58a1a1a8d1
Add ``scripts/seed_junction_load.py``, a backend-agnostic script that
bulk-inserts synthetic parent rows (dashboards, slices, users, roles,
tables, dbs) and many-to-many junction rows for the four largest
association tables targeted by the composite-PK migration:
``dashboard_slices``, ``slice_user``, ``dashboard_user``,
``dashboard_roles``.
Designed for measuring migration runtime at varying scales — run with
a series of size flags (100K / 1M / 5M / 10M for the target table)
and time the migration at each scale to verify the predicted
``O(N log N)`` extrapolation against real numbers.
Properties:
- **Reproducible**: deterministic cross-product walk through parent IDs
produces a stable pair sequence; re-running is replayable.
- **Idempotent**: re-running with the same target is a no-op; with a
higher target, only new rows are added.
- **Backend-agnostic**: connects via Superset's standard ``DATABASE_*``
env vars (or ``SUPERSET__SQLALCHEMY_DATABASE_URI``). Branches on
dialect for ``BINARY(16)`` vs ``UUID`` vs TEXT/BLOB UUID columns.
- **Batched**: bulk INSERT 10K rows per statement.
- **Per-phase timing**: logs elapsed wall time for the parents phase,
the junctions phase as a whole, and per junction-table.
- **Avoidance set**: loads existing junction pairs into a Python set
so re-runs on top of pre-existing data don't collide on the
uniqueness constraint.
Usage (inside the Superset container):
docker exec superset-superset-1 \\
/app/.venv/bin/python /app/scripts/seed_junction_load.py \\
--dashboard-slices 1000000
Defaults target a "large multi-team install" shape: 1M
``dashboard_slices``, 100K each ``slice_user`` / ``dashboard_user``,
10K ``dashboard_roles``. Override per-table via flags.
Tested locally on MySQL (the user's current eval stack):
- 200/100/100/50 row mini-run produced expected counts.
- Re-running at the same target is a no-op (idempotent).
- ``--dry-run`` plans without writing.
Junction tables not yet covered (``sqlatable_user``, ``rls_filter_*``,
``report_schedule_user``) are typically small in production and
require additional parent seeding (RLS filters, report schedules)
that wasn't worth the scope here. Adding them is straightforward by
extending ``JUNCTIONS`` and writing the corresponding parent seeder.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Superset
A modern, enterprise-ready business intelligence web application.
Documentation
- User Guide — For analysts and business users. Explore data, build charts, create dashboards, and connect databases.
- Administrator Guide — Install, configure, and operate Superset. Covers security, scaling, and database drivers.
- Developer Guide — Contribute to Superset or build on its REST API and extension framework.
Why Superset? | Supported Databases | Release Notes | Get Involved | 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
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
- Ask and answer questions on StackOverflow using the apache-superset tag
- Join our community's Slack and please read our Slack Community Guidelines
- Join our dev@superset.apache.org Mailing list. To join, simply send an email to dev-subscribe@superset.apache.org
- If you want to help troubleshoot GitHub Issues involving the numerous database drivers that Superset supports, please consider adding your name and the databases you have access to on the Superset Database Familiarity Rolodex
- Join Superset's Town Hall and Operational Model recurring meetings. Meeting info is available on the Superset Community Calendar
Contributor Guide
Interested in contributing? Check out our Developer Guide to find resources around contributing along with a detailed guide on how to set up a development environment.
Resources
- Superset "In the Wild" - see who's using Superset, and add your organization 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
-
Getting Started with Superset
-
Deploying Superset
-
Recordings of Past Superset Community Events
-
Visualizations
Repo Activity
