# Zendesk Source Connector for Confluent Cloud
Zendesk is a customer service system for tracking, prioritizing, and solving
customer support tickets. The fully managed Zendesk Source connector for
Confluent Cloud copies data into Apache Kafka® from various Zendesk support tables such as
`tickets`, `ticket_audits`, `ticket_fields`, `groups`,
`organizations`, `satisfaction_ratings`, among others. The connector streams
data to Zendesk using the [Zendesk Support API](https://developer.zendesk.com/rest_api/docs/support/introduction). See
[Supported tables](#cc-zendesk-supported-tables) for more information.
#### NOTE
* This Quick Start is for the fully managed Confluent Cloud connector. If you are
installing the connector locally for Confluent Platform, see [Zendesk Source Connector for
Confluent Platform](https://docs.confluent.io/kafka-connectors/zendesk/current/).
* If you require private networking for fully managed connectors, make sure to set up the proper
networking beforehand. For more information, see [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud).
## Features
The Zendesk Source connector provides the following features:
* **Topics created automatically**: The connector can automatically create Kafka topics.
* **At least once delivery**: The connector guarantees that records are delivered at least once to the Kafka topic.
* **Supported data formats**: The connector supports Avro, JSON Schema (JSON-SR), Protobuf, and JSON (schemaless) output formats. You must enable [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf).
* **Offset management capabilities**: Supports offset management. For more information, see [Manage custom offsets](#cc-zendesk-source-custom-offsets).
For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.
## Limitations
Be sure to review the following information.
* For connector limitations, see [Zendesk Source Connector](limits.md#cc-zendesk-source-limits) limitations.
* If you plan to use one or more Single Message Transformations (SMTs), see [SMT Limitations](single-message-transforms.md#cc-single-message-transforms-limitations).
## Supported tables
See the following dropdown list for supported Zendesk tables.
### **Supported tables**
- activities
- apps
- audit_logs
- automations
- bookmarks
- brands
- custom_roles
- groups
- group_memberships
- locales
- macros
- organizations
- organization_fields
- organization_subscriptions
- organization_memberships
- recipient_addresses
- requests
- resource_collections
- satisfaction_ratings
- satisfaction_reasons
- sharing_agreements
- suspended_tickets
- targets
- target_failures
- tickets
- ticket_audits
- ticket_fields
- ticket_forms
- ticket_metrics
- triggers
- trigger_categories
- users
- user_fields
- views
- workspaces
## Manage custom offsets
You can manage the offsets for this connector. Offsets provide information on the
point in the system from which the connector is accessing data. For more
information, see [Manage Offsets for Fully Managed Connectors in Confluent Cloud](offsets.md#connect-custom-offsets).
**To manage offsets**:
- Manage offsets using Confluent Cloud APIs. For more information, see [Connect offsets API reference](https://docs.confluent.io/cloud/current/ccloud/offsets-connect-v-1/).
### Get the current offset
To get the current offset, make a `GET` request that specifies the environment, Kafka cluster, and connector name.
```bash
GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets
Host: https://api.confluent.cloud
```
**Response:**
Successful calls return HTTP `200` with a JSON payload that describes the offset.
```bash
{
"id": "lcc-example123",
"name": "{connector_name}",
"offsets": [
{
"partition": {
"name": "tickets"
},
"offset": {
"updated_at": 1712559408
}
},
{
"partition": {
"name": "targets"
},
"offset": {
"created_at": 1607376776000
}
},
{
"partition": {
"name": "users"
},
"offset": {
"updated_at": 1712639446
}
},
{
"partition": {
"name": "ticket_audits"
},
"offset": {
"created_at": 1607359500000
}
}
],
"metadata": {
"observed_at": "2024-03-28T17:57:48.139635200Z"
}
}
```
Responses include the following information:
- The position of latest offset.
- The observed time of the offset in the metadata portion of the payload. The `observed_at` time
indicates a snapshot in time for when the API retrieved the offset. A running connector is always updating
its offsets. Use `observed_at` to get a sense for the gap between real time and the time at which the request
was made. By default, offsets are observed every minute. Calling `GET` repeatedly will fetch more recently
observed offsets.
- Information about the connector.
### Update the offset
To update the offset, make a `POST` request that specifies the environment, Kafka cluster, and connector
name. Include a JSON payload that specifies new offset and a patch type.
```bash
POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request
Host: https://api.confluent.cloud
{
"type": "PATCH",
"offsets": [
{
"partition": {
"name": "tickets"
},
"offset": {
"updated_at": 1554687029
}
},
{
"partition": {
"name": "targets"
},
"offset": {
"created_at": 1554687029
}
},
{
"partition": {
"name": "users"
},
"offset": {
"updated_at": 1554687029
}
},
{
"partition": {
"name": "ticket_audits"
},
"offset": {
"created_at": 1554687029
}
}
]
}
```
Considerations:
- You can only make one offset change at a time for a given connector.
