Welcome to the Feast blog! Here you'll find articles about feature store development, new features, and community updates.

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What is Feast?

Feast (Feature Store) is an open-source feature store that helps teams operate production ML systems at scale by allowing them to define, manage, validate, and serve features for production AI/ML.

Feast's feature store is composed of two foundational components: (1) an offline store for historical feature extraction used in model training and an (2) online store for serving features at low-latency in production systems and applications.

Feast is a configurable operational data system that re-uses existing infrastructure to manage and serve machine learning features to realtime models. For more details, please review our architecture.

Concretely, Feast provides:

• A Python SDK for programmatically defining features, entities, sources, and (optionally) transformations

• A Python SDK for reading and writing features to configured offline and online data stores

• An optional feature server for reading and writing features (useful for non-python languages)

• A UI for viewing and exploring information about features defined in the project

• A CLI tool for viewing and updating feature information

Feast allows ML platform teams to:

• Make features consistently available for training and low-latency serving by managing an offline store (to process historical data for scale-out batch scoring or model training), a low-latency online store (to power real-time prediction), and a battle-tested feature server (to serve pre-computed features online).

• Avoid data leakage by generating point-in-time correct feature sets so data scientists can focus on feature engineering rather than debugging error-prone dataset joining logic. This ensures that future feature values do not leak to models during training.

• Decouple ML from data infrastructure by providing a single data access layer that abstracts feature storage from feature retrieval, ensuring models remain portable as you move from training models to serving models, from batch models to real-time models, and from one data infra system to another.

Who is Feast for?

Feast helps ML platform/MLOps teams with DevOps experience productionize real-time models. Feast also helps these teams build a feature platform that improves collaboration between data engineers, software engineers, machine learning engineers, and data scientists.

• 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.

What Feast is not?

Feast is not

• An ETL / ELT system. Feast is not a general purpose data pipelining system. Users often leverage tools like dbt to manage upstream data transformations. Feast does support some transformations.

• A data orchestration tool: Feast does not manage or orchestrate complex workflow DAGs. It relies on upstream data pipelines to produce feature values and integrations with tools like Airflow to make features consistently available.

• A data warehouse: Feast is not a replacement for your data warehouse or the source of truth for all transformed data in your organization. Rather, Feast is a lightweight downstream layer that can serve data from an existing data warehouse (or other data sources) to models in production.

• A database: Feast is not a database, but helps manage data stored in other systems (e.g. BigQuery, Snowflake, DynamoDB, Redis) to make features consistently available at training / serving time

Feast does not fully solve

• reproducible model training / model backtesting / experiment management: Feast captures feature and model metadata, but does not version-control datasets / labels or manage train / test splits. Other tools like DVC, MLflow, and Kubeflow are better suited for this.

• batch feature engineering: Feast supports on-demand and streaming transformations. Feast is also investing in supporting batch transformations.

• native streaming feature integration: Feast enables users to push streaming features, but does not pull from streaming sources or manage streaming pipelines.

• lineage: Feast helps tie feature values to model versions, but is not a complete solution for capturing end-to-end lineage from raw data sources to model versions. Feast also has community contributed plugins with DataHub and Amundsen.

• data quality / drift detection: Feast has experimental integrations with Great Expectations, but is not purpose built to solve data drift / data quality issues. This requires more sophisticated monitoring across data pipelines, served feature values, labels, and model versions.

Example use cases

Many companies have used Feast to power real-world ML use cases such as:

• Personalizing online recommendations by leveraging pre-computed historical user or item features.

• Online fraud detection, using features that compare against (pre-computed) historical transaction patterns

• Churn prediction (an offline model), generating feature values for all users at a fixed cadence in batch

• Credit scoring, using pre-computed historical features to compute the probability of default

How can I get started?

Explore the following resources to get started with Feast:

• Quickstart is the fastest way to get started with Feast

• Concepts describes all important Feast API concepts

• Architecture describes Feast's overall architecture.

• Tutorials shows full examples of using Feast in machine learning applications.

• Running Feast with Snowflake/GCP/AWS provides a more in-depth guide to using Feast.

• Reference contains detailed API and design documents.

• Contributing contains resources for anyone who wants to contribute to Feast.

The list below contains the functionality that contributors are planning to develop for Feast.

• We welcome contribution to all items in the roadmap!

• Natural Language Processing
• Data Sources
• Offline Stores
• Online Stores
• Feature Engineering
• Streaming
• Deployments
• Feature Serving

  • Python Client
• • Data profiling and validation (Great Expectations)

• Feature Discovery and Governance

  • Python SDK for browsing feature registry

  • CLI for browsing feature registry

  • Model-centric feature tracking (feature services)

  • Feast Lineage Explorer

Use Python to serve your features.

Why should you use Python to Serve features for Machine Learning?

Python has emerged as the primary language for machine learning, and this extends to feature serving and there are five main reasons Feast recommends using a microservice written in Python.

1. Python is the language of Machine Learning

You should meet your users where they are. Python’s popularity in the machine learning community is undeniable. Its simplicity and readability make it an ideal language for writing and understanding complex algorithms. Python boasts a rich ecosystem of libraries such as TensorFlow, PyTorch, XGBoost, and scikit-learn, which provide robust support for developing and deploying machine learning models and we want Feast in this ecosystem.

2. Precomputation is The Way

Precomputing features is the recommended optimal path to ensure low latency performance. Reducing feature serving to a lightweight database lookup is the ideal pattern, which means the marginal overhead of Python should be tolerable. Precomputation ensures product experiences for downstream services are also fast. Slow user experiences are bad user experiences. Precompute and persist data as much as you can.

3. Serving features in another language can lead to skew

Ensuring that features used during model training (offline serving) and online serving are available in production to make real-time predictions is critical. When features are initially developed, they are typically written in Python. This is due to the convenience and efficiency provided by Python's data manipulation libraries. However, in a production environment, there is often interest or pressure to rewrite these features in a different language, like Java, Go, or C++, for performance reasons. This reimplementation introduces a significant risk: training and serving skew. Note that there will always be some minor exceptions (e.g., any Time Since Last Event types of features) but this should not be the rule.

Training and serving skew occurs when there are discrepancies between the features used during model training and those used during prediction. This can lead to degraded model performance, unreliable predictions, and reduced velocity in releasing new features and new models. The process of rewriting features in another language is prone to errors and inconsistencies, which exacerbate this issue.

4. Reimplementation is Excessive

Rewriting features in another language is not only risky but also resource-intensive. It requires significant time and effort from engineers to ensure that the features are correctly translated. This process can introduce bugs and inconsistencies, further increasing the risk of training and serving skew. Additionally, maintaining two versions of the same feature codebase adds unnecessary complexity and overhead. More importantly, the opportunity cost of this work is high and requires twice the amount of resourcing. Reimplementing code should only be done when the performance gains are worth the investment. Features should largely be precomputed so the latency performance gains should not be the highest impact work that your team can accomplish.

5. Use existing Python Optimizations

Rather than switching languages, it is more efficient to optimize the performance of your feature store while keeping Python as the primary language. Optimization is a two step process.

Step 1: Quantify latency bottlenecks in your feature calculations

Use tools like CProfile to understand latency bottlenecks in your code. This will help you prioritize the biggest inefficiencies first. When we initially launched Python native transformations in Python, profiling the code helped us identify that Pandas resulted in a 10x overhead due to type conversion.

Step 2: Optimize your feature calculations

As mentioned, precomputation is the recommended path. In some cases, you may want fully synchronous writes from your data producer to your online feature store, in which case you will want your feature computations and writes to be very fast. In this case, we recommend optimizing the feature calculation code first.

You should optimize your code using libraries, tools, and caching. For example, identify whether your feature calculations can be optimized through vectorized calculations in NumPy; explore tools like Numba for faster execution; and cache frequently accessed data using tools like an lru_cache.

Lastly, Feast will continue to optimize serving in Python and making the overall infrastructure more performant. This will better serve the community.

So we recommend focusing on optimizing your feature-specific code, reporting latency bottlenecks to the maintainers, and contributing to help the infrastructure be more performant.

By keeping features in Python and optimizing performance, you can ensure consistency between training and serving, reduce the risk of errors, and focus on launching more product experiences for your customers.

