# Quickstart
## What is Feast?
Feast (Feature Store) is an open-source feature store designed to facilitate the management and serving of machine learning features in a way that supports both batch and real-time applications.
* *For Data Scientists*: Feast is a tool where you can easily define, store, and retrieve your features for both model development and model deployment. By using Feast, you can focus on what you do best: build features that power your AI/ML models and maximize the value of your data.
* *For MLOps Engineers*: Feast is a library that allows you to connect your existing infrastructure (e.g., online database, application server, microservice, analytical database, and orchestration tooling) that enables your Data Scientists to ship features for their models to production using a friendly SDK without having to be concerned with software engineering challenges that occur from serving real-time production systems. By using Feast, you can focus on maintaining a resilient system, instead of implementing features for Data Scientists.
* *For Data Engineers*: Feast provides a centralized catalog for storing feature definitions allowing one to maintain a single source of truth for feature data. It provides the abstraction for reading and writing to many different types of offline and online data stores. Using either the provided python SDK or the feature server service, users can write data to the online and/or offline stores and then read that data out again in either low-latency online scenarios for model inference, or in batch scenarios for model training.
* *For AI Engineers*: Feast provides a platform designed to scale your AI applications by enabling seamless integration of richer data and facilitating fine-tuning. With Feast, you can optimize the performance of your AI models while ensuring a scalable and efficient data pipeline.
For more info refer to [Introduction to feast](../README.md)
## Prerequisites
* Ensure that you have Python (3.9 or above) installed.
* It is recommended to create and work in a virtual environment:
```sh
# create & activate a virtual environment
python -m venv venv/
source venv/bin/activate
```
## Overview
In this tutorial we will:
1. Deploy a local feature store with a **Parquet file offline store** and **Sqlite online store**.
2. Build a training dataset using our time series features from our **Parquet files**.
3. Ingest batch features ("materialization") and streaming features (via a Push API) into the online store.
4. Read the latest features from the offline store for batch scoring
5. Read the latest features from the online store for real-time inference.
6. Explore the (experimental) Feast UI
***Note*** - Feast provides a python SDK as well as an optional [hosted service](../reference/feature-servers/python-feature-server.md) for reading and writing feature data to the online and offline data stores. The latter might be useful when non-python languages are required.
For this tutorial, we will be using the python SDK.
In this tutorial, we'll use Feast to generate training data and power online model inference for a
ride-sharing driver satisfaction prediction model. Feast solves several common issues in this flow:
1. **Training-serving skew and complex data joins:** Feature values often exist across multiple tables. Joining
these datasets can be complicated, slow, and error-prone.
* Feast joins these tables with battle-tested logic that ensures _point-in-time_ correctness so future feature
values do not leak to models.
2. **Online feature availability:** At inference time, models often need access to features that aren't readily
available and need to be precomputed from other data sources.
* Feast manages deployment to a variety of online stores (e.g. DynamoDB, Redis, Google Cloud Datastore) and
ensures necessary features are consistently _available_ and _freshly computed_ at inference time.
3. **Feature and model versioning:** Different teams within an organization are often unable to reuse
features across projects, resulting in duplicate feature creation logic. Models have data dependencies that need
to be versioned, for example when running A/B tests on model versions.
* Feast enables discovery of and collaboration on previously used features and enables versioning of sets of
features (via _feature services_).
* _(Experimental)_ Feast enables light-weight feature transformations so users can re-use transformation logic
across online / offline use cases and across models.
## Step 1: Install Feast
Install the Feast SDK and CLI using pip:
* In this tutorial, we focus on a local deployment. For a more in-depth guide on how to use Feast with Snowflake /
GCP / AWS deployments, see [Running Feast with Snowflake/GCP/AWS](../how-to-guides/feast-snowflake-gcp-aws/)
{% tabs %}
{% tab title="Bash" %}
```bash
pip install feast
```
{% endtab %}
{% endtabs %}
## Step 2: Create a feature repository
Bootstrap a new feature repository using `feast init` from the command line.
{% tabs %}
{% tab title="Bash" %}
```bash
feast init my_project
cd my_project/feature_repo
```
{% endtab %}
{% endtabs %}
{% tabs %}
{% tab title="Output" %}
```
Creating a new Feast repository in /home/Jovyan/my_project.
```
{% endtab %}
{% endtabs %}
Let's take a look at the resulting demo repo itself. It breaks down into
* `data/` contains raw demo parquet data
* `feature_definitions.py` contains demo feature definitions
* `feature_store.yaml` contains a demo setup configuring where data sources are
* `test_workflow.py` showcases how to run all key Feast commands, including defining, retrieving, and pushing features.
You can run this with `python test_workflow.py`.