- This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information,
see **Get the status of an offset request**.
- For source connectors, the connector attempts to read from the position defined by the requested offsets.
**Response:**
Successful calls return HTTP `202 Accepted` with a JSON payload that describes the offset.
```bash
{
"id": "lcc-example123",
"name": "{connector_name}",
"offsets": [
{
"partition": {
"name": "tickets"
},
"offset": {
"date_updated": 1618184736
}
}
],
"requested_at": "2024-03-28T17:58:45.606796307Z",
"type": "PATCH"
}
```
Responses include the following information:
- The requested position of the offsets in the source.
- The time of the request to update the offset.
- Information about the connector.
### Delete the offset
To delete the offset, make a `POST` request that specifies the environment, Kafka cluster, and connector
name. Include a JSON payload that specifies the delete type.
```bash
POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request
Host: https://api.confluent.cloud
{
"type": "DELETE"
}
```
Considerations:
- Delete requests delete the offset for the provided partition and reset to the base state. A
delete request is as if you created a fresh new connector.
- This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information,
see **Get the status of an offset request**.
- Do not issue delete and patch requests at the same time.
- For source connectors, the connector attempts to read from the position defined in the base state.
**Response**:
Successful calls return HTTP `202 Accepted` with a JSON payload that describes the result.
```bash
{
"id": "lcc-example123",
"name": "{connector_name}",
"offsets": [],
"requested_at": "2024-03-28T17:59:45.606796307Z",
"type": "DELETE"
}
```
Responses include the following information:
- Empty offsets.
- The time of the request to delete the offset.
- Information about Kafka cluster and connector.
- The type of request.
### Get the status of an offset request
To get the status of a previous offset request, make a `GET` request that specifies the environment, Kafka cluster, and connector
name.
```bash
GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request/status
Host: https://api.confluent.cloud
```
Considerations:
- The status endpoint always shows the status of the most recent PATCH/DELETE operation.
**Response**:
Successful calls return HTTP `200` with a JSON payload that describes the result. The following is an example
of an applied patch.
```bash
{
"request": {
"id": "lcc-example123",
"name": "{connector_name}",
"offsets": [
{
"partition": {
"name": "tickets"
},
"offset": {
"date_updated": 1618184736
}
}
],
"requested_at": "2024-03-28T17:58:45.606796307Z",
"type": "PATCH"
},
"status": {
"phase": "APPLIED",
"message": "The Connect framework-managed offsets for this connector have been altered successfully. However, if this connector manages offsets externally, they will need to be manually altered in the system that the connector uses."
},
"previous_offsets": [
{
"partition": {
"name": "brands"
},
"offset": {
"updated_at": 1666023665000
}
},
{
"partition": {
"name": "apps"
},
"offset": {
"updated_at": 1713982063000
}
}
],
"applied_at": "2024-03-28T17:58:48.079141883Z"
}
```
Responses include the following information:
- The original request, including the time it was made.
- The status of the request: applied, pending, or failed.
- The time you issued the status request.
- The previous offsets. These are the offsets that the connector last updated
prior to updating the offsets. Use these to try to restore the state of your connector
if a patch update causes your connector to fail or to return a connector to its
previous state after rolling back.
### JSON payload
The table below offers a description of the unique fields in the JSON payload for managing offsets of the Zendesk Source connector.
| Field | Definition | Required/Optional |
|--------------|----------------------------------------------------|---------------------|
| `created_at` | The UNIX timestamp when the table row was created. | Required |
| `updated_at` | The UNIX timestamp when the row was last updated. | Required |
## Quick Start
Use this quick start to get up and running with the Confluent Cloud Zendesk Source
connector. The quick start provides the basics of selecting the connector and
configuring it to stream events.
Prerequisites
: - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.
- The Confluent CLI installed and configured for the cluster. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html).
- [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
- Authorization and credentials to access the Zendesk service URL.
- Zendesk API: [Support APIs](https://https//developer.zendesk.com/rest_api/docs/support/introduction) must be enabled for the Zendesk account.
- Either the `oauth2` or `password` mechanisms should be enabled for the Zendesk account. For additional information, see [Using the API dashboard: Enabling password or token access](https://support.zendesk.com/hc/en-us/articles/115002555167-Using-the-API-dashboard).
- Certain tables, such as `custom_roles`, can only be accessed if the Zendesk Account is an Enterprise account. For more information, see [Custom Agent Roles](https://developer.zendesk.com/rest_api/docs/support/custom_roles).