Embrace Python for feature serving, and leverage its strengths to build robust and reliable machine learning systems.

A feature transformation is a function that takes some set of input data and returns some set of output data. Feature transformations can happen on either raw data or derived data.

Feature Transformation Engines

Feature transformations can be executed by three types of "transformation engines":

1. The Feast Feature Server

2. An Offline Store (e.g., Snowflake, BigQuery, DuckDB, Spark, etc.)

3. A Stream processor (e.g., Flink or Spark Streaming)

The three transformation engines are coupled with the communication pattern used for writes.

Importantly, this implies that different feature transformation code may be used under different transformation engines, so understanding the tradeoffs of when to use which transformation engine/communication pattern is extremely critical to the success of your implementation.

In general, we recommend transformation engines and network calls to be chosen by aligning it with what is most appropriate for the data producer, feature/model usage, and overall product.

Feast uses a Push Model to push features to the online store.

This has two important consequences: (1) communication patterns between the Data Producer (i.e., the client) and Feast (i.e,. the server) and (2) feature computation and feature value write patterns to Feast's online store.

Data Producers (i.e., services that generate data) send data to Feast so that Feast can write feature values to the online store. That data can be either raw data where Feast computes and stores the feature values or precomputed feature values.

Communication Patterns

There are two ways a client (or Data Producer) can send data to the online store:

1. Synchronously

   • Using a synchronous API call for a small number of entities or a single entity (e.g., using the push or write_to_online_store methods) or the Feature Server's push endpoint)

2. Asynchronously

   • Using an asynchronous API call for a small number of entities or a single entity (e.g., using the push or write_to_online_store methods) or the Feature Server's push endpoint)

   • Using a "batch job" for a large number of entities (e.g., using a batch materialization engine)

Note, in some contexts, developers may "batch" a group of entities together and write them to the online store in a single API call. This is a common pattern when writing data to the online store to reduce write loads but we would not qualify this as a batch job.

Feature Value Write Patterns

Writing feature values to the online store (i.e., the server) can be done in two ways: Precomputing the transformations client-side or Computing the transformations On Demand server-side.

Combining Approaches

In some scenarios, a combination of Precomputed and On Demand transformations may be optimal.

When selecting feature value write patterns, one must consider the specific requirements of your application, the acceptable correctness of the data, the latency tolerance, and the computational resources available. Making deliberate choices can help the performance and reliability of your service.

There are two ways the client can write feature values to the online store:

1. Precomputing transformations

2. Computing transformations On Demand

3. Hybrid (Precomputed + On Demand)

1. Precomputing Transformations

Precomputed transformations can happen outside of Feast (e.g., via some batch job or streaming application) or inside of the Feast feature server when writing to the online store via the push or write-to-online-store api.

2. Computing Transformations On Demand

On Demand transformations can only happen inside of Feast at either (1) the time of the client's request or (2) when the data producer writes to the online store.

3. Hybrid (Precomputed + On Demand)

The hybrid approach allows for precomputed transformations to happen inside or outside of Feast and have the On Demand transformations happen at client request time. This is particularly convenient for "Time Since Last" types of features (e.g., time since purchase).

Tradeoffs

When deciding between synchronous and asynchronous data writes, several tradeoffs should be considered:

• Data Consistency: Asynchronous writes allow Data Producers to send data without waiting for the write operation to complete, which can lead to situations where the data in the online store is stale. This might be acceptable in scenarios where absolute freshness is not critical. However, for critical operations, such as calculating loan amounts in financial applications, stale data can lead to incorrect decisions, making synchronous writes essential.

• Correctness: The risk of data being out-of-date must be weighed against the operational requirements. For instance, in a lending application, having up-to-date feature data can be crucial for correctness (depending upon the features and raw data), thus favoring synchronous writes. In less sensitive contexts, the eventual consistency offered by asynchronous writes might be sufficient.

• Service Coupling: Synchronous writes result in tighter coupling between services. If a write operation fails, it can cause the dependent service operation to fail as well, which might be a significant drawback in systems requiring high reliability and independence between services.

• Application Latency: Asynchronous writes typically reduce the perceived latency from the client's perspective because the client does not wait for the write operation to complete. This can enhance the user experience and efficiency in environments where operations are not critically dependent on immediate data freshness.

The table below can help guide the most appropriate data write and feature computation strategies based on specific application needs and data sensitivity.

Introduction

Role-Based Access Control (RBAC) is a security mechanism that restricts access to resources based on the roles of individual users within an organization. In the context of the Feast, RBAC ensures that only authorized users or groups can access or modify specific resources, thereby maintaining data security and operational integrity.

Functional Requirements

The RBAC implementation in Feast is designed to:

• Assign Permissions: Allow administrators to assign permissions for various operations and resources to users or groups based on their roles.

• Seamless Integration: Integrate smoothly with existing business code without requiring significant modifications.

• Backward Compatibility: Maintain support for non-authorized models as the default to ensure backward compatibility.

Business Goals

The primary business goals of implementing RBAC in the Feast are:

1. Feature Sharing: Enable multiple teams to share the feature store while ensuring controlled access. This allows for collaborative work without compromising data security.

2. Access Control Management: Prevent unauthorized access to team-specific resources and spaces, governing the operations that each user or group can perform.

Reference Architecture

Feast operates as a collection of connected services, each enforcing authorization permissions. The architecture is designed as a distributed microservices system with the following key components:

• Service Endpoints: These enforce authorization permissions, ensuring that only authorized requests are processed.

• Client Integration: Clients authenticate with feature servers by attaching authorization token to each request.

• Service-to-Service Communication: This is always granted.

Permission Model

The RBAC system in Feast uses a permission model that defines the following concepts:

• Resource: An object within Feast that needs to be secured against unauthorized access.

• Action: A logical operation performed on a resource, such as Create, Describe, Update, Delete, Read, or write operations.

• Policy: A set of rules that enforce authorization decisions on resources. The default implementation uses role-based policies.

Authorization Architecture

The authorization architecture in Feast is built with the following components:

• Token Extractor: Extracts the authorization token from the request header.

• Token Parser: Parses the token to retrieve user details.

• Policy Enforcer: Validates the secured endpoint against the retrieved user details.

• Token Injector: Adds the authorization token to each secured request header.

Feast project structure

The top-level namespace within Feast is a project. Users define one or more feature views within a project. Each feature view contains one or more features. These features typically relate to one or more entities. A feature view must always have a data source, which in turn is used during the generation of training datasets and when materializing feature values into the online store. You can read more about Feast projects in the project page.

Data ingestion

For offline use cases that only rely on batch data, Feast does not need to ingest data and can query your existing data (leveraging a compute engine, whether it be a data warehouse or (experimental) Spark / Trino). Feast can help manage pushing streaming features to a batch source to make features available for training.

For online use cases, Feast supports ingesting features from batch sources to make them available online (through a process called materialization), and pushing streaming features to make them available both offline / online. We explore this more in the next concept page (Data ingestion)

Feature registration and retrieval

Features are registered as code in a version controlled repository, and tie to data sources + model versions via the concepts of entities, feature views, and feature services. We explore these concepts more in the upcoming concept pages. These features are then stored in a registry, which can be accessed across users and services. The features can then be retrieved via SDK API methods or via a deployed feature server which exposes endpoints to query for online features (to power real time models).

Feast supports several patterns of feature retrieval.

Projects provide complete isolation of feature stores at the infrastructure level. This is accomplished through resource namespacing, e.g., prefixing table names with the associated project. Each project should be considered a completely separate universe of entities and features. It is not possible to retrieve features from multiple projects in a single request. We recommend having a single feature store and a single project per environment (dev, staging, prod).

Users define one or more feature views within a project. Each feature view contains one or more features. These features typically relate to one or more entities. A feature view must always have a data source, which in turn is used during the generation of training datasets and when materializing feature values into the online store.

The concept of a "project" provide the following benefits:

Logical Grouping: Projects group related features together, making it easier to manage and track them.

Feature Definitions: Within a project, you can define features, including their metadata, types, and sources. This helps standardize how features are created and consumed.

Isolation: Projects provide a way to isolate different environments, such as development, testing, and production, ensuring that changes in one project do not affect others.

