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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.
A remote feature server on AWS Lambda is also available.

Example

Initializing a feature server

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)

Retrieving features

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

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 that timestamps need to be strings.
curl -X POST "http://localhost:6566/push" -d '{
"push_source_name": "driver_hourly_stats_push_source",
"df": {
"driver_id": [1001],
"event_timestamp": ["2022-05-13 10:59:42"],
"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
import pandas as pd
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))
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On this page
Overview
CLI
Deploying as a service
Example
Initializing a feature server
Retrieving features
Pushing features to the online and offline stores