- A few Zendesk configuration settings may need to be enabled to ensure export is possible. For example, `satisfaction_ratings` can only be exported if this option is enabled. For more information, see [Support API: Satisfaction Ratings](https://developer.zendesk.com/rest_api/docs/support/satisfaction_ratings).
### Using the Confluent Cloud Console
#### Step 1: Launch your Confluent Cloud cluster
To create and launch a Kafka cluster in Confluent Cloud, see [Create a kafka cluster in Confluent Cloud](../get-started/index.md#cloud-create-kafka-cluster).
#### Step 2: Add a connector
In the left navigation menu, click **Connectors**. If you already have connectors in your cluster, click **+ Add
connector**.
#### Step 3: Select your connector
Click the **Zendesk Source** connector card.
#### Step 4: Enter the connector details
#### NOTE
* Make sure you have all your [prerequisites](#cc-zendesk-source-prereqs) completed.
* An asterisk ( \* ) designates a required entry.
At the **Add Zendesk Source Connector** screen, complete the following:
### Kafka access
1. Select the way you want to provide **Kafka Cluster credentials**. You can
choose one of the following options:
- **My account**: This setting allows your connector to globally access everything
that you have access to. With a user account, the connector uses an API key and
secret to access the Kafka cluster. This option is not recommended for production.
- **Service account**: This setting limits the access for your connector by using a
[service account](service-account.md#s3-cloud-service-account). This option is recommended for
production.
- **Use an existing API key**: This setting allows you to specify an API key and a
secret pair. You can use an existing pair or create a new one. This method is not
recommended for production environments.
#### NOTE
Freight clusters support only service accounts for Kafka authentication.
2. Click **Continue**.
### Authentication
1. Add the Zendesk authentication details:
- **Zendesk Service URL**: The URL where the connector gets Zendesk
source data. For example, `https://.zendesk.com`
- **Endpoint Authentication type**: Choose either `basis` or
`bearer` for the authentication type. For more information, see
[OAuth tokens](https://developer.zendesk.com/documentation/ticketing/working-with-oauth/creating-and-using-oauth-tokens-with-the-api/)
in the Zendesk docs.
2. Click **Continue**.
### Configuration
- **Zendesk tables**: The Zendesk tables the connector exports and
writes to Kafka. To balance the load between workers, order the
tables by their expected size or throughput requirement. For the
list of supported tables, see [Supported tables](#cc-zendesk-supported-tables).
- **Topic Name Pattern**: The pattern to use for the topic name,
where the `${entityName}` literal is replaced with each entity
name. If `${entityName}` is not specified, the connector writes
all records to a single topic named `ZD_${entityName}`. A valid
topic pattern should follow the regex
`[a-zA-Z0-9\\.\\-\\_]*(\\$\\{entityName\\})?[a-zA-Z0-9\\.\\-\\_]*`.
- **Zendesk start time (ISO 8601)**: Rows updated after the time
entered are processed by the connector. The value should be
formatted using the ISO 8601 format `yyyy-MM-dd'T'HH:mm:SS`. If
left blank, the default time is set to the time the connector is
launched minus one minute.
**Output messages**
- **Select output record value format**: Select the output record value format (data going to the Kafka topic). Valid values are AVRO, JSON, JSON_SR (JSON Schema), or PROTOBUF.
[Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for
example, Avro, JSON Schema, or Protobuf).
#### NOTE
For Schema Registry-based output formats, the connector attempts to deduce the schema based
on the source API response returned. The connector registers a new schema for
every NULL and NOT NULL value of an optional field in the API response. For this
reason, the connector may register schema versions at a much higher rate than
expected.
### **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?](../sr/faqs-cc.md#faq-schema-contexts).
- **Maximum Batch Size**: The maximum number of records to return and write to Kafka at one time.
- **Maximum In Flight Requests**: The maximum number of requests that can be in-flight at once.
- **Maximum Poll Interval (ms)**: The time in milliseconds between requests to fetch changed or updated entities.
- **Request Interval (ms)**: The time in milliseconds to wait before checking for updated records.
- **Maximum Retries**: The maximum number of times to retry on errors before failing the task.
- **Retry Backoff (ms)**: The time in milliseconds to wait after an error before a retry attempt is made.
**Auto-restart policy**
- **Enable Connector Auto-restart**: Enables the auto-restart behavior of the connector and its
task in the event of user-actionable errors. Defaults to `true`, enabling the connector to
automatically restart in case of user-actionable errors. Set this property to `false` to
disable auto-restart for failed connectors. If disabled, you must manually restart the connector.
**Additional Configs**
- **Value Converter Decimal Format**: Specifies the `JSON` or `JSON_SR` serialization format for Connect `DECIMAL` logical type values with two allowed literals:
`BASE64` to serialize `DECIMAL` logical types as base64 encoded binary data, and
`NUMERIC` to serialize `DECIMAL` logical type values in `JSON` or `JSON_SR` as a number representing the decimal value.
