[FSTORE-2106] Event-time-based windows for feature monitoring configurations

docs/user_guides/fs/feature_group/feature_monitoring.md

@@ -162,6 +162,35 @@ Additionally, you can specify the percentage of feature data on which statistics
 
 See the API reference for [`FeatureMonitoringConfig.with_detection_window`][hsfs.core.feature_monitoring_config.FeatureMonitoringConfig.with_detection_window].
 
+#### Time basis of the windows
+
+Rolling windows select rows by an event-time feature when the Feature Group declares one, and by commit time otherwise.
+The `event_time` parameter of `create_scheduled_statistics` and `create_feature_monitoring` overrides that default for the whole configuration, detection and reference windows alike.
+Pass a feature name to use another timestamp, date or epoch feature of the Feature Group, or `False` to select rows by commit time.
+
+=== "Python"
+
+    ```python
+    # windows over the transaction time, the Feature Group event_time (default)
+    fg_monitoring_config = trans_fg.create_feature_monitoring(
+        name="trans_fg_amount_monitoring",
+    )
+
+    # windows over another time feature of the Feature Group
+    fg_monitoring_config = trans_fg.create_feature_monitoring(
+        name="trans_fg_amount_monitoring_by_settlement",
+        event_time="settlement_date",
+    )
+
+    # windows over the time the rows were written (commit time)
+    fg_monitoring_config = trans_fg.create_feature_monitoring(
+        name="trans_fg_amount_monitoring_by_commit",
+        event_time=False,
+    )
+    ```
+
+See [Time basis](../feature_monitoring/scheduled_statistics.md#time-basis) for how the two bases differ.
+
 ### Step 4: (Optional) Define a reference window
 
 When setting up feature monitoring for a Feature Group, you can compare the detection statistics against a reference window of feature data.

docs/user_guides/fs/feature_group/statistics.md

@@ -93,15 +93,25 @@ Users can schedule periodic statistics computation that take into consideration
 By default, the `compute_statistics` method computes statistics on the most recent version of the data available in a feature group.
 Users can provide a specific time using the `wallclock_time` parameter, to compute the statistics for a previous version of the data.
 
-Hopsworks can compute statistics of external feature groups.
-As external feature groups are read only from an Hopsworks perspective, statistics computation can be triggered using the `compute_statistics` method.
-
 === "Python"
 
     ```python
     fg.compute_statistics(wallclock_time="20220611 20:00")
     ```
 
+### External feature groups
+
+External feature groups own the same built-in `ingestion_stats` configuration as cached and stream feature groups, but no data is ingested into Hopsworks for them, so it never runs on its own.
+Calling `compute_statistics` on the external feature group, or clicking "Compute statistics" in the UI, runs it: the statistics job reads the external source and profiles it like an internal feature group.
+Saving an external feature group with statistics enabled runs it once as well.
+External feature groups have no commit history, so their statistics carry the computation time only and `compute_statistics` takes no time argument.
+
+=== "Python"
+
+    ```python
+    external_fg.compute_statistics()
+    ```
+
 ## Inspect statistics
 
 You can also create a new feature group through the UI.

docs/user_guides/fs/feature_monitoring/scheduled_statistics.md

@@ -35,6 +35,22 @@ Taking a Feature Group as an example, the figure above describes how these windo
 - A _rolling window_ covering a variable subset of feature data (e.g., feature data written last week).
   It helps you analyze the properties of **newly inserted feature data**.
 
+### Time basis
+
+A rolling window needs a notion of time to decide which rows fall inside it.
+Hopsworks supports two bases, chosen once per configuration and shared by the detection and reference windows:
+
+- _Event time_: rows are selected by the value of an event-time feature, so a window such as "last week" contains the rows whose event time falls in that week regardless of when they were written.
+  Backfills and late arrivals land in the window of their event time.
+  This is the default for Feature Groups and Feature Views that declare an `event_time`, and it also works on Feature Groups without time travel and on external Feature Groups.
+- _Commit time_: rows are selected by the time they were written to the Feature Group, using time travel.
+  A window such as "last week" contains the rows committed during that week.
+  This is the default when no event-time feature is declared, and it requires a time-travel enabled Feature Group.
+
+An expanding window reads the latest snapshot on either basis, with no time filter.
+Statistics computed on an event-time window are stored with the event-time bounds of the window, and statistics computed on a commit-time window with its commit bounds.
+The `event_time` parameter of `create_scheduled_statistics` and `create_feature_monitoring` selects the basis; see the guides linked below.
+
 See more details on how to define a detection window for your Feature Groups and Feature Views in the Feature Monitoring Guides for [Feature Groups](../feature_group/feature_monitoring.md) and [Feature Views](../feature_view/feature_monitoring.md).
 
 !!! info "Next steps"

docs/user_guides/fs/feature_monitoring/statistics_comparison.md

@@ -41,6 +41,9 @@ Taking a Feature View as an example, the figure above describes how these window
 - A _specific value_.
   It helps you target the analysis of feature data to a **specific feature and statistics metric**.
 
+Rolling and expanding reference windows use the same time basis as the detection window of the configuration, either the event-time feature or the commit time.
+See [Time basis](scheduled_statistics.md#time-basis) in the scheduled statistics guide.
+
 See more details on how to define a reference window for your Feature Groups and Training Datasets in the Feature Monitoring guides for [Feature Groups](../feature_group/feature_monitoring.md) and [Feature Views](../feature_view/feature_monitoring.md).
 
 ## Comparison criteria

docs/user_guides/fs/feature_view/feature_monitoring.md

@@ -133,6 +133,31 @@ Additionally, you can specify the percentage of feature data on which statistics
 
 See the API reference for [`FeatureMonitoringConfig.with_detection_window`][hsfs.core.feature_monitoring_config.FeatureMonitoringConfig.with_detection_window].
 
+#### Time basis of the windows
+
+Rolling windows select rows by the event-time feature of the Feature View's left Feature Group when it declares one, and by commit time otherwise.
+The `event_time` parameter of `create_scheduled_statistics` and `create_feature_monitoring` overrides that default for the whole configuration, detection and reference windows alike.
+Pass the name of a timestamp, date or epoch feature from any Feature Group in the query, or `False` to select rows by commit time.
+With event time the joined Feature Groups contribute their current rows, whereas with commit time the same commit interval is applied to every Feature Group in the query.
+
+=== "Python"
+
+    ```python
+    # windows over a time feature of a joined Feature Group
+    fm_monitoring_config = trans_fv.create_feature_monitoring(
+        name="trans_fv_amount_monitoring_by_event_time",
+        event_time="datetime",
+    )
+
+    # windows over the time the rows were written (commit time)
+    fm_monitoring_config = trans_fv.create_feature_monitoring(
+        name="trans_fv_amount_monitoring_by_commit",
+        event_time=False,
+    )
+    ```
+
+See [Time basis](../feature_monitoring/scheduled_statistics.md#time-basis) for how the two bases differ.
+
 ### Step 6: (Optional) Define a reference window
 
 When setting up feature monitoring for a Feature View, the reference can be either a reference window of feature data or a training dataset.
@@ -251,6 +276,7 @@ A Feature View can also monitor the inference data of a model served in producti
 This is the feature-view entry point to [Model Monitoring](../../mlops/model_monitoring/index.md).
 
 It targets the feature view's logging feature group, so feature logging must be enabled with `feature_view.enable_logging()`, and filters the detection window by the given model name and version.
+The windows select inference rows by their `log_time`, the time the prediction was logged.
 The reference defaults to the training dataset version used to train the model.
 
 === "Python"