{% tabs %}
{% tab title="feature_store.yaml" %}
```yaml
project: my_project
# By default, the registry is a file (but can be turned into a more scalable SQL-backed registry)
registry: data/registry.db
# The provider primarily specifies default offline / online stores & storing the registry in a given cloud
provider: local
online_store:
type: sqlite
path: data/online_store.db
entity_key_serialization_version: 3
```
{% endtab %}
{% tab title="feature_definitions.py" %}
```python
# This is an example feature definition file
from datetime import timedelta
import pandas as pd
from feast import (
Entity,
FeatureService,
FeatureView,
Field,
FileSource,
Project,
PushSource,
RequestSource,
)
from feast.on_demand_feature_view import on_demand_feature_view
from feast.types import Float32, Float64, Int64
# Define a project for the feature repo
project = Project(name="my_project", description="A project for driver statistics")
# Define an entity for the driver. You can think of an entity as a primary key used to
# fetch features.
driver = Entity(name="driver", join_keys=["driver_id"])
# Read data from parquet files. Parquet is convenient for local development mode. For
# production, you can use your favorite DWH, such as BigQuery. See Feast documentation
# for more info.
driver_stats_source = FileSource(
name="driver_hourly_stats_source",
path="%PARQUET_PATH%",
timestamp_field="event_timestamp",
created_timestamp_column="created",
)
# Our parquet files contain sample data that includes a driver_id column, timestamps and
# three feature column. Here we define a Feature View that will allow us to serve this
# data to our model online.
driver_stats_fv = FeatureView(
# The unique name of this feature view. Two feature views in a single
# project cannot have the same name, and names must be unique across
# all feature view types (regular, stream, on-demand) to avoid conflicts
# during `feast apply`.
name="driver_hourly_stats",
entities=[driver],
ttl=timedelta(days=1),
# The list of features defined below act as a schema to both define features
# for both materialization of features into a store, and are used as references
# during retrieval for building a training dataset or serving features
schema=[
Field(name="conv_rate", dtype=Float32),
Field(name="acc_rate", dtype=Float32),
Field(name="avg_daily_trips", dtype=Int64, description="Average daily trips"),
],
online=True,
source=driver_stats_source,
# Tags are user defined key/value pairs that are attached to each
# feature view
tags={"team": "driver_performance"},
)
# Define a request data source which encodes features / information only
# available at request time (e.g. part of the user initiated HTTP request)
input_request = RequestSource(
name="vals_to_add",
schema=[
Field(name="val_to_add", dtype=Int64),
Field(name="val_to_add_2", dtype=Int64),
],
)
# Define an on demand feature view which can generate new features based on
# existing feature views and RequestSource features
@on_demand_feature_view(
sources=[driver_stats_fv, input_request],
schema=[
Field(name="conv_rate_plus_val1", dtype=Float64),
Field(name="conv_rate_plus_val2", dtype=Float64),
],
)
def transformed_conv_rate(inputs: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame()
df["conv_rate_plus_val1"] = inputs["conv_rate"] + inputs["val_to_add"]
df["conv_rate_plus_val2"] = inputs["conv_rate"] + inputs["val_to_add_2"]
return df
# This groups features into a model version
driver_activity_v1 = FeatureService(
name="driver_activity_v1",
features=[
driver_stats_fv[["conv_rate"]], # Sub-selects a feature from a feature view
transformed_conv_rate, # Selects all features from the feature view
],
)
driver_activity_v2 = FeatureService(
name="driver_activity_v2", features=[driver_stats_fv, transformed_conv_rate]
)
# Defines a way to push data (to be available offline, online or both) into Feast.
driver_stats_push_source = PushSource(
name="driver_stats_push_source",
batch_source=driver_stats_source,
)
# Defines a slightly modified version of the feature view from above, where the source
# has been changed to the push source. This allows fresh features to be directly pushed
# to the online store for this feature view.
driver_stats_fresh_fv = FeatureView(
name="driver_hourly_stats_fresh",
entities=[driver],
ttl=timedelta(days=1),
schema=[
Field(name="conv_rate", dtype=Float32),
Field(name="acc_rate", dtype=Float32),
Field(name="avg_daily_trips", dtype=Int64),
],
online=True,
source=driver_stats_push_source, # Changed from above
tags={"team": "driver_performance"},
)
# Define an on demand feature view which can generate new features based on
# existing feature views and RequestSource features
@on_demand_feature_view(
sources=[driver_stats_fresh_fv, input_request], # relies on fresh version of FV
schema=[
Field(name="conv_rate_plus_val1", dtype=Float64),
Field(name="conv_rate_plus_val2", dtype=Float64),
],
)
def transformed_conv_rate_fresh(inputs: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame()
df["conv_rate_plus_val1"] = inputs["conv_rate"] + inputs["val_to_add"]
df["conv_rate_plus_val2"] = inputs["conv_rate"] + inputs["val_to_add_2"]
return df
driver_activity_v3 = FeatureService(
name="driver_activity_v3",
features=[driver_stats_fresh_fv, transformed_conv_rate_fresh],
)
```
{% endtab %}
{% endtabs %}
The `feature_store.yaml` file configures the key overall architecture of the feature store.