- **Key Converter Schema ID Serializer**: The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers.
- **Value Converter Reference Subject Name Strategy**: Sets the subject reference name strategy for values. Valid entries are `DefaultReferenceSubjectNameStrategy` or `QualifiedReferenceSubjectNameStrategy`. You can use this strategy only with `PROTOBUF` format; the default strategy is `DefaultReferenceSubjectNameStrategy`.
- **Value Converter Connect Meta Data**: Enables the Connect converter to add its metadata to the output schema. Applies to Avro converters.
- **Value Converter Value Subject Name Strategy**: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
- **Key Converter Key Subject Name Strategy**: Determines how to construct the subject name for key schema registration.
- **Value Converter Schema ID Serializer**: The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers.
**Transforms**
- **Single Message Transformations**: To add a new SMT, see [Add transforms](single-message-transforms.md#cc-single-message-transforms-ui).
For more information about unsupported SMTs, see
[Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms).
**Processing position**
- **Set offsets**: Click **Set offsets** to define a specific offset for
this connector to begin procession data from. For more information
on managing offsets, see [Manage offsets](offsets.md#connect-custom-offsets).
For all property values and definitions, see [Configuration Properties](#cc-zendesk-source-config-properties).
- Click **Continue**.
### Sizing
Based on the number of topic partitions you select, you will be provided
with a recommended number of tasks.
1. To change the number of tasks, use the Range Slider to select the
desired number of tasks.
2. Click **Continue**.
### Review and Launch
1. Verify the connection details by previewing the running configuration.
2. After you’ve validated that the properties are configured to your
satisfaction, click **Launch**.
The status for the connector should go from **Provisioning** to
**Running**.
#### Step 5: Check for records
Verify that records are being produced at the Kafka topic.
For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.
### 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](#cc-zendesk-source-prereqs) completed.
#### Step 1: List the available connectors
Enter the following command to list available connectors:
```none
confluent connect plugin list
```
#### Step 2: List the connector configuration properties
Enter the following command to show the connector configuration properties:
```none
confluent connect plugin describe
```
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. See
[Configuration Properties](#cc-zendesk-source-config-properties) for additional configuration property
values and descriptions.
```json
{
"connector.class": "ZendeskSource",
"name": "ZendeskSource_0",
"kafka.auth.mode": "KAFKA_API_KEY",
"kafka.api.key": "",
"kafka.api.secret": "",
"zendesk.url": "https://.zendesk.com",
"zendesk.tables": "tickets, groups, users",
"zendesk.user": "",
"zendesk.password": "*********************************",
"output.data.format": "AVRO",
"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](service-account.md#s3-cloud-service-account), specify the **Resource ID** in the property `kafka.service.account.id=`. To list the available service account resource IDs, use the following command:
```bash
confluent iam service-account list
```
For example:
```bash
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
```
* Enter the Zendesk connection details.
- `"zendesk.url"`: The URL where the connector gets Zendesk source data. For example, `https://.zendesk.com```.
- `"zendesk.tables"`: A comma-separated list of Zendesk tables the
connector exports and writes to Kafka. To balance the load between workers,
order the tables by their expected size or throughput requirement. For the
list of supported tables, see [Supported tables](#cc-zendesk-supported-tables).
* Enter the authentication details. The example shows the default `basic` authentication properties `"zendesk.user"` and `"zendesk.password"`. You can use the properties `"zendesk.auth.type": "bearer"` and `"bearer.token": ""` to authenticate. This is a single string that is sent in the HTTP `Authorization` header.
* `"output.data.format"`: Enter an output data format (data going to the Kafka topic): AVRO, JSON_SR (JSON Schema), PROTOBUF, or JSON (schemaless). [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
#### NOTE
For Schema Registry-based output formats, the connector attempts to deduce the schema based
on the source API response returned. The connector registers a new schema for
every NULL and NOT NULL value of an optional field in the API response. For this
reason, the connector may register schema versions at a much higher rate than
expected.
* `"tasks.max"`: Enter the number of [tasks](/platform/current/connect/concepts.html#tasks) to use with the connector. Only one task per connector is supported.
* **Transforms and Predicates**: See the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation for details.
See [Configuration Properties](#cc-zendesk-source-config-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:
```none
confluent connect cluster create --config-file .json
```
For example:
```none
confluent connect cluster create --config-file zendesk-source-config.json
```
Example output:
```none
Created connector ZendeskSource_0 lcc-do6vzd
```
#### Step 5: Check the connector status
Enter the following command to check the connector status:
```none
confluent connect cluster list
```
Example output:
```none
ID | Name | Status | Type | Trace
+------------+--------------------------+---------+--------+-------+
lcc-do6vzd | ZendeskSource_0 | RUNNING | source | |
```
#### Step 6: Check for records.
Verify that records are being produced at the Kafka topic.
