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 integrates with the Dataiku DSS API to perform various operations related to security management, specifically including updating code environment packages within a DSS instance. It allows users to interact programmatically with DSS resources such as code environments, users, groups, and other security-related entities.
The "Update Code Env Packages" operation under the "Security" resource updates the packages installed in a specified DSS code environment. This is useful for maintaining or upgrading the software dependencies of code environments used in DSS projects, ensuring that the environments have the required libraries and versions for execution.
Common scenarios:
- Automating package updates in DSS code environments after adding new dependencies.
- Synchronizing code environment packages across multiple DSS instances.
- Integrating package update workflows into CI/CD pipelines for DSS projects.
Practical example:
A data engineer wants to update the Python packages in a DSS code environment named "my-env" to include the latest versions of pandas and numpy. Using this node, they can trigger the update operation with the appropriate parameters, automating the process without manual intervention in the DSS UI.
Properties
| Name | Meaning |
|---|---|
| Query Parameters | A collection of optional query parameters to customize the API request. Includes many possible keys such as active, allUsers, archivePath, codeEnvName, deletionMode, forceRebuildEnv, includeLibs, limit, name, versions, wait, and many others. For this operation, relevant parameters would typically include codeEnvName (the name of the code environment to update), forceRebuildEnv (boolean to force rebuilding the environment), and wait (boolean to wait for the operation to complete). |
| Resource | Fixed to "Security" for this context. |
| Operation | Fixed to "Update Code Env Packages" for this context. |
Note: The full list of query parameters is extensive and generic for many operations; for this specific operation, the key parameters are those related to identifying the code environment and controlling the update behavior.
Output
The node outputs the response from the Dataiku DSS API call:
- If the response is JSON, it returns it as a JSON object in the
jsonoutput field. - If the response includes binary data (not typical for this operation), it would be returned as binary data prepared by the node.
- For this operation, the output will typically be a JSON object indicating the status or result of the package update request, such as success confirmation or details about the updated environment.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires credentials providing:
- The DSS server URL.
- An API authentication token (user API key) with sufficient permissions to update code environments.
- No additional external services are needed beyond the Dataiku DSS API.
- The node uses HTTP requests to communicate with the DSS REST API.
Troubleshooting
- Missing Credentials Error: The node throws an error if the required API credentials are not provided. Ensure that the API key and DSS server URL are correctly configured in n8n credentials.
- Required Parameter Missing: The node validates required parameters like
codeEnvName. If missing, it throws an error specifying which parameter is required. - API Request Failures: Errors from the DSS API (e.g., permission denied, invalid environment name) will be surfaced as node errors with messages from the API. Check the API key permissions and the correctness of parameters.
- Timeouts or Long Operations: Updating code environment packages may take time. Use the
waitparameter to control whether the node waits for completion or returns immediately. - Unexpected Response Format: If the API returns non-JSON or unexpected data, the node attempts to parse it but may return raw text or binary data.
Links and References
- Dataiku DSS API Documentation
- Managing Code Environments in Dataiku DSS
- Dataiku DSS REST API Reference
This summary focuses on the "Security" resource and the "Update Code Env Packages" operation, describing how the node constructs the API request, handles input properties, and processes the output accordingly.