Actions364
- API Service Actions
- Bundles Automation-Side Actions
- Bundles Design-Side Actions
- Connection Actions
- Continuous Activity 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
- Dataset Actions
- 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
- Get Last Metric Values
- Get Metadata
- Get Schema
- Get Single Metric History
- List Datasets
- List Partitions
- List Tables
- List Tables Schemas
- Prepare Tables Import
- Run Checks
- Set Metadata
- Set Schema
- Synchronize Hive Metastore
- Update Dataset Settings
- Update From Hive Metastore
- Dataset Statistic Actions
- Discussion Actions
- DSS Administration Actions
- Flow Documentation Actions
- Insight Actions
- Internal Metric Actions
- Job Actions
- Library Actions
- LLM Mesh Actions
- Long Task Actions
- Machine Learning - Experiment Tracking Actions
- Machine Learning - Lab Actions
- 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
- Delete Visual Analysis
- Deploy Trained Model to Flow
- Download Model Documentation of Trained Model
- Generate Model Documentation From Custom Template
- 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
- Start Training ML Task
- Update User Metadata for Trained Model
- Update Visual Analysis
- Machine Learning - Saved Model Actions
- Compute Partial Dependencies of Version
- 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
- Get Version Scoring PMML
- Get Version Snippet
- Import MLflow Version From File or Path
- List Saved Models
- List Versions
- Set Version Active
- Set Version User Meta
- Update Saved Model
- Macro Actions
- Managed Folder Actions
- Meaning Actions
- Model Comparison Actions
- Notebook Actions
- Plugin Actions
- 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
- 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
- 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 Actions
- Project Deployer Actions
- Create Deployment
- Create Infra
- Create Project
- Delete Bundle
- Delete Deployment
- Delete Infra
- Delete Project
- Get Deployment
- Get Deployment Governance Status
- Get Deployment Settings
- Get Deployment Status
- Get Infra
- Get Infra Settings
- Get Project
- Get Project Settings
- Save Deployment Settings
- Save Infra Settings
- Save Project Settings
- Update Deployment
- Upload Bundle
- Project Folder Actions
- Recipe Actions
- Scenario Actions
- Security Actions
- SQL Query Actions
- Streaming Endpoint Actions
- Webapp Actions
- Wiki Actions
- Workspace Actions
Job → Get Job Logs
AI-generatedOverview
The node interacts with the Dataiku DSS API to perform various operations on different resources within a Dataiku DSS instance. Specifically, for the Job resource and the Get Job Logs operation, it retrieves the logs content of a specified job or one of its activities if specified. This is useful for monitoring job execution, debugging issues, or auditing job runs in Dataiku DSS projects.
Typical use cases include:
- Fetching logs after running a job to verify success or diagnose failures.
- Automating log retrieval for scheduled jobs to integrate with external monitoring systems.
- Extracting logs for compliance or audit purposes.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique identifier of the Dataiku DSS project containing the job. |
| Job ID | The unique identifier of the job whose logs are to be retrieved. |
| Query Parameters | Optional additional parameters to filter or modify the log retrieval request (e.g., pagination, filters). |
Output
The output contains the logs content of the specified job in the json field under the key logs. The logs are returned as a string representing the textual content of the job's execution logs.
If the logs are binary or file-based (not typical for job logs), the node would prepare binary data accordingly, but for this operation, the output is textual logs in JSON format.
Example output structure:
[
{
"json": {
"logs": "Log content text here..."
}
}
]
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Needs valid API credentials including the DSS server URL and a user API key for authentication.
- The node uses HTTP requests to communicate with the Dataiku DSS REST API.
- No additional external services are required beyond the Dataiku DSS API.
Troubleshooting
- Missing Credentials Error: If the API credentials are not provided or invalid, the node will throw an error indicating missing credentials. Ensure that the API key and server URL are correctly configured.
- Required Parameter Missing: The node validates required parameters such as Project Key and Job ID. If these are missing, it throws descriptive errors. Provide all mandatory fields.
- API Request Failures: Network issues, incorrect URLs, or permission problems can cause API call failures. Check network connectivity, API endpoint correctness, and user permissions in Dataiku DSS.
- Parsing Errors: If the response from the API is not valid JSON or unexpected, parsing may fail. This could indicate an API change or server issue.
- Empty Logs: If no logs are found for the given job ID, the output might be empty or contain a "204 No Content" status. Verify the job ID and that the job has generated logs.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Jobs API Reference
- n8n Documentation on Creating Custom Nodes
This summary focuses on the Job resource and the Get Job Logs operation as requested, based on static analysis of the provided source code and input properties.