Feast demo
Overview
This tutorial demonstrates the use of on demand feature views and stream ingestion in Feast. It extends the Feast default demo data to include on demand transformations and pushing to the online store from stream events.
It also includes miscellaneous code samples for embedding in tutorials / blog posts.
Setup
Setting up Feast
Install Feast using pip
pip install feast
We have already set up a feature repository in feature_repo/.
Deploy the feature store by running apply from within the feature_repo/ folder
cd feature_repo/
feast apply
Output:
Created entity driver
Created feature view driver_hourly_stats
Created on demand feature view transformed_conv_rate
Created sqlite table feast_demo_driver_hourly_stats
Next we load features into the online store using the materialize-incremental command. This command will load the
latest feature values from a data source into the online store.
CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize-incremental $CURRENT_TIME
Output:
Materializing 1 feature views to 2021-09-20 13:36:06-04:00 into the sqlite online store.
driver_hourly_stats from 2021-06-12 17:43:32-04:00 to 2021-09-20 13:36:06-04:00:
100%|███████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1509.29it/s]
Fetch features
python get_features_demo.py
Output:
--- Historical features ---
driver_id event_timestamp val_to_add val_to_add_2 string_feature conv_rate acc_rate avg_daily_trips conv_rate_plus_val1 conv_rate_plus_val2
360 1001 2021-04-12 10:59:42+00:00 1 10 test 0.701558 0.195824 566 1.701558 10.701558
721 1002 2021-04-12 08:12:10+00:00 2 20 test 0.775499 0.947109 890 2.775499 20.775499
1082 1003 2021-04-12 16:40:26+00:00 3 30 test 0.186658 0.245490 971 3.186658 30.186658
1445 1004 2021-04-12 15:01:12+00:00 4 40 test 0.891017 0.118256 154 4.891017 40.891017
--- Online features ---
acc_rate : [0.8408732414245605]
avg_daily_trips : [714]
conv_rate_plus_val1 : [1000.8123571872711]
conv_rate_plus_val2 : [2000.8123571872711]
driver_id : [1001]
string_feature : ['test']
--- Simulate a stream event ingestion of the hourly stats df ---
driver_id event_timestamp created conv_rate acc_rate avg_daily_trips string_feature
0 1001 2021-05-13 10:59:42 2021-05-13 10:59:42 1.0 1.0 1000 test2
--- Online features again with updated values---
acc_rate : [1.0]
avg_daily_trips : [1000]
conv_rate_plus_val1 : [1001.0]
conv_rate_plus_val2 : [2001.0]
driver_id : [1001]
string_feature : ['test2']
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