For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.
## 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](/platform/current/connect/kafka_connectors.html).
#### NOTE
These are properties for the fully managed cloud connector. If you are installing
the connector locally for Confluent Platform, see [Zendesk Source Connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/zendesk/current/).
### 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, whenever possible.
* Type: string
* Valid Values: SERVICE_ACCOUNT, KAFKA_API_KEY
* 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 do you want to name your topic(s)?
`topic.name.pattern`
: The pattern to use for the topic name, where the `${entityName}` literal will be replaced with each entity name. If `${entityName}` is not specified all the records will be written to a single topic. A valid topic pattern should follow the regex [a-zA-Z0-9.-_]\*(${entityName})?[a-zA-Z0-9.-_]\*
* Type: string
* Default: ZD_${entityName}
* Valid Values: Must match the regex `[a-zA-Z0-9\.\-\_]*(\$\{entityName\})?[a-zA-Z0-9\.\-\_]*`
* 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
### How should we connect to Zendesk?
`zendesk.url`
: The zendesk service url that connector will connect to.
* Type: string
* Importance: high
`zendesk.auth.type`
: Authentication type of the endpoint. Valid values are `basic` and `bearer`
* Type: string
* Default: basic
* Valid Values: basic, bearer
* Importance: high
`zendesk.tables`
: The Zendesk tables that are to be exported and written to Kafka. To avail a reasonable load balance between workers, the tables could be ordered by their expected size or throughput.
* Type: list
* Importance: high
`zendesk.since`
: Rows updated after this time will be processed by the connector. If left blank, the default time will be set to the time this connector is launched minus 1 minute. The value should be formatted as ISO 8601. Example format yyyy-MM-dd’T’HH:mm:SS.
* Type: string
* Importance: medium
### Authorization: Basic
`zendesk.user`
: The username to be used with an endpoint requiring authentication.
* Type: string
* Importance: high
`zendesk.password`
: The password to be used with an endpoint requiring authentication.
* Type: password
* Importance: high
### Authorization: Bearer
`bearer.token`
: The bearer authentication token to be used when `auth.type=bearer`. The supplied token will be used as the value of `Authorization` header in HTTP requests.
* Type: password
* Importance: high
### Connection details
`max.batch.size`
: The maximum number of records that should be returned and written to Kafka at one time.
* Type: int
* Default: 100
* Importance: low
`max.in.flight.requests`
: The maximum number of requests that may be in-flight at once.
* Type: int
* Default: 10
* Importance: low
`max.poll.interval.ms`
: The time in milliseconds between requests to fetch changed or updated entities.
* Type: long
* Default: 3000 (3 seconds)
* Importance: low
`request.interval.ms`
: The time in milliseconds to wait before checking for updated records.
* Type: long
* Default: 15000 (15 seconds)
* Importance: low
`max.retries`
: The maximum number of times to retry on errors before failing the task.
* Type: int
* Default: 10
* Importance: low
`retry.backoff.ms`
: The time in milliseconds to wait following an error before a retry attempt is made.
* Type: long
* Default: 3000 (3 seconds)
* 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
* Default: JSON
* Importance: high
### Number of tasks for this connector
`tasks.max`
: Maximum number of tasks for the connector.
* Type: int
* Valid Values: [1,…]
* Importance: high
### Auto-restart policy
`auto.restart.on.user.error`
: Enable connector to automatically restart on user-actionable errors.
* Type: boolean
* Default: true
* Importance: medium
### Additional Configs
`header.converter`
: The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
* Type: string
* Importance: low
`producer.override.compression.type`
: The compression type for all data generated by the producer. Valid values are none, gzip, snappy, lz4, and zstd.
* Type: string
* Importance: low
`value.converter.allow.optional.map.keys`
: Allow optional string map key when converting from Connect Schema to Avro Schema. Applicable for Avro Converters.
* Type: boolean
* Importance: low
`value.converter.auto.register.schemas`
: Specify if the Serializer should attempt to register the Schema.
* Type: boolean
* Importance: low
`value.converter.connect.meta.data`
: Allow the Connect converter to add its metadata to the output schema. Applicable for Avro Converters.
* Type: boolean
* Importance: low
`value.converter.enhanced.avro.schema.support`
: Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.
