Rebased on master and updated the tests for the new raise_for_access on the
query_context-only update path:
- the unit test now mocks raise_for_access (so it no longer hits an unmocked
g.user) and asserts access is enforced while ownership is relaxed; adds a
forbidden-access case.
- the integration test also accepts ChartNotFoundError, since a no-access user
is filtered by the DAO access filter before the explicit check is reached.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Three @patch decorators but only two function parameters meant the mock for
superset.commands.chart.update.g was never captured and its .user was never
set. Added mock_u_g as the third parameter and set mock_u_g.user = gamma so
all three g objects are consistent.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
UpdateChartCommand skips the ownership check for "query_context-only" updates
(payload == {query_context, query_context_generation:true}) so report workers
and the UI's lazy query_context backfill can run as non-owners. But it skipped
ALL authorization, so any user with can_write on Chart could rewrite the
query_context of a chart they don't own (CWE-639).
Replace the unconditional skip with security_manager.raise_for_access(chart=...)
on that path. That still permits the legitimate non-owner flows (admins, owners,
and any user with access to the chart's datasource — which includes viewers who
can render the chart and the report executor), while rejecting users who cannot
access the chart at all.
DRAFT: needs CI / manual validation that the report-execution screenshot path
(executor user) passes raise_for_access in all configurations before merge.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Joe Li <joe@preset.io>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Joe Li <joe@preset.io>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Joe Li <joe@preset.io>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Joe Li <joe@preset.io>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: codeant-ai-for-open-source[bot] <244253245+codeant-ai-for-open-source[bot]@users.noreply.github.com>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Joe Li <joe@preset.io>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Joe Li <joe@preset.io>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: codeant-ai-for-open-source[bot] <244253245+codeant-ai-for-open-source[bot]@users.noreply.github.com>
Co-authored-by: Diego Pucci <diegopucci.me@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: codeant-ai-for-open-source[bot] <244253245+codeant-ai-for-open-source[bot]@users.noreply.github.com>
`❗ @${pull.user.login} Your base branch \`${currentBranch}\` has ` +
'also updated `superset/migrations`.\n' +
'\n' +
'**Please consider rebasing your branch and [resolving potential db migration conflicts](https://github.com/apache/superset/blob/master/CONTRIBUTING.md#merging-db-migrations).**',
'**Please consider rebasing your branch and [resolving potential db migration conflicts](https://superset.apache.org/docs/contributing/development#merging-db-migrations).**',
body: '⚠️ **DEPRECATED WORKFLOW** - Ephemeral environment shutdown and build artifacts deleted. Please migrate to the new Superset Showtime system for future PRs.'
body: `@${{ github.actor }} Ephemeral environment spinning up at http://${{ steps.get-ip.outputs.ip }}:8080. Credentials are 'admin'/'admin'. Please allow several minutes for bootstrapping and startup.`
Looks like your PR contains new `.js` or `.jsx` files:
```
${{steps.check.outputs.js_files_added}}
```
As decided in [SIP-36](https://github.com/apache/superset/issues/9101), all new frontend code should be written in TypeScript. Please convert above files to TypeScript then re-request review.
description:'Use Cypress Dashboard (true/false) [paid service - trigger manually when needed]. You MUST provide a branch and/or PR number below for this to work.'
description:"Use Cypress Dashboard (true/false) [paid service - trigger manually when needed]. You MUST provide a branch and/or PR number below for this to work."
We hope to see you in our [Slack](https://apache-superset.slack.com/) community too! Not signed up? Use our [Slack App](http://bit.ly/join-superset-slack) to self-register.
Before evaluating any code path for security issues, read [`SECURITY.md`](SECURITY.md). It is the canonical, authoritative source for Apache Superset's security model and is referenced by both human reporters and automated scanners.
In short, the test for whether a finding is in scope is one question:
> *Does it let a principal perform an action the role and capability matrix in `SECURITY.md` does not entitle them to?*
If yes, it is in scope. If no, it is not.
The three trust boundaries are:
1.**The Admin role** is a fully trusted operational principal. Anything an Admin can do through documented configuration, API, or UI is an intended capability, not a vulnerability.
2.**The operator** owns deployment-time decisions (secrets, network exposure, feature-flag selection, connector and codec choices, notification destinations, third-party plugins). Misconfiguration at this layer is a deployment defect, not a Superset vulnerability.
3.**The codebase** is responsible for enforcing the role and capability matrix wherever it exposes functionality to a principal: API routes, command and DAO layers, UI handlers, background jobs, and any other entry point. A missing or incorrect enforcement check is in scope no matter where it lives.
The security model assumes that operator-controlled infrastructure, including the metadata database, cache backends, message brokers, secret stores, and deployment environment, remains within the operator's trust boundary. Vulnerabilities must demonstrate a security boundary violation by an attacker who does not already control those systems.
Route-level authorization in this codebase uses one of three Flask-AppBuilder decorators depending on the route type:
-`@protect()` for REST API routes (`ModelRestApi` / `BaseApi`)
-`@has_access_api` for legacy view routes
-`@has_access` for legacy HTML view routes
Object-level authorization via `security_manager.raise_for_access(...)` applies to data-bearing resources: dashboards, charts, datasets and datasources, queries, database and table access, and query contexts. Other resources (annotations, tags, CSS templates, reports, RLS rules, and similar) rely on the route-level decorator plus DAO `base_filters` for ownership scoping; the absence of `raise_for_access` on these resources is by design, not a finding. Code that omits the per-object gate on a route that returns or mutates a specific data-bearing object is in scope; code that follows the correct pattern for its resource class can still contain injection, SSRF, XSS, or other classes of finding unrelated to authorization, which are evaluated separately.
The full role and capability matrix, in-scope and out-of-scope class lists, and CVE aggregation rules are in [`SECURITY.md`](SECURITY.md). Defer to that document for any specifics.
**Requirements for findings filed by automated tooling**
Automated scanners (LLM-based code scanners, static analyzers, dependency tools) that file findings against this codebase must, in each finding, name:
1. The specific role and capability matrix row in [`SECURITY.md`](SECURITY.md) the finding believes is violated.
2. The principal the finding assumes the attacker holds (Public, Gamma, sql_lab, Alpha, Admin, Embedded guest token, or a custom role with explicit capability grants).
Findings that cannot identify both should be filed as questions, not vulnerabilities. This requirement exists to ensure every reported issue is testable against the published security model and to keep speculative or pattern-match-only reports out of the triage queue.
## Key Directories
```
@@ -80,9 +138,34 @@ superset/
- **UPDATING.md**: Add breaking changes here
- **Docstrings**: Required for new functions/classes
A modern, enterprise-ready business intelligence web application.
### Documentation
- **[User Guide](https://superset.apache.org/user-docs/)** — For analysts and business users. Explore data, build charts, create dashboards, and connect databases.
- **[Administrator Guide](https://superset.apache.org/admin-docs/)** — Install, configure, and operate Superset. Covers security, scaling, and database drivers.
- **[Developer Guide](https://superset.apache.org/developer-docs/)** — Contribute to Superset or build on its REST API and extension framework.
[**Why Superset?**](#why-superset) |
[**Supported Databases**](#supported-databases) |
[**Installation and Configuration**](#installation-and-configuration) |
Superset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena, [and more](https://superset.apache.org/docs/configuration/databases)) that has a Python DB-API driver and a SQLAlchemy dialect.
Superset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena, [and more](https://superset.apache.org/docs/databases)) 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](https://superset.apache.org/docs/configuration/databases).
**A more comprehensive list of supported databases** along with the configuration instructions can be found [here](https://superset.apache.org/docs/databases).
Want to add support for your datastore or data engine? Read more [here](https://superset.apache.org/docs/frequently-asked-questions#does-superset-work-with-insert-database-engine-here) about the technical requirements.
@@ -159,20 +189,25 @@ Try out Superset's [quickstart](https://superset.apache.org/docs/quickstart/) gu
and please read our [Slack Community Guidelines](https://github.com/apache/superset/blob/master/CODE_OF_CONDUCT.md#slack-community-guidelines)
- [Join our dev@superset.apache.org Mailing list](https://lists.apache.org/list.html?dev@superset.apache.org). To join, simply send an email to [dev-subscribe@superset.apache.org](mailto: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](https://docs.google.com/spreadsheets/d/1U1qxiLvOX0kBTUGME1AHHi6Ywel6ECF8xk_Qy-V9R8c/edit#gid=0)
- Join Superset's Town Hall and [Operational Model](https://preset.io/blog/the-superset-operational-model-wants-you/) recurring meetings. Meeting info is available on the [Superset Community Calendar](https://superset.apache.org/community)
to find resources around contributing along with a detailed guide on
how to set up a development environment.
## Resources
- [Superset "In the Wild"](https://superset.apache.org/inTheWild) - see who's using Superset, and [add your organization](https://github.com/apache/superset/edit/master/RESOURCES/INTHEWILD.yaml) to the list!
- [Feature Flags](https://github.com/apache/superset/blob/master/RESOURCES/FEATURE_FLAGS.md) - the status of Superset's Feature Flags.
- [Feature Flags](https://superset.apache.org/docs/configuration/feature-flags) - the status of Superset's Feature Flags.
- [Standard Roles](https://github.com/apache/superset/blob/master/RESOURCES/STANDARD_ROLES.md) - How RBAC permissions map to roles.
- [Superset Wiki](https://github.com/apache/superset/wiki) - Tons of additional community resources: best practices, community content and other information.
- [Superset SIPs](https://github.com/orgs/apache/projects/170) - The status of Superset's SIPs (Superset Improvement Proposals) for both consensus and implementation status.
# Part 2: Verify RSA key - this is the same as running `gpg --verify {release}.asc {release}` and comparing the RSA key and email address against the KEYS file # noqa: E501
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
-->
# Superset Feature Flags
This is a list of the current Superset optional features. See config.py for default values. These features can be turned on/off by setting your preferred values in superset_config.py to True/False respectively
## In Development
These features are considered **unfinished** and should only be used on development environments.
[//]: # "PLEASE KEEP THE LIST SORTED ALPHABETICALLY"
- ALERT_REPORT_TABS
- DATE_RANGE_TIMESHIFTS_ENABLED
- ENABLE_ADVANCED_DATA_TYPES
- PRESTO_EXPAND_DATA
- SHARE_QUERIES_VIA_KV_STORE
- TAGGING_SYSTEM
- CHART_PLUGINS_EXPERIMENTAL
## In Testing
These features are **finished** but currently being tested. They are usable, but may still contain some bugs.
[//]: # "PLEASE KEEP THE LIST SORTED ALPHABETICALLY"
These features flags currently default to True and **will be removed in a future major release**. For this current release you can turn them off by setting your config to False, but it is advised to remove or set these flags in your local configuration to **True** so that you do not experience any unexpected changes in a future release.
[//]: # "PLEASE KEEP THE LIST SORTED ALPHABETICALLY"
To ensure engineering focus remains on verified risks and to manage high reporting volumes, all reports must meet the following criteria:
- Plain Text Format: In accordance with Apache guidelines, please provide all details in plain text within the email body. Avoid sending PDFs, Word documents, or password-protected archives.
- Mandatory AI Disclosure: If you utilized Large Language Models (LLMs) or AI tools to identify a flaw or assist in writing a report, you must disclose this in your submission so our triage team can contextualize the findings.
- Human-Verified PoC: All submissions must include a manual, step-by-step Proof of Concept (PoC) performed on a supported release. Raw AI outputs, hypothetical chat transcripts, or unverified scanner logs will be closed as Invalid.
We kindly ask you to include the following information in your report to assist our developers in triaging and remediating issues efficiently:
- Version/Commit: The specific version of Apache Superset or the Git commit hash you are using.
- Configuration: A sanitized copy of your `superset_config.py` file or any config overrides.
- Environment: Your deployment method (e.g., Docker Compose, Helm, or source) and relevant OS/Browser details.
- Impacted Component: Identification of the affected area (e.g., Python backend, React frontend, or a specific database connector).
- Expected vs. Actual Behavior: A clear description of the intended system behavior versus the observed vulnerability.
- Detailed Reproduction Steps: Clear, manual steps to reproduce the vulnerability.
## Security Model
This section defines what Apache Superset considers a security issue and what it does not. It is the canonical reference for reporters, the Apache Superset Security Team, and any automated tool (LLM-based scanner, static analyzer, dependency tool) that needs to constrain its hypotheses to behaviors that genuinely violate the project's security policy.
The model is intentionally written in terms of principals, trust boundaries, and capability surface rather than in terms of specific files, functions, or libraries. New code paths inherit the model automatically.
