# Jira Source Connector for Confluent Cloud The fully managed Jira Source connector for Confluent Cloud moves data from Jira to Apache Kafka® topics. This connector polls data from Jira through [Jira v2 APIs](https://developer.atlassian.com/cloud/jira/platform/rest/v2/), converts data into Kafka records, and moves the records into the Kafka topic. Each row from a Jira table is converted into exactly one Kafka record. #### NOTE * This Quick Start is for the fully managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see [Jira Source Connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/jira/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 Jira Source connector supports the following features: * **At least once delivery**: The connector guarantees that records are delivered at least once to the Kafka topic (if the file row parsed is valid). * **Supports multiple tasks**: The connector supports running one or more tasks. More tasks may improve performance. One Jira resource (table) is covered by one task only. * **Offset management capabilities**: Supports offset management. For more information, see [Manage custom offsets](#cc-jira-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 [Jira Source Connector](limits.md#cc-jira-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). ## Jira Resources The connector can fetch from the following resources: * **changelogs** : See [Get changelogs](https://developer.atlassian.com/cloud/jira/platform/rest/v2/#api-rest-api-2-issue-issueIdOrKey-changelog-get). * **issue_comments** : See [Get comments](https://developer.atlassian.com/cloud/jira/platform/rest/v2/#api-rest-api-2-issue-issueIdOrKey-comment-get). * **issue_transitions** : See [Get transitions](https://developer.atlassian.com/cloud/jira/platform/rest/v2/#api-rest-api-2-issue-issueIdOrKey-transitions-get). * **issues** : See [Get issue](https://developer.atlassian.com/cloud/jira/platform/rest/v2/#api-rest-api-2-issue-issueIdOrKey-get). * **project_categories** : See [Project categories](https://developer.atlassian.com/cloud/jira/platform/rest/v2/#api-group-Project-categories). * **project_types** : See [Project types](https://developer.atlassian.com/cloud/jira/platform/rest/v2/#api-group-Project-types). * **projects** : See [Get projects paginated](https://developer.atlassian.com/cloud/jira/platform/rest/v2/api-group-projects/#api-rest-api-2-project-search-get). * **resolutions** : See [Get resolutions](https://developer.atlassian.com/cloud/jira/platform/rest/v2/#api-group-Issue-resolutions). * **roles** : See [Get project roles for project](https://developer.atlassian.com/cloud/jira/platform/rest/v2/#api-rest-api-2-project-projectIdOrKey-role-get). * **users** : See [Get user](https://developer.atlassian.com/cloud/jira/platform/rest/v2/#api-rest-api-2-user-get). * **versions** : See [Get project versions](https://developer.atlassian.com/cloud/jira/platform/rest/v2/#api-rest-api-2-project-projectIdOrKey-versions-get). * **worklogs** : See [Get issue worklogs](https://developer.atlassian.com/cloud/jira/platform/rest/v2/#api-rest-api-2-issue-issueIdOrKey-worklog-get). ## Task Distribution The Jira Source connector groups resources to process related data within a single task. The `tasks.max` configuration defines the maximum number of tasks, but the connector might use fewer, depending on the resources configured in `jira.resources`. The connector uses the following grouping logic: * Issue Grouping: When you include issues in your configuration, it forms the basis of a task. Any of its dependent resources that are also specified in `jira.resources` will be processed in the same task. * The dependent resources for issues are: `changelogs`, `issue_comments`, `issue_transitions`, `resolutions`, and `worklogs`. * Project Grouping: When you include projects, it forms a separate task group. * The dependent resource for projects is `versions`. * Other Resources: Any other resources not part of these groups (for example, `users` or `project_categories`) are distributed among the remaining available tasks. *Example 1: Grouping with Dependents* Given the following configuration, where dependents are included: ```bash "tasks.max": "4" "jira.resources": "issues, resolutions, versions, worklogs, projects, project_categories" ``` Even though `tasks.max` is set to 4, the connector only creates three tasks because there are only three resource groups to process. The distribution is as follows: * **Task 1**: Processes `projects` and its requested dependent, `versions`. * **Task 2**: Processes `issues` and its requested dependents, `resolutions` and `worklogs`. * **Task 3**: Processes the remaining resource, `project_categories`. *Example 2: Grouping without Dependents* Given a configuration where only primary resources are requested: ```bash "tasks.max": "2" "jira.resources": "issues, projects" ``` The dependents are not included because they were not specified in `jira.resources`. The distribution is: * **Task 1**: Processes `issues`. * **Task 2**: Processes `projects`. ## 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": "users" }, "offset": { "date_updated": null } }, { "partition": { "name": "projects" }, "offset": { "date_updated": null } } ], "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": "users" }, "offset": { "date_updated": "2024-07-13 13:34" } }, { "partition": { "name": "projects" }, "offset": { "date_updated": "2024-07-13 10:03" } } ] } ``` 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**. - The `date_updated` follows a `yyyy-MM-dd HH:mm` format. - 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": "issues" }, "offset": { "date_updated": "2024-07-13 13:34" } } ], "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": "issues" }, "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": "issues" }, "offset": { "date_updated": "2023-11-07 18:44" } } ], "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 Jira Source connector. | Field | Definition | Required/Optional | |----------------|-----------------------------------------------------------------------|---------------------| | `name` | The name of the table. | Required | | `date_updated` | The Unix timestamp of the last update in the row of the target table. | Required | ## Quick Start Use this quick start to get up and running with the Confluent Cloud Jira Source connector. The quick start provides the basics of selecting the connector and configuring it to get data from one or more Jira resources. 