The provider value sets default offline and online stores.
* The offline store provides the compute layer to process historical data (for generating training data & feature
values for serving).
* The online store is a low latency store of the latest feature values (for powering real-time inference).
Valid values for `provider` in `feature_store.yaml` are:
* local: use a SQL registry or local file registry. By default, use a file / Dask based offline store + SQLite online store
* gcp: use a SQL registry or GCS file registry. By default, use BigQuery (offline store) + Google Cloud Datastore (online store)
* aws: use a SQL registry or S3 file registry. By default, use Redshift (offline store) + DynamoDB (online store)
Note that there are many other offline / online stores Feast works with, including Spark, Azure, Hive, Trino, and
PostgreSQL via community plugins. See [Third party integrations](third-party-integrations.md) for all supported data sources.
A custom setup can also be made by following [Customizing Feast](../how-to-guides/customizing-feast/).
### Inspecting the raw data
The raw feature data we have in this demo is stored in a local parquet file. The dataset captures hourly stats of a driver in a ride-sharing app.
```python
import pandas as pd
pd.read_parquet("data/driver_stats.parquet")
```
## Step 3: Run sample workflow
There's an included `test_workflow.py` file which runs through a full sample workflow:
1. Register feature definitions through `feast apply`
2. Generate a training dataset (using `get_historical_features`)
3. Generate features for batch scoring (using `get_historical_features`)
4. Ingest batch features into an online store (using `materialize_incremental`)
5. Fetch online features to power real time inference (using `get_online_features`)
6. Ingest streaming features into offline / online stores (using `push`)
7. Verify online features are updated / fresher
We'll walk through some snippets of code below and explain
### Step 4: Register feature definitions and deploy your feature store
The `apply` command scans python files in the current directory for feature view/entity definitions, registers the
objects, and deploys infrastructure. In this example, it reads `feature_definitions.py` and sets up SQLite online store tables. Note that we had specified SQLite as the default online store by
configuring `online_store` in `feature_store.yaml`.
{% tabs %}
{% tab title="Bash" %}
```bash
feast apply
```
{% endtab %}
{% endtabs %}
{% tabs %}
{% tab title="Output" %}
```
Created entity driver
Created feature view driver_hourly_stats
Created feature view driver_hourly_stats_fresh
Created on demand feature view transformed_conv_rate
Created on demand feature view transformed_conv_rate_fresh
Created feature service driver_activity_v3
Created feature service driver_activity_v1
Created feature service driver_activity_v2
Created sqlite table my_project_driver_hourly_stats_fresh
Created sqlite table my_project_driver_hourly_stats
```
{% endtab %}
{% endtabs %}
### Step 5: Generating training data or powering batch scoring models
To train a model, we need features and labels. Often, this label data is stored separately (e.g. you have one table storing user survey results and another set of tables with feature values). Feast can help generate the features that map to these labels.
Feast needs a list of **entities** (e.g. driver ids) and **timestamps**. Feast will intelligently join relevant
tables to create the relevant feature vectors. There are two ways to generate this list:
1. The user can query that table of labels with timestamps and pass that into Feast as an _entity dataframe_ for
training data generation.
2. The user can also query that table with a *SQL query* which pulls entities. See the documentation on [feature retrieval](https://docs.feast.dev/getting-started/concepts/feature-retrieval) for details
* Note that we include timestamps because we want the features for the same driver at various timestamps to be used in a model.
#### Generating training data
{% tabs %}
{% tab title="Python" %}
```python
from datetime import datetime
import pandas as pd
from feast import FeatureStore
# Note: see https://docs.feast.dev/getting-started/concepts/feature-retrieval for
# more details on how to retrieve for all entities in the offline store instead
entity_df = pd.DataFrame.from_dict(
{
# entity's join key -> entity values
"driver_id": [1001, 1002, 1003],
# "event_timestamp" (reserved key) -> timestamps
# Each timestamp acts as the upper bound for the point-in-time join:
# Feast retrieves the latest feature values at or before this time,
# preventing data leakage from future events.
"event_timestamp": [
datetime(2021, 4, 12, 10, 59, 42),
datetime(2021, 4, 12, 8, 12, 10),
datetime(2021, 4, 12, 16, 40, 26),
],
# (optional) label name -> label values. Feast does not process these
"label_driver_reported_satisfaction": [1, 5, 3],
# values we're using for an on-demand transformation
"val_to_add": [1, 2, 3],
"val_to_add_2": [10, 20, 30],
}
)
store = FeatureStore(repo_path=".")