### Trust Boundaries
Apache Superset's threat model assumes three trust boundaries.
1.*The Admin role* is a fully trusted operational principal. Anything an Admin can do through the documented user interface, REST API, or configuration system is an intended capability, not a vulnerability, even if individually powerful or destructive. The Admin role is, by policy, equivalent to operating-system-level trust over the Apache Superset application. This is unavoidable rather than aspirational: an Admin can, for example, register new database connections of arbitrary type, execute arbitrary SQL through SQL Lab, render Jinja templates that resolve to SQL or rendered HTML, and override application configuration. Granting Admin is functionally equivalent to granting shell access on the host, which is the reasoning behind treating it as a trust boundary in the sense of MITRE CNA Operational Rules 4.1.
2.*The operator* is whoever deploys, configures, and runs Apache Superset. Behaviors that depend on deployment-time decisions are the operator's responsibility, not Apache Superset's. This includes the values of secrets, the network reachability of the application and its data sources, the choice of database connectors and cache backends, the selection of feature flags, the destinations of notifications, and the trust placed in third-party plugins. Defaults that fail closed are the responsibility of the Apache Superset codebase. Defaults that fail open must be accompanied by a documented hardening requirement; applying that hardening is the operator's responsibility, while shipping an undocumented or unflagged fail-open default is a codebase issue.
3.*The Apache Superset codebase* is responsible for enforcing the role and capability matrix below across its product surface. A failure to enforce, anywhere in that surface, is in scope. The codebase's commitments are limited to the role and capability matrix and to controls Apache Superset's own documentation (this file and the linked Security documentation) explicitly positions as security boundaries; configurable hardening that operators can layer on top is treated separately under *Vulnerability Scope* below.
### Roles and Capabilities
Apache Superset ships with the following first-class principals. Detailed permission definitions live in the [Security documentation](https://superset.apache.org/docs/security).
| Principal | Read data | Write objects | Execute SQL | Manage databases | Manage users, roles, RLS |
|---|---|---|---|---|---|
| Public (anonymous) | none by default | no | no | no | no |
| Gamma | only granted datasets | own charts and dashboards on granted datasets | no by default (requires the `sql_lab` role) | no | no |
| Alpha | all data sources | own charts, dashboards, and datasets | no by default (requires the `sql_lab` role) | data upload to existing databases only | no |
| Admin | all | all | yes | yes | yes |
| Embedded guest token | data sources reachable through the embedded dashboards the token authorizes | no | no | no | no |
The `sql_lab` role is *additive*: it grants the SQL Lab permission set on top of the base role above, and is the only path by which Gamma or Alpha gain SQL execution capability. Database access is still scoped per the base role's grants. Admin includes SQL Lab access by default.
Deployments may grant or revoke individual view-menu permissions, which shifts the boundary for that deployment but does not redefine the model. Any custom role created by an operator inherits the same principle: its capabilities are whatever the operator has explicitly granted it. The Public principal follows the same rule: operators may grant the Public role read access to specific datasets or dashboards (typically for anonymous reporting use cases), which shifts the boundary for that deployment without redefining the model.
### Vulnerability Scope
The test for whether a finding is in scope is a single question:
> *Does this finding let a principal perform an action the role and capability matrix above does not entitle them to?*
If yes, it is in scope. If no, it is out of scope. The lists below apply that test to the classes Apache Superset most commonly receives reports about; they are illustrative, not exhaustive.
*In Scope*
- A user, embedded guest, or anonymous visitor reads, modifies, or deletes data outside their granted set. Includes object-level access bypass on charts, dashboards, datasets, saved queries, tags, annotations, and similar per-object endpoints, and row-level-security rule bypass.
- A user supplies input that the codebase should sanitize or parameterize but does not, causing arbitrary SQL, template code, or scripts to execute. Includes injection through Jinja templates, SQL-construction paths, and any field the codebase passes to a query engine or template engine.
- A user bypasses authentication, fixates or reuses another user's session, or reaches an authenticated endpoint without logging in.
- An embedded guest token authorizes actions outside the dashboard it was issued for, or can be forged, replayed, or escalated to a higher principal.
- Apache Superset, acting on behalf of an unprivileged user, fetches an outbound URL the user controls in a feature where Apache Superset itself, not the operator, controls the outbound destination set (server-side request forgery).
- An Apache Superset default fails open without an accompanying documented hardening requirement. The codebase is responsible for shipping fail-closed defaults or for documenting the hardening required when a default fails open; failures of that responsibility are in scope (see *Trust Boundaries*).
- A user bypasses a control Apache Superset documents specifically as a security boundary. This includes row-level security, the access checks tied to the role and capability matrix above, and any feature whose documentation positions it as security-relevant. The codebase commits to enforcing those controls; bypasses are in scope regardless of which principal triggers them.
- A user causes a script to execute in another user's browser through a field the codebase renders to that other user (cross-site scripting), or causes cross-origin leakage of authenticated session state or data.
- A user reaches a route, page, or API endpoint that requires a role they do not have.
*Out of Scope*
- Any action an Admin role can perform through documented configuration, API, or UI. The Admin role is a trusted operational principal by policy. Per MITRE CNA Operational Rules 4.1, a qualifying vulnerability must violate a security policy; behavior within a documented trust boundary does not.
- Deployment or operator decisions: the values of secrets and tokens, whether internal networks are reachable from the server, which database connectors or cache backends are enabled, which feature flags are set, where notifications are delivered, and which third-party plugins are loaded.
- Compromise, modification, or malicious control of trusted backend infrastructure. Apache Superset assumes the integrity of its metastore, cache backends (for example Redis or Memcached), message brokers, secret stores, and other operator-managed infrastructure. Findings that require an attacker to read from, write to, or otherwise tamper with these systems, including injecting malicious state, serialized objects, cache entries, task metadata, configuration, or database records, are post-compromise scenarios and do not constitute vulnerabilities in Apache Superset itself. A finding remains in scope only if an unprivileged user can cause such modification through a vulnerability in Apache Superset.
- The continued presence of expired key-value or metastore-cache entries that have not yet been deleted from the metadata database. Such entries are excluded from reads once expired, are purged opportunistically on write, and are removed in bulk by the scheduled `prune_key_value` maintenance task; their lingering until purged is an eventual-cleanup property, not a security boundary, and does not constitute a vulnerability.
- How a downstream application (spreadsheet program, email client, browser handling user-downloaded files) interprets output Apache Superset produced for it.
- Findings without a reproducible proof of concept against a supported release. The burden of demonstrating exploitability rests with the reporter; findings closed for lack of a proof of concept may be refiled if one is later produced.
- Brute force, rate limiting, denial of service, or resource exhaustion that does not bypass a documented control.
- Missing security headers, banner or version disclosure, user or object enumeration through error messages or timing, and similar low-impact information disclosure that does not enable a further concrete exploit.
- Bypasses of configurable defense-in-depth hardening that Apache Superset does not document as a security boundary. Apache Superset is not a SQL or database firewall: operator-deployable filters such as SQL function or table denylists, URI restrictions on already-authorized database connectors, and similar belt-and-braces controls are provided to let operators layer hardening on top of the role and capability matrix, not as firewall-grade guarantees the codebase commits to. Findings against such hardening are improvements, not vulnerabilities, unless the documentation positions the specific control as security-relevant.
- Hardening suggestions that improve defense in depth but do not violate the security model.
Findings in third-party dependencies fall into two cases. A finding in a transitive dependency, or in an operator-selected dependency that Apache Superset does not ship, is out of scope and should be reported to the dependency's maintainers. A finding caused by Apache Superset pinning a known-vulnerable version of a direct dependency it ships, or using a dependency in a way that creates a vulnerability the dependency itself does not have, remains in scope. Dependency findings in the official Apache Superset Docker image that fall into the first case can be remediated by extending the image at release time.
When uncertain whether a finding falls in scope, please file it through the reporting process above. The triage team will classify it and explain the reasoning if it is closed as out of scope.
**Outcome of Reports**
Reports that are deemed out of scope for a CVE but represent valid security best practices or hardening opportunities are typically converted into public GitHub issues, where the community can contribute fixes alongside the maintainers. The triage decision and reasoning are communicated back to the reporter in either case.
**Vulnerability Aggregation & CVE Attribution**
In accordance with MITRE CNA Operational Rules (4.1.10, 4.1.11, and 4.2.13), Apache Superset issues CVEs based on the underlying architectural root cause rather than the number of affected endpoints or exploit payloads.
- Aggregation: If multiple exploit vectors stem from the same programmatic failure or shared vulnerable code, they must be aggregated into a single, comprehensive report.
- Independent Fixes: Separate CVEs will only be assigned if the vulnerabilities reside in decoupled architectural modules and can be fixed independently of one another.
Reports that fail to aggregate related findings will be merged during triage to ensure an accurate and defensible CVE record.
**Your responsible disclosure and collaboration are invaluable.**
- [Common Vulnerabilities and Exposures by release](https://superset.apache.org/docs/security/cves)
- [How Security Vulnerabilities are Reported & Handled in Apache Superset (Blog)](https://preset.io/blog/how-security-vulnerabilities-are-reported-and-handled-in-apache-superset/)
**For LLM agents and automated scanners**
This file is the canonical source of truth for what Apache Superset considers a security vulnerability. Automated tooling (LLM-based code scanners, static analyzers, dependency tools) should treat the Security Model section as authoritative when classifying findings. The repository's [AGENTS.md](AGENTS.md) file contains a short pointer to this document for agents that read AGENTS.md as their entry point.
@@ -24,6 +24,200 @@ assists people when migrating to a new version.
## Next
### Duration formatter precision
The `DURATION` number formatter now uses `Intl.DurationFormat` for locale-aware output. By default, sub-second fields are omitted, so values that previously displayed fractional seconds with `pretty-ms`, such as `10500` milliseconds rendering as `10.5s`, now render as `10s`.
To preserve sub-second precision in custom duration formatters, enable `formatSubMilliseconds`.
### Cache warmup authenticates via SUPERSET_CACHE_WARMUP_USER
The `cache-warmup` Celery task now drives a real WebDriver session for reliable authentication and reads the user to authenticate as from the new `SUPERSET_CACHE_WARMUP_USER` config option. It no longer consults `CACHE_WARMUP_EXECUTORS` for the warmup path. `SUPERSET_CACHE_WARMUP_USER` defaults to `None`, so the task fails fast with a clear message until you set it. Operators who previously relied on `CACHE_WARMUP_EXECUTORS` for cache warmup must set `SUPERSET_CACHE_WARMUP_USER` to a dedicated least-privilege user with access to the dashboards they want warmed up before the next warmup run.
### YDB now uses a native sqlglot dialect
YDB SQL parsing now relies on the dedicated [`ydb-sqlglot-plugin`](https://pypi.org/project/ydb-sqlglot-plugin/) dialect, which registers itself with sqlglot automatically. YDB users must install this plugin (e.g., via `pip install "apache-superset[ydb]"`) to avoid a `ValueError` when Superset parses YDB queries.
### Embedded dashboards enforce configured Allowed Domains for postMessage
The embedded dashboard page now validates the origin of incoming `postMessage` events against the dashboard's configured **Allowed Domains**. The server-rendered embedded page exposes the configured domains in its bootstrap payload, and the frontend rejects message events whose origin is not in that list.
Enforcement only applies when the Allowed Domains list is non-empty. If the list is empty (the default), any origin is accepted, so there is no behavior change for embeds that did not configure Allowed Domains.
### Default guest/async JWT secrets are rejected at startup
Superset already refuses to start in production (non-debug, non-testing) when `SECRET_KEY` is left at its built-in default, and when `GUEST_TOKEN_JWT_SECRET` is left at its default while `EMBEDDED_SUPERSET` is enabled. This behavior is extended to `GLOBAL_ASYNC_QUERIES_JWT_SECRET`: if the `GLOBAL_ASYNC_QUERIES` feature flag is enabled and the secret is still the publicly known default (`test-secret-change-me`), Superset logs a clear error and refuses to start.
As with the existing `SECRET_KEY` check, this only fails in production. In debug mode, testing mode, or under the test runner, a warning is logged instead of exiting, so local development is unaffected.
To resolve the error, set a strong random value in `superset_config.py`:
The check is only active when the relevant feature is enabled, so deployments that do not use global async queries (or embedding) are not affected.
### Dataset import validates catalog against the target connection
Importing a dataset now validates the `catalog` field against the target database connection. When the connection has multi-catalog disabled (`allow_multi_catalog` off) and the dataset's catalog is not the connection's default catalog, the import fails instead of silently persisting the non-default catalog. This matches the validation already enforced on the dataset update path and prevents imported datasets from querying an unintended database.