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). For additional information, see [Cloud connector limitations](limits.md#cc-jira-source-limits). - Authorization and credentials to access the Jira server. ### 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 **Jira Source** connector card.  #### Step 4: Enter the connector details #### NOTE * Make sure you have all your [prerequisites](#cc-jira-source-prereqs) completed. * An asterisk ( \* ) designates a required entry. At the **Add Jira 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. Configure the authentication properties: **How should we connect to your Jira server?** - **Jira URL**: The Jira server URL. For example: `https://.atlassian.net`. - **Jira Username**: Username or email associated with the Jira account. - **Jira API key**: API key for the Jira account. 2. Click **Continue**. ### Configuration - **Topic Name Pattern**: The pattern to use for the topic name where the `${resourceName}` literal will be replaced with each resource name. **Jira details** - **Since**: Records created or updated after this time will be processed by the connector. The expected format for `jira.since` is `yyyy-MM-dd HH:mm`. - **Jira resources**: The resources that are to be extracted and written to Kafka. - **Lookback Milliseconds**: The number of milliseconds to look back when querying for issues. This creates a time window (lastUpdatedDate - X ms) to (currentTime) to handle potential race conditions and ensure no issues are missed. Default is 0 (disabled) and the maximum value is 9,00,000 ms (15 min). - **Max JIRA Results**: Maximum number of issues or users returned per API page. Higher values reduce API call count but can increase memory pressure. The users endpoint enforces a hard server-side cap of 1000 regardless of this setting. **Output messages** - **Select output record value format**: Select the output record value format (data going to the Kafka topic). Valid options are AVRO, JSON, JSON_SR (JSON Schema), or PROTOBUF. Confluent Cloud Schema Registry 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**: Defaults to `100` records. - **Maximum In Flight Requests**: The maximum number of requests that may be in flight at one time. Defaults to `10` requests. - **Maximum Poll Interval (ms)**: The time in milliseconds (ms) between requests to fetch changed or updated resources. Defaults to `3000` ms (3 seconds). - **Request Interval (ms)**: The time in milliseconds to wait before checking for updated records. Defaults to `15000` ms (15 seconds). - **Maximum Retries**: The maximum number of times the connector retries a task before the task fails. Defaults to `10` retries. - **Retry Backoff (ms)**: The time in milliseconds to wait following an error before a retry attempt is made. Defaults to `3000` ms (3 seconds). **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-jira-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 in 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 To set up and run the connector using the Confluent CLI, complete the following steps. #### NOTE Make sure you have all your [prerequisites](#cc-jira-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. ```json { "connector.class": "JiraSource", "name": "JiraSourceConnector_0", "kafka.auth.mode": "SERVICE_ACCOUNT", "kafka.service.account.id": "", "jira.url": "https://.atlassian.net", "jira.username": "", "jira.api.token": "*********************************", "jira.since": "2020-06-14 09:30", "jira.resources": "issues, users, worklogs", "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](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 GitHub connection details. * `"jira.url"`: The Jira server URL. For example: `https://.atlassian.net`. * `"jira.api.token"`: API key for the Jira account. * `"jira.since"`: Records created or updated after this time will be processed by the connector. The expected format for this property is `yyyy-MM-dd HH:mm`. Note that the value should be configured according to the timezone set in the Jira environment for the `jira.username` configured. * `"jira.resources"`: One or more resources that the connector extracts and writes to Kafka. See [Jira Resources](#cc-jira-source-jira-resources) for details. * `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). For additional information, see [Cloud connector limitations](limits.md#cc-jira-source-limits). #### 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"`: The connector supports running one or more tasks. More tasks may improve performance. See [Configuration Properties](#cc-jira-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 jira-source-config.json ``` Example output: ```none Created connector JiraSourceConnector_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 | JiraSourceConnector_0 | RUNNING | source | | ``` #### Step 6: Check the Kafka topic. 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). ### 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 ### Which topic name pattern do you want to send data to? `topic.name.pattern` : The pattern to use for the topic name, where the `${resourceName}` literal will be replaced with each resource name.
