PagerDuty Sink Connector for Confluent Cloud¶

The fully-managed PagerDuty Sink connector for Confluent Cloud reads records from an Apache Kafka® topic and creates PagerDuty incidents.

Note

This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see PagerDuty Sink Connector for Confluent Platform.

Features¶

The PagerDuty Sink connector supports the following features:

At least once delivery: This connector guarantees that records from the Kafka topic are delivered at least once.
Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance.
Automatic retries: If the PagerDuty Sink connector fails to connect to the PagerDuty endpoint it automatically retries the connection using exponential backoff.

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 Pagerduty 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 PagerDuty Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to a PagerDuty directory.

Prerequisites

Authorized access to a 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.
A PagerDuty API key.
Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
At least one source Kafka topic must exist in your Confluent Cloud cluster before creating the sink connector.

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 3: Select your connector¶

Click the PagerDuty Sink connector card.

Step 4: Enter the connector details¶

Note

Ensure you have all your prerequisites completed.
An asterisk ( * ) designates a required entry.

At the Add PagerDuty 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.

Note

Configuration properties that are not shown in the Cloud Console use the default values. See Configuration Properties for all property values and definitions.

Select an Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR, or PROTOBUF. 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?.
- Maximum Retry Time (ms): If the PagerDuty Sink connector fails to connect to the PagerDuty endpoint it automatically retries the connection using exponential backoff. This property controls how long the connector retries requests (in milliseconds). The default value is 10000 ms (10 seconds). Be sure to set this value to at least 1000 ms (1 second).
- Behavior on Error: The connector’s behavior if the Kafka record does not contain an expected field. Valid options are log, fail (the default), and ignore. If you select log, the connector logs errors and skips malformed records. Select fail and the connector task fails. ignore skips the error.
- Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.
See Configuration Properties for all property values and definitions.
Click Continue.

Step 5: Check for PagerDuty incidents¶

Verify that incidents are being produced on the PagerDuty host.

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¶

To set up and run the connector using the Confluent CLI, complete the following steps.

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": "PagerDutySink",
  "topics": "incidents",
  "input.data.format": "JSON",
  "name": "PagerDutySinkConnector_0",
  "kafka.api.key": "****************",
  "kafka.api.secret": "*********************************",
  "pagerduty.api.key": "a1b2CDe3...",
  "tasks.max": "1",
}

Note the following property definitions:

"connector.class": Identifies the connector plugin name.
"topics": Enter the topic name or a comma-separated list of topic names.
"input.data.format": Sets (data coming from the Kafka topic): AVRO, PROTOBUF, or JSON_SR. A valid schema must be available in Schema Registry.
"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
```
"pagerduty.api.key": PagerDuty API key with write permissions to create incidents. For more information, see the PagerDuty docs.
"tasks.max": Enter the maximum number of tasks for the connector to use. More tasks may improve performance.

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 3: 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 pagerduty-sink-config.json

Example output:

Created connector PagerDutySinkConnector_0 lcc-do6vzd

Step 4: Check the connector status.¶

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID           |             Name              | Status  | Type | Trace
+------------+-------------------------------+---------+------+-------+
lcc-do6vzd   | PagerDutySinkConnector_0      | RUNNING | sink |       |

Step 5: Check for PagerDuty incidents¶

Verify that incidents are being produced on the PagerDuty host.

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 and PROTOBUF. Note that you need to have Confluent Cloud Schema Registry configured

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

PagerDuty details¶

pagerduty.api.key

PagerDuty API key with write permissions to create incidents.

Type: password
Importance: high

pagerduty.max.retry.time.ms

In case of error, while executing a post request, the connector will retry until this time (in ms) elapses. The default value is 10000 (10 seconds). It’s recommended to set this value to be at least 1 second.

Type: int
Default: 10000 (10 seconds)
Valid Values: [1000,…]
Importance: low

behavior.on.error

The connector’s behavior if the kafka record does not contain an expected field. Valid options are ‘log’, ‘fail’ ans ‘ignore’. ‘log’ will log and skip the malformed records, ‘fail’ will fail the connector and ‘ignore’ will ignore the error.

Type: string
Default: fail
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] for non-dedicated clusters and [60000,…] for dedicated clusters
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] for non-dedicated clusters and [1,…] for dedicated clusters
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.