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fix(docs): fix 404s in documentation (#38974)
Co-authored-by: Evan Rusackas <evan@preset.io>
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@@ -64,7 +64,7 @@ There are two approaches to making dashboards publicly accessible:
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3. Edit each dashboard's properties and add the "Public" role
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4. Only dashboards with the Public role explicitly assigned are visible to anonymous users
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See the [Public role documentation](/admin-docs/security/security#public) for more details.
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See the [Public role documentation](/admin-docs/security/#public) for more details.
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#### Embedding a Public Dashboard
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@@ -111,7 +111,7 @@ FEATURE_FLAGS = {
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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.
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:::note
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When embedding with SSO, also set `SESSION_COOKIE_SAMESITE = 'None'` and `SESSION_COOKIE_SECURE = True`. See [Security documentation](/docs/security/securing_superset) for details.
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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.
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:::
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## CSRF settings
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@@ -20,7 +20,7 @@ To help make the problem somewhat tractable—given that Apache Superset has no
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To strive for data consistency (regardless of the timezone of the client) the Apache Superset backend tries to ensure that any timestamp sent to the client has an explicit (or semi-explicit as in the case with [Epoch time](https://en.wikipedia.org/wiki/Unix_time) which is always in reference to UTC) timezone encoded within.
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The challenge however lies with the slew of [database engines](/admin-docs/databases#installing-drivers-in-docker) which Apache Superset supports and various inconsistencies between their [Python Database API (DB-API)](https://www.python.org/dev/peps/pep-0249/) implementations combined with the fact that we use [Pandas](https://pandas.pydata.org/) to read SQL into a DataFrame prior to serializing to JSON. Regrettably Pandas ignores the DB-API [type_code](https://www.python.org/dev/peps/pep-0249/#type-objects) relying by default on the underlying Python type returned by the DB-API. Currently only a subset of the supported database engines work correctly with Pandas, i.e., ensuring timestamps without an explicit timestamp are serializd to JSON with the server timezone, thus guaranteeing the client will display timestamps in a consistent manner irrespective of the client's timezone.
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The challenge however lies with the slew of [database engines](/user-docs/databases#installing-drivers-in-docker) which Apache Superset supports and various inconsistencies between their [Python Database API (DB-API)](https://www.python.org/dev/peps/pep-0249/) implementations combined with the fact that we use [Pandas](https://pandas.pydata.org/) to read SQL into a DataFrame prior to serializing to JSON. Regrettably Pandas ignores the DB-API [type_code](https://www.python.org/dev/peps/pep-0249/#type-objects) relying by default on the underlying Python type returned by the DB-API. Currently only a subset of the supported database engines work correctly with Pandas, i.e., ensuring timestamps without an explicit timestamp are serialized to JSON with the server timezone, thus guaranteeing the client will display timestamps in a consistent manner irrespective of the client's timezone.
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For example the following is a comparison of MySQL and Presto,
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@@ -149,7 +149,7 @@ For production clusters it's recommended to build own image with this step done
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Superset requires a Python DB-API database driver and a SQLAlchemy
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dialect to be installed for each datastore you want to connect to.
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See [Install Database Drivers](/admin-docs/databases#installing-database-drivers) for more information.
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See [Install Database Drivers](/user-docs/databases#installing-database-drivers) for more information.
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It is recommended that you refer to versions listed in
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[pyproject.toml](https://github.com/apache/superset/blob/master/pyproject.toml)
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instead of hard-coding them in your bootstrap script, as seen below.
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