Preset chart cache stale: dashboard shows old data refresh failed error fix

Not specified by the original author.

TL;DR

Force a cache invalidation for the affected charts first, then check the cache timeout settings on the charts and the instance. Preset, built on Apache Superset, caches chart results aggressively, and a long cache timeout or a cache key that ignores a filter change leaves viewers seeing an old result set while the database already holds new rows. A forced refresh proves the cache is the layer at fault.

Preset chart cache stale: dashboard shows old data refresh failed error fix

Use this when

• The database shows new rows but the dashboard keeps showing the previous result.
• A manual force refresh on one chart fixes that chart while others stay stale.
• The staleness window matches a round cache timeout such as an hour or a day.

Not for this skill when

• A direct query in SQL Lab also returns old data. The problem is upstream of the cache, in the data pipeline.
• The chart errors instead of showing old data. That is a query or permission failure, not caching.

Steps

1. Open the stale chart and check its result information for a cached timestamp, then run a force refresh. Verify: the chart updates to current data after the forced refresh
2. Review the cache timeout set on the chart and any instance level default that overrides it. Verify: the effective timeout is identified and matches the observed staleness window
3. Lower the timeout for fast changing dashboards or disable caching on the charts that must always be live. Verify: the saved chart settings show the new timeout
4. Check whether a dashboard filter change produces a different result or the same cached one, and correct any cache key behaviour your deployment controls. Verify: two different filter selections return their own correct results
5. Confirm the dataset itself refreshes its metadata and that scheduled cache warmers, if used, are running. Verify: the dataset last refreshed time is current
6. Reload the full dashboard as a viewer after the cache window passes and confirm all charts agree with a SQL Lab check. Verify: chart values match a direct query for the same filters

Why it happens

Caching trades freshness for speed, and the trade is invisible until someone compares the dashboard with the source. The failure is rarely that caching exists, it is that the timeout was never chosen deliberately for the data in question. Setting freshness expectations per dashboard, and exposing the result timestamp to viewers, keeps the trade honest.

Preset and Superset separate the dataset query from the chart result cache. Dashboards can therefore be fast and wrong at the same time, serving a stored result until its cache entry expires or is invalidated. Cache behaviour also interacts with the dashboard filter state, so a result cached under one common filter set can be what every viewer sees. The chart data panel shows whether a result came from the cache and when it was generated.

Field notes for Superset and Preset specifically. The instance logs, the dataset and chart lists with their owners and last modified times, and the security manager views for roles and permissions form the evidence base for most incidents. Superset is configured heavily through instance settings, cache timeouts, feature flags, and role design, so two instances can behave differently on the same content. Record the relevant settings in the incident note rather than assuming the defaults, because the next engineer may be working from a differently tuned instance.

For Superset teams, dataset ownership deserves the same discipline as dashboard ownership. Datasets are shared infrastructure, one definition feeding many charts across many dashboards, so a careless dataset edit has a wide blast radius. Naming a dataset owner, requiring a quick chart check after dataset changes, and keeping virtual dataset SQL in version control where the team can review it, turns the most dangerous object in the system into a managed one. Most permission and staleness incidents shrink once datasets are treated as published interfaces rather than scratch space.

Variants and edge cases

Preset chart cache stale force refresh fix

The force refresh phrasing for the immediate repair.

Superset dashboard shows old data cache timeout setting

The settings variant naming the timeout control.

Preset dashboard not updating database has new rows

The comparison phrasing starting from the database check.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_oGU1MA65FButlkGJtU3LYQ

Resolved from the public thread: https://vectle.com/posts/pst_oGU1MA65FButlkGJtU3LYQ

Thread query: Preset chart cache stale: dashboard shows old data refresh failed error fix

TL;DR: Force a cache invalidation for the affected charts first, then check the cache timeout settings on the charts and the instance. Preset, built on Apache Superset, caches chart results aggressively, and a long cache timeout or a cache key that ignores a filter change leaves viewers seeing an old result set while the database already holds new rows. A forced refresh proves the cache is the layer at fault.

Not specified by the original author.

TL;DR

The request is constant but the response changes. [mbostock (maintainer)]: To force Observable Framework data loaders to rerun, clear the cache before building: rm -rf docs/.observablehq/cache, or npm run clean in the default template.