If you relied on importing datasets with a non-default catalog, enable "Allow changing catalogs" on the target connection, or set the dataset's catalog to the connection's default before importing.
### Extension supply-chain controls (denylist + version policy)
Two opt-in static gates control which extensions are allowed to load:
-`EXTENSION_DENYLIST` refuses extensions matching an id (every version) or `id@version` (a single version), e.g. `["compromised-extension", "other-ext@1.2.3"]`.
-`EXTENSION_VERSION_POLICY` enforces a minimum version per extension id, e.g. `{"acme.widget": "1.2.0"}` (PEP 440 comparison); a release below the minimum is refused.
Both default to empty (no behavior change). They apply to both the `LOCAL_EXTENSIONS` and `EXTENSIONS_PATH` load paths.
### Dynamic Group By respects the sort toggle for display values
The Dynamic Group By chart customization now orders its display values according to the "Sort display control values" toggle: ascending (A–Z), descending (Z–A), or the dataset's source order when the toggle is unset. Previously the dropdown always sorted alphabetically. Existing dashboards where the toggle was never set will show options in source order instead of A–Z; open the customization and enable the toggle to restore alphabetical ordering.
### Granular Export Controls
A new feature flag `GRANULAR_EXPORT_CONTROLS` introduces three fine-grained permissions that replace the legacy `can_csv` permission:
When the feature flag is enabled, these permissions are enforced on both the frontend (disabled buttons with tooltips) and backend (403 responses from API endpoints). When disabled, legacy `can_csv` behavior is preserved.
**Migration behavior:** All three new permissions are granted to every role that currently has `can_csv`, preserving existing access. Admins can then selectively revoke individual export permissions from specific roles as needed.
### Deck.gl MapBox viewport and opacity controls are functional
The Deck.gl MapBox chart's **Opacity**, **Default longitude**, **Default latitude**, and **Zoom** controls were previously non-functional — changing them had no effect on the rendered map. These controls are now wired up correctly.
**Behavior change for existing charts:** Previously, the viewport controls had hard-coded default values (`-122.405293`, `37.772123`, zoom `11` — San Francisco) that were stored in each chart's `form_data` but never applied. The map always used `fitBounds` to center on the data. With this fix, those stored values are now respected, which means existing MapBox charts may open centered on the old default coordinates instead of fitting to data bounds.
**To restore fit-to-data behavior:** Open the chart in Explore, clear the **Default longitude**, **Default latitude**, and **Zoom** fields in the Viewport section, and re-save the chart.
### Combined datasource list endpoint
Added a new combined datasource list endpoint at `GET /api/v1/datasource/` to serve datasets and semantic views in one response.
- The endpoint is available to users with at least one of `can_read` on `Dataset` or `SemanticView`.
- Semantic views are included only when the `SEMANTIC_LAYERS` feature flag is enabled.
- The endpoint enforces strict `order_column` validation and returns `400` for invalid sort columns.
### ClickHouse minimum driver version bump
The minimum required version of `clickhouse-connect` has been raised to `>=0.13.0`. If you are using the ClickHouse connector, please upgrade your `clickhouse-connect` package. The `_mutate_label` workaround that appended hash suffixes to column aliases has also been removed, as it is no longer needed with modern versions of the driver.
### MCP Tool Observability
MCP (Model Context Protocol) tools now include enhanced observability instrumentation for monitoring and debugging:
**Two-layer instrumentation:**
1.**Middleware layer** (`LoggingMiddleware`): Automatically logs all MCP tool calls with `duration_ms` and `success` status in the audit log (Action Log UI, logs table)
2.**Sub-operation tracking**: All 19 MCP tools include granular `event_logger.log_context()` blocks for tracking individual operations like validation, database writes, and query execution
**Security note:** Sensitive parameters (passwords, API keys, tokens) are automatically redacted in logs as `[REDACTED]`.
### Distributed Coordination Backend
A new `DISTRIBUTED_COORDINATION_CONFIG` configuration provides a unified Redis-based backend for real-time coordination features in Superset. This backend enables:
- **Pub/sub messaging** for real-time event notifications between workers
- **Atomic distributed locking** using Redis SET NX EX (more performant than database-backed locks)
- **Event-based coordination** for background task management
The distributed coordination is used by the Global Task Framework (GTF) for abort notifications and task completion signaling, and will eventually replace `GLOBAL_ASYNC_QUERIES_CACHE_BACKEND` as the standard signaling backend. Configuring this is recommended for Redis enabled production deployments.
Example configuration in `superset_config.py`:
```python
DISTRIBUTED_COORDINATION_CONFIG={
"CACHE_TYPE":"RedisCache",
"CACHE_KEY_PREFIX":"signal_",
"CACHE_REDIS_URL":"redis://localhost:6379/1",
"CACHE_DEFAULT_TIMEOUT":300,
}
```
See `superset/config.py` for complete configuration options.
### WebSocket config for GAQ with Docker
[35896](https://github.com/apache/superset/pull/35896) and [37624](https://github.com/apache/superset/pull/37624) updated documentation on how to run and configure Superset with Docker. Specifically for the WebSocket configuration, a new `docker/superset-websocket/config.example.json` was added to the repo, so that users could copy it to create a `docker/superset-websocket/config.json` file. The existing `docker/superset-websocket/config.json` was removed and git-ignored, so if you're using GAQ / WebSocket make sure to:
- Stash/backup your existing `config.json` file, to re-apply it after (will get git-ignored going forward)
- Update the `volumes` configuration for the `superset-websocket` service in your `docker-compose.override.yml` file, to include the `docker/superset-websocket/config.json` file. For example:
- Auto-discovery: create `superset/examples/my_dataset/data.parquet` to add a new example
- Parquet is an Apache project format: compressed (~27% smaller), self-describing schema
- YAML configs define datasets, charts, and dashboards declaratively
- Removed Python-based data generation from individual example files
#### Test Data Reorganization
- Moved `big_data.py` to `superset/cli/test_loaders.py` - better reflects its purpose as a test utility
- Fixed inverted logic for `--load-test-data` flag (now correctly includes .test.yaml files when flag is set)
- Clarified CLI flags:
- `--force` / `-f`: Force reload even if tables exist
- `--only-metadata` / `-m`: Create table metadata without loading data
- `--load-test-data` / `-t`: Include test dashboards and .test.yaml configs
- `--load-big-data` / `-b`: Generate synthetic stress-test data
#### Bug Fixes
- Fixed numpy array serialization for PostgreSQL (converts complex types to JSON strings)
- Fixed KeyError for `allow_csv_upload` field in database configs (now optional with default)
- Fixed test data loading logic that was incorrectly filtering files
### MCP Service
The MCP (Model Context Protocol) service enables AI assistants and automation tools to interact programmatically with Superset.
@@ -128,6 +322,28 @@ See `superset/mcp_service/PRODUCTION.md` for deployment guides.
- [35062](https://github.com/apache/superset/pull/35062): Changed the function signature of `setupExtensions` to `setupCodeOverrides` with options as arguments.
### Breaking Changes
- [37370](https://github.com/apache/superset/pull/37370): The `APP_NAME` configuration variable no longer controls the browser window/tab title or other frontend branding. Application names should now be configured using the theme system with the `brandAppName` token. The `APP_NAME` config is still used for backend contexts (MCP service, logs, etc.) and serves as a fallback if `brandAppName` is not set.
- **Migration:**
```python
# Before (Superset 5.x)
APP_NAME = "My Custom App"
# After (Superset 6.x) - Option 1: Use theme system (recommended)
# After (Superset 6.x) - Option 2: Temporary fallback
# Keep APP_NAME for now (will be used as fallback for brandAppName)
APP_NAME = "My Custom App"
# But you should migrate to THEME_DEFAULT.token.brandAppName
```
- **Note:** For dark mode, set the same tokens in `THEME_DARK` configuration.
- [36317](https://github.com/apache/superset/pull/36317): The `CUSTOM_FONT_URLS` configuration option has been removed. Use the new per-theme `fontUrls` token in `THEME_DEFAULT` or database-managed themes instead.
- **Before:**
```python
@@ -142,7 +358,7 @@ See `superset/mcp_service/PRODUCTION.md` for deployment guides.
"fontUrls": [
"https://fonts.example.com/myfont.css",
],
# ... other tokens
# ... other tokens
}
}
```
@@ -165,14 +381,14 @@ Note: Pillow is now a required dependency (previously optional) to support image
- [33116](https://github.com/apache/superset/pull/33116) In Echarts Series charts (e.g. Line, Area, Bar, etc.) charts, the `x_axis_sort_series` and `x_axis_sort_series_ascending` form data items have been renamed with `x_axis_sort` and `x_axis_sort_asc`.
There's a migration added that can potentially affect a significant number of existing charts.
- [32317](https://github.com/apache/superset/pull/32317) The horizontal filter bar feature is now out of testing/beta development and its feature flag `HORIZONTAL_FILTER_BAR` has been removed.
- [31590](https://github.com/apache/superset/pull/31590) Marks the begining of intricate work around supporting dynamic Theming, and breaks support for [THEME_OVERRIDES](https://github.com/apache/superset/blob/732de4ac7fae88e29b7f123b6cbb2d7cd411b0e4/superset/config.py#L671) in favor of a new theming system based on AntD V5. Likely this will be in disrepair until settling over the 5.x lifecycle.
- [32432](https://github.com/apache/superset/pull/31260) Moves the List Roles FAB view to the frontend and requires `FAB_ADD_SECURITY_API` to be enabled in the configuration and `superset init` to be executed.
- [31590](https://github.com/apache/superset/pull/31590) Marks the beginning of intricate work around supporting dynamic Theming, and breaks support for [THEME_OVERRIDES](https://github.com/apache/superset/blob/732de4ac7fae88e29b7f123b6cbb2d7cd411b0e4/superset/config.py#L671) in favor of a new theming system based on AntD V5. Likely this will be in disrepair until settling over the 5.x lifecycle.
- [32432](https://github.com/apache/superset/pull/32432) Moves the List Roles FAB view to the frontend and requires `FAB_ADD_SECURITY_API` to be enabled in the configuration and `superset init` to be executed.
- [34319](https://github.com/apache/superset/pull/34319) Drill to Detail and Drill By is now supported in Embedded mode, and also with the `DASHBOARD_RBAC` FF. If you don't want to expose these features in Embedded / `DASHBOARD_RBAC`, make sure the roles used for Embedded / `DASHBOARD_RBAC`don't have the required permissions to perform D2D actions.
## 5.0.0
- [31976](https://github.com/apache/superset/pull/31976) Removed the `DISABLE_LEGACY_DATASOURCE_EDITOR` feature flag. The previous value of the feature flag was `True` and now the feature is permanently removed.
- [31959](https://github.com/apache/superset/pull/32000) Removes CSV_UPLOAD_MAX_SIZE config, use your web server to control file upload size.
- [32000](https://github.com/apache/superset/pull/32000) Removes CSV_UPLOAD_MAX_SIZE config, use your web server to control file upload size.
- [31959](https://github.com/apache/superset/pull/31959) Removes the following endpoints from data uploads: `/api/v1/database/<id>/<file type>_upload` and `/api/v1/database/<file type>_metadata`, in favour of new one (Details on the PR). And simplifies permissions.
- [31844](https://github.com/apache/superset/pull/31844) The `ALERT_REPORTS_EXECUTE_AS` and `THUMBNAILS_EXECUTE_AS` config parameters have been renamed to `ALERT_REPORTS_EXECUTORS` and `THUMBNAILS_EXECUTORS` respectively. A new config flag `CACHE_WARMUP_EXECUTORS` has also been introduced to be able to control which user is used to execute cache warmup tasks. Finally, the config flag `THUMBNAILS_SELENIUM_USER` has been removed. To use a fixed executor for async tasks, use the new `FixedExecutor` class. See the config and docs for more info on setting up different executor profiles.
- [31894](https://github.com/apache/superset/pull/31894) Domain sharding is deprecated in favor of HTTP2. The `SUPERSET_WEBSERVER_DOMAINS` configuration will be removed in the next major version (6.0)
@@ -180,7 +396,7 @@ Note: Pillow is now a required dependency (previously optional) to support image
- [31774](https://github.com/apache/superset/pull/31774): Fixes the spelling of the `USE-ANALAGOUS-COLORS` feature flag. Please update any scripts/configuration item to use the new/corrected `USE-ANALOGOUS-COLORS` flag spelling.