* Type: boolean
* Importance: low
`value.converter.enhanced.protobuf.schema.support`
: Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.flatten.unions`
: Whether to flatten unions (oneofs). Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.generate.index.for.unions`
: Whether to generate an index suffix for unions. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.generate.struct.for.nulls`
: Whether to generate a struct variable for null values. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.int.for.enums`
: Whether to represent enums as integers. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.latest.compatibility.strict`
: Verify latest subject version is backward compatible when use.latest.version is true.
* Type: boolean
* Importance: low
`value.converter.object.additional.properties`
: Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.
* Type: boolean
* Importance: low
`value.converter.optional.for.nullables`
: Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.optional.for.proto2`
: Whether proto2 optionals are supported. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.use.latest.version`
: Use latest version of schema in subject for serialization when auto.register.schemas is false.
* Type: boolean
* Importance: low
`value.converter.use.optional.for.nonrequired`
: Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.
* Type: boolean
* Importance: low
`value.converter.wrapper.for.nullables`
: Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`value.converter.wrapper.for.raw.primitives`
: Whether a wrapper message should be interpreted as a raw primitive at root level. Applicable for Protobuf Converters.
* Type: boolean
* Importance: low
`key.converter.key.schema.id.serializer`
: The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers.
* Type: string
* Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer
* Importance: low
`key.converter.key.subject.name.strategy`
: How to construct the subject name for key schema registration.
* Type: string
* Default: TopicNameStrategy
* Importance: low
`value.converter.decimal.format`
: Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:
BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
* Type: string
* Default: BASE64
* Importance: low
`value.converter.flatten.singleton.unions`
: Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.
* Type: boolean
* Default: false
* Importance: low
`value.converter.reference.subject.name.strategy`
: Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.
* Type: string
* Default: DefaultReferenceSubjectNameStrategy
* Importance: low
`value.converter.value.schema.id.serializer`
: The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers.
* Type: string
* Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer
* Importance: low
`value.converter.value.subject.name.strategy`
: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
* Type: string
* Default: TopicNameStrategy
* Importance: low
## Frequently asked questions
Find answers to frequently asked questions about the Zendesk Source connector for Confluent Cloud.
### Authentication and OAuth
#### Why do I get `401 Unauthorized` errors with OAuth authentication?
This error occurs when OAuth token authentication fails with the Zendesk API or Confluent Cloud Schema Registry. Common causes include:
* **Token invalidation:** The OAuth/OIDC token expired or was invalidated.
* **Token size limits:** The authorization header exceeds the maximum allowed size (approximately 8 KB for Confluent Cloud Schema Registry).
* **Rate limiting on token exchange:** Concurrent token exchanges may interfere with each other on the STS token exchange endpoint.
**Resolution:**
1. **Verify token validity:** Ensure your OAuth tokens are valid and not expired.
2. **Check header size:** If using OIDC tokens, verify the `Authorization` header does not exceed size limits.
3. **Use API key authentication temporarily:** For initial data imports, consider using API key authentication (`basic` authentication with `zendesk.user` and `zendesk.password`), then switch to OAuth (`bearer` authentication) for ongoing operations.
4. **Configure retry settings:** Ensure the connector has appropriate retry configurations for transient authentication failures.
Example configuration for basic authentication:
```json
{
"zendesk.auth.type": "basic",
"zendesk.user": "",
"zendesk.password": ""
}
```
Example configuration for bearer authentication:
```json
{
"zendesk.auth.type": "bearer",
"bearer.token": ""
}
```
### Rate limiting and API throttling
#### Why am I seeing `HTTP 429 Too Many Requests` errors?
This error indicates that the connector has exceeded the Zendesk API rate limits. Rate limits are enforced by Zendesk to prevent API abuse and ensure fair usage.
**Common causes:**
* **High request volume:** The connector is making too many API requests in a short time period.
* **Multiple connectors:** Multiple connectors are accessing the same Zendesk account simultaneously.
* **Zendesk account tier limits:** Different Zendesk account types have different rate limits.
**Resolution:**
1. **Reduce polling frequency:** Increase the `zendesk.since` time interval to reduce the number of API calls.
2. **Monitor Zendesk API usage:** Check your Zendesk account’s API usage dashboard to understand current rate limit consumption.
3. **Implement backoff strategy:** The connector automatically retries with exponential backoff. Ensure retry configurations are appropriate.
4. **Contact Zendesk support:** If rate limits are consistently exceeded, contact Zendesk to discuss your account’s rate limit allocation.
5. **Stagger connector tasks:** If running multiple connectors, consider staggering their polling schedules to distribute API load.
The connector resumes data syncing automatically once the API becomes available again.
#### Why does the connector fail with `HTTP 429` errors for Schema Registry?
This error occurs when the connector exceeds Confluent Cloud Schema Registry rate limits, causing intermittent failures.
**Resolution:**
1. **Monitor Schema Registry usage:** Check your Confluent Cloud Schema Registry usage in the Confluent Cloud Console.
