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:
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))
Last updated 3 months ago