feat: Enable ingestion without event timestamp (#5625)

Author: franciscojavierarceo

README.md

@@ -109,11 +109,26 @@ print(training_df.head())
 ```
 
 ### 6. Load feature values into your online store
+
+**Option 1: Incremental materialization (recommended)**
 ```commandline
 CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
 feast materialize-incremental $CURRENT_TIME
 ```
 
+**Option 2: Full materialization with timestamps**
+```commandline
+CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
+feast materialize 2021-04-12T00:00:00 $CURRENT_TIME
+```
+
+**Option 3: Simple materialization without timestamps**
+```commandline
+feast materialize --disable-event-timestamp
+```
+
+The `--disable-event-timestamp` flag allows you to materialize all available feature data using the current datetime as the event timestamp, without needing to specify start and end timestamps. This is useful when your source data lacks proper event timestamp columns.
+
 ```commandline
 Materializing feature view driver_hourly_stats from 2021-04-14 to 2021-04-15 done!
 ```

docs/getting-started/components/online-store.md

@@ -1,18 +1,18 @@
-# Online store
-
-Feast uses online stores to serve features at low latency.
-Feature values are loaded from data sources into the online store through _materialization_, which can be triggered through the `materialize` command.
-
-The storage schema of features within the online store mirrors that of the original data source.
-One key difference is that for each [entity key](../concepts/entity.md), only the latest feature values are stored.
-No historical values are stored.
-
-Here is an example batch data source:
-
-![](../../.gitbook/assets/image%20%286%29.png)
-
-Once the above data source is materialized into Feast (using `feast materialize`), the feature values will be stored as follows:
-
-![](../../.gitbook/assets/image%20%285%29.png)
-
+# Online store
+
+Feast uses online stores to serve features at low latency.
+Feature values are loaded from data sources into the online store through _materialization_, which can be triggered through the `materialize` command (either with specific timestamps or using `--disable-event-timestamp` to materialize all data with current timestamps).
+
+The storage schema of features within the online store mirrors that of the original data source.
+One key difference is that for each [entity key](../concepts/entity.md), only the latest feature values are stored.
+No historical values are stored.
+
+Here is an example batch data source:
+
+![](../../.gitbook/assets/image%20%286%29.png)
+
+Once the above data source is materialized into Feast (using `feast materialize` with timestamps or `feast materialize --disable-event-timestamp`), the feature values will be stored as follows:
+
+![](../../.gitbook/assets/image%20%285%29.png)
+
 Features can also be written directly to the online store via [push sources](../../reference/data-sources/push.md) .

docs/getting-started/components/overview.md

@@ -7,7 +7,7 @@
 * **Create Batch Features:** ELT/ETL systems like Spark and SQL are used to transform data in the batch store.
 * **Create Stream Features:** Stream features are created from streaming services such as Kafka or Kinesis, and can be pushed directly into Feast via the [Push API](../../reference/data-sources/push.md).
 * **Feast Apply:** The user (or CI) publishes versioned controlled feature definitions using `feast apply`. This CLI command updates infrastructure and persists definitions in the object store registry.
-* **Feast Materialize:** The user (or scheduler) executes `feast materialize` which loads features from the offline store into the online store.
+* **Feast Materialize:** The user (or scheduler) executes `feast materialize` (with timestamps or `--disable-event-timestamp` to materialize all data with current timestamps) which loads features from the offline store into the online store.
 * **Model Training:** A model training pipeline is launched. It uses the Feast Python SDK to retrieve a training dataset that can be used for training models.
 * **Get Historical Features:** Feast exports a point-in-time correct training dataset based on the list of features and entity dataframe provided by the model training pipeline.
 * **Deploy Model:** The trained model binary (and list of features) are deployed into a model serving system. This step is not executed by Feast.

docs/getting-started/concepts/data-ingestion.md

@@ -64,11 +64,17 @@ materialize_python = PythonOperator(
 
 #### How to run this in the CLI
 
+**With timestamps:**
 ```bash
 CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
 feast materialize-incremental $CURRENT_TIME
 ```
 
+**Simple materialization (for data without event timestamps):**
+```bash
+feast materialize --disable-event-timestamp
+```
+
 #### How to run this on Airflow
 
 ```python

docs/getting-started/quickstart.md

@@ -499,13 +499,19 @@ print(training_df.head())
 We now serialize the latest values of features since the beginning of time to prepare for serving. Note, `materialize_incremental` serializes all new features since the last `materialize` call, or since the time provided minus the `ttl` timedelta. In this case, this will be `CURRENT_TIME - 1 day` (`ttl` was set on the `FeatureView` instances in [feature_repo/feature_repo/example_repo.py](feature_repo/feature_repo/example_repo.py)).
 
