Python feature server
Overview
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.
CLI
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.
Deploying as a service
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.
Example
Initializing a feature server
Here's an example of how to start the Python feature server with a local feature repo:
Copy $ 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 ) Retrieving features
After the server starts, we can execute cURL commands from another terminal tab:
Copy $ 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:
Copy curl -X POST \
"http://localhost:6566/get-online-features" \
-d '{
"feature_service": <feature-service-name>,
"entities": {
"driver_id": [1001, 1002, 1003]
}
}' | jq Pushing features to the online and offline stores
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)
Copy 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:
Copy 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)) 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.
Obtaining a self-signed TLS certificate and key
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.
Copy openssl req -x509 -newkey rsa:2048 -keyout key.pem -out cert.pem -days 365 -nodes The above command will generate two files
key.pem : certificate private key
cert.pem: certificate public key
Starting the Online Server in TLS(SSL) Mode
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.
Copy feast serve --key /path/to/key.pem --cert /path/to/cert.pem Online Feature Server Permissions and Access Control
API Endpoints and Permissions
Endpoint
Resource Type
Permission
Description
FeatureView,OnDemandFeatureView
Get online features from the feature store
Write Online, Write Offline, Write Online and Offline
Push features to the feature store (online, offline, or both)
Write features to the online store
Materialize features within a specified time range
Incrementally materialize features up to a specified timestamp
How to configure Authentication and Authorization ?
Please refer the page for more details on how to configure authentication and authorization.