- [31582](https://github.com/apache/superset/pull/31582) Removed the legacy Area, Bar, Event Flow, Heatmap, Histogram, Line, Sankey, and Sankey Loop charts. They were all automatically migrated to their ECharts counterparts with the exception of the Event Flow and Sankey Loop charts which were removed as they were not actively maintained and not widely used. If you were using the Event Flow or Sankey Loop charts, you will need to find an alternative solution.
- [31198](https://github.com/apache/superset/pull/31198) Disallows by default the use of the following ClickHouse functions: "version", "currentDatabase", "hostName".
- [29798](https://github.com/apache/superset/pull/29798) Since 3.1.0, the intial schedule for an alert or report was mistakenly offset by the specified timezone's relation to UTC. The initial schedule should now begin at the correct time.
- [29798](https://github.com/apache/superset/pull/29798) Since 3.1.0, the initial schedule for an alert or report was mistakenly offset by the specified timezone's relation to UTC. The initial schedule should now begin at the correct time.
- [30021](https://github.com/apache/superset/pull/30021) The `dev` layer in our Dockerfile no long includes firefox binaries, only Chromium to reduce bloat/docker-build-time.
- [30099](https://github.com/apache/superset/pull/30099) Translations are no longer included in the default docker image builds. If your environment requires translations, you'll want to set the docker build arg `BUILD_TRANSLATIONS=true`.
- [31262](https://github.com/apache/superset/pull/31262) NOTE: deprecated `pylint` in favor of `ruff` as our only python linter. Only affect development workflows positively (not the release itself). It should cover most important rules, be much faster, but some things linting rules that were enforced before may not be enforce in the exact same way as before.
@@ -193,7 +409,7 @@ Note: Pillow is now a required dependency (previously optional) to support image
- [25166](https://github.com/apache/superset/pull/25166) Changed the default configuration of `UPLOAD_FOLDER` from `/app/static/uploads/` to `/static/uploads/`. It also removed the unused `IMG_UPLOAD_FOLDER` and `IMG_UPLOAD_URL` configuration options.
- [30284](https://github.com/apache/superset/pull/30284) Deprecated GLOBAL_ASYNC_QUERIES_REDIS_CONFIG in favor of the new GLOBAL_ASYNC_QUERIES_CACHE_BACKEND configuration. To leverage Redis Sentinel, set CACHE_TYPE to RedisSentinelCache, or use RedisCache for standalone Redis
- [31961](https://github.com/apache/superset/pull/31961) Upgraded React from version 16.13.1 to 17.0.2. If you are using custom frontend extensions or plugins, you may need to update them to be compatible with React 17.
- [31260](https://github.com/apache/superset/pull/31260) Docker images now use `uv pip install` instead of `pip install` to manage the python envrionment. Most docker-based deployments will be affected, whether you derive one of the published images, or have custom bootstrap script that install python libraries (drivers)
- [31260](https://github.com/apache/superset/pull/31260) Docker images now use `uv pip install` instead of `pip install` to manage the python environment. Most docker-based deployments will be affected, whether you derive one of the published images, or have custom bootstrap script that install python libraries (drivers)
### Potential Downtime
@@ -270,7 +486,7 @@ Note: Pillow is now a required dependency (previously optional) to support image
- [26462](https://github.com/apache/superset/issues/26462): Removes the Profile feature given that it's not actively maintained and not widely used.
- [26377](https://github.com/apache/superset/pull/26377): Removes the deprecated Redirect API that supported short URLs used before the permalink feature.
- [26329](https://github.com/apache/superset/issues/26329): Removes the deprecated `DASHBOARD_NATIVE_FILTERS` feature flag. The previous value of the feature flag was `True` and now the feature is permanently enabled.
- [25510](https://github.com/apache/superset/pull/25510): Reenforces that any newly defined Python data format (other than epoch) must adhere to the ISO 8601 standard (enforced by way of validation at the API and database level) after a previous relaxation to include slashes in addition to dashes. From now on when specifying new columns, dataset owners will need to use a SQL expression instead to convert their string columns of the form %Y/%m/%d etc. to a `DATE`, `DATETIME`, etc. type.
- [25510](https://github.com/apache/superset/pull/25510): Reinforces that any newly defined Python data format (other than epoch) must adhere to the ISO 8601 standard (enforced by way of validation at the API and database level) after a previous relaxation to include slashes in addition to dashes. From now on when specifying new columns, dataset owners will need to use a SQL expression instead to convert their string columns of the form %Y/%m/%d etc. to a `DATE`, `DATETIME`, etc. type.
- [26372](https://github.com/apache/superset/issues/26372): Removes the deprecated `GENERIC_CHART_AXES` feature flag. The previous value of the feature flag was `True` and now the feature is permanently enabled.
until curl -sf --max-time 5 -o /dev/null "$url"; do
attempt=$((attempt + 1))
if [ "$attempt" -ge "$max_attempts" ]; then
echo "ERROR: webpack dev server did not serve $url after $max_attempts attempts (~5 minutes)." >&2
echo "Is the dev server running? With BUILD_SUPERSET_FRONTEND_IN_DOCKER=false you must start it on the host (e.g. 'npm run dev' in superset-frontend)." >&2
exit 1
fi
sleep 2
done
echo "Webpack dev server is ready; starting nginx."
WEBDRIVER_BASEURL=f"http://superset_app{os.environ.get('SUPERSET_APP_ROOT','/')}/"# When using docker compose baseurl should be http://superset_nginx{ENV{BASEPATH}}/ # noqa: E501
## Core Principle: Stories Are the Single Source of Truth
When working on the Storybook-to-MDX documentation system:
**ALWAYS fix the story first. NEVER add workarounds to the generator.**
## Why This Matters
The generator (`scripts/generate-superset-components.mjs`) should be lightweight - it extracts data from stories and passes it through. When you add special cases to the generator:
- It becomes harder to maintain
- Stories diverge from their docs representation
- Future stories need to know about generator quirks
When you fix stories to match the expected patterns:
- Stories work identically in Storybook and Docs
- The generator stays simple and predictable
- Patterns are consistent and learnable
## Story Patterns for Docs Generation
### Required Structure
```tsx
// Use inline export default (NOT const meta = ...; export default meta)
exportdefault{
title:'Components/MyComponent',
component: MyComponent,
};
// Name interactive stories with Interactive prefix
exportconstInteractiveMyComponent: Story={
args:{
// Default prop values
},
argTypes:{
// Control definitions - MUST be at story level, not meta level
propName:{
control:{type:'select'},
options:['a','b','c'],
description:'What this prop does',
},
},
};
```
### For Components with Variants (size × style grids)
```tsx
constsizes=['small','medium','large'];
constvariants=['primary','secondary','danger'];
InteractiveButton.parameters={
docs:{
gallery:{
component:'Button',
sizes,
styles: variants,
sizeProp:'size',
styleProp:'variant',
},
},
};
```
### For Components Requiring Children
```tsx
InteractiveIconTooltip.parameters={
docs:{
// Component descriptors with dot notation for nested components
Each section maintains its own version history and can be versioned independently.
@@ -36,23 +37,45 @@ Each section maintains its own version history and can be versioned independentl
To create a new version for any section, use the Docusaurus version command with the appropriate plugin ID or use our automated scripts:
#### Before You Cut
The cut snapshots whatever's on disk into a frozen historical version, including auto-generated content (database pages from `superset/db_engine_specs/`, API reference from `static/resources/openapi.json`, component pages from Storybook stories). The cut script refreshes these via `generate:smart` before snapshotting, but the **`databases.json` diagnostics file** needs special care to capture full detail:
1.**Canonical release cut**: download the `database-diagnostics` artifact from a green `Python-Integration` run on master, place it at `docs/src/data/databases.json`, then run the cut script with `--skip-generate` to preserve it. This is what the production deploy uses and includes full Flask-context diagnostics (driver versions, feature support matrix, etc.).
2.**Local dev cut**: just run the script normally. `generate:smart` will regenerate `databases.json` using your local Flask environment — accurate to whatever drivers/extras you have installed, but typically less complete than the CI artifact.
3.**No Flask available**: also fine — the database generator falls back to AST parsing of engine spec files. The MDX pages are still correct; only the diagnostics JSON is leaner.
Also: confirm `master` CI is green, and that your local checkout matches the SHA you intend to cut from.
#### Using Automated Scripts (Required)
**⚠️ Important:** Always use these custom commands instead of the native Docusaurus commands. These scripts ensure that both the Docusaurus versioning system AND the `versions-config.json` file are updated correctly.
**⚠️ Important:** Always use these custom commands instead of the native Docusaurus commands. These scripts ensure that both the Docusaurus versioning system AND the `versions-config.json` file are updated correctly, AND that auto-generated content is refreshed before snapshotting.
```bash
# Main Documentation
yarn version:add:docs 1.2.0
yarn version:add:user_docs 1.2.0
# Developer Portal
yarn version:add:developer_portal 1.2.0
# Admin Docs
yarn version:add:admin_docs 1.2.0
# Component Playground (when enabled)
# Developer Docs
yarn version:add:developer_docs 1.2.0
# Component Playground
yarn version:add:components 1.2.0
```
What the script does:
1. Refreshes auto-generated content via `generate:smart` (database pages, API reference, component pages).
2. Calls `yarn docusaurus docs:version` (or the per-section equivalent) to snapshot the section.
3. Freezes any data-file imports (`@site/static/*.json`, `../../data/*.json`) into a snapshot-local `_versioned_data/` dir so the historical version doesn't silently mutate when the source files change.
4. Adjusts relative import paths (`../../src/...` → `../../../src/...`) for files now one directory deeper.
5. Updates `versions-config.json` and `<section>_versions.json`.
**Do NOT use** the native Docusaurus commands directly (`yarn docusaurus docs:version`), as they will:
- ❌ Create version files but NOT update `versions-config.json`
- ❌ Skip auto-gen refresh, freezing whatever was on disk
- ❌ Skip data-import freezing, leaving the snapshot pointed at live data
- ❌ Cause versions to not appear in dropdown menus
- ❌ Require manual fixes to synchronize the configuration
@@ -75,7 +98,7 @@ If creating versions manually, you'll need to:
Users can configure automated alerts and reports to send dashboards or charts to an email recipient or Slack channel.
- *Alerts* are sent when a SQL condition is reached
- *Reports* are sent on a schedule
Alerts and reports are disabled by default. To turn them on, you'll need to change configuration settings and install a suitable headless browser in your environment.
## Requirements
### Commons
#### In your `superset_config.py` or `superset_config_docker.py`
- `"ALERT_REPORTS"` [feature flag](/admin-docs/configuration/configuring-superset#feature-flags) must be turned to True.
- `beat_schedule` in CeleryConfig must contain schedule for `reports.scheduler`.
- At least one of those must be configured, depending on what you want to use:
- emails: `SMTP_*` settings
- Slack messages: `SLACK_API_TOKEN`
- Users can customize the email subject by including date code placeholders, which will automatically be replaced with the corresponding UTC date when the email is sent. To enable this functionality, activate the `"DATE_FORMAT_IN_EMAIL_SUBJECT"` [feature flag](/admin-docs/configuration/configuring-superset#feature-flags). This enables date formatting in email subjects, preventing all reporting emails from being grouped into the same thread (optional for the reporting feature).
- Use date codes from [strftime.org](https://strftime.org/) to create the email subject.
- If no date code is provided, the original string will be used as the email subject.
##### Disable dry-run mode
Screenshots will be taken but no messages actually sent as long as `ALERT_REPORTS_NOTIFICATION_DRY_RUN = True`, its default value in `docker/pythonpath_dev/superset_config.py`. To disable dry-run mode and start receiving email/Slack notifications, set `ALERT_REPORTS_NOTIFICATION_DRY_RUN` to `False` in [superset config](https://github.com/apache/superset/blob/master/docker/pythonpath_dev/superset_config.py).
#### In your `Dockerfile`
You'll need to extend the Superset image to include a headless browser. Your options include:
- Use Playwright with Chromium: this is the recommended approach as of version 4.1.x or greater. Playwright always uses Chromium — the `WEBDRIVER_TYPE` config setting has no effect when Playwright is active. A working example of a Dockerfile that installs these tools is provided under "Building your own production Docker image" on the [Docker Builds](/admin-docs/installation/docker-builds#building-your-own-production-docker-image) page. Enable the `PLAYWRIGHT_REPORTS_AND_THUMBNAILS` feature flag in your config to activate it.
- Use Firefox (Selenium): you'll need to install geckodriver and Firefox. Set `WEBDRIVER_TYPE` to `"firefox"` in your `superset_config.py`.
- Use Chrome (Selenium): you'll need to install Chrome. Set `WEBDRIVER_TYPE` to `"chrome"` in your `superset_config.py`.
In Superset versions <=4.0x, users installed Firefox or Chrome and that was documented here.
