Examples for dummy timestamps

dummy_timestamp_examples

# Exmaple 1 : Sample File based data creation

import pandas as pd
from datetime import datetime

from feast import Entity, FeatureView, Feature, ValueType, Field, FileSource
from feast.types import String

df = pd.DataFrame({
    "employee_id": [1, 2],
    "department": ["Engineering", "HR"],
    "dummy_event_timestamp": [datetime(2024, 1, 1)] * 2
})

# Save to file
df.to_parquet("employee_metadata.parquet", index=False)

# Feature Definition

employee = Entity(name="employee_id", join_keys=["employee_id"])

source = FileSource(
    path="employee_metadata.parquet",
    timestamp_field="dummy_event_timestamp"
)

employee_metadata_fv = FeatureView(
    name="employee_metadata",
    entities=["employee_id"],
    ttl=None,
    schema=[
        Feature(name="department", dtype=String),
    ],
    source=source,
    online=True,
)
=========================================================

# Exmaple 2 : Sample PostgreSQL based data creation

# Create a table with dummy timestamps:

CREATE TABLE employee_metadata (
    employee_id INT PRIMARY KEY,
    department TEXT,
    dummy_event_timestamp TIMESTAMP DEFAULT '2024-01-01'
);

INSERT INTO employee_metadata (employee_id, department)
VALUES (1, 'Engineering'), (2, 'HR');

# Feature Definition

postgres_source = PostgreSQLSource(
    name="employee_metadata_source",
    query="SELECT * FROM employee_metadata",
    timestamp_field="dummy_event_timestamp"
)

# Feature view with dummy timestamp
employee_metadata_fv = FeatureView(
    name="employee_metadata",
    entities=["employee_id"],
    ttl=None,
    schema=[
        Field(name="department", dtype=String),
    ],
    source=postgres_source,
    online=True,
)