sadpandajoe ca32d9b422 fix(dashboard): keep More filters reachable after applying a cross-filter
When a horizontal filter bar has enough native filters to overflow into the
"More filters" dropdown, applying a cross-filter (which prepends a chip to the
bar's item list) could make the overflowed native filters vanish from BOTH the
bar and the dropdown, while the "More filters" button itself also disappeared,
leaving the hidden filters unreachable. Clearing the cross-filter restored them.

Root cause: when the item set changes, DropdownContainer resets its positional
overflow index and re-measures. If that measurement runs against a transient
mid-reflow layout, it can conclude "nothing overflows" and latch that verdict
(the recalculation effect's dependencies do not change again, so it never
self-corrects). Because the trigger's visibility is derived solely from the
overflow count, that single bad verdict both strands the surplus filters in the
clipped bar and removes the trigger to reach them.

Fix: treat a post-item-change "nothing overflows" read as provisional and run a
single requestAnimationFrame confirmation pass that re-measures once the browser
has reflowed, keeping the trigger mounted across the confirmation window. The
confirmation is armed on every item-set change (so a fit->overflow transition is
covered, not only the already-overflowing case) and is versioned and cancelled
so a superseded frame from a rapid second change cannot clobber the newer state.
This extends the intent of #38193 (which guarded only the transient reset
window) to also cover a settled bad read, and is in the same overflow/button
visibility area as #28060.

Adds DropdownContainer.overflow.test.tsx, which drives the real overflow
recalculation (mocking only the two measurement sources) and covers: a clean
re-measurement after a prepended chip, the transient-latch regression, the
fit->overflow transition, over-correction (genuine fit drops the trigger), and
re-entrant item-set changes.
2026-06-24 02:13:43 +00:00
2024-04-15 11:21:42 -06:00

Superset

License Latest Release on Github Build Status PyPI version PyPI GitHub Stars Contributors Last Commit Open Issues Open PRs Get on Slack Documentation

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

Repo Activity

Performance Stats of apache/superset - Last 28 days
Languages
Python 38%
TypeScript 37.7%
Jupyter Notebook 21.3%
HTML 2.4%
JavaScript 0.3%
Other 0.2%