Only the worker container needs the browser.
### Slack integration
To send alerts and reports to Slack channels, you need to create a new Slack Application on your workspace.
1. Connect to your Slack workspace, then head to [https://api.slack.com/apps].
2. Create a new app.
3. Go to "OAuth & Permissions" section, and give the following scopes to your app:
- `incoming-webhook`
- `files:write`
- `chat:write`
- `channels:read`
- `groups:read`
4. At the top of the "OAuth and Permissions" section, click "install to workspace".
5. Select a default channel for your app and continue.
(You can post to any channel by inviting your Superset app into that channel).
6. The app should now be installed in your workspace, and a "Bot User OAuth Access Token" should have been created. Copy that token in the `SLACK_API_TOKEN` variable of your `superset_config.py`.
7. Ensure the feature flag `ALERT_REPORT_SLACK_V2` is set to True in `superset_config.py`
8. Restart the service (or run `superset init`) to pull in the new configuration.
Note: when you configure an alert or a report, the Slack channel list takes channel names without the leading '#' e.g. use `alerts` instead of `#alerts`.
#### Large Slack Workspaces (10k+ channels)
For workspaces with many channels, fetching the complete channel list can take several minutes and may encounter Slack API rate limits. Add the following to your `superset_config.py`:
When a report includes file attachments (CSV, PDF, or PNG screenshots), the request is sent as `multipart/form-data` instead. In that case, each top-level payload field (`name`, `text`, `description`, `url`) becomes its own form field, and nested structures like `header` are serialized as a JSON-encoded string in their own field. Every attachment is added as a repeated form field named `files`:
Webhook consumers should branch on `Content-Type`: parse the body as JSON when `application/json`, or read the individual form fields (decoding `header` as JSON) when `multipart/form-data`.
#### HTTPS Enforcement
To require HTTPS webhook URLs (recommended for production), set:
```python
ALERT_REPORTS_WEBHOOK_HTTPS_ONLY = True
```
When enabled, Superset rejects webhook configurations that use `http://` URLs.
#### Retry Behavior
Superset automatically retries webhook deliveries on `429 Too Many Requests` and `5xx` server errors using exponential backoff.
### Kubernetes-specific
- You must have a `celery beat` pod running. If you're using the chart included in the GitHub repository under [helm/superset](https://github.com/apache/superset/tree/master/helm/superset), you need to put `supersetCeleryBeat.enabled = true` in your values override.
- You can see the dedicated docs about [Kubernetes installation](/admin-docs/installation/kubernetes) for more details.
### Docker Compose specific
#### You must have in your `docker-compose.yml`
- A Redis message broker
- PostgreSQL DB instead of SQLlite
- One or more `celery worker`
- A single `celery beat`
This process also works in a Docker swarm environment, you would just need to add `Deploy:` to the Superset, Redis and Postgres services along with your specific configs for your swarm.
### Detailed config
The following configurations need to be added to the `superset_config.py` file. This file is loaded when the image runs, and any configurations in it will override the default configurations found in the `config.py`.
You can find documentation about each field in the default `config.py` in the GitHub repository under [superset/config.py](https://github.com/apache/superset/blob/master/superset/config.py).
You need to replace default values with your custom Redis, Slack and/or SMTP config.
Superset uses Celery beat and Celery worker(s) to send alerts and reports.
- The beat is the scheduler that tells the worker when to perform its tasks. This schedule is defined when you create the alert or report.
- The worker will process the tasks that need to be performed when an alert or report is fired.
In the `CeleryConfig`, only the `beat_schedule` is relevant to this feature, the rest of the `CeleryConfig` can be changed for your needs.
Alternatively, you can assign a function to `ALERT_MINIMUM_INTERVAL` and/or `REPORT_MINIMUM_INTERVAL`. This is useful to dynamically retrieve a value as needed:
For security, Superset rewrites external links in alert/report email HTML so
they go through a warning page before the user is navigated to the external
site. Internal links (matching your configured base URL) are not affected.
```python
# Disable external link redirection entirely (default: True)
ALERT_REPORTS_ENABLE_LINK_REDIRECT = False
```
The feature uses `WEBDRIVER_BASEURL_USER_FRIENDLY` (or `WEBDRIVER_BASEURL`)
to determine which hosts are internal.
## Troubleshooting
There are many reasons that reports might not be working. Try these steps to check for specific issues.
### Confirm feature flag is enabled and you have sufficient permissions
If you don't see "Alerts & Reports" under the *Manage* section of the Settings dropdown in the Superset UI, you need to enable the `ALERT_REPORTS` feature flag (see above). Enable another feature flag and check to see that it took effect, to verify that your config file is getting loaded.
Log in as an admin user to ensure you have adequate permissions.
### Check the logs of your Celery worker
This is the best source of information about the problem. In a docker compose deployment, you can do this with a command like `docker logs superset_worker --since 1h`.
### Check web browser and webdriver installation
To take a screenshot, the worker visits the dashboard or chart using a headless browser, then takes a screenshot. If you are able to send a chart as CSV or text but can't send as PNG, your problem may lie with the browser.
If you are handling the installation of the headless browser on your own, do your own verification to ensure that the headless browser opens successfully in the worker environment.
### Send a test email
One symptom of an invalid connection to an email server is receiving an error of `[Errno 110] Connection timed out` in your logs when the report tries to send.
Confirm via testing that your outbound email configuration is correct. Here is the simplest test, for an un-authenticated email SMTP email service running on port 25. If you are sending over SSL, for instance, study how [Superset's codebase sends emails](https://github.com/apache/superset/blob/master/superset/utils/core.py#L818) and then test with those commands and arguments.
Start Python in your worker environment, replace all example values, and run:
- Some cloud hosts disable outgoing unauthenticated SMTP email to prevent spam. For instance, [Azure blocks port 25 by default on some machines](https://learn.microsoft.com/en-us/azure/virtual-network/troubleshoot-outbound-smtp-connectivity). Enable that port or use another sending method.
- Use another set of SMTP credentials that you verify works in this setup.
### Browse to your report from the worker
The worker may be unable to reach the report. It will use the value of `WEBDRIVER_BASEURL` to browse to the report. If that route is invalid, or presents an authentication challenge that the worker can't pass, the report screenshot will fail.
Check this by attempting to `curl` the URL of a report that you see in the error logs of your worker. For instance, from the worker environment, run `curl http://superset_app:8088/superset/dashboard/1/`. You may get different responses depending on whether the dashboard exists - for example, you may need to change the `1` in that URL. If there's a URL in your logs from a failed report screenshot, that's a good place to start. The goal is to determine a valid value for `WEBDRIVER_BASEURL` and determine if an issue like HTTPS or authentication is redirecting your worker.
In a deployment with authentication measures enabled like HTTPS and Single Sign-On, it may make sense to have the worker navigate directly to the Superset application running in the same location, avoiding the need to sign in. For instance, you could use `WEBDRIVER_BASEURL="http://superset_app:8088"` for a docker compose deployment, and set `"force_https": False,` in your `TALISMAN_CONFIG`.
### Duplicate report deliveries
In some deployment configurations a scheduled report can be delivered more than once around its planned time. This typically happens when more than one process is responsible for running the alerts & reports schedule (for example, multiple schedulers or Celery beat instances). To avoid duplicate emails or notifications:
- Ensure that only a **single scheduler/beat process** is configured to trigger alerts and reports for a given environment.
- If you run **multiple Celery workers**, verify that there is still only one component responsible for scheduling the report tasks (workers should execute tasks, not schedule them independently).
- Review your deployment/orchestration setup (for example systemd, Docker, or Kubernetes) to make sure the alerts & reports scheduler is **not started from multiple places by accident**.
## Scheduling Queries as Reports
You can optionally allow your users to schedule queries directly in SQL Lab. This is done by adding
extra metadata to saved queries, which are then picked up by an external scheduled (like
[Apache Airflow](https://airflow.apache.org/)).
To allow scheduled queries, add the following to `SCHEDULED_QUERIES` in your configuration file:
```python
SCHEDULED_QUERIES = {
# This information is collected when the user clicks "Schedule query",
# and saved into the `extra` field of saved queries.
[react-jsonschema-form](https://github.com/mozilla-services/react-jsonschema-form) and will add a
menu item called “Schedule” to SQL Lab. When the menu item is clicked, a modal will show up where
the user can add the metadata required for scheduling the query.
This information can then be retrieved from the endpoint `/api/v1/saved_query/` and used to
schedule the queries that have `schedule_info` in their JSON metadata. For schedulers other than
Airflow, additional fields can be easily added to the configuration file above.
:::resources
- [Tutorial: Automated Alerts and Reporting via Slack/Email in Superset](https://dev.to/ngtduc693/apache-superset-topic-5-automated-alerts-and-reporting-via-slackemail-in-superset-2gbe)
- [Blog: Integrating Slack alerts and Apache Superset for better data observability](https://medium.com/affinityanswers-tech/integrating-slack-alerts-and-apache-superset-for-better-data-observability-fd2f9a12c350)
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
*/}
---
title: AWS IAM Authentication
sidebar_label: AWS IAM Authentication
sidebar_position: 15
---
# AWS IAM Authentication for AWS Databases
Superset supports IAM-based authentication for **Amazon Aurora** (PostgreSQL and MySQL) and **Amazon Redshift**. IAM auth eliminates the need for database passwords — Superset generates a short-lived auth token using temporary AWS credentials instead.
Cross-account IAM role assumption via STS `AssumeRole` is supported, allowing a Superset deployment in one AWS account to connect to databases in a different account.
## Prerequisites
- Enable the `AWS_DATABASE_IAM_AUTH` feature flag in `superset_config.py`. IAM authentication is gated behind this flag; if it is disabled, connections using `aws_iam` fail with *"AWS IAM database authentication is not enabled."*
```python
FEATURE_FLAGS = {
"AWS_DATABASE_IAM_AUTH": True,
}
```
- `boto3` must be installed in your Superset environment:
```bash
pip install boto3
```
- The Superset server's IAM role (or static credentials) must have permission to call `sts:AssumeRole` (for cross-account) or the same-account permissions for the target service:
- **Redshift Serverless**: `redshift-serverless:GetCredentials` and `redshift-serverless:GetWorkgroup`
- SSL must be enabled on the Aurora / Redshift endpoint (required for IAM token auth).
## Configuration
IAM authentication is configured via the **encrypted_extra** field of the database connection. Access this field in the **Advanced** → **Security** section of the database connection form, under **Secure Extra**.
**3. Configure the database connection in Superset** using the `role_arn` and `external_id` from the trust policy (as shown in the configuration example above).
## Credential Caching
STS credentials are cached in memory keyed by `(role_arn, region, external_id)` with a 10-minute TTL. This reduces the number of STS API calls when multiple queries are executed with the same connection. Tokens are refreshed automatically before expiry.
Caching can be configured by providing dictionaries in
`superset_config.py` that comply with [the Flask-Caching config specifications](https://flask-caching.readthedocs.io/en/latest/#configuring-flask-caching).
The following cache configurations can be customized in this way:
- Dashboard filter state (required): `FILTER_STATE_CACHE_CONFIG`.
- Explore chart form data (required): `EXPLORE_FORM_DATA_CACHE_CONFIG`
- Metadata cache (optional): `CACHE_CONFIG`
- Charting data queried from datasets (optional): `DATA_CACHE_CONFIG`
For example, to configure the filter state cache using Redis:
```python
FILTER_STATE_CACHE_CONFIG = {
'CACHE_TYPE': 'RedisCache',
'CACHE_DEFAULT_TIMEOUT': 86400,
'CACHE_KEY_PREFIX': 'superset_filter_cache',
'CACHE_REDIS_URL': 'redis://localhost:6379/0'
}
```
## Dependencies
In order to use dedicated cache stores, additional python libraries must be installed
- For Redis: we recommend the [redis](https://pypi.python.org/pypi/redis) Python package
- Memcached: we recommend using [pylibmc](https://pypi.org/project/pylibmc/) client library as
`python-memcached` does not handle storing binary data correctly.
These libraries can be installed using pip.
## Fallback Metastore Cache
Note, that some form of Filter State and Explore caching are required. If either of these caches
are undefined, Superset falls back to using a built-in cache that stores data in the metadata
database. While it is recommended to use a dedicated cache, the built-in cache can also be used
to cache other data.