Collaboration: By organizing features within projects, teams can collaborate more effectively, with clear boundaries around the features they are responsible for.

Access Control: Projects can implement permissions, allowing different users or teams to access only the features relevant to their work.

Overview

Tags in Feast allow for efficient filtering of Feast objects when listing them in the UI, CLI, or querying the registry directly.

The way to define tags on the feast objects is through the definition file or directly in the object that will be applied to the feature store.

Examples

In this example we define a Feature View in a definition file that has a tag:

driver_stats_fv = FeatureView(
    name="driver_hourly_stats",
    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, 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"},
)

In this example we define a Stream Feature View that has a tag, in the code:

    sfv = StreamFeatureView(
        name="test kafka stream feature view",
        entities=[entity],
        schema=[],
        description="desc",
        timestamp_field="event_timestamp",
        source=stream_source,
        tags={"team": "driver_performance"},

An example of filtering feature-views with the tag team:driver_performance:

$ feast feature-views list --tags team:driver_performance              
NAME                       ENTITIES    TYPE
driver_hourly_stats        {'driver'}  FeatureView
driver_hourly_stats_fresh  {'driver'}  FeatureView

The same example of listing feature-views without tag filtering:

$ feast feature-views list
NAME                       ENTITIES    TYPE
driver_hourly_stats        {'driver'}  FeatureView
driver_hourly_stats_fresh  {'driver'}  FeatureView
transformed_conv_rate_fresh  {'driver'}  OnDemandFeatureView
transformed_conv_rate        {'driver'}  OnDemandFeatureView

Feast datasets allow for conveniently saving dataframes that include both features and entities to be subsequently used for data analysis and model training. Data Quality Monitoring was the primary motivation for creating dataset concept.

Dataset's metadata is stored in the Feast registry and raw data (features, entities, additional input keys and timestamp) is stored in the offline store.

Dataset can be created from:

1. Results of historical retrieval

2. [planned] Logging request (including input for on demand transformation) and response during feature serving

3. [planned] Logging features during writing to online store (from batch source or stream)

Creating a saved dataset from historical retrieval

To create a saved dataset from historical features for later retrieval or analysis, a user needs to call get_historical_features method first and then pass the returned retrieval job to create_saved_dataset method. create_saved_dataset will trigger the provided retrieval job (by calling .persist() on it) to store the data using the specified storage behind the scenes. Storage type must be the same as the globally configured offline store (e.g it's impossible to persist data to a different offline source). create_saved_dataset will also create a SavedDataset object with all of the related metadata and will write this object to the registry.

from feast import FeatureStore
from feast.infra.offline_stores.bigquery_source import SavedDatasetBigQueryStorage

store = FeatureStore()

historical_job = store.get_historical_features(
    features=["driver:avg_trip"],
    entity_df=...,
)

dataset = store.create_saved_dataset(
    from_=historical_job,
    name='my_training_dataset',
    storage=SavedDatasetBigQueryStorage(table_ref='<gcp-project>.<gcp-dataset>.my_training_dataset'),
    tags={'author': 'oleksii'}
)

dataset.to_df()

Saved dataset can be retrieved later using the get_saved_dataset method in the feature store:

dataset = store.get_saved_dataset('my_training_dataset')
dataset.to_df()

Check out our tutorial on validating historical features to see how this concept can be applied in a real-world use case.

Overview

Generally, Feast supports several patterns of feature retrieval:

1. Training data generation (via feature_store.get_historical_features(...))

2. Offline feature retrieval for batch scoring (via feature_store.get_historical_features(...))

3. Online feature retrieval for real-time model predictions

   • via the SDK: feature_store.get_online_features(...)

   • via deployed feature server endpoints: requests.post('http://localhost:6566/get-online-features', data=json.dumps(online_request))

Each of these retrieval mechanisms accept:

• some way of specifying entities (to fetch features for)

• some way to specify the features to fetch (either via feature services, which group features needed for a model version, or feature references)

Before beginning, you need to instantiate a local FeatureStore object that knows how to parse the registry (see more details)

For code examples of how the below work, inspect the generated repository from feast init -t [YOUR TEMPLATE] (gcp, snowflake, and aws are the most fully fleshed).

Concepts

Before diving into how to retrieve features, we need to understand some high level concepts in Feast.

Feature Services

A feature service is an object that represents a logical group of features from one or more feature views. Feature Services allows features from within a feature view to be used as needed by an ML model. Users can expect to create one feature service per model version, allowing for tracking of the features used by models.

from driver_ratings_feature_view import driver_ratings_fv
from driver_trips_feature_view import driver_stats_fv

driver_stats_fs = FeatureService(
    name="driver_activity",
    features=[driver_stats_fv, driver_ratings_fv[["lifetime_rating"]]]
)

Feature services are used during

• The generation of training datasets when querying feature views in order to find historical feature values. A single training dataset may consist of features from multiple feature views.

• Retrieval of features for batch scoring from the offline store (e.g. with an entity dataframe where all timestamps are now())

• Retrieval of features from the online store for online inference (with smaller batch sizes). The features retrieved from the online store may also belong to multiple feature views.

Feature services enable referencing all or some features from a feature view.

Retrieving from the online store with a feature service

from feast import FeatureStore
feature_store = FeatureStore('.')  # Initialize the feature store

feature_service = feature_store.get_feature_service("driver_activity")
features = feature_store.get_online_features(
    features=feature_service, entity_rows=[entity_dict]
)

Retrieving from the offline store with a feature service

from feast import FeatureStore
feature_store = FeatureStore('.')  # Initialize the feature store

feature_service = feature_store.get_feature_service("driver_activity")
feature_store.get_historical_features(features=feature_service, entity_df=entity_df)

Feature References

This mechanism of retrieving features is only recommended as you're experimenting. Once you want to launch experiments or serve models, feature services are recommended.

Feature references uniquely identify feature values in Feast. The structure of a feature reference in string form is as follows: <feature_view>:<feature>

Feature references are used for the retrieval of features from Feast:

online_features = fs.get_online_features(
    features=[
        'driver_locations:lon',
        'drivers_activity:trips_today'
    ],
    entity_rows=[
        # {join_key: entity_value}
        {'driver': 'driver_1001'}
    ]
)

It is possible to retrieve features from multiple feature views with a single request, and Feast is able to join features from multiple tables in order to build a training dataset. However, it is not possible to reference (or retrieve) features from multiple projects at the same time.

Event timestamp

The timestamp on which an event occurred, as found in a feature view's data source. The event timestamp describes the event time at which a feature was observed or generated.

Event timestamps are used during point-in-time joins to ensure that the latest feature values are joined from feature views onto entity rows. Event timestamps are also used to ensure that old feature values aren't served to models during online serving.

Dataset

A dataset is a collection of rows that is produced by a historical retrieval from Feast in order to train a model. A dataset is produced by a join from one or more feature views onto an entity dataframe. Therefore, a dataset may consist of features from multiple feature views.

Dataset vs Feature View: Feature views contain the schema of data and a reference to where data can be found (through its data source). Datasets are the actual data manifestation of querying those data sources.

Dataset vs Data Source: Datasets are the output of historical retrieval, whereas data sources are the inputs. One or more data sources can be used in the creation of a dataset.

Retrieving historical features (for training data or batch scoring)

Feast abstracts away point-in-time join complexities with the get_historical_features API.

We go through the major steps, and also show example code. Note that the quickstart templates generally have end-to-end working examples for all these cases.

entity_df = pd.DataFrame.from_dict(
    {
        "driver_id": [1001, 1002, 1003, 1004, 1001],
        "event_timestamp": [
            datetime(2021, 4, 12, 10, 59, 42),
            datetime(2021, 4, 12, 8, 12, 10),
            datetime(2021, 4, 12, 16, 40, 26),
            datetime(2021, 4, 12, 15, 1, 12),
            datetime.now()
        ]
    }
)
training_df = store.get_historical_features(
    entity_df=entity_df,
    features=store.get_feature_service("model_v1"),
).to_df()
print(training_df.head())

from feast import FeatureStore

store = FeatureStore(repo_path=".")

