rusackas commented on code in PR #42953:
URL: https://github.com/apache/superset/pull/42953#discussion_r3746773906
##########
docs/admin_docs/configuration/cache.mdx:
##########
@@ -134,6 +134,43 @@ CELERY_CONFIG = CustomCeleryConfig
This will cache the top 5 most popular dashboards every hour. For other
strategies, check the `superset/tasks/cache.py` file.
+### Warming Up Native Filter Options
+
+Native filter Value-type dropdown option queries (e.g. `SELECT DISTINCT column
FROM table`) are
+cached the same way as chart data, via `DATA_CACHE_CONFIG`. However, the
strategies above only warm
+up chart render queries, so the first user to open a dashboard's filter
dropdown after a cache entry
+expires still triggers a fresh database query.
+
+The `native_filter_options` strategy pre-populates the cache for these
dropdown queries. It reads
+each dashboard's `native_filter_configuration`, builds the same
`filter_select` chart-data query the
+frontend would send, and executes it as the configured
`SUPERSET_CACHE_WARMUP_USER`:
+
+```python
+class CustomCeleryConfig(CeleryConfig):
+ beat_schedule = {
+ **CeleryConfig.beat_schedule,
+ 'cache-warmup-native-filters': {
+ 'task': 'cache-warmup',
+ 'schedule': crontab(minute=0, hour=3), # daily at 03:00
Review Comment:
Good catch — added a note that the schedule needs to be at least as frequent
as the effective native filter cache TTL, since the daily example only makes
sense for TTLs of a day or more.
##########
docs/admin_docs/configuration/cache.mdx:
##########
@@ -134,6 +134,43 @@ CELERY_CONFIG = CustomCeleryConfig
This will cache the top 5 most popular dashboards every hour. For other
strategies, check the `superset/tasks/cache.py` file.
+### Warming Up Native Filter Options
+
+Native filter Value-type dropdown option queries (e.g. `SELECT DISTINCT column
FROM table`) are
+cached the same way as chart data, via `DATA_CACHE_CONFIG`. However, the
strategies above only warm
+up chart render queries, so the first user to open a dashboard's filter
dropdown after a cache entry
+expires still triggers a fresh database query.
+
+The `native_filter_options` strategy pre-populates the cache for these
dropdown queries. It reads
+each dashboard's `native_filter_configuration`, builds the same
`filter_select` chart-data query the
+frontend would send, and executes it as the configured
`SUPERSET_CACHE_WARMUP_USER`:
+
+```python
+class CustomCeleryConfig(CeleryConfig):
+ beat_schedule = {
+ **CeleryConfig.beat_schedule,
+ 'cache-warmup-native-filters': {
+ 'task': 'cache-warmup',
+ 'schedule': crontab(minute=0, hour=3), # daily at 03:00
+ 'kwargs': {
+ 'strategy_name': 'native_filter_options',
+ 'dashboard_ids': [1, 2, 3],
+ },
+ },
+ }
+```
+
+Requirements and limitations:
+
+- `SUPERSET_CACHE_WARMUP_USER` must be set to a user with access to the
dashboards and datasets
+ referenced by the native filters.
+- `DATA_CACHE_CONFIG` must be configured (Redis recommended).
Review Comment:
Fair point — the default `NullCache` and
`NATIVE_FILTER_OPTIONS_CACHE_TIMEOUT = -1` both silently no-op the warm-up.
Called both out explicitly.
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