continues processing and ‘fail’ stops the connector in case of an error.
- Type: string
- Default: log
- Importance: low
Consumer configuration¶
max.poll.interval.ms
The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).
- Type: long
- Default: 300000 (5 minutes)
- Valid Values: [60000,…,1800000]
- Importance: low
max.poll.records
The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.
- Type: long
- Default: 500
- Valid Values: [1,…,500]
- Importance: low
Number of tasks for this connector¶
tasks.max
Maximum number of tasks for the connector.
- Type: int
- Valid Values: [1,…]
- Importance: high
Next Steps¶
For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.