For example, to use the built-in cache to store chart data, use the following config:
```python
DATA_CACHE_CONFIG = {
"CACHE_TYPE": "SupersetMetastoreCache",
"CACHE_KEY_PREFIX": "superset_results", # make sure this string is unique to avoid collisions
# Extend the default CeleryConfig to add cache warmup schedule
class CustomCeleryConfig(CeleryConfig):
beat_schedule = {
**CeleryConfig.beat_schedule,
'cache-warmup-hourly': {
'task': 'cache-warmup',
'schedule': crontab(minute=0, hour='*'), # hourly
'kwargs': {
'strategy_name': 'top_n_dashboards',
'top_n': 5,
'since': '7 days ago',
},
},
}
CELERY_CONFIG = CustomCeleryConfig
```
This will cache the top 5 most popular dashboards every hour. For other
strategies, check the `superset/tasks/cache.py` file.
## Caching Thumbnails
This is an optional feature that can be turned on by activating its [feature flag](/admin-docs/configuration/configuring-superset#feature-flags) on config:
```
FEATURE_FLAGS = {
"THUMBNAILS": True,
"THUMBNAILS_SQLA_LISTENERS": True,
}
```
By default thumbnails are rendered per user, and will fall back to the Selenium user for anonymous users.
To always render thumbnails as a fixed user (`admin` in this example), use the following configuration:
```python
from superset.tasks.types import FixedExecutor
THUMBNAIL_EXECUTORS = [FixedExecutor("admin")]
```
For this feature you will need a cache system and celery workers. All thumbnails are stored on cache
and are processed asynchronously by the workers.
An example config where images are stored on S3 could be:
```python
from flask import Flask
from s3cache.s3cache import S3Cache
...
class CeleryConfig(object):
broker_url = "redis://localhost:6379/0"
imports = (
"superset.sql_lab",
"superset.tasks.thumbnails",
)
result_backend = "redis://localhost:6379/0"
worker_prefetch_multiplier = 10
task_acks_late = True
CELERY_CONFIG = CeleryConfig
def init_thumbnail_cache(app: Flask) -> S3Cache:
return S3Cache("bucket_name", 'thumbs_cache/')
THUMBNAIL_CACHE_CONFIG = init_thumbnail_cache
```
Using the above example cache keys for dashboards will be `superset_thumb__dashboard__{ID}`. You can
Thumbnail and screenshot endpoints return `ETag` response headers based on the cached content digest. Clients can use conditional requests to avoid downloading unchanged images:
```
GET /api/v1/chart/42/thumbnail/
If-None-Match: "abc123..."
→ 304 Not Modified (if unchanged)
→ 200 OK (with new image if changed)
```
This is particularly useful for embedded dashboards and external integrations that periodically poll for updated screenshots — unchanged thumbnails return immediately with no payload.
## Distributed Coordination Backend
Superset supports an optional distributed coordination (`DISTRIBUTED_COORDINATION_CONFIG`) for
high-performance distributed operations. This configuration enables:
- **Distributed locking**: Moves lock operations from the metadata database to Redis, improving
performance and reducing metastore load
- **Real-time event notifications**: Enables instant pub/sub messaging for task abort signals and
completion notifications instead of polling-based approaches
:::note
This requires Redis or Valkey specifically—it uses Redis-specific features (pub/sub, `SET NX EX`)
that are not available in general Flask-Caching backends.
:::
### Configuration
The distributed coordination uses Flask-Caching style configuration for consistency with other cache
backends. Configure `DISTRIBUTED_COORDINATION_CONFIG` in `superset_config.py`:
Individual lock acquisitions can override this value when needed.
### Database-Only Mode
When `DISTRIBUTED_COORDINATION_CONFIG` is not configured, Superset uses database-backed operations:
- **Locking**: Uses the KeyValue table with periodic cleanup of expired entries
- **Event notifications**: Uses database polling instead of pub/sub
While database-backed operations work reliably, the Redis backend is recommended for production
deployments where low latency and reduced database load are important.
:::resources
- [Blog: The Data Engineer's Guide to Lightning-Fast Superset Dashboards](https://preset.io/blog/the-data-engineers-guide-to-lightning-fast-apache-superset-dashboards/)
- [Blog: Accelerating Dashboards with Materialized Views](https://preset.io/blog/accelerating-apache-superset-dashboards-with-materialized-views/)
At the very least, you'll want to change `SECRET_KEY` and `SQLALCHEMY_DATABASE_URI`. Continue reading for more about each of these.
## Specifying a SECRET_KEY
### Adding an initial SECRET_KEY
Superset requires a user-specified SECRET_KEY to start up. This requirement was [added in version 2.1.0 to force secure configurations](https://preset.io/blog/superset-security-update-default-secret_key-vulnerability/). Add a strong SECRET_KEY to your `superset_config.py` file like:
Properly setting up metadata store is beyond the scope of this documentation. We recommend
using a hosted managed service such as [Amazon RDS](https://aws.amazon.com/rds/) or
[Google Cloud Databases](https://cloud.google.com/products/databases?hl=en) to handle
service and supporting infrastructure and backup strategy.
:::
To configure Superset metastore set `SQLALCHEMY_DATABASE_URI` config key on `superset_config`
to the appropriate connection string.
## Running on a WSGI HTTP Server
While you can run Superset on NGINX or Apache, we recommend using Gunicorn in async mode. This
enables impressive concurrency even and is fairly easy to install and configure. Please refer to the
documentation of your preferred technology to set up this Flask WSGI application in a way that works
well in your environment. Here’s an async setup known to work well in production:
```
-w 10 \
-k gevent \
--worker-connections 1000 \
--timeout 120 \
-b 0.0.0.0:6666 \
--limit-request-line 0 \
--limit-request-field_size 0 \
--statsd-host localhost:8125 \
"superset.app:create_app()"
```
Refer to the [Gunicorn documentation](https://docs.gunicorn.org/en/stable/design.html) for more
information. _Note that the development web server (`superset run` or `flask run`) is not intended
for production use._
If you're not using Gunicorn, you may want to disable the use of `flask-compress` by setting
`COMPRESS_REGISTER = False` in your `superset_config.py`.
Currently, the Google BigQuery Python SDK is not compatible with `gevent`, due to some dynamic monkeypatching on python core library by `gevent`.
So, when you use `BigQuery` datasource on Superset, you have to use `gunicorn` worker type except `gevent`.
## HTTPS Configuration
You can configure HTTPS upstream via a load balancer or a reverse proxy (such as nginx) and do SSL/TLS Offloading before traffic reaches the Superset application. In this setup, local traffic from a Celery worker taking a snapshot of a chart for Alerts & Reports can access Superset at a `http://` URL, from behind the ingress point.
You can also configure [SSL in Gunicorn](https://docs.gunicorn.org/en/stable/settings.html#ssl) (the Python webserver) if you are using an official Superset Docker image.
## Configuration Behind a Load Balancer
If you are running superset behind a load balancer or reverse proxy (e.g. NGINX or ELB on AWS), you
may need to utilize a healthcheck endpoint so that your load balancer knows if your superset
instance is running. This is provided at `/health` which will return a 200 response containing “OK”
if the webserver is running.
If the load balancer is inserting `X-Forwarded-For/X-Forwarded-Proto` headers, you should set
`ENABLE_PROXY_FIX = True` in the superset config file (`superset_config.py`) to extract and use the
headers.
In case the reverse proxy is used for providing SSL encryption, an explicit definition of the
`X-Forwarded-Proto` may be required. For the Apache webserver this can be set as follows:
```
RequestHeader set X-Forwarded-Proto "https"
```
## Configuring the application root
*Please be advised that this feature is in BETA.*
Superset supports running the application under a non-root path. The root path
prefix can be specified in one of three ways:
- Customizing the [Flask entrypoint](https://github.com/apache/superset/blob/master/superset/app.py#L29)
by passing the `superset_app_root` variable; or
- Setting the `SUPERSET_APP_ROOT` environment variable to the desired prefix; or
- Setting the `APPLICATION_ROOT` config in your `superset_config.py` file.
Note, the prefix should start with a `/`.
### Customizing the Flask entrypoint
To configure a prefix, e.g `/analytics`, pass the `superset_app_root` argument to
`create_app` when calling flask run either through the `FLASK_APP`
# Will allow user self registration, allowing to create Flask users from Authorized User
AUTH_USER_REGISTRATION = True
# The default user self registration role
AUTH_USER_REGISTRATION_ROLE = "Public"
```
In case you want to assign the `Admin` role on new user registration, it can be assigned as follows:
```python
AUTH_USER_REGISTRATION_ROLE = "Admin"
```
If you encounter the [issue](https://github.com/apache/superset/issues/13243) of not being able to list users from the Superset main page settings, although a newly registered user has an `Admin` role, please re-run `superset init` to sync the required permissions. Below is the command to re-run `superset init` using docker compose.
```
docker-compose exec superset superset init
```
Then, create a `CustomSsoSecurityManager` that extends `SupersetSecurityManager` and overrides
`oauth_user_info`:
```python
import logging
from superset.security import SupersetSecurityManager
class CustomSsoSecurityManager(SupersetSecurityManager):
For public OAuth2 clients that cannot securely store a client secret, enable Proof Key for Code Exchange (PKCE) by adding `code_challenge_method` to the `remote_app` configuration:
```python
OAUTH_PROVIDERS = [
{
'name': 'myProvider',
'remote_app': {
'client_id': 'myClientId',
'client_secret': 'mySecret', # may be empty for pure public clients
PKCE (`S256`) is recommended for all OAuth2 flows, even when a client secret is present, as it protects against authorization code interception attacks.
## LDAP Authentication
FAB supports authenticating user credentials against an LDAP server.
To use LDAP you must install the [python-ldap](https://www.python-ldap.org/en/latest/installing.html) package.
See [FAB's LDAP documentation](https://flask-appbuilder.readthedocs.io/en/latest/security.html#authentication-ldap)
for details.
## Mapping LDAP or OAUTH groups to Superset roles
AUTH_ROLES_MAPPING in Flask-AppBuilder is a dictionary that maps from LDAP/OAUTH group names to FAB roles.
It is used to assign roles to users who authenticate using LDAP or OAuth.
### Mapping OAUTH groups to Superset roles
The following `AUTH_ROLES_MAPPING` dictionary would map the OAUTH group "superset_users" to the Superset roles "Gamma" as well as "Alpha", and the OAUTH group "superset_admins" to the Superset role "Admin".
```python
AUTH_ROLES_MAPPING = {
"superset_users": ["Gamma","Alpha"],
"superset_admins": ["Admin"],
}
```
### Mapping LDAP groups to Superset roles
The following `AUTH_ROLES_MAPPING` dictionary would map the LDAP DN "cn=superset_users,ou=groups,dc=example,dc=com" to the Superset roles "Gamma" as well as "Alpha", and the LDAP DN "cn=superset_admins,ou=groups,dc=example,dc=com" to the Superset role "Admin".
Note: This requires `AUTH_LDAP_SEARCH` to be set. For more details, please see the [FAB Security documentation](https://flask-appbuilder.readthedocs.io/en/latest/security.html).
### Syncing roles at login
You can also use the `AUTH_ROLES_SYNC_AT_LOGIN` configuration variable to control how often Flask-AppBuilder syncs the user's roles with the LDAP/OAUTH groups. If `AUTH_ROLES_SYNC_AT_LOGIN` is set to True, Flask-AppBuilder will sync the user's roles each time they log in. If `AUTH_ROLES_SYNC_AT_LOGIN` is set to False, Flask-AppBuilder will only sync the user's roles when they first register.
## Flask app Configuration Hook
`FLASK_APP_MUTATOR` is a configuration function that can be provided in your environment, receives
the app object and can alter it in any way. For example, add `FLASK_APP_MUTATOR` into your
`superset_config.py` to setup session cookie expiration time to 24 hours:
To support a diverse set of users, Superset has some features that are not enabled by default. For
example, some users have stronger security restrictions, while some others may not. So Superset
allows users to enable or disable some features by config. For feature owners, you can add optional
functionalities in Superset, but will be only affected by a subset of users.
You can enable or disable features with flag from `superset_config.py`:
```python
FEATURE_FLAGS = {
'PRESTO_EXPAND_DATA': False,
}
```
A current list of feature flags can be found in the [Feature Flags](/admin-docs/configuration/feature-flags) documentation.
## Security Configuration
### HASH_ALGORITHM
Controls the hashing algorithm used for internal checksums and cache keys (thumbnails, cache keys, etc.). The default is `sha256`, which satisfies environments with stricter compliance requirements (e.g., FedRAMP). Set it to `md5` to retain the legacy behavior from older Superset deployments:
```python
HASH_ALGORITHM = "sha256" # default; set to "md5" for legacy behavior
```
A companion `HASH_ALGORITHM_FALLBACKS` list (default: `["md5"]`) lets UUID lookups fall back to older algorithms, which enables gradual migration without breaking existing entries. Set it to `[]` for strict mode (use only `HASH_ALGORITHM`).
