Claude Code c6fd7e71c9 experiment: add missing db.session.add() when Explore saves a chart to a new dashboard
Same removed-cascade_backrefs pattern as the ReportSchedule test
fixtures (see the "add missing db.session.add() in report-schedule
test fixtures" commit), found in real production code this time: the
legacy Explore "save as" -> "new dashboard" flow
(superset/views/core.py, the new_dashboard_name branch of
SliceAddView.save_or_overwrite_slice) constructs a brand-new, transient
Dashboard, then does `dash.slices.append(slc)` where `slc` is already
persistent. Appending a persistent Slice into a transient Dashboard's
`slices` collection also populates the reverse `Slice.dashboards`
backref - under SQLAlchemy 1.4 that implicitly cascaded the new
Dashboard into the session; under 2.0 (cascade_backrefs removed) it no
longer does, so `db.session.commit()` silently persisted nothing and
the "new dashboard" the user asked for was never created.

Confirmed directly: constructing a transient Dashboard, appending a
persistent Slice to its `.slices`, and committing left `dash.id` as
None (and logged
"SAWarning: Object of type <Dashboard> not in session, add operation
along 'Slice.dashboards' won't proceed" from Superset's own versioning
listener's flush). Adding `db.session.add(dash)` before the append
fixes it - verified the same probe then leaves `dash.id` populated
after commit.

No existing test exercises this specific new_dashboard_name path, so
verified via a standalone repro script against this branch's real
sqlalchemy==2.0.51/flask-sqlalchemy==3.1.1 rather than a test
assertion; ran tests/integration_tests/core_tests.py in full (47
passed, 2 skipped) to confirm no regression to the adjacent saveas/
overwrite paths that do have coverage.
2026-08-10 04:39:14 -07:00
2024-04-15 11:21:42 -06:00

Superset

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Superset logo (light)

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

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:

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

Languages
Python 40.9%
TypeScript 38%
Jupyter Notebook 18.3%
HTML 2.2%
JavaScript 0.3%
Other 0.2%