ServiceNow Source Connector for Confluent Cloud¶

The fully-managed ServiceNow Source connector for Confluent Cloud is used to poll for additions and changes made in a ServiceNow table (see the ServiceNow documentation) and get these changes into Apache Kafka® in real time. The connector consumes data from a ServiceNow table to add and update records in a Kafka topic.

Note

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

Features¶

The ServiceNow 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.
Automatic retries: When network failures occur, the connector automatically retries the request. The property retry.max.times controls how many times retries are attempted. An exponential backoff is added to each retry interval.
Elasticity: The connector allows you to configure two parameters that enforce the throughput limit: batch.max.rows and poll.interval.s. The connector defaults to 10000 records and a 30 second polling interval. If a large number of updates occur within the given interval, the connector will paginate records according to configurable batch size. Note that since ServiceNow provides precision to one second, the ServiceNow Connector provides one second as the lowest poll.interval.s configuration property setting.
Supports one task: The connector supports running one task only. That is, one table is handled by one task.
Supported data formats: The connector supports Avro, JSON Schema (JSON-SR), Protobuf, and JSON (schemaless) output formats. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf). See Schema Registry Enabled Environments for additional information.
Offset management capabilities: Supports offset management. For more information, see Manage custom offsets.

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 ServiceNow Source 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.

Manage custom offsets¶

Custom offsets for managed connectors is Early Access

Confluent uses Early Access releases to gather feedback. This service should be used only for evaluation and non-production testing purposes, or to provide feedback to Confluent, particularly as it becomes more widely available in follow-on preview editions.

Early Access is intended for evaluation use in development and testing environments only and not for production use. The warranty, SLA, and Support Services provisions of your agreement with Confluent do not apply to Early Access. Confluent considers Early Access to be a Proof of Concept as defined in the Confluent Cloud Terms of Service. Confluent may discontinue providing preview releases of the Early Access releases at any time at the sole discretion of Confluent.

You can manage the offsets for this connector. Offsets provide information on the point in the source system from which the connector accesses data. For more information, see Manage Offsets for Fully-Managed Connectors in Confluent Cloud.

To manage offsets:

Manage offsets using Confluent Cloud APIs. For more information, see Cluster API reference.

To get the current offset, make a GET request that specifies the environment, Kafka cluster, and connector name.

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.

{
    "id": "lcc-example123",
    "name": "{connector_name}",
     "offsets": [
        {
           "partition": {
              "tablename": "u_tvshows"
           },
           "offset": {
              "offset": 108,
              "schema": "eyJzY2hlbWEiOnsidHlwZSI6InN0cnVjdCIsImZpZWxkcyI6W3sidHlwZ_TRUNCATED",
              "time": 1713344828000,
              "url": "sys_updated_on>=2024-04-17 09:06:05"
           }
        }
     ],
    "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.

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.

POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request
Host: https://api.confluent.cloud

 {
     "type": "PATCH",
      "offsets": [
         {
            "partition": {
               "tablename": "u_tvshows"
            },
            "offset": {
               "offset": 108,
               "schema": "eyJzY2hlbWEiOnsidHlwZSI6InN0cnVjdCIsImZpZWxkcyI6W3sidHlwZ_TRUNCATED",
               "time": 1713344828000,
               "url": "sys_updated_on>=2024-04-17 09:06:05"
            }
         }
      ]
 }

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.

{
    "id": "lcc-example123",
    "name": "{connector_name}",
    "offsets": [
       {
          "partition": {
             "tablename": "u_tvshows"
          },
          "offset": {
             "offset": 108,
             "schema": "eyJzY2hlbWEiOnsidHlwZSI6InN0cnVjdCIsImZpZWxkcyI6W3sidHlwZS_TRUNCATED",
             "time": 1713344828000,
             "url": "sys_updated_on>=2024-04-17 09:06:05"
          }
       }
    ]
    "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.

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.

 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.

{
  "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.

To get the status of a previous offset request, make a GET request that specifies the environment, Kafka cluster, and connector name.

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.

{
   "request": {
      "id": "lcc-example123",
      "name": "{connector_name}",
      "offsets": [
         {
            "partition": {
               "tablename": "u_tvshows"
            },
            "offset": {
               "offset": 108,
               "schema": "eyJzY2hlbWEiOnsidHlwZSI6InN0cnVjdCIsImZpZWxkcyI6W3sidHlwZ_TRUNCATED",
               "time": 1713344828000,
               "url": "sys_updated_on>=2024-04-17 09:06:05"
            }
         }
      ],
      "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": {
            "tablename": "u_tvshows"
         },
         "offset": {
            "offset": 2,
            "schema": "eyJzY2hlbWEiOnsidHlwZSI6InN0cnVjdCIsImZpZWxkcyI6W3sidHlwZSI6_TRUNCATED",
            "time": 1713346385000,
            "url": "sys_updated_on>=2024-04-17 09:33:04"
         }
      }
   ],
   "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 ServiceNow Source connector.

