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Salesforce Bulk API Source Connector for Confluent Cloud

Note

If you are installing the connector locally for Confluent Platform, see Salesforce Bulk API Source Connector for Confluent Platform.

The Salesforce Bulk API Source connector for Confluent Cloud integrates Salesforce.com with Apache Kafka®. This connector pulls records and captures changes from Salesforce.com using the Salesforce Bulk Query API.

Features

The Salesforce Bulk API Source connector provides the following features:

  • At least once delivery: The connector guarantees that records are delivered at least once to the Kafka topic. If the connector restarts, there could be duplicate records in the Kafka topic.
  • Supported data formats: The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) output data. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Environment Limitations for additional information.
  • Tasks per connector: Organizations can run multiple connectors with a limit of one task per connector (that is, "tasks.max": "1").
  • Supported SObjects: See the following lists for supported and unsupported Salesforce objects. See Confluent Cloud connector limitations for additional information.

The following Salesforce objects are supported by this connector:

  • Account
  • Campaign
  • CampaignMember
  • Case
  • Contact
  • Contract
  • Event
  • Group
  • Lead
  • Opportunity
  • OpportunityContactRole
  • OpportunityLineItem
  • Period
  • PricebookEntry
  • Product2
  • Task
  • TaskFeed
  • TaskRelation
  • User
  • UserRole

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Limitations

Be sure to review the following information.

Quick Start

Use this quick start to get up and running with the Salesforce Bulk API Source connector. The quick start provides the basics of selecting the connector and configuring it to capture records and record changes from Salesforce.

Prerequisites
  • 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.

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster.

See the Quick Start for Apache Kafka using Confluent Cloud for installation instructions.

Step 2: Add a connector.

In the left navigation menu, click Data integration, and then click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector.

Click the Salesforce Bulk API Source connector icon.

Salesforce Bulk API Source Connector Icon

Important

At least one topic must exist in your Confluent Cloud cluster before creating the connector.

Step 4: Set up the connection.

Complete the following steps and click Continue.

Note

  • Make sure you have all your prerequisites completed.
  • An asterisk ( * ) designates a required entry.
  1. Enter a connector name.
  2. Select the way you want to provide Kafka Cluster credentials. You can either select a service account resource ID or you can enter an API key and secret (or generate these in the Cloud Console).
  3. Add your Salesforce connection details.
    • Salesforce instance is optional. If not entered, this property defaults to https://login.salesforce.com. The connector uses the endpoint specified in the authentication response from Salesforce. The remaining Salesforce credentials are required.
    • Salesforce credentials: Enter the username, password, and password token to authenticate with Salesforce.
    • Salesforce Object is the SObject that the connector polls for new and changed records.
    • Poll interval (ms) is optional. Enter a time in milliseconds (ms) that the connector waits before polling the SObject again. The default is 30000 ms (30 seconds). The maximum value allowed is 300000 (5 minutes).
    • Enable Batching is optional. Enable batching to use PK Chunking for batching records. The default value is false.
    • Enter a Salesforce since date. The connector pulls data starting from this date. The required format is yyyy-MM-dd.
    • Max Retry Time in Milliseconds: If an error occurs when the connector makes a request to Salesforce, it retries until the time in milliseconds (ms) elapses. The default value is 30000 ms (30 seconds). The minimum value is 1000 ms (1 second).
  4. Select the Output Kafka record value format (data coming from the connector): AVRO, JSON (schemaless), JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Environment Limitations for additional information.
  5. Enter the number of tasks in use by the connector. Organizations can run multiple connectors with a limit of one task per connector (that is, "tasks.max": "1").
  6. Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.

See Configuration Properties for all property values and descriptions.

Step 5: Launch the connector.

Verify the connection details by previewing the running configuration. Once you’ve validated that the properties are configured to your satisfaction, click Launch.

Tip

For information about previewing your connector output, see Connector Data Previews.

Launch the connector

Step 6: Check the connector status.

The status for the connector should go from Provisioning to Running. It may take a few minutes.

Check the connector status

Step 7: Check the Kafka topic.

After the connector is running, verify that messages are populating your Kafka topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

See also

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.

../_images/topology.png

Using the Confluent CLI

Complete the following steps to set up and run the connector using the Confluent CLI.

Important

  • Make sure you have all your prerequisites completed.
  • At least one topic must exist in your Confluent Cloud cluster before creating the connector.
  • The example commands use Confluent CLI version 2. For more information see, Confluent CLI v2.