# Get the latest feature values for unique entities
entity_sql = f"""
    SELECT
        driver_id,
        CURRENT_TIMESTAMP() as event_timestamp
    FROM {store.get_data_source("driver_hourly_stats_source").get_table_query_string()}
    WHERE event_timestamp BETWEEN '2021-01-01' and '2021-12-31'
    GROUP BY driver_id
"""
batch_scoring_features = store.get_historical_features(
    entity_df=entity_sql,
    features=store.get_feature_service("model_v2"),
).to_df()
# predictions = model.predict(batch_scoring_features)

Step 1: Specifying Features

Feast accepts either:

• feature services, which group features needed for a model version

• feature references

Example: querying a feature service (recommended)

training_df = store.get_historical_features(
    entity_df=entity_df,
    features=store.get_feature_service("model_v1"),
).to_df()

Example: querying a list of feature references

training_df = store.get_historical_features(
    entity_df=entity_df,
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_daily_features:daily_miles_driven"
    ],
).to_df()

Step 2: Specifying Entities

Feast accepts either a Pandas dataframe as the entity dataframe (including entity keys and timestamps) or a SQL query to generate the entities.

Both approaches must specify the full entity key needed as well as the timestamps. Feast then joins features onto this dataframe.

Example: entity dataframe for generating training data

entity_df = pd.DataFrame.from_dict(
    {
        "driver_id": [1001, 1002, 1003, 1004, 1001],
        "event_timestamp": [
            datetime(2021, 4, 12, 10, 59, 42),
            datetime(2021, 4, 12, 8, 12, 10),
            datetime(2021, 4, 12, 16, 40, 26),
            datetime(2021, 4, 12, 15, 1, 12),
            datetime.now()
        ]
    }
)
training_df = store.get_historical_features(
    entity_df=entity_df,
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_daily_features:daily_miles_driven"
    ],
).to_df()

Example: entity SQL query for generating training data

You can also pass a SQL string to generate the above dataframe. This is useful for getting all entities in a timeframe from some data source.

entity_sql = f"""
    SELECT
        driver_id,
        event_timestamp
    FROM {store.get_data_source("driver_hourly_stats_source").get_table_query_string()}
    WHERE event_timestamp BETWEEN '2021-01-01' and '2021-12-31'
"""
training_df = store.get_historical_features(
    entity_df=entity_sql,
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_daily_features:daily_miles_driven"
    ],
).to_df()

Retrieving online features (for model inference)

Feast will ensure the latest feature values for registered features are available. At retrieval time, you need to supply a list of entities and the corresponding features to be retrieved. Similar to get_historical_features, we recommend using feature services as a mechanism for grouping features in a model version.

Note: unlike get_historical_features, the entity_rows do not need timestamps since you only want one feature value per entity key.

There are several options for retrieving online features: through the SDK, or through a feature server

from feast import RepoConfig, FeatureStore
from feast.repo_config import RegistryConfig

repo_config = RepoConfig(
    registry=RegistryConfig(path="gs://feast-test-gcs-bucket/registry.pb"),
    project="feast_demo_gcp",
    provider="gcp",
)
store = FeatureStore(config=repo_config)

features = store.get_online_features(
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_daily_features:daily_miles_driven",
    ],
    entity_rows=[
        {
            "driver_id": 1001,
        }
    ],
).to_dict()

import requests
import json

online_request = {
    "features": [
        "driver_hourly_stats:conv_rate",
    ],
    "entities": {"driver_id": [1001, 1002]},
}
r = requests.post('http://localhost:6566/get-online-features', data=json.dumps(online_request))
print(json.dumps(r.json(), indent=4, sort_keys=True))

The Feast feature registry is a central catalog of all feature definitions and their related metadata. Feast uses the registry to store all applied Feast objects (e.g. Feature views, entities, etc). It allows data scientists to search, discover, and collaborate on new features. The registry exposes methods to apply, list, retrieve and delete these objects, and is an abstraction with multiple implementations.

Feast comes with built-in file-based and sql-based registry implementations. By default, Feast uses a file-based registry, which stores the protobuf representation of the registry as a serialized file in the local file system. For more details on which registries are supported, please see Registries.

Updating the registry

We recommend users store their Feast feature definitions in a version controlled repository, which then via CI/CD automatically stays synced with the registry. Users will often also want multiple registries to correspond to different environments (e.g. dev vs staging vs prod), with staging and production registries with locked down write access since they can impact real user traffic. See Running Feast in Production for details on how to set this up.

Accessing the registry from clients

Users can specify the registry through a feature_store.yaml config file, or programmatically. We often see teams preferring the programmatic approach because it makes notebook driven development very easy:

Option 1: programmatically specifying the registry

repo_config = RepoConfig(
    registry=RegistryConfig(path="gs://feast-test-gcs-bucket/registry.pb"),
    project="feast_demo_gcp",
    provider="gcp",
    offline_store="file",  # Could also be the OfflineStoreConfig e.g. FileOfflineStoreConfig
    online_store="null",  # Could also be the OnlineStoreConfig e.g. RedisOnlineStoreConfig
)
store = FeatureStore(config=repo_config)

Option 2: specifying the registry in the project's feature_store.yaml file

project: feast_demo_aws
provider: aws
registry: s3://feast-test-s3-bucket/registry.pb
online_store: null
offline_store:
  type: file

Instantiating a FeatureStore object can then point to this:

store = FeatureStore(repo_path=".")

An offline store is an interface for working with historical time-series feature values that are stored in data sources. The OfflineStore interface has several different implementations, such as the BigQueryOfflineStore, each of which is backed by a different storage and compute engine. For more details on which offline stores are supported, please see Offline Stores.

Offline stores are primarily used for two reasons:

1. Building training datasets from time-series features.

2. Materializing (loading) features into an online store to serve those features at low-latency in a production setting.

Offline stores are configured through the feature_store.yaml. When building training datasets or materializing features into an online store, Feast will use the configured offline store with your configured data sources to execute the necessary data operations.

Only a single offline store can be used at a time. Moreover, offline stores are not compatible with all data sources; for example, the BigQuery offline store cannot be used to query a file-based data source.

Please see Push Source for more details on how to push features directly to the offline store in your feature store.

The Feature Server is a core architectural component in Feast, designed to provide low-latency feature retrieval and updates for machine learning applications.

It is a REST API server built using FastAPI and exposes a limited set of endpoints to serve features, push data, and support materialization operations. The server is scalable, flexible, and designed to work seamlessly with various deployment environments, including local setups and cloud-based systems.

Motivation

In machine learning workflows, real-time access to feature values is critical for enabling low-latency predictions. The Feature Server simplifies this requirement by:

1. Serving Features: Allowing clients to retrieve feature values for specific entities in real-time, reducing the complexity of direct interactions with the online store.

2. Data Integration: Providing endpoints to push feature data directly into the online or offline store, ensuring data freshness and consistency.

3. Scalability: Supporting horizontal scaling to handle high request volumes efficiently.

4. Standardized API: Exposing HTTP/JSON endpoints that integrate seamlessly with various programming languages and ML pipelines.

5. Secure Communication: Supporting TLS (SSL) for secure data transmission in production environments.

Architecture

The Feature Server operates as a stateless service backed by two key components:

• Online Store: The primary data store used for low-latency feature retrieval.

• Registry: The metadata store that defines feature sets, feature views, and their relationships to entities.

Key Features

1. RESTful API: Provides standardized endpoints for feature retrieval and data pushing.

2. CLI Integration: Easily managed through the Feast CLI with commands like feast serve.

3. Flexible Deployment: Can be deployed locally, via Docker, or on Kubernetes using Helm charts.

4. Scalability: Designed for distributed deployments to handle large-scale workloads.

5. TLS Support: Ensures secure communication in production setups.

Endpoints Overview

Overview

The Feast permissions model allows to configure granular permission policies to all the resources defined in a feature store.

The configured permissions are stored in the Feast registry and accessible through the CLI and the registry APIs.

The permission authorization enforcement is performed when requests are executed through one of the Feast (Python) servers

• The online feature server (REST)

• The offline feature server (Arrow Flight)

• The registry server (gRPC)

Note that there is no permission enforcement when accessing the Feast API with a local provider.

Concepts

The permission model is based on the following components:

• A resource is a Feast object that we want to secure against unauthorized access.

  • We assume that the resource has a name attribute and optional dictionary of associated key-value tags.