* Type: string * Default: ${resourceName} * 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 your Jira server? `jira.url` : Jira server url, e.g. “[https://server-name.atlassian.net](https://server-name.atlassian.net)”.
* Type: string * Importance: high `jira.username` : Username or email associated with the Jira account.
* Type: string * Importance: high `jira.api.token` : API key for this Jira account.
* Type: password * Importance: high ### Jira details `jira.since` : Records created or updated after this time will be processed by the connector. The expected format for jira.since is yyyy-MM-dd HH:mm. Note that the value should be configured according to timezone set by the user(defined in Jira Username config) in Jira environment.
* Type: string * Importance: medium `jira.resources` : The resources that are to be extracted and written to Kafka.
* Type: list * Importance: high `jira.lookback.ms` : The number of milliseconds to look back when querying for issues. This creates a time window (lastUpdatedDate - X ms) to (currentTime) to handle potential race conditions and ensure no issues are missed. Default is 0 (disabled) and the maximum value is 9,00,000 ms (15 min)
* Type: long * Default: 0 * Valid Values: [0,…,900000] * Importance: medium `jira.max.results` : Maximum number of issues or users returned per API page. Higher values reduce API call count but can increase memory pressure. The users endpoint enforces a hard server-side cap of 1000 regardless of this setting.
* Type: int * Default: 100 * Valid Values: [1,…,2000] * Importance: low ### 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 Jira Source connector for Confluent Cloud. ### Deployment model and product fit #### Can I run Jira Source as a self-managed connector on my own Connect cluster? Yes. The Jira Source connector is available both as a fully managed connector on Confluent Cloud and as a self-managed connector for Confluent Platform. For self-managed connector documentation, see [Jira Source Connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/jira/current/overview.html). ### Configuration and resource selection #### How do I configure the connector to pull data from specific projects or boards? Currently, the Jira Source connector does not support filtering by specific project IDs, board IDs, or JQL queries. The `jira.resources` configuration property specifies the types of Jira objects to retrieve (for example, `issues`, `projects`), not specific instances. When you configure a resource type, the connector pulls all records of that type from your Jira instance. **Available options:** * Use the `jira.since` property to limit data by timestamp. Only records created or updated after this time are processed: ```json { "jira.since": "2025-01-01 00:00" } ``` #### NOTE The `jira.since` value should be configured according to the timezone set in your Jira environment for the `jira.username` you configured. * Apply filtering downstream using SMTs or stream processing. This approach does not reduce the API load on your Jira instance but allows you to route or filter records after they reach Kafka. **Common use case:** If your Jira instance contains thousands of projects but you only need data from specific ones, the connector still fetches all projects and issue data. To manage API rate limits, consider: * Reducing the number of records fetched per poll using `jira.resources.per.poll.count` to lower per-request load on the Jira API. To decrease how often the connector polls Jira, adjust `request.interval.ms`. * Starting with a recent `jira.since` value to minimize initial data volume. * Using downstream filters to process only relevant records. #### What Jira resources can the connector extract? The connector supports the following Jira resources via the `jira.resources` configuration property: * `issues` - Jira issues. * `changelogs` - Issue change history (dependent on `issues`). * `issue_comments` - Comments on issues (dependent on `issues`). * `issue_transitions` - Issue workflow transitions (dependent on `issues`). * `resolutions` - Issue resolution types (dependent on `issues`). * `worklogs` - Time tracking entries (dependent on `issues`.) * `projects` - Jira projects. * `versions` - Project versions (dependent on `projects`). * `project_categories` - Project categories. * `project_types` - Project types. * `roles` - Project roles. * `users` - Jira users. For resource grouping and task distribution details, see [Task