Field	Definition	Required/Optional
`tablename`	The name of the ServiceNow table.	Required
`time`	Timestamp is used as the offset for all records.	Required
`url`	`url` contains a query parameter used by the ServiceNow API. The connector uses a combination of `url` and `offset` for paginated queries. `url` can be null. If the data is paginated, `url` uses the following format: `sys_updated_on>=<datetime>` If `url` is null, the query parameter for the ServiceNow API would look like this: `?sysparm_query=sys_updated_on>=1713344828000^ORDERBYsys_updated_on&sysparm_limit=<batch.max.rows>` If `url` is not null, the query parameter for the ServiceNow API would look like this: `?sysparm_query=sys_updated_on>=2024-04-17 09:06:05^ORDERBYsys_updated_on&sysparm_limit=<batch.max.rows>&sysparm_offset=108`	Optional
`schema`	Base64 encoded schema of the last record polled.	Required
`offset`	In paginated queries, `offset` contains the value for the ServiceNow `sysparm_offset` parameter. The connector uses a combination of `offset` and `url` for paginated queries. If the query is not paginated, `offset` can be null.	Optional

Quick Start¶

Use this quick start to get up and running with the Confluent Cloud ServiceNow 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 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.
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.
You must have the ServiceNow instance URL, table name, and connector authentication details. For details, see the ServiceNow docs.

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 ServiceNow Source connector card.

Step 4: Enter the connector details¶

Note

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

At the Add ServiceNow Source Connector screen, complete the following:

Select the topic you want to send data to from the Topics list. To create a new topic, click +Add new topic.

Add the ServiceNow authentication details:
- ServiceNow Instance URL: The ServiceNow instance URL. For example: https://<instancename>.service-now.com/.
- ServiceNow table name: The ServiceNow table name or database view name.
- ServiceNow Username: The ServiceNow basic authentication user name.
- ServiceNow Password: The ServiceNow basic authentication password.
Click Continue.

Add the following details:
- Select the output record value format (data going to the Kafka topic): AVRO, JSON, JSON_SR (JSON Schema), or PROTOBUF. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf). For more information, see Schema Registry Enabled Environments.
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?.
- HTTP request timeout (ms): HTTP request timeout in milliseconds.
- Maximum number of times to retry request: Maximum number of times to retry the request.
- Poll interval (ms)t: Frequency in seconds to poll for new data in each table. The minimum poll interval is one second and the maximum poll interval is 60 seconds.
- Max rows per batch: Maximum number of rows to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.
- Starting time in UTC (yyyy-MM-dd): Time to start fetching all updates/creation. Default uses the time connector launched. Note that the time is in UTC and has the required format: yyyy-MM-dd.
- Transforms and Predicates: For more details, see the Single Message Transforms (SMT) documentation.
For all property values and definitions, see Configuration Properties .
Click Continue.

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 Managed and Custom Connectors 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 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": "ServiceNowSource",
  "name": "ServiceNowSource_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "****************",
  "kafka.api.secret": "************************************************",
  "kafka.topic": "<topic-name>",
  "output.data.format": "AVRO",
  "servicenow.url": "<instance-URL>",
  "servicenow.table": "<table-name>",
  "servicenow.user": "<username>",
  "servicenow.password": "<password>",
  "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 the topic name where data is sent.
“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 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.
"servicenow.<>": Enter the instance URL, table name (or database view name), and connector authentication details. For additional information, see the ServiceNow docs. An instance URL looks like this: https://<instancename>.service-now.com/.
"tasks.max": Enter the number of tasks to use with the connector. The connector supports running one task only. That is, one table is handled by one task.

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 cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file servicenow-source-config.json

Example output:

Created connector ServiceNowSource_0 lcc-do6vzd

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  | Trace
+------------+-------------------------------+---------+--------+-------+
lcc-do6vzd   | ServiceNowSource_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 Managed and Custom Connectors 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.

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

Which topic do you want to send data to?¶

kafka.topic

Identifies the topic name to write the data to.

Type: string
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

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

ServiceNow details¶

servicenow.url

ServiceNow Instance URL.

Type: string
Importance: high

servicenow.table

ServiceNow table name.

Type: string
Importance: high

servicenow.user

ServiceNow basic authentication username.

Type: string
Importance: high

servicenow.password

ServiceNow basic authentication password.

Type: password
Importance: high

connection.timeout.ms

HTTP request timeout in milliseconds.

Type: int
Default: 50000 (50 seconds)
Importance: low

retry.max.times

Maximum number of times to retry request.

Type: int
Default: 3
Importance: low

poll.interval.s

Frequency in seconds to poll for new data in each table. The minimum poll interval is 1 second and the maximum poll interval is 60 seconds.

Type: int
Default: 30
Importance: medium

ServiceNow query details¶

batch.max.rows

Maximum number of rows to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.

Type: int
Default: 10000
Valid Values: [1,…,10000]
Importance: low

servicenow.since

Time to start fetching all updates/creation. Default uses the time connector launched. Note that the time is in UTC and has required format: yyyy-MM-dd.

Type: string
Importance: medium

servicenow.view.variable.prefix

Prefix to be used for Service Now Database Views for timestamp and id columns. The prefix will be pre-appended to the columns sys_updated_on and sys_id i.e. {servicenow.view.variable.prefix}_sys_updated_on and {servicenow.view.variable.prefix}_sys_id to support sourcing data from views with prefix on these columns.

Type: string
Importance: medium

Number of tasks for this connector¶

tasks.max

Maximum number of tasks for the connector.

Type: int
Valid Values: [1,…,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.