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, enabling users to perform a wide range of operations on Dataiku DSS resources directly from n8n workflows. Specifically for the SQL Query resource and the Stream Query operation, it streams the results of an existing SQL query execution in Dataiku DSS.
Typical use cases include:
- Retrieving large SQL query results incrementally without waiting for the entire query to complete.
- Integrating Dataiku DSS SQL query results into automated data pipelines or dashboards.
- Monitoring and processing query outputs in real-time or near-real-time scenarios.
For example, a user might start a SQL query execution in Dataiku DSS using another operation, then use this Stream Query operation to fetch and process the results as they become available.
Properties
| Name | Meaning |
|---|---|
| Query ID | The unique identifier of the SQL query whose results you want to stream. |
| Query Parameters | Optional key-value pairs to parameterize the query execution (only applicable if supported). |
Note: The provided properties JSON includes many options, but for the SQL Query - Stream Query operation, only the "Query ID" and "Query Parameters" are relevant.
Output
The node outputs the streamed results of the specified SQL query in the json field of the output items. Each item corresponds to a chunk or batch of the query result data streamed from Dataiku DSS.
If the response is binary (e.g., downloading files), the node prepares the binary data accordingly, but for streaming SQL query results, the output is JSON structured data representing rows or records returned by the query.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires valid API credentials for Dataiku DSS (an API key credential).
- The node uses HTTP requests to interact with the Dataiku DSS REST API.
- No additional external services or environment variables are required beyond the configured Dataiku DSS API credentials.
Troubleshooting
- Missing Credentials Error: If the node throws an error about missing Dataiku DSS API credentials, ensure that the API key credential is properly configured in n8n.
- Missing Query ID: The node requires a valid Query ID to stream results. If not provided, it will throw an error.
- HTTP Errors: Errors from the Dataiku DSS API (e.g., 404 Not Found if the query ID does not exist) will be surfaced as node errors. Verify the Query ID and permissions.
- Timeouts or Large Result Sets: Streaming large query results may take time; ensure network stability and consider pagination or limits if supported.
- Invalid Query Parameters: If query parameters are used, ensure they match the expected format and names defined in the Dataiku DSS query.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS SQL Queries API
- n8n Documentation on Creating Custom Nodes
This summary focuses on the SQL Query resource and the Stream Query operation as requested, based on static analysis of the provided source code and property definitions.