 {% tabs %}
-{% tab title="Bash" %}
+{% tab title="Bash (with timestamp)" %}
 ```bash
 CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
 
 feast materialize-incremental $CURRENT_TIME
 ```
 {% endtab %}
+{% tab title="Bash (simple)" %}
+```bash
+# Alternative: Materialize all data using current timestamp (for data without event timestamps)
+feast materialize --disable-event-timestamp
+```
+{% endtab %}
 {% endtabs %}
 
 {% tabs %}

docs/reference/feast-cli-commands.md

@@ -152,18 +152,30 @@ feast init -t gcp my_feature_repo
 
 ## Materialize
 
-Load data from feature views into the online store between two dates
+Load data from feature views into the online store.
 
+**With timestamps:**
 ```bash
 feast materialize 2020-01-01T00:00:00 2022-01-01T00:00:00
 ```
 
-Load data for specific feature views into the online store between two dates
+**Without timestamps (uses current datetime):**
+```bash
+feast materialize --disable-event-timestamp
+```
+
+Load data for specific feature views:
 
 ```text
 feast materialize -v driver_hourly_stats 2020-01-01T00:00:00 2022-01-01T00:00:00
 ```
 
+```text
+feast materialize --disable-event-timestamp -v driver_hourly_stats
+```
+
+The `--disable-event-timestamp` flag is useful when your source data lacks event timestamp columns, allowing you to materialize all available data using the current datetime as the event timestamp.
+
 ```text
 Materializing 1 feature views from 2020-01-01 to 2022-01-01
 

docs/reference/feature-servers/python-feature-server.md

@@ -200,6 +200,52 @@ requests.post(
     data=json.dumps(push_data))
 ```
 
+### Materializing features
+
+The Python feature server also exposes an endpoint for materializing features from the offline store to the online store.
+
+**Standard materialization with timestamps:**
+```bash
+curl -X POST "http://localhost:6566/materialize" -d '{
+    "start_ts": "2021-01-01T00:00:00",
+    "end_ts": "2021-01-02T00:00:00",
+    "feature_views": ["driver_hourly_stats"]
+}' | jq
+```
+
+**Materialize all data without event timestamps:**
+```bash
+curl -X POST "http://localhost:6566/materialize" -d '{
+    "feature_views": ["driver_hourly_stats"],
+    "disable_event_timestamp": true
+}' | jq
+```
+
+When `disable_event_timestamp` is set to `true`, the `start_ts` and `end_ts` parameters are not required, and all available data is materialized using the current datetime as the event timestamp. This is useful when your source data lacks proper event timestamp columns.
+
+Or from Python:
+```python
+import json
+import requests
+
+# Standard materialization
+materialize_data = {
+    "start_ts": "2021-01-01T00:00:00",
+    "end_ts": "2021-01-02T00:00:00",
+    "feature_views": ["driver_hourly_stats"]
+}
+
+# Materialize without event timestamps
+materialize_data_no_timestamps = {
+    "feature_views": ["driver_hourly_stats"],
+    "disable_event_timestamp": True
+}
+
+requests.post(
+    "http://localhost:6566/materialize",
+    data=json.dumps(materialize_data))
+```
+
 ## Starting the feature server in TLS(SSL) mode
 
 Enabling TLS mode ensures that data between the Feast client and server is transmitted securely. For an ideal production environment, it is recommended to start the feature server in TLS mode.

