Google Git
Sign in
apache / superset / refs/heads/fix-query-cache
commit35d1a6c21c53b84723ca571f7a9d6248ac70bac6[log] [tgz]
authorBeto Dealmeida <roberto@dealmeida.net>Thu Nov 13 12:50:38 2025 -0500
committerBeto Dealmeida <roberto@dealmeida.net>Thu Nov 13 12:50:38 2025 -0500
treea5f724e2dc41183a32e4a600598a788cbdc47616
parentadd087cbfe97cba551ba51657e4ee5f0631262c3 [diff]
fix: render Jinja templates in ORDER BY adhoc metrics

When processing adhoc metrics in ORDER BY clauses during query execution,
Jinja templates were not being rendered because `processed=True` was
passed without providing template processing.

This commit:
1. Updates adhoc_metric_to_sqla() to apply template processing even when
   processed=True (meaning SQL is already sanitized)
2. Passes template_processor when converting orderby adhoc metrics
3. Removes obsolete test that expected error handling removed in commit
   add087cbfe

The fix ensures that:
- During validation: SQL is sanitized but Jinja templates are preserved
  (template_processor=None)
- During execution: Jinja templates are rendered (template_processor
  provided, processed=True skips re-sanitization)

Fixes test: test_chart_data_table_chart_with_time_grain_filter

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2 files changed
tree: a5f724e2dc41183a32e4a600598a788cbdc47616
  1. .claude/
  2. .cursor/
  3. .devcontainer/
  4. .github/
  5. ASF/
  6. CHANGELOG/
  7. docker/
  8. docs/
  9. helm/
  10. RELEASING/
  11. requirements/
  12. RESOURCES/
  13. scripts/
  14. superset/
  15. superset-core/
  16. superset-embedded-sdk/
  17. superset-extensions-cli/
  18. superset-frontend/
  19. superset-websocket/
  20. tests/
  21. CLAUDE.md ⇨ AGENTS.md
  22. GEMINI.md ⇨ AGENTS.md
  23. GPT.md ⇨ AGENTS.md
  24. .asf.yaml
  25. .codecov.yml
  26. .coveragerc
  27. .dockerignore
  28. .editorconfig
  29. .flaskenv
  30. .fossa.yml
  31. .gitattributes
  32. .gitignore
  33. .gitmodules
  34. .markdownlint.json
  35. .pre-commit-config.yaml
  36. .pylintrc
  37. .rat-excludes
  38. AGENTS.md
  39. CHANGELOG.md
  40. CODE_OF_CONDUCT.md
  41. CONTRIBUTING.md
  42. docker-compose-image-tag.yml
  43. docker-compose-light.yml
  44. docker-compose-non-dev.yml
  45. docker-compose.yml
  46. Dockerfile
  47. dockerize.Dockerfile
  48. INSTALL.md
  49. LICENSE.txt
  50. lintconf.yaml
  51. LINTING_ARCHITECTURE.md
  52. Makefile
  53. MANIFEST.in
  54. NOTICE
  55. pyproject.toml
  56. pytest.ini
  57. README.md
  58. setup.py
  59. superset_text.yml
  60. UPDATING.md
README.md

Superset

License Latest Release on Github Build Status PyPI version Coverage Status PyPI Get on Slack Documentation

A modern, enterprise-ready business intelligence web application.

Why Superset? | Supported Databases | Installation and Configuration | Release Notes | Get Involved | Contributor Guide | Resources | Organizations Using Superset

Why Superset?

Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

Superset provides:

  • A no-code interface for building charts quickly
  • A powerful, web-based SQL Editor for advanced querying
  • A lightweight semantic layer for quickly defining custom dimensions and metrics
  • Out of the box support for nearly any SQL database or data engine
  • A wide array of beautiful visualizations to showcase your data, ranging from simple bar charts to geospatial visualizations
  • Lightweight, configurable caching layer to help ease database load
  • Highly extensible security roles and authentication options
  • An API for programmatic customization
  • A cloud-native architecture designed from the ground up for scale

Screenshots & Gifs

Video Overview

superset-video-1080p.webm

Large Gallery of Visualizations


Craft Beautiful, Dynamic Dashboards


No-Code Chart Builder


Powerful SQL Editor


Supported Databases

Superset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena, and more) that has a Python DB-API driver and a SQLAlchemy dialect.

Here are some of the major database solutions that are supported:

A more comprehensive list of supported databases along with the configuration instructions can be found here.

Want to add support for your datastore or data engine? Read more here about the technical requirements.

Installation and Configuration

Try out Superset's quickstart guide or learn about the options for production deployments.

Get Involved

Contributor Guide

Interested in contributing? Check out our CONTRIBUTING.md to find resources around contributing along with a detailed guide on how to set up a development environment.

Resources

  • Superset “In the Wild” - open a PR to add your org to the list!
  • Feature Flags - the status of Superset's Feature Flags.
  • Standard Roles - How RBAC permissions map to roles.
  • Superset Wiki - Tons of additional community resources: best practices, community content and other information.
  • Superset SIPs - The status of Superset's SIPs (Superset Improvement Proposals) for both consensus and implementation status.

Understanding the Superset Points of View

Repo Activity