2. **Upgrade cluster type:** Consider upgrading to a Dedicated cluster, where certain limits (such as Schema Registry requests) scale automatically with the number of CKUs.
3. **Reduce schema operations:** Minimize schema updates and registrations by ensuring schemas are stable before production use.
For more information about cluster types and limits.
### Data synchronization and table selection
#### Why are some Zendesk tables not syncing data to Kafka?
This can occur for several reasons related to table configuration and Zendesk account permissions.
**Common causes:**
* **Table not included in configuration:** The table is not listed in the `zendesk.tables` property.
* **Zendesk account restrictions:** Some tables (such as `custom_roles`) require an Enterprise Zendesk account.
* **Feature not enabled:** Certain tables (such as `satisfaction_ratings`) can only be exported if the corresponding feature is enabled in your Zendesk account.
* **API permissions:** The API user does not have permissions to access the table.
**Resolution:**
1. **Verify table configuration:** Check that the table is included in the `zendesk.tables` comma-separated list. For supported tables, see [Supported tables](#cc-zendesk-supported-tables).
2. **Check Zendesk account type:** Verify your Zendesk account tier supports the table. For example, `custom_roles` requires an Enterprise account. See [Custom Agent Roles](https://developer.zendesk.com/rest_api/docs/support/custom_roles) in the Zendesk documentation.
3. **Enable required features:** For tables like `satisfaction_ratings`, ensure the feature is enabled in your Zendesk admin settings. See [Support API: Satisfaction Ratings](https://developer.zendesk.com/rest_api/docs/support/satisfaction_ratings) in the Zendesk documentation.
4. **Review API user permissions:** Ensure the API user has appropriate read permissions for all requested tables.
#### How do I handle missing or incomplete data in Kafka topics?
Missing or incomplete data can result from configuration issues, API errors, or offset management problems.
**Troubleshooting checklist:**
1. **Check connector status:** Verify the connector is in `RUNNING` state without errors.
2. **Review connector logs:** Look for error messages, API failures, or authentication issues in the connector logs.
3. **Verify offset positions:** Use the [offset management API](#cc-zendesk-source-custom-offsets) to check current offset positions and ensure they are progressing.
4. **Check for API errors:** Review the error topic `error-lcc-` for failed records.
5. **Monitor Zendesk API health:** Verify Zendesk API is operational and not experiencing outages.
6. **Validate table configuration:** Ensure all required tables are listed in `zendesk.tables`.
If data is consistently missing, consider resetting offsets to re-sync from a specific point in time. See [Manage custom offsets](#cc-zendesk-source-custom-offsets) for details.
### Offset management
#### How do I reset offsets to re-sync data from a specific time?
You can use the offset management API to reset offsets and re-sync data from a specific point in time.
**Steps:**
1. **Stop the connector:** Pause or stop the connector to prevent offset updates during the reset.
2. **Determine the target timestamp:** Identify the UNIX timestamp (in seconds) from which you want to re-sync data. For example, `1712559408` represents April 8, 2024.
3. **Update offsets:** Use the `PATCH` operation to update offsets for the relevant tables. See [Manage custom offsets](#cc-zendesk-source-custom-offsets) for the complete API reference.
Example offset reset for the `tickets` table:
```bash
POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request
Host: https://api.confluent.cloud
{
"type": "PATCH",
"offsets": [
{
"partition": {
"name": "tickets"
},
"offset": {
"updated_at": 1712559408
}
}
]
}
```
1. **Verify the update:** Use the status API to confirm the offset update was applied successfully.
2. **Resume the connector:** Restart the connector to begin syncing from the new offset position.
#### WARNING
Resetting offsets may cause duplicate records in Kafka if the data has already been synced. Ensure downstream applications can handle duplicates or use idempotent processing.
#### What do the `created_at` and `updated_at` offset fields mean?
The Zendesk Source connector uses these timestamp fields to track the last synced position for each table:
* **created_at:** Tracks the creation timestamp of the last synced record. Used for tables that support filtering by creation time.
* **updated_at:** Tracks the last update timestamp of the last synced record. Used for tables that support filtering by update time.
Different Zendesk tables use different offset fields based on the Zendesk API’s filtering capabilities:
* Tables like `tickets` and `users` use `updated_at` because the Zendesk API supports incremental updates based on modification time.
* Tables like `ticket_audits` use `created_at` because they are append-only and filter by creation time.
For the complete list of offset fields for each table, see [Manage custom offsets](#cc-zendesk-source-custom-offsets).
### Schema and data formats
#### Why do I get schema-related errors when using Avro or JSON Schema format?
Schema-related errors occur when Confluent Cloud Schema Registry is not properly configured or when there are schema compatibility issues.