• An action is a logical operation executed on the secured resource, like:

  • create: Create an instance.

  • describe: Access the instance state.

  • update: Update the instance state.

  • delete: Delete an instance.

  • read: Read both online and offline stores.

  • read_online: Read the online store.

  • read_offline: Read the offline store.

  • write: Write on any store.

  • write_online: Write to the online store.

  • write_offline: Write to the offline store.

• A policy identifies the rule for enforcing authorization decisions on secured resources, based on the current user.

  • A default implementation is provided for role-based policies, using the user roles to grant or deny access to the requested actions on the secured resources.

The Permission class identifies a single permission configured on the feature store and is identified by these attributes:

• name: The permission name.

• types: The list of protected resource types. Defaults to all managed types, e.g. the ALL_RESOURCE_TYPES alias. All sub-classes are included in the resource match.

• name_patterns: A list of regex patterns to match resource names. If any regex matches, the Permission policy is applied. Defaults to [], meaning no name filtering is applied.

• required_tags: Dictionary of key-value pairs that must match the resource tags. Defaults to None, meaning that no tags filtering is applied.

• actions: The actions authorized by this permission. Defaults to ALL_VALUES, an alias defined in the action module.

• policy: The policy to be applied to validate a client request.

To simplify configuration, several constants are defined to streamline the permissions setup:

• In module feast.feast_object:

  • ALL_RESOURCE_TYPES is the list of all the FeastObject types.

  • ALL_FEATURE_VIEW_TYPES is the list of all the feature view types, including those not inheriting from FeatureView type like OnDemandFeatureView.

• In module feast.permissions.action:

  • ALL_ACTIONS is the list of all managed actions.

  • READ includes all the read actions for online and offline store.

  • WRITE includes all the write actions for online and offline store.

  • CRUD includes all the state management actions to create, describe, update or delete a Feast resource.

Given the above definitions, the feature store can be configured with granular control over each resource, enabling partitioned access by teams to meet organizational requirements for service and data sharing, and protection of sensitive information.

The feast CLI includes a new permissions command to list the registered permissions, with options to identify the matching resources for each configured permission and the existing resources that are not covered by any permission.

Definition examples

This permission definition grants access to the resource state and the ability to read all of the stores for any feature view or feature service to all users with the role super-reader:

Permission(
    name="feature-reader",
    types=[FeatureView, FeatureService],
    policy=RoleBasedPolicy(roles=["super-reader"]),
    actions=[AuthzedAction.DESCRIBE, *READ],
)

This example grants permission to write on all the data sources with risk_level tag set to high only to users with role admin or data_team:

Permission(
    name="ds-writer",
    types=[DataSource],
    required_tags={"risk_level": "high"},
    policy=RoleBasedPolicy(roles=["admin", "data_team"]),
    actions=[AuthzedAction.WRITE],
)

The following permission grants authorization to read the offline store of all the feature views including risky in the name, to users with role trusted:

Permission(
    name="reader",
    types=[FeatureView],
    name_patterns=".*risky.*", # Accepts both `str` or `list[str]` types
    policy=RoleBasedPolicy(roles=["trusted"]),
    actions=[AuthzedAction.READ_OFFLINE],
)

Authorization configuration

In order to leverage the permission functionality, the auth section is needed in the feature_store.yaml configuration. Currently, Feast supports OIDC and Kubernetes RBAC authorization protocols.

The default configuration, if you don't specify the auth configuration section, is no_auth, indicating that no permission enforcement is applied.

The auth section includes a type field specifying the actual authorization protocol, and protocol-specific fields that are specified in Authorization Manager.

An Authorization Manager is an instance of the AuthManager class that is plugged into one of the Feast servers to extract user details from the current request and inject them into the permission framework.

Two authorization managers are supported out-of-the-box:

• One using a configurable OIDC server to extract the user details.

• One using the Kubernetes RBAC resources to extract the user details.

These instances are created when the Feast servers are initialized, according to the authorization configuration defined in their own feature_store.yaml.

Feast servers and clients must have consistent authorization configuration, so that the client proxies can automatically inject the authorization tokens that the server can properly identify and use to enforce permission validations.

Design notes

The server-side implementation of the authorization functionality is defined here. Few of the key models, classes to understand the authorization implementation on the client side can be found here.

Configuring Authorization

The authorization is configured using a dedicated auth section in the feature_store.yaml configuration.

Note: As a consequence, when deploying the Feast servers with the Helm charts, the feature_store_yaml_base64 value must include the auth section to specify the authorization configuration.

No Authorization

This configuration applies the default no_auth authorization:

project: my-project
auth:
  type: no_auth
...

OIDC Authorization

With OIDC authorization, the Feast client proxies retrieve the JWT token from an OIDC server (or Identity Provider) and append it in every request to a Feast server, using an Authorization Bearer Token.

The server, in turn, uses the same OIDC server to validate the token and extract the user roles from the token itself.

Some assumptions are made in the OIDC server configuration:

• The OIDC token refers to a client with roles matching the RBAC roles of the configured Permissions (*)

• The roles are exposed in the access token that is passed to the server

• The JWT token is expected to have a verified signature and not be expired. The Feast OIDC token parser logic validates for verify_signature and verify_exp so make sure that the given OIDC provider is configured to meet these requirements.

• The preferred_username should be part of the JWT token claim.

(*) Please note that the role match is case-sensitive, e.g. the name of the role in the OIDC server and in the Permission configuration must be exactly the same.

For example, the access token for a client app of a user with reader role should have the following resource_access section:

{
  "resource_access": {
    "app": {
      "roles": [
        "reader"
      ]
    }
  }
}

An example of feast OIDC authorization configuration on the server side is the following:

project: my-project
auth:
  type: oidc
  client_id: _CLIENT_ID__
  auth_discovery_url: _OIDC_SERVER_URL_/realms/master/.well-known/openid-configuration
...

In case of client configuration, the following settings username, password and client_secret must be added to specify the current user:

auth:
  type: oidc
  ...
  username: _USERNAME_
  password: _PASSWORD_
  client_secret: _CLIENT_SECRET__

Below is an example of feast full OIDC client auth configuration:

project: my-project
auth:
  type: oidc
  client_id: test_client_id
  client_secret: test_client_secret
  username: test_user_name
  password: test_password
  auth_discovery_url: http://localhost:8080/realms/master/.well-known/openid-configuration

Kubernetes RBAC Authorization

With Kubernetes RBAC Authorization, the client uses the service account token as the authorizarion bearer token, and the server fetches the associated roles from the Kubernetes RBAC resources.