:::note
This setting affects internal Superset operations only, not user passwords or authentication tokens. Changing it in an existing deployment may invalidate cached values but does not require a database migration.
:::
## SQL Lab Query History Pruning
SQL Lab query history is stored in the metadata database and is **not** pruned by default. To trim older rows, enable the `prune_query` Celery beat task by uncommenting it in `CELERY_BEAT_SCHEDULE` and choosing a retention window:
Adjust `retention_period_days` to control how long query rows are kept. Companion opt-in tasks (`prune_logs`, `prune_tasks`) exist for pruning the logs and tasks tables; see the commented-out examples in `superset/config.py`. Without enabling these tasks, the metadata database will grow unbounded over time.
:::resources
- [Blog: Feature Flags in Apache Superset](https://preset.io/blog/feature-flags-in-apache-superset-and-preset/)
The superset cli allows you to import and export datasources from and to YAML. Datasources include
databases. The data is expected to be organized in the following hierarchy:
:::info
Superset's ZIP-based import/export also covers **dashboards**, **charts**, and **saved queries**, exercised through the UI and REST API. The [Dashboard Import Overwrite Behavior](#dashboard-import-overwrite-behavior) and [UUIDs in API Responses](#uuids-in-api-responses) sections below document the behavior shared across all asset types.
:::
```text
├──databases
| ├──database_1
| | ├──table_1
| | | ├──columns
| | | | ├──column_1
| | | | ├──column_2
| | | | └──... (more columns)
| | | └──metrics
| | | ├──metric_1
| | | ├──metric_2
| | | └──... (more metrics)
| | └── ... (more tables)
| └── ... (more databases)
```
:::note
When you export a database connection, the `masked_encrypted_extra` field (used for sensitive connection parameters such as service account JSON, OAuth tokens, and other encrypted credentials) is included in the export. When importing on another instance, these values are decrypted and re-encrypted using the destination instance's `SECRET_KEY`. Ensure the receiving instance has a valid `SECRET_KEY` configured before importing.
:::
## Exporting Datasources to YAML
You can print your current datasources to stdout by running:
```bash
superset export_datasources
```
To save your datasources to a ZIP file run:
```bash
superset export_datasources -f <filename>
```
By default, default (null) values will be omitted. Use the -d flag to include them. If you want back
references to be included (e.g. a column to include the table id it belongs to) use the -b flag.
Alternatively, you can export datasources using the UI:
1. Open **Sources -> Databases** to export all tables associated to a single or multiple databases.
(**Tables** for one or more tables)
2. Select the items you would like to export.
3. Click **Actions -> Export** to YAML
4. If you want to import an item that you exported through the UI, you will need to nest it inside
its parent element, e.g. a database needs to be nested under databases a table needs to be nested
inside a database element.
In order to obtain an **exhaustive list of all fields** you can import using the YAML import run:
```bash
superset export_datasource_schema
```
As a reminder, you can use the `-b` flag to include back references.
## Importing Datasources
In order to import datasources from a ZIP file, run:
```bash
superset import_datasources -p <path / filename>
```
The optional username flag **-u** sets the user used for the datasource import. The default is 'admin'. Example:
When importing a dashboard ZIP with the **overwrite** option enabled, any existing charts that are part of the dashboard are **replaced** rather than duplicated. This applies to:
- Charts whose UUID matches a chart already present in the target instance
- The full chart configuration (query, visualization type, columns, metrics) is replaced by the imported version
If you import without the overwrite flag, existing charts with conflicting UUIDs are left unchanged and the import skips those objects. Use overwrite when you want to push a fully updated dashboard (including chart definitions) from a development or staging environment to production.
## UUIDs in API Responses
The REST API POST endpoints for **datasets**, **charts**, and **dashboards** include the auto-generated `uuid` field in the response body:
```json
{
"id": 42,
"uuid": "b8a8d5c3-1234-4abc-8def-0123456789ab",
...
}
```
UUIDs remain stable across import/export cycles and can be used for cross-environment workflows — for example, recording a UUID when creating a chart in development and using it to identify the matching chart after importing into production.
## Legacy Importing Datasources
### From older versions of Superset to current version
When using Superset version 4.x.x to import from an older version (2.x.x or 3.x.x) importing is supported as the command `legacy_import_datasources` and expects a JSON or directory of JSONs. The options are `-r` for recursive and `-u` for specifying a user. Example of legacy import without options:
```bash
superset legacy_import_datasources -p <path or filename>
```
### From older versions of Superset to older versions
When using an older Superset version (2.x.x & 3.x.x) of Superset, the command is `import_datasources`. ZIP and YAML files are supported and to switch between them the feature flag `VERSIONED_EXPORT` is used. When `VERSIONED_EXPORT` is `True`, `import_datasources` expects a ZIP file, otherwise YAML. Example:
```bash
superset import_datasources -p <path or filename>
```
When `VERSIONED_EXPORT` is `False`, if you supply a path all files ending with **yaml** or **yml** will be parsed. You can apply
additional flags (e.g. to search the supplied path recursively):
```bash
superset import_datasources -p <path> -r
```
The sync flag **-s** takes parameters in order to sync the supplied elements with your file. Be
careful this can delete the contents of your meta database. Example:
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
-->
# MCP Server Deployment & Authentication
Superset includes a built-in [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server that lets AI assistants -- Claude, ChatGPT, and other MCP-compatible clients -- interact with your Superset instance. Through MCP, clients can list dashboards, query datasets, execute SQL, create charts, and more.
This guide covers how to run, secure, and deploy the MCP server.
:::tip Looking for user docs?
See **[Using AI with Superset](/user-docs/using-superset/using-ai-with-superset)** for a guide on what AI can do with Superset and how to connect your AI client.
Both containers share the same `superset_config.py`, so authentication settings, database connections, and feature flags stay in sync.
### Multi-Pod (Kubernetes)
For high-availability deployments, configure Redis so that replicas share session state:
```mermaid
flowchart TD
LB["Load Balancer"] --> M1["MCP Pod 1"]
LB --> M2["MCP Pod 2"]
LB --> M3["MCP Pod 3"]
M1 --> R[("Redis<br/>(session store)")]
M2 --> R
M3 --> R
M1 --> DB[("Postgres")]
M2 --> DB
M3 --> DB
```
**superset_config.py:**
```python
MCP_STORE_CONFIG = {
"enabled": True,
"CACHE_REDIS_URL": "redis://redis-host:6379/0",
"event_store_max_events": 100,
"event_store_ttl": 3600,
}
```
When `CACHE_REDIS_URL` is set, the MCP server uses a Redis-backed EventStore for session management, allowing replicas to share state. Without Redis, each pod manages its own in-memory sessions and stateful MCP interactions may fail when requests hit different replicas.
---
## Configuration Reference
All MCP settings go in `superset_config.py`. Defaults are defined in `superset/mcp_service/mcp_config.py`.
### Core
| Setting | Default | Description |
|---------|---------|-------------|
| `MCP_SERVICE_HOST` | `"localhost"` | Host the MCP server binds to |
| `MCP_SERVICE_PORT` | `5008` | Port the MCP server binds to |
| `MCP_SERVICE_URL` | `None` | Public base URL for MCP-generated links (set this when behind a reverse proxy) |
| `MCP_DEBUG` | `False` | Enable debug logging |
| `MCP_DEV_USERNAME` | -- | Superset username for development mode (no auth) |
| `MCP_RBAC_ENABLED` | `True` | Enforce Superset's role-based access control on MCP tool calls. When `True`, each tool checks that the authenticated user has the required FAB permission before executing. Disable only for testing or trusted-network deployments. |
| `MCP_DISABLED_TOOLS` | `set()` | Set of tool names to remove from the MCP server at startup. Disabled tools are never advertised to AI clients during tool discovery. Useful when a custom extension tool should replace a built-in Superset tool. See [Disabling built-in tools](#disabling-built-in-tools). |
| `MCP_USER_RESOLVER` | `None` | Custom function `(app, access_token) -> username` to extract a Superset username from a validated JWT token. When `None`, the default resolver checks `preferred_username`, `username`, `email`, and `sub` claims in that order. |
### Response Size Guard
Limits response sizes to prevent exceeding LLM context windows:
| `event_store_max_events` | `100` | Maximum events retained per session |
| `event_store_ttl` | `3600` | Event TTL in seconds |
### Tool Search
By default the MCP server exposes a lightweight tool-search interface instead of advertising every tool at once. This reduces the initial context sent to the LLM by ~70%, which lowers cost and latency. The AI client discovers tools on demand by calling `search_tools` and then invokes them via `call_tool`.
```python
MCP_TOOL_SEARCH_CONFIG = {
"enabled": True,
"strategy": "bm25", # "bm25" (natural language) or "regex"
| `max_results` | `5` | Maximum tools returned per search query |
| `always_visible` | See above | Tools that always appear in `list_tools`, regardless of search |
| `include_schemas` | `False` | When `False` (default, "summary mode"), search results omit `inputSchema` entirely and include a lightweight `parameters_hint` listing top-level parameter names. Set to `True` to include the full `inputSchema` in search results. Full schemas are always used when a tool is actually invoked via `call_tool`. |
| `compact_schemas` | `True` | Strip `$defs` / `$ref` and replace with `{"type": "object"}` in search results to reduce token cost. Only takes effect when `include_schemas=True` — ignored in summary mode. |
| `max_description_length` | `300` | Truncate tool descriptions in search results (0 = no truncation). Applies in both summary and full-schema modes. |
:::tip
Set `enabled: False` to revert to the traditional "show all tools at once" behavior, which some clients or workflows may prefer.
:::
Tool search reduces the initial token cost from ~15–20K tokens (full catalog) down to ~4–5K tokens (pinned tools + search interface) — roughly 85% savings at the start of each conversation.
### Session & CSRF
These values are flat-merged into the Flask app config used by the MCP server process:
```python
MCP_SESSION_CONFIG = {
"SESSION_COOKIE_HTTPONLY": True,
"SESSION_COOKIE_SECURE": False,
"SESSION_COOKIE_SAMESITE": "Lax",
"SESSION_COOKIE_NAME": "superset_session",
"PERMANENT_SESSION_LIFETIME": 86400,
}
MCP_CSRF_CONFIG = {
"WTF_CSRF_ENABLED": True,
"WTF_CSRF_TIME_LIMIT": None,
}
```
---
## Access Control
### RBAC Enforcement
The MCP server respects Superset's full role-based access control (RBAC). Every authenticated user can only access the data and operations their Superset roles permit — the same rules that apply in the Superset UI apply through MCP.
Each tool declares one or more required FAB permissions. The table below maps tool groups to their permission requirements:
| Tool group | Required FAB permission |
|------------|------------------------|
| `list_charts`, `get_chart_info`, `get_chart_data`, `get_chart_preview`, `generate_chart`, `update_chart` | `can_read` on `Chart` (read), `can_write` on `Chart` (mutate) |
| `list_dashboards`, `get_dashboard_info`, `generate_dashboard`, `add_chart_to_existing_dashboard` | `can_read` on `Dashboard` (read), `can_write` on `Dashboard` (mutate) |
| `list_datasets`, `get_dataset_info`, `create_virtual_dataset` | `can_read` on `Dataset` (read), `can_write` on `Dataset` (mutate) |
| `list_databases`, `get_database_info` | `can_read` on `Database` |
| `execute_sql` | `can_execute_sql_query` on `SQLLab` |
| `open_sql_lab_with_context` | `can_read` on `SQLLab` |
| `save_sql_query` | `can_write` on `SavedQuery` |
| `health_check` | None (public) |
To disable RBAC checking globally (for trusted-network deployments or testing), set:
```python
# superset_config.py
MCP_RBAC_ENABLED = False
```
:::warning
Disabling RBAC removes all permission checks from MCP tool calls. Only do this on isolated, internal deployments where all MCP users are trusted admins.
:::
### Audit Log
All MCP tool calls are recorded in Superset's action log. You can view them at **Settings → Action Log** (admin only). Each log entry records:
- The tool name (e.g., `mcp.generate_chart.db_write`)
- The authenticated user
- A timestamp
This makes MCP activity fully auditable alongside regular Superset activity. The action log uses the same event logger as the rest of Superset, so existing log ingestion pipelines (e.g., sending logs to Elasticsearch or a SIEM) capture MCP events automatically.
