The Python feature server is an HTTP endpoint that serves features with JSON I/O. This enables users to write and read features from the online store using any programming language that can make HTTP requests.
There is a CLI command that starts the server: feast serve. By default, Feast uses port 6566; the port be overridden with a --port flag.
One can deploy a feature server by building a docker image that bundles in the project's feature_store.yaml. See this helm chart for an example on how to run Feast on Kubernetes.
Here's an example of how to start the Python feature server with a local feature repo:
$ feast init feature_repo
Creating a new Feast repository in /home/tsotne/feast/feature_repo.
$ cd feature_repo
$ feast apply
Created entity driver
Created feature view driver_hourly_stats
Created feature service driver_activity
Created sqlite table feature_repo_driver_hourly_stats
$ feast materialize-incremental $(date +%Y-%m-%d)
Materializing 1 feature views to 2021-09-09 17:00:00-07:00 into the sqlite online store.
driver_hourly_stats from 2021-09-09 16:51:08-07:00 to 2021-09-09 17:00:00-07:00:
100%|████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 295.24it/s]
$ feast serve
09/10/2021 10:42:11 AM INFO:Started server process [8889]
INFO: Waiting for application startup.
09/10/2021 10:42:11 AM INFO:Waiting for application startup.
INFO: Application startup complete.
09/10/2021 10:42:11 AM INFO:Application startup complete.
INFO: Uvicorn running on http://127.0.0.1:6566 (Press CTRL+C to quit)
09/10/2021 10:42:11 AM INFO:Uvicorn running on http://127.0.0.1:6566 (Press CTRL+C to quit)After the server starts, we can execute cURL commands from another terminal tab:
$ curl -X POST \
"http://localhost:6566/get-online-features" \
-d '{
"features": [
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips"
],
"entities": {
"driver_id": [1001, 1002, 1003]
}
}' | jq
{
"metadata": {
"feature_names": [
"driver_id",
"conv_rate",
"avg_daily_trips",
"acc_rate"
]
},
"results": [
{
"values": [
1001,
0.7037263512611389,
308,
0.8724706768989563
],
"statuses": [
"PRESENT",
"PRESENT",
"PRESENT",
"PRESENT"
],
"event_timestamps": [
"1970-01-01T00:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z"
]
},
{
"values": [
1002,
0.038169607520103455,
332,
0.48534533381462097
],
"statuses": [
"PRESENT",
"PRESENT",
"PRESENT",
"PRESENT"
],
"event_timestamps": [
"1970-01-01T00:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z"
]
},
{
"values": [
1003,
0.9665873050689697,
779,
0.7793770432472229
],
"statuses": [
"PRESENT",
"PRESENT",
"PRESENT",
"PRESENT"
],
"event_timestamps": [
"1970-01-01T00:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z"
]
}
]
}It's also possible to specify a feature service name instead of the list of features:
curl -X POST \
"http://localhost:6566/get-online-features" \
-d '{
"feature_service": <feature-service-name>,
"entities": {
"driver_id": [1001, 1002, 1003]
}
}' | jq
The Python feature server also exposes an endpoint for push sources. This endpoint allows you to push data to the online and/or offline store.
The request definition for PushMode is a string parameter to where the options are: ["online", "offline", "online_and_offline"].
Note: timestamps need to be strings, and might need to be timezone aware (matching the schema of the offline store)
curl -X POST "http://localhost:6566/push" -d '{
"push_source_name": "driver_stats_push_source",
"df": {
"driver_id": [1001],
"event_timestamp": ["2022-05-13 10:59:42+00:00"],
"created": ["2022-05-13 10:59:42"],
"conv_rate": [1.0],
"acc_rate": [1.0],
"avg_daily_trips": [1000]
},
"to": "online_and_offline"
}' | jq
or equivalently from Python:
import json
import requests
from datetime import datetime
event_dict = {
"driver_id": [1001],
"event_timestamp": [str(datetime(2021, 5, 13, 10, 59, 42))],
"created": [str(datetime(2021, 5, 13, 10, 59, 42))],
"conv_rate": [1.0],
"acc_rate": [1.0],
"avg_daily_trips": [1000],
"string_feature": "test2",
}
push_data = {
"push_source_name":"driver_stats_push_source",
"df":event_dict,
"to":"online",
}
requests.post(
"http://localhost:6566/push",
data=json.dumps(push_data))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.
In development mode we can generate a self-signed certificate for testing. In an actual production environment it is always recommended to get it from a trusted TLS certificate provider.
openssl req -x509 -newkey rsa:2048 -keyout key.pem -out cert.pem -days 365 -nodesThe above command will generate two files
key.pem: certificate private keycert.pem: certificate public key
To start the feature server in TLS mode, you need to provide the private and public keys using the --key and --cert arguments with the feast serve command.
feast serve --key /path/to/key.pem --cert /path/to/cert.pem| Endpoint | Resource Type | Permission | Description |
|---|---|---|---|
| /get-online-features | FeatureView,OnDemandFeatureView | Read Online | Get online features from the feature store |
| /retrieve-online-documents | FeatureView | Read Online | Retrieve online documents from the feature store for RAG |
| /push | FeatureView | Write Online, Write Offline, Write Online and Offline | Push features to the feature store (online, offline, or both) |
| /write-to-online-store | FeatureView | Write Online | Write features to the online store |
| /materialize | FeatureView | Write Online | Materialize features within a specified time range |
| /materialize-incremental | FeatureView | Write Online | Incrementally materialize features up to a specified timestamp |
Please refer the page for more details on how to configure authentication and authorization.