An example of Kubernetes RBAC authorization configuration is the following:

```yaml project: my-project auth: type: kubernetes ... ```

In case the client cannot run on the same cluster as the servers, the client token can be injected using the LOCAL_K8S_TOKEN environment variable on the client side. The value must refer to the token of a service account created on the servers cluster and linked to the desired RBAC roles.

Setting Up Kubernetes RBAC for Feast

To ensure the Kubernetes RBAC environment aligns with Feast's RBAC configuration, follow these guidelines:

• The roles defined in Feast Permission instances must have corresponding Kubernetes RBAC Role names.

• The Kubernetes RBAC Role must reside in the same namespace as the Feast service.

• The client application can run in a different namespace, using its own dedicated ServiceAccount.

• Finally, the RoleBinding that links the client ServiceAccount to the RBAC Role must be defined in the namespace of the Feast service.

If the above rules are satisfied, the Feast service must be granted permissions to fetch RoleBinding instances from the local namespace.

A provider is an implementation of a feature store using specific feature store components (e.g. offline store, online store) targeting a specific environment (e.g. GCP stack).

Providers orchestrate various components (offline store, online store, infrastructure, compute) inside an environment. For example, the gcp provider supports BigQuery as an offline store and Datastore as an online store, ensuring that these components can work together seamlessly. Feast has three built-in providers (local, gcp, and aws) with default configurations that make it easy for users to start a feature store in a specific environment. These default configurations can be overridden easily. For instance, you can use the gcp provider but use Redis as the online store instead of Datastore.

If the built-in providers are not sufficient, you can create your own custom provider. Please see this guide for more details.

Please see feature_store.yaml for configuring providers.

Feature values in Feast are modeled as time-series records. Below is an example of a driver feature view with two feature columns (trips_today, and earnings_today):

The above table can be registered with Feast through the following feature view:

from feast import Entity, FeatureView, Field, FileSource
from feast.types import Float32, Int64
from datetime import timedelta

driver = Entity(name="driver", join_keys=["driver_id"])

driver_stats_fv = FeatureView(
    name="driver_hourly_stats",
    entities=[driver],
    schema=[
        Field(name="trips_today", dtype=Int64),
        Field(name="earnings_today", dtype=Float32),
    ],
    ttl=timedelta(hours=2),
    source=FileSource(
        path="driver_hourly_stats.parquet"
    )
)

Feast is able to join features from one or more feature views onto an entity dataframe in a point-in-time correct way. This means Feast is able to reproduce the state of features at a specific point in the past.

Given the following entity dataframe, imagine a user would like to join the above driver_hourly_stats feature view onto it, while preserving the trip_success column:

The timestamps within the entity dataframe above are the events at which we want to reproduce the state of the world (i.e., what the feature values were at those specific points in time). In order to do a point-in-time join, a user would load the entity dataframe and run historical retrieval:

# Read in entity dataframe
entity_df = pd.read_csv("entity_df.csv")

training_df = store.get_historical_features(
    entity_df=entity_df,
    features = [
        'driver_hourly_stats:trips_today',
        'driver_hourly_stats:earnings_today'
    ],
)

For each row within the entity dataframe, Feast will query and join the selected features from the appropriate feature view data source. Feast will scan backward in time from the entity dataframe timestamp up to a maximum of the TTL time specified.

Below is the resulting joined training dataframe. It contains both the original entity rows and joined feature values:

Three feature rows were successfully joined to the entity dataframe rows. The first row in the entity dataframe was older than the earliest feature rows in the feature view and could not be joined. The last row in the entity dataframe was outside of the TTL window (the event happened 11 hours after the feature row) and also couldn't be joined.

We integrate with a wide set of tools and technologies so you can make Feast work in your existing stack. Many of these integrations are maintained as plugins to the main Feast repo.

Integrations

See Functionality and Roadmap

Standards

In order for a plugin integration to be highlighted, it must meet the following requirements:

1. The plugin must have tests. Ideally it would use the Feast universal tests (see this guide for an example), but custom tests are fine.

2. The plugin must have some basic documentation on how it should be used.

3. The author must work with a maintainer to pass a basic code review (e.g. to ensure that the implementation roughly matches the core Feast implementations).

In order for a plugin integration to be merged into the main Feast repo, it must meet the following requirements:

1. The PR must pass all integration tests. The universal tests (tests specifically designed for custom integrations) must be updated to test the integration.

2. There is documentation and a tutorial on how to use the integration.

3. The author (or someone else) agrees to take ownership of all the files, and maintain those files going forward.

4. If the plugin is being contributed by an organization, and not an individual, the organization should provide the infrastructure (or credits) for integration tests.

Links & Resources

• Come say hi on Slack!

  • As a part of the Linux Foundation, we ask community members to adhere to the Linux Foundation Code of Conduct

• GitHub Repository: Find the complete Feast codebase on GitHub.

  • Community Governance Doc: See the governance model of Feast, including who the maintainers are and how decisions are made.

• Google Folder: This folder is used as a central repository for all Feast resources. For example:

  • Design proposals in the form of Request for Comments (RFC).

  • User surveys and meeting minutes.

  • Slide decks of conferences our contributors have spoken at.

• Feast Linux Foundation Wiki: Our LFAI wiki page contains links to resources for contributors and maintainers.

How can I get help?

• GitHub Issues: Found a bug or need a feature? Create an issue on GitHub.

Push vs Pull Model

Feast uses a Push Model, i.e., Data Producers push data to the feature store and Feast stores the feature values in the online store, to serve features in real-time.

In a Pull Model, Feast would pull data from the data producers at request time and store the feature values in the online store before serving them (storing them would actually be unnecessary). This approach would incur additional network latency as Feast would need to orchestrate a request to each data producer, which would mean the latency would be at least as long as your slowest call. So, in order to serve features as fast as possible, we push data to Feast and store the feature values in the online store.

The trade-off with the Push Model is that strong consistency is not guaranteed out of the box. Instead, strong consistency has to be explicitly designed for in orchestrating the updates to Feast and the client usage.

The significant advantage with this approach is that Feast is read-optimized for low-latency feature retrieval.

How to Push

Implicit in the Push model are decisions about how and when to push feature values to the online store.

From a developer's perspective, there are three ways to push feature values to the online store with different tradeoffs.

They are discussed further in the Write Patterns section.

Feature views

A feature view is defined as a collection of features.

• In the online settings, this is a stateful collection of features that are read when the get_online_features method is called.

• In the offline setting, this is a stateless collection of features that are created when the get_historical_features method is called.

A feature view is an object representing a logical group of time-series feature data as it is found in a data source. Depending on the kind of feature view, it may contain some lightweight (experimental) feature transformations (see [Beta] On demand feature views).

Feature views consist of:

• a data source

• zero or more entities

  • If the features are not related to a specific object, the feature view might not have entities; see feature views without entities below.

• a name to uniquely identify this feature view in the project.

• (optional, but recommended) a schema specifying one or more features (without this, Feast will infer the schema by reading from the data source)

• (optional, but recommended) metadata (for example, description, or other free-form metadata via tags)

• (optional) a TTL, which limits how far back Feast will look when generating historical datasets

Feature views allow Feast to model your existing feature data in a consistent way in both an offline (training) and online (serving) environment. Feature views generally contain features that are properties of a specific object, in which case that object is defined as an entity and included in the feature view.

from feast import BigQuerySource, Entity, FeatureView, Field
from feast.types import Float32, Int64

driver = Entity(name="driver", join_keys=["driver_id"])

driver_stats_fv = FeatureView(
    name="driver_activity",
    entities=[driver],
    schema=[
        Field(name="trips_today", dtype=Int64),
        Field(name="rating", dtype=Float32),
    ],
    source=BigQuerySource(
        table="feast-oss.demo_data.driver_activity"
    )
)

Feature views are used during

• The generation of training datasets by querying the data source of feature views in order to find historical feature values. A single training dataset may consist of features from multiple feature views.

• Loading of feature values into an online store. Feature views determine the storage schema in the online store. Feature values can be loaded from batch sources or from stream sources.

• Retrieval of features from the online store. Feature views provide the schema definition to Feast in order to look up features from the online store.

Feature views without entities

If a feature view contains features that are not related to a specific entity, the feature view can be defined without entities (only timestamps are needed for this feature view).

from feast import BigQuerySource, FeatureView, Field
from feast.types import Int64

global_stats_fv = FeatureView(
    name="global_stats",
    entities=[],
    schema=[
