Actions364
- Continuous Activity Actions
- Dataset Actions
- Get Last Metric Values
- Get Metadata
- Get Schema
- Get Single Metric History
- List Datasets
- List Partitions
- Compute Metrics
- Create Dataset
- Create Managed Dataset
- Delete Data
- Delete Dataset
- Execute Tables Import
- Get Column Lineage
- Get Data
- Get Data - Alternative Version
- Get Dataset Settings
- Get Full Info
- List Tables
- List Tables Schemas
- Prepare Tables Import
- Run Checks
- Set Metadata
- Set Schema
- Synchronize Hive Metastore
- Update Dataset Settings
- Update From Hive Metastore
- API Service Actions
- Bundles Automation-Side Actions
- Bundles Design-Side Actions
- Connection Actions
- Dashboard Actions
- Data Collection Actions
- Data Quality Actions
- Compute Rules on Specific Partition
- Create Data Quality Rules Configuration
- Delete Rule
- Get Data Quality Project Current Status
- Get Data Quality Project Timeline
- Get Data Quality Rules Configuration
- Get Dataset Current Status
- Get Dataset Current Status per Partition
- Get Last Outcome on Specific Partition
- Get Last Rule Results
- Get Rule History
- Update Rule Configuration
- DSS Administration Actions
- Job Actions
- Library Actions
- Dataset Statistic Actions
- Discussion Actions
- Flow Documentation Actions
- Insight Actions
- Internal Metric Actions
- LLM Mesh Actions
- Machine Learning - Lab Actions
- Delete Visual Analysis
- Deploy Trained Model to Flow
- Download Model Documentation of Trained Model
- Generate Model Documentation From Custom Template
- Start Training ML Task
- Update User Metadata for Trained Model
- Update Visual Analysis
- Adjust Forecasting Parameters and Algorithm
- Compute Partial Dependencies of Trained Model
- Compute Subpopulation Analysis of Trained Model
- Create ML Task
- Create Visual Analysis
- Create Visual Analysis and ML Task
- Generate Model Documentation From Default Template
- Generate Model Documentation From File Template
- Get ML Task Settings
- Get ML Task Status
- Get Model Snippet
- Get Partial Dependencies of Trained Model
- Get Scoring Jar of Trained Model
- Get Scoring PMML of Trained Model
- Get Subpopulation Analysis of Trained Model
- Get Trained Model Details
- Get Visual Analysis
- List ML Tasks of Project
- List ML Tasks of Visual Analyses
- List Visual Analyses
- Reguess ML Task
- Machine Learning - Saved Model Actions
- Compute Partial Dependencies of Version
- Get Version Scoring PMML
- Get Version Snippet
- Import MLflow Version From File or Path
- List Saved Models
- List Versions
- Set Version Active
- Compute Subpopulation Analysis of Version
- Create Saved Model
- Delete Version
- Download Model Documentation of Version
- Evaluate MLflow Model Version
- Generate Model Documentation From Custom Template
- Generate Model Documentation From Default Template
- Generate Model Documentation From File Template
- Get MLflow Model Version Metadata
- Get Partial Dependencies of Version
- Get Saved Model
- Get Subpopulation Analysis of Version
- Get Version Details
- Get Version Scoring Jar
- Set Version User Meta
- Update Saved Model
- Long Task Actions
- Machine Learning - Experiment Tracking Actions
- Macro Actions
- Plugin Actions
- Download Plugin
- Fetch From Git Remote
- Get File Detail From Plugin
- Get Git Remote Info
- Get Plugin Settings
- Install Plugin From Git
- Install Plugin From Store
- List Files in Plugin
- List Git Branches
- List Plugin Usages
- Move File or Folder in Plugin
- Add Folder to Plugin
- Create Development Plugin
- Create Plugin Code Env
- Delete File From Plugin
- Delete Git Remote Info
- Delete Plugin
- Download File From Plugin
- Move Plugin to Dev Environment
- Pull From Git Remote
- Push to Git Remote
- Rename File or Folder in Plugin
- Reset to Local Head State
- Reset to Remote Head State
- Set Git Remote Info
- Set Plugin Settings
- Update Plugin Code Env
- Update Plugin From Git
- Update Plugin From Store
- Update Plugin From Zip Archive
- Upload File to Plugin
- Upload Plugin
- Project Deployer Actions
- Get Deployment Settings
- Get Deployment Status
- Create Deployment
- Create Infra
- Create Project
- Delete Bundle
- Delete Deployment
- Delete Infra
- Delete Project
- Get Deployment
- Get Deployment Governance Status
- Get Infra
- Get Infra Settings
- Get Project
- Get Project Settings
- Save Deployment Settings
- Save Infra Settings
- Save Project Settings
- Update Deployment
- Upload Bundle
- SQL Query Actions
- Wiki Actions
- Managed Folder Actions
- Meaning Actions
- Model Comparison Actions
- Notebook Actions
- Project Actions
- Project Folder Actions
- Recipe Actions
- Scenario Actions
- Security Actions
- Streaming Endpoint Actions
- Webapp Actions
- Workspace Actions
Overview
This node interacts with the Dataiku DSS API to perform various operations related to managing long-running tasks, among many other resources. Specifically for the "Long Task" resource and the "Get Running Task State" operation, it retrieves the current state of a running long task identified by a Job ID. This is useful in scenarios where you need to monitor the progress or status of asynchronous or lengthy processes within Dataiku DSS.
Practical examples include:
- Monitoring the execution state of a data processing job that runs asynchronously.
- Checking if a long task has completed, failed, or is still running before proceeding with dependent workflow steps.
- Integrating Dataiku DSS long task status checks into automated workflows for alerting or conditional logic.
Properties
| Name | Meaning |
|---|---|
| Job ID | The identifier of the long-running task (job) whose state you want to retrieve. |
| Query Parameters | Optional additional query parameters as key-value pairs to customize the API request. |
The "Query Parameters" collection supports multiple optional parameters such as active, activity, allUsers, limit, page, wait, etc., which can be used to filter or control the API response. For this specific operation, these parameters are appended as URL query strings.
Output
The node outputs JSON data representing the state of the requested running long task. The structure corresponds directly to the Dataiku DSS API response for the long task's state endpoint. It typically includes details such as:
- Current status of the task (e.g., running, completed, failed).
- Progress information.
- Any relevant metadata about the task execution.
If the operation involves downloading files or binary content (not applicable for this operation), the node would output binary data accordingly.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires valid API credentials (an API key credential) for authentication with the Dataiku DSS API.
- The node uses HTTP requests to communicate with the Dataiku DSS REST API endpoints.
Troubleshooting
- Missing Credentials: If the API credentials are not set or invalid, the node will throw an error indicating missing Dataiku DSS API credentials.
- Missing Required Parameters: The node validates required parameters like Job ID for this operation. Omitting these will cause errors specifying which parameter is missing.
- API Errors: Errors returned from the Dataiku DSS API (e.g., 404 if the job does not exist, 401 for unauthorized access) will be surfaced as node errors with descriptive messages.
- Network Issues: Connectivity problems to the Dataiku DSS server will result in request failures; ensure the server URL and network access are correct.
- Unexpected Response Format: If the API returns unexpected data, parsing errors may occur; verify the API version compatibility.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Long Tasks API Reference
- n8n Documentation on Creating Custom Nodes
This summary focuses on the "Long Task" resource and the "Get Running Task State" operation as requested, based on static analysis of the provided source code and property definitions.