**Common errors:**
* **Schema not found (error code: 40403):** The schema does not exist in Schema Registry and auto-registration is disabled.
* **Schema Registry authentication errors:** The connector cannot authenticate with Schema Registry.
* **Schema incompatibility errors:** A schema update violates the configured compatibility mode.
**Resolution:**
1. **Enable Schema Registry:** Ensure Confluent Cloud Schema Registry is enabled for your environment. See [Quick Start for Schema Management on Confluent Cloud](../get-started/schema-registry.md#cloud-sr-config).
2. **Verify authentication:** Confirm the connector has valid API credentials for Schema Registry.
3. **Enable auto-registration:** Set `value.converter.auto.register.schemas=true` to allow automatic schema registration:
```json
{
"output.data.format": "AVRO",
"value.converter.auto.register.schemas": "true"
}
```
4. **Check Schema Registry mode:** Ensure Schema Registry is in `READWRITE` mode (not `READONLY`).
5. **Review compatibility settings:** Verify the subject compatibility mode allows your schema changes.
#### NOTE
For Schema Registry-based output formats, the connector attempts to deduce the schema based
on the source API response returned. The connector registers a new schema for
every NULL and NOT NULL value of an optional field in the API response. For this
reason, the connector may register schema versions at a much higher rate than
expected.
### Configuration and connectivity
#### What authentication types does the Zendesk connector support?
The Zendesk Source connector supports the following authentication types:
* **Basic authentication** (`basic`): Uses username and password or username and API token. Configure with `zendesk.user` and `zendesk.password`.
* **Bearer authentication** (`bearer`): Uses a bearer token. Configure with `bearer.token`.
* **OAuth2 authentication** (`oauth2`): Uses OAuth 2.0 with Client Credentials grant type. Configure with `oauth2.token.url`, `oauth2.client.id`, and `oauth2.client.secret`.
* **No authentication** (`none`): No authentication required.
#### Why does the connector fail with connection timeout or DNS errors?
Connection failures typically indicate network connectivity issues between Confluent Cloud and your Zendesk instance.
**Common causes:**
* **Incorrect Zendesk URL:** The `zendesk.url` is malformed or incorrect.
* **DNS resolution failure:** The Zendesk subdomain cannot be resolved.
* **Network restrictions:** Firewall rules or network policies block Confluent Cloud egress traffic.
* **Private network configuration:** For `PRIVATE_LINK` or `PCC` clusters, outbound traffic may not be configured.
**Resolution:**
1. **Verify Zendesk URL:** Ensure the `zendesk.url` is correct and uses the format `https://.zendesk.com`.
2. **Test connectivity:** Use external tools (such as `curl` or `ping`) to verify the Zendesk URL is accessible from the internet.
3. **Check cluster network type:** Confirm your Confluent Cloud cluster’s network configuration. For `PRIVATE_LINK` or `PCC` clusters, ensure network connectivity to Zendesk is properly configured.
4. **Review firewall rules:** If using IP allowlisting, ensure Confluent Cloud egress IPs are permitted.
For more information about network configuration, see [Prerequisites](#cc-zendesk-source-prereqs).
### Performance and tasks
#### Why is the connector slow or taking a long time to sync data?
Slow data synchronization can result from several factors related to Zendesk API performance, data volume, and connector configuration.
**Common causes:**
* **Large data volume:** Syncing large Zendesk tables with millions of records takes time, especially on initial sync.
* **Zendesk API rate limits:** The connector is being throttled by Zendesk API rate limits.
* **Single task limitation:** The Zendesk Source connector supports only one task (`tasks.max=1`), limiting parallelism.
* **Network latency:** High latency between Confluent Cloud and Zendesk API endpoints.
**Resolution:**
1. **Monitor API rate limits:** Check if the connector is being rate-limited by reviewing connector logs for `HTTP 429` errors.
2. **Optimize table selection:** Only sync tables you need by configuring `zendesk.tables` with the minimal required set.
3. **Order tables by size:** In `zendesk.tables`, list smaller tables first to balance load and see faster results.
4. **Use appropriate polling intervals:** Configure `zendesk.since` to an appropriate value for your use case (default is to sync all historical data).
5. **Be patient on initial sync:** The first sync of large tables can take hours or days. Subsequent incremental syncs will be much faster.
#### NOTE
The Zendesk Source connector supports only one task per connector (`tasks.max=1`). Increasing this value will not improve performance.
## Next Steps
For an example that shows fully managed Confluent Cloud connectors in action with
Confluent Cloud for Apache Flink, see the [Cloud ETL Demo](/platform/current/tutorials/examples/cloud-etl/docs/index.html).
This example also shows how to use Confluent CLI to manage your resources in
Confluent Cloud.
[](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)