        Field(name="total_trips_today_by_all_drivers", dtype=Int64),
    ],
    source=BigQuerySource(
        table="feast-oss.demo_data.global_stats"
    )
)

Feature inferencing

If the schema parameter is not specified in the creation of the feature view, Feast will infer the features during feast apply by creating a Field for each column in the underlying data source except the columns corresponding to the entities of the feature view or the columns corresponding to the timestamp columns of the feature view's data source. The names and value types of the inferred features will use the names and data types of the columns from which the features were inferred.

Entity aliasing

"Entity aliases" can be specified to join entity_dataframe columns that do not match the column names in the source table of a FeatureView.

This could be used if a user has no control over these column names or if there are multiple entities are a subclass of a more general entity. For example, "spammer" and "reporter" could be aliases of a "user" entity, and "origin" and "destination" could be aliases of a "location" entity as shown below.

It is suggested that you dynamically specify the new FeatureView name using .with_name and join_key_map override using .with_join_key_map instead of needing to register each new copy.

from feast import BigQuerySource, Entity, FeatureView, Field
from feast.types import Int32, Int64

location = Entity(name="location", join_keys=["location_id"])

location_stats_fv= FeatureView(
    name="location_stats",
    entities=[location],
    schema=[
        Field(name="temperature", dtype=Int32),
        Field(name="location_id", dtype=Int64),
    ],
    source=BigQuerySource(
        table="feast-oss.demo_data.location_stats"
    ),
)

from location_stats_feature_view import location_stats_fv

temperatures_fs = FeatureService(
    name="temperatures",
    features=[
        location_stats_fv
            .with_name("origin_stats")
            .with_join_key_map(
                {"location_id": "origin_id"}
            ),
        location_stats_fv
            .with_name("destination_stats")
            .with_join_key_map(
                {"location_id": "destination_id"}
            ),
    ],
)

Field

A field or feature is an individual measurable property. It is typically a property observed on a specific entity, but does not have to be associated with an entity. For example, a feature of a customer entity could be the number of transactions they have made on an average month, while a feature that is not observed on a specific entity could be the total number of posts made by all users in the last month. Supported types for fields in Feast can be found in sdk/python/feast/types.py.

Fields are defined as part of feature views. Since Feast does not transform data, a field is essentially a schema that only contains a name and a type:

from feast import Field
from feast.types import Float32

trips_today = Field(
    name="trips_today",
    dtype=Float32
)

Together with data sources, they indicate to Feast where to find your feature values, e.g., in a specific parquet file or BigQuery table. Feature definitions are also used when reading features from the feature store, using feature references.

Feature names must be unique within a feature view.

Each field can have additional metadata associated with it, specified as key-value tags.

[Alpha] On demand feature views

On demand feature views allows data scientists to use existing features and request time data (features only available at request time) to transform and create new features. Users define python transformation logic which is executed in both the historical retrieval and online retrieval paths.

Currently, these transformations are executed locally. This is fine for online serving, but does not scale well to offline retrieval.

Why use on demand feature views?

This enables data scientists to easily impact the online feature retrieval path. For example, a data scientist could

1. Call get_historical_features to generate a training dataframe

2. Iterate in notebook on feature engineering in Pandas

3. Copy transformation logic into on demand feature views and commit to a dev branch of the feature repository

4. Verify with get_historical_features (on a small dataset) that the transformation gives expected output over historical data

5. Verify with get_online_features on dev branch that the transformation correctly outputs online features

6. Submit a pull request to the staging / prod branches which impact production traffic

from feast import Field, RequestSource
from feast.on_demand_feature_view import on_demand_feature_view
from feast.types import Float64

# 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=PrimitiveFeastType.INT64),
        Field(name="val_to_add_2": dtype=PrimitiveFeastType.INT64),
    ]
)

# Use the input data and feature view features to create new features
@on_demand_feature_view(
   sources=[
       driver_hourly_stats_view,
       input_request
   ],
   schema=[
     Field(name='conv_rate_plus_val1', dtype=Float64),
     Field(name='conv_rate_plus_val2', dtype=Float64)
   ]
)
def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
    df = pd.DataFrame()
    df['conv_rate_plus_val1'] = (features_df['conv_rate'] + features_df['val_to_add'])
    df['conv_rate_plus_val2'] = (features_df['conv_rate'] + features_df['val_to_add_2'])
    return df

[Alpha] Stream feature views

A stream feature view is an extension of a normal feature view. The primary difference is that stream feature views have both stream and batch data sources, whereas a normal feature view only has a batch data source.

Stream feature views should be used instead of normal feature views when there are stream data sources (e.g. Kafka and Kinesis) available to provide fresh features in an online setting. Here is an example definition of a stream feature view with an attached transformation:

from datetime import timedelta

from feast import Field, FileSource, KafkaSource, stream_feature_view
from feast.data_format import JsonFormat
from feast.types import Float32

driver_stats_batch_source = FileSource(
    name="driver_stats_source",
    path="data/driver_stats.parquet",
    timestamp_field="event_timestamp",
)

driver_stats_stream_source = KafkaSource(
    name="driver_stats_stream",
    kafka_bootstrap_servers="localhost:9092",
    topic="drivers",
    timestamp_field="event_timestamp",
    batch_source=driver_stats_batch_source,
    message_format=JsonFormat(
        schema_json="driver_id integer, event_timestamp timestamp, conv_rate double, acc_rate double, created timestamp"
    ),
    watermark_delay_threshold=timedelta(minutes=5),
)

@stream_feature_view(
    entities=[driver],
    ttl=timedelta(seconds=8640000000),
    mode="spark",
    schema=[
        Field(name="conv_percentage", dtype=Float32),
        Field(name="acc_percentage", dtype=Float32),
    ],
    timestamp_field="event_timestamp",
    online=True,
    source=driver_stats_stream_source,
)
def driver_hourly_stats_stream(df: DataFrame):
    from pyspark.sql.functions import col

    return (
        df.withColumn("conv_percentage", col("conv_rate") * 100.0)
        .withColumn("acc_percentage", col("acc_rate") * 100.0)
        .drop("conv_rate", "acc_rate")
    )

See here for a example of how to use stream feature views to register your own streaming data pipelines in Feast.

Getting started

Which programming language should I use to run Feast in a microservice architecture?

We recommend Python.

Do you have any examples of how Feast should be used?

The quickstart is the easiest way to learn about Feast. For more detailed tutorials, please check out the tutorials page.

Concepts

Do feature views have to include entities?

No, there are feature views without entities.

How does Feast handle model or feature versioning?

Feast expects that each version of a model corresponds to a different feature service.

Feature views once they are used by a feature service are intended to be immutable and not deleted (until a feature service is removed). In the future, feast plan and feast apply will throw errors if it sees this kind of behavior.

What is the difference between data sources and the offline store?

The data source itself defines the underlying data warehouse table in which the features are stored. The offline store interface defines the APIs required to make an arbitrary compute layer work for Feast (e.g. pulling features given a set of feature views from their sources, exporting the data set results to different formats). Please see data sources and offline store for more details.

Is it possible to have offline and online stores from different providers?

Yes, this is possible. For example, you can use BigQuery as an offline store and Redis as an online store.

Functionality

How do I run get_historical_features without providing an entity dataframe?

Feast does not provide a way to do this right now. This is an area we're actively interested in contributions for. See GitHub issue

Does Feast provide security or access control?

Feast currently does not support any access control other than the access control required for the Provider's environment (for example, GCP and AWS permissions).

It is a good idea though to lock down the registry file so only the CI/CD pipeline can modify it. That way data scientists and other users cannot accidentally modify the registry and lose other team's data.

Does Feast support streaming sources?

Yes. In earlier versions of Feast, we used Feast Spark to manage ingestion from stream sources. In the current version of Feast, we support push based ingestion. Feast also defines a stream processor that allows a deeper integration with stream sources.

Does Feast support feature transformation?

There are several kinds of transformations:

• On demand transformations (See docs)

  • These transformations are Pandas transformations run on batch data when you call get_historical_features and at online serving time when you call `get_online_features.

  • Note that if you use push sources to ingest streaming features, these transformations will execute on the fly as well

• Batch transformations (WIP, see RFC)

  • These will include SQL + PySpark based transformations on batch data sources.

• Streaming transformations (RFC in progress)

Does Feast have a Web UI?

Yes. See documentation.

Does Feast support composite keys?

A feature view can be defined with multiple entities. Since each entity has a unique join_key, using multiple entities will achieve the effect of a composite key.

What are the performance/latency characteristics of Feast?

Feast is designed to work at scale and support low latency online serving. See our benchmark blog post for details.

Does Feast support embeddings and list features?

Yes. Specifically:

• Simple lists / dense embeddings:

  • BigQuery supports list types natively