infra/templates/README.md.jinja2

@@ -107,11 +107,26 @@ print(training_df.head())
 ```
 
 ### 6. Load feature values into your online store
+
+**Option 1: Incremental materialization (recommended)**
 ```commandline
 CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
 feast materialize-incremental $CURRENT_TIME
 ```
 
+**Option 2: Full materialization with timestamps**
+```commandline
+CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
+feast materialize 2021-04-12T00:00:00 $CURRENT_TIME
+```
+
+**Option 3: Simple materialization without timestamps**
+```commandline
+feast materialize --disable-event-timestamp
+```
+
+The `--disable-event-timestamp` flag allows you to materialize all available feature data using the current datetime as the event timestamp, without needing to specify start and end timestamps. This is useful when your source data lacks proper event timestamp columns.
+
 ```commandline
 Materializing feature view driver_hourly_stats from 2021-04-14 to 2021-04-15 done!
 ```

sdk/python/feast/cli/cli.py

@@ -303,17 +303,26 @@ def registry_dump_command(ctx: click.Context):
 
 
 @cli.command("materialize")
-@click.argument("start_ts")
-@click.argument("end_ts")
+@click.argument("start_ts", required=False)
+@click.argument("end_ts", required=False)
 @click.option(
     "--views",
     "-v",
     help="Feature views to materialize",
     multiple=True,
 )
+@click.option(
+    "--disable-event-timestamp",
+    is_flag=True,
+    help="Materialize all available data using current datetime as event timestamp (useful when source data lacks event timestamps)",
+)
 @click.pass_context
 def materialize_command(
-    ctx: click.Context, start_ts: str, end_ts: str, views: List[str]
+    ctx: click.Context,
+    start_ts: Optional[str],
+    end_ts: Optional[str],
+    views: List[str],
+    disable_event_timestamp: bool,
 ):
     """
     Run a (non-incremental) materialization job to ingest data into the online store. Feast
@@ -322,13 +331,35 @@ def materialize_command(
     Views will be materialized.
 
     START_TS and END_TS should be in ISO 8601 format, e.g. '2021-07-16T19:20:01'
+
+    If --disable-event-timestamp is used, timestamps are not required and all available data will be materialized using the current datetime as the event timestamp.
     """
     store = create_feature_store(ctx)
 
+    if disable_event_timestamp:
+        if start_ts or end_ts:
+            raise click.UsageError(
+                "Cannot specify START_TS or END_TS when --disable-event-timestamp is used"
+            )
+        now = datetime.now()
+        # Query all available data and use current datetime as event timestamp
+        start_date = datetime(
+            1970, 1, 1
+        )  # Beginning of time to capture all historical data
+        end_date = now
+    else:
+        if not start_ts or not end_ts:
+            raise click.UsageError(
+                "START_TS and END_TS are required unless --disable-event-timestamp is used"
+            )
+        start_date = utils.make_tzaware(parser.parse(start_ts))
+        end_date = utils.make_tzaware(parser.parse(end_ts))
+
     store.materialize(
         feature_views=None if not views else views,
-        start_date=utils.make_tzaware(parser.parse(start_ts)),
-        end_date=utils.make_tzaware(parser.parse(end_ts)),
+        start_date=start_date,
+        end_date=end_date,
+        disable_event_timestamp=disable_event_timestamp,
     )
 
 

sdk/python/feast/feature_server.py

@@ -5,6 +5,7 @@
 import time
 import traceback
 from contextlib import asynccontextmanager
+from datetime import datetime
 from importlib import resources as importlib_resources
 from typing import Any, Dict, List, Optional, Union
 
@@ -73,9 +74,10 @@ class PushFeaturesRequest(BaseModel):
 
 
 class MaterializeRequest(BaseModel):
-    start_ts: str
-    end_ts: str
+    start_ts: Optional[str] = None
+    end_ts: Optional[str] = None
     feature_views: Optional[List[str]] = None
+    disable_event_timestamp: bool = False
 
 
 class MaterializeIncrementalRequest(BaseModel):
@@ -432,10 +434,27 @@ def materialize(request: MaterializeRequest) -> None:
                 resource=_get_feast_object(feature_view, True),
                 actions=[AuthzedAction.WRITE_ONLINE],
             )
+
+        if request.disable_event_timestamp:
+            # Query all available data and use current datetime as event timestamp
+            now = datetime.now()
+            start_date = datetime(
+                1970, 1, 1
+            )  # Beginning of time to capture all historical data
+            end_date = now
+        else:
+            if not request.start_ts or not request.end_ts:
+                raise ValueError(
+                    "start_ts and end_ts are required when disable_event_timestamp is False"
+                )
+            start_date = utils.make_tzaware(parser.parse(request.start_ts))
+            end_date = utils.make_tzaware(parser.parse(request.end_ts))
+
         store.materialize(
-            utils.make_tzaware(parser.parse(request.start_ts)),
-            utils.make_tzaware(parser.parse(request.end_ts)),
+            start_date,
+            end_date,
             request.feature_views,
+            disable_event_timestamp=request.disable_event_timestamp,
         )
 
     @app.post("/materialize-incremental", dependencies=[Depends(inject_user_details)])