Distribution](#cc-jira-source-task-distribution). ### Authentication and credentials #### Why does my connector fail with authentication or authorization errors? Common authentication issues include: * **Invalid API token:** Verify your `jira.api.token` is correct and has not expired. Jira API tokens can be generated in your Atlassian account settings. * **Incorrect username:** The `jira.username` must be the email address associated with your Jira account, not your display name. * **Insufficient permissions:** The Jira user account must have read access to all resources specified in `jira.resources`. Verify the user has appropriate permissions in your Jira instance. * **Jira URL format:** Ensure `jira.url` uses the correct format: `https://.atlassian.net` for Jira Cloud instances. **Verification steps:** 1. Test your credentials using a curl request: ```bash curl -u ":" \ "https://.atlassian.net/rest/api/2/myself" ``` 2. If the curl request succeeds but the connector fails, verify the API token is correctly configured in the connector (check for extra whitespace or encoding issues). 3. For on-premises Jira Server or Data Center instances, consult the [self-managed connector documentation](/platform/current/connect/kafka_connectors.html) for authentication requirements. ### Schema and data format #### Why does my connector fail with schema compatibility errors? Jira’s dynamic field structure can cause schema evolution conflicts in Confluent Cloud Schema Registry. Common error messages include: ```text Schema being registered is incompatible with an earlier schema ``` **Root cause:** Jira custom fields and field configurations vary across projects and issue types. When the connector encounters different field structures, it may attempt to register incompatible schemas. **Resolution:** Set the Schema Registry compatibility mode to `NONE` for Jira Source topics before starting the connector: ### Using the Confluent Cloud Console 1. Navigate to **Stream Governance** > **Schema Registry** > **Schemas** 2. Find the schema for your Jira topic 3. Click **Edit** and set **Compatibility mode** to `NONE` ### Using the Confluent CLI ```bash confluent schema-registry config update \ --subject -value \ --config compatibility=NONE ``` #### NOTE Setting compatibility to `NONE` allows the schema to evolve without validation. This is typically safe for Jira data because Jira manages the field structure. However, downstream consumers must handle schema variations appropriately. #### Why does my sink connector fail to consume from Jira Source topics? The Jira Source connector serializes record keys as `STRING` format, not Avro. If your sink connector expects Avro keys, you will see deserialization errors: ```text Error deserializing Avro message for id -1 ``` **Resolution:** Configure your sink connector to expect STRING keys: ```json { "input.key.format": "STRING", "input.value.format": "AVRO" } ``` Alternatively, use the `Cast` Single Message Transform to convert the key format if needed by your downstream system. ### Performance and polling #### How can I control the rate at which the connector polls Jira? Use the following configuration properties to manage polling behavior: * `request.interval.ms` - Milliseconds to wait between polling requests (default: `15000` ms / 15 seconds). * `max.batch.size` - Maximum number of records to fetch per resource per poll (default: `100`) **Example configuration for slower polling:** ```json { "request.interval.ms": "300000", "jira.resources.per.poll.count": "50" } ``` This configuration polls every 5 minutes and fetches `50` records per resource per poll. #### Why is the connector repeatedly fetching the same records? Common causes: * **Static offset:** Verify you have not manually set the offset to a fixed value. The connector automatically manages offsets based on the `date_updated` field in Jira records. * **Timezone mismatch:** The `jira.since` value should be configured according to the timezone set in your Jira environment for the configured `jira.username`. Timezone mismatches can cause the connector to miss or re-fetch records. * **Clock skew:** If the Confluent Cloud environment clock differs significantly from your Jira server clock, the connector may re-fetch recent records. This is typically self-correcting after initial sync. * **Offset reset:** Check if the connector offset was deleted or reset. Review the connector logs for offset-related messages. ## 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)