Add ttl example (4.6)

.github/workflows/mkdocs-test.yml

@@ -42,4 +42,4 @@ jobs:
           kill -9 $SERVER_PID
 
       - name: mike deploy docs
-        run: mike deploy 3.2-SNAPSHOT dev -u
+        run: mike deploy test-version

docs/user_guides/fs/feature_group/index.md

@@ -8,4 +8,5 @@ This section serves to provide guides and examples for the common usage of abstr
 - [Data Types and Schema management](data_types.md)
 - [Statistics](statistics.md)
 - [Data Validation](data_validation.md)
-- [Feature Monitoring](feature_monitoring.md)
+- [Feature Monitoring](feature_monitoring.md)
+- [Time-To-Live (TTL)](ttl.md)

docs/user_guides/fs/feature_group/ttl.md

@@ -0,0 +1,143 @@
+## Feature Group TTL Usage Guide
+
+Time To Live (TTL) is a feature that automatically expires data in feature groups after a specified time period.
+This guide explains when and how to use TTL in your feature groups.
+
+### Use Case: When to Use TTL
+
+TTL is particularly useful for feature groups that contain time-sensitive data that becomes stale or irrelevant after a certain period.
+Common use cases include:
+
+- **Regulatory compliance**: Data that must be automatically purged after a retention period for privacy or compliance reasons (e.g., GDPR, HIPAA)
+- **Cost optimization**: Reducing storage costs by automatically removing outdated data that is no longer needed for model inference
+- **Data freshness**: Ensuring that only recent, relevant data is available for online serving, preventing models from using stale features
+
+For example, if you're building a recommendation system, you might want user interaction features (like "items viewed in the last hour") to automatically expire after 1 hour, ensuring your model only uses current, relevant data.
+
+---
+
+## Getting Started
+
+### Creating a Feature Group with TTL
+
+When creating a new feature group, you can enable TTL by specifying the `ttl` parameter.
+The TTL value determines how long data will remain in the feature group before being automatically expired.
+The TTL is calculated based on the `event_time` column.
+Data rows where `event_time` is older than the TTL period will be automatically removed.
+
+```python
+from datetime import datetime, timezone
+import pandas as pd
+
+# Assume you already have a feature store handle
+# fs = ...
+
+now = datetime.now(timezone.utc)
+df = pd.DataFrame(
+    {
+        "id": [0, 1, 2],
+        "timestamp": [now, now, now],
+        "feature1": [10, 20, 30],
+        "feature2": ["a", "b", "c"],
+    }
+)
+
+# Create a feature group with TTL enabled (60 seconds)
+fg = fs.create_feature_group(
+    name="fg_ttl_example",
+    version=1,
+    primary_key=["id"],
+    event_time="timestamp",
+    online_enabled=True,
+    ttl=60,  # TTL in seconds - data will expire after 60 seconds
+)
+
+fg.insert(
+    df,
+    write_options={
+        "start_offline_materialization": False,
+        "wait_for_online_ingestion": True,
+    },
+)
+
+# After 60 seconds, reading online will return empty data
+fg.read(online=True)  # Returns empty DataFrame after TTL expires
+```
+
+For detailed API reference on all possible types of TTL values, see the [FeatureStore.create_feature_group API documentation](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/feature_store_api/#create_feature_group).
+
+---
+
+## Managing TTL on Existing Feature Groups
+
+### Updating the TTL Value
+
+You can change the TTL value for an existing feature group at any time.
+This is useful when you need to adjust the retention period based on changing requirements.
+
+```python
+# Get your existing feature group
+fg = fs.get_feature_group(
+    name="fg_ttl_example",
+    version=1,
+)
+
+# Update TTL to a new value (120 seconds = 2 minutes)
+fg.enable_ttl(ttl=120)
+```
+
+After updating the TTL, the new retention period will apply to all future data insertions and will affect when existing data expires.
+
+---
+
+### Disabling and Re-enabling TTL
+
+You can temporarily disable TTL on a feature group if you need to retain data indefinitely, and then re-enable it later.
+
+#### Disabling TTL
+
+```python
+# Disable TTL - data will no longer expire automatically
+fg.disable_ttl()
+```
+
+#### Re-enabling TTL
+
+When re-enabling TTL, you have two options:
+
+1. **Re-enable with the previous TTL value**: If you don't specify a TTL value, the feature group will use the last TTL value that was set.
+
+    ```python
+    # Re-enable TTL using the previous TTL value
+    fg.enable_ttl()
+    ```
+
+2. **Re-enable with a new TTL value**: Specify a new TTL value when re-enabling.
+
+    ```python
+    # Re-enable TTL with a new value (90 seconds)
+    fg.enable_ttl(ttl=90)
+    ```
+
+**Important**: If TTL was never set on the feature group before, you must provide a TTL value when enabling it.
+Otherwise, TTL cannot be enabled.
+
+---
+
+### Enabling TTL on an Existing Feature Group
+
+If you created a feature group without TTL initially, you can enable it later:
+
+```python
+# Get an existing feature group that was created without TTL
+fg = fs.get_feature_group(
+    name="fg_existing_no_ttl",
+    version=1,
+)
+
+# Enable TTL for the first time (60 seconds)
+fg.enable_ttl(ttl=60)
+```
+
+Once enabled, TTL will apply to all data in the feature group based on the `event_time` column.
+For detailed API reference on all possible types of TTL values and additional options, see the [FeatureGroup.enable_ttl API documentation](https://docs.hopsworks.ai/hopsworks-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#enable_ttl).

mkdocs.yml

@@ -87,6 +87,7 @@ nav:
               - Notification: user_guides/fs/feature_group/notification.md
               - On-Demand Transformations: user_guides/fs/feature_group/on_demand_transformations.md
               - Online Ingestion Observability: user_guides/fs/feature_group/online_ingestion_observability.md
+              - Time-To-Live (TTL): user_guides/fs/feature_group/ttl.md
           - Feature View:
               - user_guides/fs/feature_view/index.md
               - Overview: user_guides/fs/feature_view/overview.md