training_df = store.get_historical_features(
entity_df=entity_df,
features=[
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips",
"transformed_conv_rate:conv_rate_plus_val1",
"transformed_conv_rate:conv_rate_plus_val2",
],
).to_df()
print("----- Feature schema -----\n")
print(training_df.info())
print()
print("----- Example features -----\n")
print(training_df.head())
```
{% endtab %}
{% endtabs %}
{% tabs %}
{% tab title="Output" %}
```bash
----- Feature schema -----
RangeIndex: 3 entries, 0 to 2
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 driver_id 3 non-null int64
1 event_timestamp 3 non-null datetime64[ns, UTC]
2 label_driver_reported_satisfaction 3 non-null int64
3 val_to_add 3 non-null int64
4 val_to_add_2 3 non-null int64
5 conv_rate 3 non-null float32
6 acc_rate 3 non-null float32
7 avg_daily_trips 3 non-null int32
8 conv_rate_plus_val1 3 non-null float64
9 conv_rate_plus_val2 3 non-null float64
dtypes: datetime64[ns, UTC](1), float32(2), float64(2), int32(1), int64(4)
memory usage: 336.0 bytes
None
----- Example features -----
driver_id event_timestamp label_driver_reported_satisfaction \
0 1001 2021-04-12 10:59:42+00:00 1
1 1002 2021-04-12 08:12:10+00:00 5
2 1003 2021-04-12 16:40:26+00:00 3
val_to_add val_to_add_2 conv_rate acc_rate avg_daily_trips \
0 1 10 0.800648 0.265174 643
1 2 20 0.644141 0.996602 765
2 3 30 0.855432 0.546345 954
conv_rate_plus_val1 conv_rate_plus_val2
0 1.800648 10.800648
1 2.644141 20.644141
2 3.855432 30.855432
```
{% endtab %}
{% endtabs %}
#### Run offline inference (batch scoring)
To power a batch model, we primarily need to generate features with the `get_historical_features` call, but using the current timestamp
{% tabs %}
{% tab title="Python" %}
```python
entity_df["event_timestamp"] = pd.to_datetime("now", utc=True)
training_df = store.get_historical_features(
entity_df=entity_df,
features=[
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips",
"transformed_conv_rate:conv_rate_plus_val1",
"transformed_conv_rate:conv_rate_plus_val2",
],
).to_df()
print("\n----- Example features -----\n")
print(training_df.head())
```
{% endtab %}
{% endtabs %}
{% tabs %}
{% tab title="Output" %}
```
----- Example features -----
driver_id event_timestamp \
0 1001 2024-04-19 14:58:16.452895+00:00
1 1002 2024-04-19 14:58:16.452895+00:00
2 1003 2024-04-19 14:58:16.452895+00:00
label_driver_reported_satisfaction val_to_add val_to_add_2 conv_rate \
0 1 1 10 0.535773
1 5 2 20 0.171976
2 3 3 30 0.275669
acc_rate avg_daily_trips conv_rate_plus_val1 conv_rate_plus_val2
0 0.689705 428 1.535773 10.535773
1 0.737113 369 2.171976 20.171976
2 0.156630 116 3.275669 30.275669
```
{% endtab %}
{% endtabs %}
### Step 6: Ingest batch features into your online store
We now serialize the latest values of features since the beginning of time to prepare for serving. Note, `materialize_incremental` serializes all new features since the last `materialize` call, or since the time provided minus the `ttl` timedelta. In this case, this will be `CURRENT_TIME - 1 day` (`ttl` was set on the `FeatureView` instances in `feature_definitions.py`).
{% tabs %}
{% tab title="Bash (with timestamp)" %}
```bash
CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize-incremental $CURRENT_TIME
```
{% endtab %}
{% tab title="Bash (simple)" %}
```bash
# Alternative: Materialize all data using current timestamp (for data without event timestamps)
feast materialize --disable-event-timestamp
```
{% endtab %}
{% endtabs %}
{% tabs %}
{% tab title="Output" %}
```bash
Materializing 2 feature views to 2024-04-19 10:59:58-04:00 into the sqlite online store.
driver_hourly_stats from 2024-04-18 15:00:46-04:00 to 2024-04-19 10:59:58-04:00:
100%|| 5/5 [00:00