Test Stream Features

Import libraries and select your workspace

import tecton
import pandas
from datetime import datetime

ws = tecton.get_workspace("prod")

Load a Stream Feature View

fv = ws.get_feature_view("last_transaction_amount_sql")
fv.summary()

Start a Streaming Job to view real-time streaming features

note

This section only applies to Spark streaming features. These methods must be run on a Spark cluster.

The run_stream method will start a Spark Structured Streaming job and write the results to the specified temporary table.

fv.run_stream(output_temp_table="output_temp_table")

The temporary table can then be queried to view real-time results. Run this code in a separate notebook cell.

# Query the result from the streaming output table.
display(spark.sql("SELECT * FROM output_temp_table ORDER BY timestamp DESC LIMIT 5"))

	user_id	timestamp	amt
0	user_469998441571	2022-06-07 18:31:24	54.46
1	user_460877961787	2022-06-07 18:31:21	73.02
2	user_650387977076	2022-06-07 18:31:20	46.05
3	user_699668125818	2022-06-07 18:31:17	59.24
4	user_394495759023	2022-06-07 18:31:15	11.38

Get a Range of Feature Values from Offline Feature Store

from_source=True can be passed in in order to bypass the offline store and compute features on-the-fly against the raw data source. This is useful for testing the expected output of feature values.

Use from_source=False (default) to see what data is materialized in the offline store.

result_dataframe = fv.get_features_in_range(
    start_time=datetime(2022, 5, 1), end_time=datetime(2022, 5, 2), from_source=True
).to_pandas()
display(result_dataframe)

	user_id	amt	_valid_from	_valid_to
0	user_1	76.45	2022-05-01 01:50:51	2022-05-02 00:00:00
1	user_2	45.8	2022-05-01 02:05:39	2022-05-02 03:51:28
2	user_2	1.43	2022-05-01 03:51:28	2022-05-02 00:00:00
3	user_3	52.31	2022-05-01 02:41:42	2022-05-02 00:00:00
4	user_4	64.15	2022-05-01 04:48:27	2022-05-02 00:00:00

Read the Latest Features from Online Feature Store

fv.get_online_features({"user_id": "user_3"}).to_dict()

Out: {"amt": 180.6}

Read Historical Features from Offline Feature Store with Time-Travel

Create a events DataFrame with events to look up. For more information on the events dataframe, check out Selecting Sample Keys and Timestamps.

events = pandas.DataFrame(
    {
        "user_id": ["user_3", "user_5"],
        "timestamp": [datetime(2022, 5, 1, 19), datetime(2022, 5, 6, 10)],
    }
)
display(events)

	user_id	timestamp
0	user_3	2022-05-01 19:00:00
1	user_5	2022-05-06 10:00:00

from_source=True can be passed in in order to bypass the offline store and compute features on-the-fly against the raw data source. However, this will be slower than reading feature data that has been materialized to the offline store.

features_df = fv.get_features_for_events(events, from_source=True).to_pandas()
display(features_df)

	user_id	timestamp	last_transaction_amount_sql__amt
0	user_3	2022-05-01 19:00:00	52.31
1	user_5	2022-05-06 10:00:00	58.68