### Middleware Pipeline
Every MCP request passes through a middleware stack before reaching the tool function. The default stack (assembled in `build_middleware_list()` in `server.py`) is:
| Middleware | Purpose | Default |
|------------|---------|---------|
| `StructuredContentStripperMiddleware` | Strips `structuredContent` from responses for Claude.ai bridge compatibility | Enabled |
| `LoggingMiddleware` | Logs each tool call with user, parameters, and duration | Enabled |
| `GlobalErrorHandlerMiddleware` | Catches unhandled exceptions and sanitizes sensitive data before it reaches the client | Enabled |
| `ResponseSizeGuardMiddleware` | Estimates token count, warns at 80% of limit, blocks at limit | Enabled (configurable via `MCP_RESPONSE_SIZE_CONFIG`) |
| `ResponseCachingMiddleware` | Caches read-heavy tool responses (in-memory or Redis) | Disabled (enable via `MCP_CACHE_CONFIG`) |
Additional middleware classes (`RateLimitMiddleware`, `FieldPermissionsMiddleware`, `PrivateToolMiddleware`) are implemented in `superset/mcp_service/middleware.py` but are not added to the default pipeline. They are available for operators who want to layer them in via a custom startup path.
### Error Sanitization
The `GlobalErrorHandlerMiddleware` automatically redacts sensitive information from all error messages before they reach the LLM client. The following are replaced with generic messages:
- **Database connection strings** — replaced with a generic connection error message
- **API keys and tokens** — redacted from error traces
- **File system paths** — stripped to prevent information disclosure
- **IP addresses** — removed from error context
This ensures that a misconfigured database connection or an unexpected exception never leaks credentials or internal topology to the LLM or its users. All regex patterns used for redaction are bounded to prevent ReDoS attacks.
---
## Performance
### Connection Pooling
Each MCP server process maintains its own SQLAlchemy connection pool to the database. For multi-worker deployments, total open connections = **workers × pool size**.
```python
# superset_config.py
SQLALCHEMY_POOL_SIZE = 5
SQLALCHEMY_MAX_OVERFLOW = 10
SQLALCHEMY_POOL_TIMEOUT = 30
SQLALCHEMY_POOL_RECYCLE = 3600 # Recycle connections after 1 hour
```
For a 3-pod Kubernetes deployment with the defaults above, expect up to 3 × (5 + 10) = 45 connections. Size your database's `max_connections` accordingly.
### Response Caching
Enable response caching for read-heavy workloads (dashboards/datasets that don't change frequently). With the in-memory backend (default when `MCP_STORE_CONFIG` is disabled), caching is per-process. Use Redis-backed caching for consistent cache hits across multiple pods:
Mutating tools (`generate_chart`, `update_chart`, `execute_sql`, `generate_dashboard`) are always excluded from caching regardless of this setting.
---
## Troubleshooting
### Server won't start
- Verify `fastmcp` is installed: `pip install fastmcp`
- Check that `MCP_DEV_USERNAME` is set if auth is disabled -- the server requires a user identity
- Confirm the port is not already in use: `lsof -i :5008`
### 401 Unauthorized
- Verify your JWT token has not expired (`exp` claim)
- Check that `MCP_JWT_ISSUER` and `MCP_JWT_AUDIENCE` match the token's `iss` and `aud` claims exactly
- For RS256 with JWKS: confirm the JWKS URI is reachable from the MCP server
- For RS256 with static key: confirm the public key string includes the `BEGIN`/`END` markers
- For HS256: confirm the secret matches between the token issuer and `MCP_JWT_SECRET`
- Enable `MCP_JWT_DEBUG_ERRORS = True` for detailed server-side logging (errors are never leaked to the client)
### Tool not found
- Ensure the MCP server and Superset share the same `superset_config.py`
- Check server logs at startup -- tool registration errors are logged with the tool name and reason
### Client can't connect
- Verify the MCP server URL is reachable from the client machine
- For Claude Desktop: fully quit the app (not just close the window) and restart after config changes
- For remote access: ensure your firewall and reverse proxy allow traffic to the MCP port
- Confirm the URL path ends with `/mcp` (e.g., `http://localhost:5008/mcp`)
### Permission errors on tool calls
- The MCP server enforces Superset's RBAC permissions -- the authenticated user must have the required roles
- In development mode, ensure `MCP_DEV_USERNAME` maps to a user with appropriate roles (e.g., Admin)
- Check `superset/security/manager.py` for the specific permission tuples required by each tool domain (e.g., `("can_execute_sql_query", "SQLLab")`)
### Response too large
- If a tool call returns an error about exceeding token limits, the response size guard is blocking an oversized result
- Reduce `page_size` or `limit` parameters, use `select_columns` to exclude large fields, or add filters to narrow results
- To adjust the threshold, change `token_limit` in `MCP_RESPONSE_SIZE_CONFIG`
- To disable the guard entirely, set `MCP_RESPONSE_SIZE_CONFIG = {"enabled": False}`
---
## Audit Events
All MCP tool calls are logged to Superset's event logger, the same system used by the web UI (viewable at **Settings → Action Log**). Each event captures:
- **User**: the resolved Superset username from the JWT or dev config
- **Timestamp**: when the operation ran
This means MCP activity is auditable alongside normal user activity. No additional configuration is required — logging is on by default whenever the event logger is enabled in your Superset deployment.
## Tool Pagination
MCP list tools (`list_datasets`, `list_charts`, `list_dashboards`, `list_databases`) use **offset pagination** via `page` (1-based) and `page_size` parameters. Responses include `page`, `page_size`, `total_count`, `total_pages`, `has_previous`, and `has_next`. To iterate through all results:
```python
# Example: fetch all charts across pages
all_charts = []
page = 1
while True:
result = mcp.list_charts(page=page, page_size=50)
all_charts.extend(result["charts"])
if not result.get("has_next"):
break
page += 1
```
## Disabling built-in tools
If you have deployed a custom tool via a Superset extension that supersedes one of the built-in Superset tools, you can suppress the built-in version so AI clients only discover your replacement. Disabled tools are removed from the server at startup and are never advertised during tool discovery.
Set `MCP_DISABLED_TOOLS` in your `superset_config.py` to a set of tool names:
Tool names match the function name used in the `@tool` decorator (e.g., `execute_sql`, `list_charts`, `health_check`). Extension-prefixed tools can also be disabled using their full prefixed name:
Specifying a tool name that does not exist logs a warning at startup and is otherwise ignored — it will not prevent the server from starting.
:::
## Security Best Practices
- **Use TLS** for all production MCP endpoints -- place the server behind a reverse proxy with HTTPS
- **Enable JWT authentication** for any internet-facing deployment
- **RBAC enforcement** -- The MCP server respects Superset's role-based access control. Users can only access data their roles permit
- **Secrets management** -- Store `MCP_JWT_SECRET`, database credentials, and API keys in environment variables or a secrets manager, never in config files committed to version control
- **Scoped tokens** -- Use `MCP_REQUIRED_SCOPES` to limit what operations a token can perform
- **Network isolation** -- In Kubernetes, restrict MCP pod network policies to only allow traffic from your AI client endpoints
- Review the **[Security documentation](/developer-docs/extensions/security)** for additional extension security guidance
---
## Next Steps
- **[Using AI with Superset](/user-docs/using-superset/using-ai-with-superset)** -- What AI can do with Superset and how to get started
- **[MCP Integration](/developer-docs/extensions/mcp)** -- Build custom MCP tools and prompts via Superset extensions
- **[Security](/developer-docs/extensions/security)** -- Security best practices for extensions
- **[Deployment](/developer-docs/extensions/deployment)** -- Package and deploy Superset extensions
Note that Superset bundles [flask-talisman](https://pypi.org/project/talisman/)
Self-described as a small Flask extension that handles setting HTTP headers that can help
protect against a few common web application security issues.
## HTML Embedding of Dashboards and Charts
There are two ways to embed a dashboard: Using the [SDK](https://www.npmjs.com/package/@superset-ui/embedded-sdk) or embedding a direct link. Note that in the latter case everybody who knows the link is able to access the dashboard.
### Embedding a Public Direct Link to a Dashboard
This works by first changing the content security policy (CSP) of [flask-talisman](https://github.com/GoogleCloudPlatform/flask-talisman) to allow for certain domains to display Superset content. Then a dashboard can be made publicly accessible, i.e. **bypassing authentication**. Once made public, the dashboard's URL can be added to an iframe in another website's HTML code.
#### Changing flask-talisman CSP
Add to `superset_config.py` the entire `TALISMAN_CONFIG` section from `config.py` and include a `frame-ancestors` section:
A chart's embed code can be generated by going to a chart's edit view and then clicking at the top right on `...` > `Share` > `Embed code`
### Enabling Embedding via the SDK
Clicking on `...` next to `EDIT DASHBOARD` on the top right of the dashboard's overview page should yield a drop-down menu including the entry "Embed dashboard".
To enable this entry, add the following line to the `.env` file:
```text
SUPERSET_FEATURE_EMBEDDED_SUPERSET=true
```
### Hiding the Logout Button in Embedded Contexts
When Superset is embedded in an application that manages authentication via SSO (OAuth2, SAML, or JWT), the logout button should be hidden since session management is handled by the parent application.
To hide the logout button in embedded contexts, add to `superset_config.py`:
```python
FEATURE_FLAGS = {
"DISABLE_EMBEDDED_SUPERSET_LOGOUT": True,
}
```
This flag only hides the logout button when Superset detects it is running inside an iframe. Users accessing Superset directly (not embedded) will still see the logout button regardless of this setting.
:::note
When embedding with SSO, also set `SESSION_COOKIE_SAMESITE = 'None'` and `SESSION_COOKIE_SECURE = True`. See [Security documentation](/admin-docs/security/securing_superset) for details.
:::
## CSRF settings
Similarly, [flask-wtf](https://flask-wtf.readthedocs.io/en/0.15.x/config/) is used to manage
some CSRF configurations. If you need to exempt endpoints from CSRF (e.g. if you are
running a custom auth postback endpoint), you can add the endpoints to `WTF_CSRF_EXEMPT_LIST`:
## SSH Tunneling
1. Turn on feature flag
- Change [`SSH_TUNNELING`](https://github.com/apache/superset/blob/eb8386e3f0647df6d1bbde8b42073850796cc16f/superset/config.py#L489) to `True`
- If you want to add more security when establishing the tunnel we allow users to overwrite the `SSHTunnelManager` class [here](https://github.com/apache/superset/blob/eb8386e3f0647df6d1bbde8b42073850796cc16f/superset/config.py#L507)
- You can also set the [`SSH_TUNNEL_LOCAL_BIND_ADDRESS`](https://github.com/apache/superset/blob/eb8386e3f0647df6d1bbde8b42073850796cc16f/superset/config.py#L508) this the host address where the tunnel will be accessible on your VPC
2. Create database w/ ssh tunnel enabled
- With the feature flag enabled you should now see ssh tunnel toggle.
- Click the toggle to enable SSH tunneling and add your credentials accordingly.
- Superset allows for two different types of authentication (Basic + Private Key). These credentials should come from your service provider.
3. Verify data is flowing
- Once SSH tunneling has been enabled, go to SQL Lab and write a query to verify data is properly flowing.
## Domain Sharding
:::note
Domain Sharding is deprecated as of Superset 5.0.0, and will be removed in Superset 6.0.0. Please Enable HTTP2 to keep more open connections per domain.
:::
Chrome allows up to 6 open connections per domain at a time. When there are more than 6 slices in
dashboard, a lot of time fetch requests are queued up and wait for next available socket.
[PR 5039](https://github.com/apache/superset/pull/5039) adds domain sharding to Superset,
and this feature will be enabled by configuration only (by default Superset doesn’t allow
cross-domain request).
Add the following setting in your `superset_config.py` file:
- `SUPERSET_WEBSERVER_DOMAINS`: list of allowed hostnames for domain sharding feature.
Please create your domain shards as subdomains of your main domain for authorization to
or add the following setting in your `superset_config.py` file if domain shards are not subdomains of main domain.
- `SESSION_COOKIE_DOMAIN = '.mydomain.com'`
## Middleware
Superset allows you to add your own middleware. To add your own middleware, update the
`ADDITIONAL_MIDDLEWARE` key in your `superset_config.py`. `ADDITIONAL_MIDDLEWARE` should be a list
of your additional middleware classes.
For example, to use `AUTH_REMOTE_USER` from behind a proxy server like nginx, you have to add a
simple middleware class to add the value of `HTTP_X_PROXY_REMOTE_USER` (or any other custom header
from the proxy) to Gunicorn’s `REMOTE_USER` environment variable.
Some files were not shown because too many files have changed in this diff
Show More
Reference in New Issue
Block a user
Blocking a user prevents them from interacting with repositories, such as opening or commenting on pull requests or issues. Learn more about blocking a user.