Evan Rusackas 10f71bdcd5 fix(docs): finish bare-relative link conversion + add lint guardrail
Copilot flagged two stragglers on editors.md where the previous
file-by-file conversion stopped halfway. Sweeping for the same
pattern across the active content tree found 76 bare relative
internal links total — 14 in this PR's already-modified files
(Copilot's two plus twelve more) and 62 in unchanged files.

Why the build doesn't catch this
─────────────────────────────────
`onBrokenLinks: 'throw'` (set in this PR) only validates *file-based*
markdown references — links whose URL ends in `.md` / `.mdx`. Those
go through Docusaurus's file resolver, which can prove the target
exists. Bare relative URL paths like `[Foo](../foo)` skip that
resolver entirely; Docusaurus emits them as raw hrefs. The browser
then resolves them against the *current* page URL, and for
trailing-slash routes that almost always lands in the wrong
directory. Page navigates client-side and 404s. The linkinator job
in CI *can* catch these, but it's `continue-on-error: true` so
findings are advisory.

What this commit does
──────────────────────
1. Fix all 76 bare relative internal links across the active docs
   tree by appending `.md` to each one (preserving anchors / query
   strings). All 76 targets resolved to real files; no link
   targets changed, only the form of the reference.

2. Fix the component-page generator. 54 of the 76 bare links lived
   in two auto-generated index files (`components/ui/index.mdx`
   and `components/design-system/index.mdx`). The next regeneration
   would have undone the manual fixes without this. The two
   emission sites in `generate-superset-components.mjs` now emit
   `.md`-suffixed links; comment at the call site explains why.

3. Add `docs/scripts/lint-docs-links.mjs` — fast source-level
   linter that scans `.md`/`.mdx` files under the active content
   trees (skipping `versioned_docs/` snapshots) and fails if it
   finds any markdown link whose URL starts with `./` or `../` and
   does not end in `.md`/`.mdx`. Excludes asset paths (.png,
   .json, etc.) and ignores fenced code blocks. Wired up as
   `yarn lint:docs-links`.

4. Add a `Lint docs links` step to `superset-docs-verify.yml`,
   running before the build step so PRs that introduce the pattern
   fail in seconds rather than at build-time / not at all. Blocking,
   not advisory — exactly the gap linkinator's `continue-on-error`
   leaves open.

Verified
────────
- `yarn lint:docs-links` exits 0 on the cleaned tree
- Re-introducing one bare link makes the linter report the exact
  file:line with the offending URL, exit code 1
- All 76 originally-flagged targets resolved to real `.md` / `.mdx`
  files; only the form of the reference changed
2026-05-13 20:17:46 -07:00
2025-12-04 13:18:34 -05:00

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
TypeScript 40.5%
Python 33.6%
Jupyter Notebook 22.5%
HTML 2.7%
JavaScript 0.4%
Other 0.2%