[eschutho]

SUMMARY

Adds a favorites strategy to the existing cache-warmup Celery beat task. On each scheduled run it picks recently active users who have favorited dashboards and warms the charts on those dashboards, so their favorites load from cache. It is gated by the CACHE_WARMUP_FAVORITES config setting (default False; not a feature flag) and runs only when an operator schedules it.

A chart is only computed if its cached result is missing or expired; charts that are already cached are left alone, so no queries are repeated needlessly.

Design notes:

• Each selected user gets their own task that runs as that user, not as SUPERSET_CACHE_WARMUP_USER, because cache keys depend on the requesting user (row-level security, per-user caching, impersonation). Access to each dashboard and chart is checked for that user.
• It reuses ChartWarmUpCacheCommand. A new only_if_stale option makes it look up the data cache first and fill misses without bypassing the cache. The existing "always force refresh" behavior is unchanged when the option is not used.
• Bounds: max_users and since on the strategy (most recent login first) and CACHE_WARMUP_FAVORITES_MAX_DASHBOARDS (default 5, newest favorites first) limit work per run. A per-result lock stops overlapping runs from computing the same result twice, and each per-user task has a 15 minute time limit and queued tasks expire after two hours. Favorites read per user are bounded in SQL. There is no per-user cooldown: the beat schedule is the throttle.
• CACHE_WARMUP_FAVORITES_QUEUE (default None) can route these tasks to a dedicated queue so they do not compete with reports, alerts, or thumbnails.
• Needs a Celery worker and a real DATA_CACHE_CONFIG backend.
• Tests check that the cache key the warm-up looks up matches the one built on the request path, and that it changes with row-level security and the running user under per-user caching, and stays stable for relative time ranges.
• Known limitation: dashboard default column filters (native filter defaults and legacy defaults) are applied, but other dashboard filter settings (time range, time column and grain, extras, ad hoc filters) are not, so a dashboard load that uses them can still miss the cache for those charts.

Example schedule:

"cache-warmup-favorites": {
    "task": "cache-warmup",
    "schedule": crontab(minute=0, hour="*/6"),
    "kwargs": {"strategy_name": "favorites", "since": "7 days ago", "max_users": 100},
}

TESTING INSTRUCTIONS

Unit tests: pytest tests/unit_tests/tasks/test_cache.py tests/unit_tests/commands/chart

Manual: configure Redis for DATA_CACHE_CONFIG and CACHE_CONFIG, run a Celery worker and beat with the schedule above and CACHE_WARMUP_FAVORITES = True. Favorite a dashboard as a user who has logged in recently, clear its cache, and confirm the worker computes its charts on the next run and computes nothing on a later run while the cache is warm.

ADDITIONAL INFORMATION

• Has associated issue:
• Required feature flags:
• Changes UI
• Includes DB Migration
• Introduces new feature or API
• Removes existing feature or API

🤖 Generated with Claude Code

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[codecov]

Codecov Report

❌ Patch coverage is 76.69173% with 31 lines in your changes missing coverage. Please review.
✅ Project coverage is 84.70%. Comparing base (a8c13c6) to head (4eeb6ca).
⚠️ Report is 13 commits behind head on master.

Files with missing lines	Patch %	Lines
superset/commands/chart/warm_up_cache.py	60.97%	14 Missing and 2 partials ⚠️
superset/tasks/cache.py	83.14%	11 Missing and 4 partials ⚠️

Additional details and impacted files

@@             Coverage Diff             @@
##           master   #45873       +/-   ##
===========================================
+ Coverage   65.62%   84.70%   +19.08%     
===========================================
  Files        3069     3069               
  Lines      205661   205788      +127     
  Branches    48336    48357       +21     
===========================================
+ Hits       134956   174315    +39359     
+ Misses      67624    28174    -39450     
- Partials     3081     3299      +218     

Flag	Coverage Δ
hive	34.49% <21.05%> (-0.02%)	⬇️
mysql	52.55% <27.06%> (-0.04%)	⬇️
postgres	52.55% <27.06%> (-0.04%)	⬇️
presto	36.22% <21.05%> (-0.02%)	⬇️
python	87.74% <76.69%> (+34.87%)	⬆️
sqlite	52.32% <27.06%> (-0.04%)	⬇️
unit	82.47% <76.69%> (?)

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