Step 1: List the available connectors.

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: Show the required connector configuration properties.

Enter the following command to show the required connector properties:

confluent connect plugin describe <connector-catalog-name>

For example:

confluent connect plugin describe SalesforceBulkApiSource

Example output:

Following are the required configs:
connector.class: SalesforceBulkApiSource
name
kafka.auth.mode
kafka.api.key
kafka.api.secret
kafka.topic
salesforce.username
salesforce.password
salesforce.password.token
salesforce.object
output.data.format
tasks.max

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": "SalesforceBulkApiSource,
  "name": "SalesforceBulkApiSource_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "<my-kafka-api-key>",
  "kafka.api.secret": "<my-kafka-api-secret>",
  "kafka.topic": "TestBulkAPI",
  "salesforce.username": "<my-username>",
  "salesforce.password": "**************",
  "salesforce.password.token": "************************",
  "salesforce.object": "<SObject-name>","
  "output.data.format": "JSON",
  "tasks.max": "1"
}

Note the following property definitions:

  • "connector.class": Identifies the connector plugin name.
  • "name": Sets a name for your new connector.

  • "kafka.auth.mode": Identifies the connector authentication mode you want to use. There are two options: SERVICE_ACCOUNT or KAFKA_API_KEY (the default). To use an API key and secret, specify the configuration properties kafka.api.key and kafka.api.secret, as shown in the example configuration (above). To use a service account, specify the Resource ID in the property kafka.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

  • ""kafka.topic": Enter a Kafka topic name. A topic must exist before launching the connector.

  • ""salesforce.<...>"": Enter the required Salesforce connection details.

  • ""salesforce.object"": The SObject that the connector polls for new and changed records.

  • "output.data.format": Sets the output Kafka record value format (data coming from the connector). Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

  • "tasks.max": Enter the number of tasks in use by the connector. Organizations can run multiple connectors with a limit of one task per connector (that is, "tasks.max": "1").

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

See Configuration Properties for all property values and descriptions.

Step 4: Load the properties file and create the connector.

Enter the following command to load the configuration and start the connector:

confluent connect create --config <file-name>.json

For example:

confluent connect create --config salesforce-bulk-api-source.json

Example output:

Created connector SalesforceBulkApiSource_0 lcc-aj3qr

Step 5: Check the connector status.

Enter the following command to check the connector status:

confluent connect list

Example output:

ID          |            Name              | Status  |  Type
+-----------+------------------------------+---------+-------+
lcc-aj3qr   | SalesforceBulkApiSource_0    | RUNNING | source

Step 6: Check the Kafka topic.

After the connector is running, verify that messages are populating your Kafka topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Configuration Properties

Use the following configuration properties with this connector.

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
  • 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
  • Type: password
  • Importance: high

Which topic do you want to send data to?

kafka.topic

Identifies the topic name to write the data to.

  • Type: string
  • Importance: high

How should we connect to Salesforce?

salesforce.instance

The URL of the Salesforce endpoint to use. The default is https://login.salesforce.com. This directs the connector to use the endpoint specified in the authentication response.

salesforce.username

The Salesforce username the connector should use.

  • Type: string
  • Importance: high
salesforce.password

The Salesforce password the connector should use.

  • Type: password
  • Importance: high
salesforce.password.token

The Salesforce security token associated with the username.

  • Type: password
  • Importance: high
salesforce.object

The Salesforce object to create topic for.

  • Type: string
  • Importance: high
poll.interval.ms

How often to query Salesforce for new records.

  • Type: int
  • Default: 30000 (30 seconds)
  • Valid Values: [8700,…,300000]
  • Importance: medium
batch.enable

Enable batching by applying PK-chunking. The default value is TRUE.

  • Type: boolean
  • Default: true
  • Importance: low
salesforce.since

CreatedDate after which the records should be pulled. Note that the time is in UTC and has required format: yyyy-MM-dd.

  • Type: string
  • Importance: medium

Connection details

request.max.retries.time.ms

In case of error when making a request to Salesforce, the connector will retry until this time (in ms) elapses. The default value is 30000 (30 seconds). Minimum value is 1 sec

  • Type: long
  • Default: 30000 (30 seconds)
  • Valid Values: [1000,…,250000]
  • Importance: low

Output messages

output.data.format

Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. 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

Number of tasks for this connector

tasks.max
  • Type: int
  • Valid Values: [1,…,1]
  • Importance: high

Next Steps

See also

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.

../_images/topology.png