Mike Bridge 8fe9a8ce4e feat(versioning): REST API endpoints + restore commands
Exposes the version surface as three new endpoints per entity type
(chart, dashboard, dataset), each carrying the standard Superset
decorator stack (``@protect()``, ``@safe``, ``@statsd_metrics``,
``@event_logger.log_this_with_context``) so they appear in FAB's
``action_log`` alongside other audited operations.

| Method | Path | Purpose |
|---|---|---|
| GET  | ``/api/v1/{resource}/<uuid>/versions/`` | List version history (oldest-first; per entry: ``version_uuid``, ``version_number``, ``transaction_id``, ``operation_type``, ``issued_at``, ``changed_by``, ``changes`` array) |
| GET  | ``/api/v1/{resource}/<uuid>/versions/<version_uuid>/`` | Read-only snapshot of the entity at the requested version (scalar fields plus ``columns`` / ``metrics`` for datasets) |
| POST | ``/api/v1/{resource}/<uuid>/versions/<version_uuid>/restore`` | Replay the snapshot onto the live entity via Continuum's ``Reverter`` (non-destructive — produces a new version row stamping the restoring user via the standard save path) |

``<version_uuid>`` is a deterministic ``UUIDv5(entity_uuid,
transaction_id)`` so it's stable across replicas and retention
pruning. Authorisation reuses the resource's existing ``can_write``
permission; workspace admins can list / restore any entity.

**Restore commands** — ``superset/commands/{chart,dashboard,dataset}/
restore_version.py`` wrap ``VersionDAO.restore_version`` in the
standard ``@transaction()`` boundary. The command resolves the
``Reverter`` once per related collection (split-revert pattern, with
``flush + expire`` between calls) so a multi-relation restore
doesn't trip Continuum's autoflush race that would otherwise mark
half the collection as ``state.deleted=True`` mid-revert.

**Save responses** — ``PUT /api/v1/{resource}/<pk>`` is updated to
include ``old_version`` / ``new_version`` (0-based numbers),
``old_transaction_id`` / ``new_transaction_id`` (stable across
pruning), and ``old_version_uuid`` / ``new_version_uuid`` body
fields so callers can correlate a save with its resulting version
row. The ``ETag`` response header in the next commit is built on
top of this, but the body fields stay — they predate the header
and remain useful for clients that don't read response headers.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-19 18:42:06 -06:00
2025-12-04 13:18:34 -05:00
2024-04-15 11:21:42 -06:00

Superset

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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

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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:

Amazon Athena   Amazon DynamoDB   Amazon Redshift   Apache Doris   Apache Drill   Apache Druid   Apache Hive   Apache Impala   Apache Kylin   Apache Pinot   Apache Solr   Apache Spark SQL   Ascend   Aurora MySQL (Data API)   Aurora PostgreSQL (Data API)   Azure Data Explorer   Azure Synapse   ClickHouse   Cloudflare D1   CockroachDB   Couchbase   CrateDB   Databend   Databricks   Denodo   Dremio   DuckDB   Elasticsearch   Exasol   Firebird   Firebolt   Google BigQuery   Google Sheets   Greenplum   Hologres   IBM Db2   IBM Netezza Performance Server   MariaDB   Microsoft SQL Server   MonetDB   MongoDB   MotherDuck   OceanBase   Oracle   Presto   RisingWave   SAP HANA   SAP Sybase   Shillelagh   SingleStore   Snowflake   SQLite   StarRocks   Superset meta database   TDengine   Teradata   TimescaleDB   Trino   Vertica   YDB   YugabyteDB

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 Developer Guide to find resources around contributing along with a detailed guide on how to set up a development environment.

Resources

Understanding the Superset Points of View

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