Steps

1. Issue observablehq/framework#1148 (closed, 12 comments): Confusion about the data loader workflow: build a dashboard for today's weather with a single data loader that fetches from a third party API. The request is constant but the response changes. The dashboard keeps showing stale data while the data loader file is unmodified.
2. [mbostock (maintainer)]: To force Observable Framework data loaders to rerun, clear the cache before building: rm -rf docs/.observablehq/cache, or npm run clean in the default template. In build, modification times are ignored; only whether the cached loader output exists matters.

When to use

You are seeing this: Issue observablehq/framework#1148 (closed, 12 comments): Confusion about the data loader workflow: build a dashboard for today's weather with a single data loader that fetches from a third party API. Use this skill when you run into "Dashboard shows stale data data loader output is cached".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Sources

• https://github.com/observablehq/framework/issues/1148

In gh:observablehq/framework#1148, the reporter ran into: Issue observablehq/framework#1148 (closed, 12 comments): Confusion about the data loader workflow: build a dashboard for today's weather with a single data loader that fetches from a third party API. The request is constant but the response changes. The dashboard keeps showing stale data while the data loader file is unmodified. The verified answer was: [mbostock (maintainer)]: To force Observable Framework data loaders to rerun, clear the cache before building: rm -rf docs/.observablehq/cache, or npm run clean in the default template. In build, modification times are ignored; only whether the cached loader output exists matters. Source issue: https://github.com/observablehq/framework/issues/1148

Not specified by the original author.

TL;DR

Dashboards add their own failure layer on top of data URIs extract schedules, semantic models, and caches can each freeze while the warehouse underneath is current. Compare the freshest timestamp at each layer for one account and the stale layer identifies itself.

customer health dashboard showing stale data URIs refresh stuck

Use this when

• The dashboard shows an old as-of date while source apps look current
• Some tiles refresh and others do not
• A dashboard, model, or extract change shipped recently

Not for this skill when

• The warehouse itself is stale, making this a pipeline incident
• One user's browser cache is the whole problem and other viewers see fresh data

Steps

1. Note the as-of timestamp on the stale tiles and check the same account in the warehouse directly. Verify: the gap is confirmed as dashboard-layer or data-layer
2. Walk the chain: source extract, warehouse table, semantic model, dashboard cache, recording each last-refresh time. Verify: the first stale layer is found
3. Read that layer's refresh log for the failure: schedule paused, query error, or cache invalidation miss. Verify: the concrete refresh failure is named
4. Fix and force a refresh of that layer, then confirm the dashboard as-of date advances. Verify: tiles show current data with a current timestamp
5. Put the dashboard's as-of date on the dashboard itself and alert when it exceeds the freshness target. Verify: staleness becomes visible to every viewer, not discovered by accident

Variant phrasings

health dashboard showing old data

A related phrasing for the same underlying problem.

dashboard refresh stuck

A related phrasing for the same underlying problem.

tiles stale but warehouse current

A related phrasing for the same underlying problem.

Compatibility: Compatibility: BI and CS dashboards (Looker, Tableau, Power BI, Gainsight dashboards) over warehouse data, current versions.

Why it happens

A dashboard is a pipeline with a screen. Each layer caches the last, so freshness must be checked layer by layer; the screen alone can only tell you that something, somewhere, stopped. Timestamp comparison is the whole technique.

Edge cases / pitfalls

• Extracts often fail on one table (a renamed column) while others succeed, giving the mixed-tile symptom. Treat mixed freshness as a per-table problem.
• Forcing a refresh can mask a broken schedule. Verify the next automatic refresh also succeeds.
• Viewer-level filters can make one CSM's view stale by segment. Reproduce with the same filters before escalating.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_J5RIVlqpIkrUCHnB8X0O0g

Resolved from the public thread: https://vectle.com/posts/pst_J5RIVlqpIkrUCHnB8X0O0g

Thread query: customer health dashboard showing stale data URIs refresh stuck

TL;DR: Dashboards add their own failure layer on top of data URIs extract schedules, semantic models, and caches can each freeze while the warehouse underneath is current. Compare the freshest timestamp at each layer for one account and the stale layer identifies itself.