  • Redshift does not support list types, so you'll need to serialize these features into strings (e.g. json or protocol buffers)

  • Feast's implementation of online stores serializes features into Feast protocol buffers and supports list types (see reference)

• Sparse embeddings (e.g. one hot encodings)

  • One way to do this efficiently is to have a protobuf or string representation of https://www.tensorflow.org/guide/sparse_tensor

Does Feast support X storage engine?

The list of supported offline and online stores can be found here and here, respectively. The roadmap indicates the stores for which we are planning to add support. Finally, our Provider abstraction is built to be extensible, so you can plug in your own implementations of offline and online stores. Please see more details about customizing Feast here.

Does Feast support using different clouds for offline vs online stores?

Yes. Using a GCP or AWS provider in feature_store.yaml primarily sets default offline / online stores and configures where the remote registry file can live. You can override the offline and online stores to be in different clouds if you wish.

What is the difference between a data source and an offline store?

The data source and the offline store are closely tied, but separate concepts. The offline store controls how feast talks to a data store for historical feature retrieval, and the data source points to specific table (or query) within a data store. Offline stores are infrastructure-level connectors to data stores like Snowflake.

Additional differences:

• Data sources may be specific to a project (e.g. feed ranking), but offline stores are agnostic and used across projects.

• A feast project may define several data sources that power different feature views, but a feast project has a single offline store.

• Feast users typically need to define data sources when using feast, but only need to use/configure existing offline stores without creating new ones.

How can I add a custom online store?

Please follow the instructions here.

Can the same storage engine be used for both the offline and online store?

Yes. For example, the Postgres connector can be used as both an offline and online store (as well as the registry).

Does Feast support S3 as a data source?

Yes. There are two ways to use S3 in Feast:

• Using Redshift as a data source via Spectrum (AWS tutorial), and then continuing with the Running Feast with Snowflake/GCP/AWS guide. See a presentation we did on this at our apply() meetup.

• Using the s3_endpoint_override in a FileSource data source. This endpoint is more suitable for quick proof of concepts that won't necessarily scale for production use cases.

Is Feast planning on supporting X functionality?

Please see the roadmap.

Project

How do I contribute to Feast?

For more details on contributing to the Feast community, see here and this here.

Feast 0.9 (legacy)

What is the difference between Feast 0.9 and Feast 0.10+?

Feast 0.10+ is much lighter weight and more extensible than Feast 0.9. It is designed to be simple to install and use. Please see this document for more details.

How do I migrate from Feast 0.9 to Feast 0.10+?

Please see this document. If you have any questions or suggestions, feel free to leave a comment on the document!

What are the plans for Feast Core, Feast Serving, and Feast Spark?

Feast Core and Feast Serving were both part of Feast Java. We plan to support Feast Serving. We will not support Feast Core; instead we will support our object store based registry. We will not support Feast Spark. For more details on what we plan on supporting, please see the roadmap.

The OpenTelemetry integration in Feast provides comprehensive monitoring and observability capabilities for your feature serving infrastructure. This component enables you to track key metrics, traces, and logs from your Feast deployment.

Motivation

Monitoring and observability are critical for production machine learning systems. The OpenTelemetry integration addresses these needs by:

1. Performance Monitoring: Track CPU and memory usage of feature servers

2. Operational Insights: Collect metrics to understand system behavior and performance

3. Troubleshooting: Enable effective debugging through distributed tracing

4. Resource Optimization: Monitor resource utilization to optimize deployments

5. Production Readiness: Provide enterprise-grade observability capabilities

Architecture

The OpenTelemetry integration in Feast consists of several components working together:

• OpenTelemetry Collector: Receives, processes, and exports telemetry data

• Prometheus Integration: Enables metrics collection and monitoring

• Instrumentation: Automatic Python instrumentation for tracking metrics

• Exporters: Components that send telemetry data to monitoring systems

Key Features

1. Automated Instrumentation: Python auto-instrumentation for comprehensive metric collection

2. Metric Collection: Track key performance indicators including:

   • Memory usage

   • CPU utilization

   • Request latencies

   • Feature retrieval statistics

3. Flexible Configuration: Customizable metric collection and export settings

4. Kubernetes Integration: Native support for Kubernetes deployments

5. Prometheus Compatibility: Integration with Prometheus for metrics visualization

Setup and Configuration

To add monitoring to the Feast Feature Server, follow these steps:

1. Deploy Prometheus Operator

Follow the Prometheus Operator documentation to install the operator.

2. Deploy OpenTelemetry Operator

Before installing the OpenTelemetry Operator:

1. Install cert-manager

2. Validate that the pods are running

3. Apply the OpenTelemetry operator:

kubectl apply -f https://github.com/open-telemetry/opentelemetry-operator/releases/latest/download/opentelemetry-operator.yaml

For additional installation steps, refer to the OpenTelemetry Operator documentation.

3. Configure OpenTelemetry Collector

Add the OpenTelemetry Collector configuration under the metrics section in your values.yaml file:

metrics:
  enabled: true
  otelCollector:
    endpoint: "otel-collector.default.svc.cluster.local:4317"  # sample
    headers:
      api-key: "your-api-key"

4. Add Instrumentation Configuration

Add the following annotations and environment variables to your deployment.yaml:

template:
  metadata:
    annotations:
      instrumentation.opentelemetry.io/inject-python: "true"

- name: OTEL_EXPORTER_OTLP_ENDPOINT
  value: http://{{ .Values.service.name }}-collector.{{ .Release.namespace }}.svc.cluster.local:{{ .Values.metrics.endpoint.port}}
- name: OTEL_EXPORTER_OTLP_INSECURE
  value: "true"

5. Add Metric Checks

Add metric checks to all manifests and deployment files:

{{ if .Values.metrics.enabled }}
apiVersion: opentelemetry.io/v1alpha1
kind: Instrumentation
metadata:
  name: feast-instrumentation
spec:
  exporter:
    endpoint: http://{{ .Values.service.name }}-collector.{{ .Release.Namespace }}.svc.cluster.local:4318
  env:
  propagators:
    - tracecontext
    - baggage
  python:
    env:
      - name: OTEL_METRICS_EXPORTER
        value: console,otlp_proto_http
      - name: OTEL_LOGS_EXPORTER
        value: otlp_proto_http
      - name: OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED
        value: "true"
{{end}}

6. Add Required Manifests

Add the following components to your chart:

• Instrumentation

• OpenTelemetryCollector

• ServiceMonitors

• Prometheus Instance

• RBAC rules

7. Deploy Feast

Deploy Feast with metrics enabled:

helm install feast-release infra/charts/feast-feature-server --set metric=true --set feature_store_yaml_base64=""

Usage

To enable OpenTelemetry monitoring in your Feast deployment:

1. Set metrics.enabled=true in your Helm values

2. Configure the OpenTelemetry Collector endpoint

3. Deploy with proper annotations and environment variables

Example configuration:

metrics:
  enabled: true
  otelCollector:
    endpoint: "otel-collector.default.svc.cluster.local:4317"

Monitoring

Once configured, you can monitor various metrics including:

• feast_feature_server_memory_usage: Memory utilization of the feature server

• feast_feature_server_cpu_usage: CPU usage statistics

• Additional custom metrics based on your configuration

These metrics can be visualized using Prometheus and other compatible monitoring tools.

An entity is a collection of semantically related features. Users define entities to map to the domain of their use case. For example, a ride-hailing service could have customers and drivers as their entities, which group related features that correspond to these customers and drivers.

driver = Entity(name='driver', join_keys=['driver_id'])

The entity name is used to uniquely identify the entity (for example to show in the experimental Web UI). The join key is used to identify the physical primary key on which feature values should be joined together to be retrieved during feature retrieval.

Entities are used by Feast in many contexts, as we explore below:

Use case #1: Defining and storing features

Feast's primary object for defining features is a feature view, which is a collection of features. Feature views map to 0 or more entities, since a feature can be associated with:

• zero entities (e.g. a global feature like num_daily_global_transactions)

• one entity (e.g. a user feature like user_age or last_5_bought_items)

• multiple entities, aka a composite key (e.g. a user + merchant category feature like num_user_purchases_in_merchant_category)

Feast refers to this collection of entities for a feature view as an entity key.

Entities should be reused across feature views. This helps with discovery of features, since it enables data scientists understand how other teams build features for the entity they are most interested in.

Feast will use the feature view concept to then define the schema of groups of features in a low-latency online store.

Use case #2: Retrieving features

At training time, users control what entities they want to look up, for example corresponding to train / test / validation splits. A user specifies a list of entity keys + timestamps they want to fetch point-in-time correct features for to generate a training dataset.

At serving time, users specify entity key(s) to fetch the latest feature values which can power real-time model prediction (e.g. a fraud detection model that needs to fetch the latest transaction user's features to make a prediction).

Feast's architecture is designed to be flexible and scalable. It is composed of several components that work together to provide a feature store that can be used to serve features for training and inference.

• Feast uses a Push Model to ingest data from different sources and store feature values in the online store. This allows Feast to serve features in real-time with low latency.

• Feast supports feature transformation for On Demand and Streaming data sources and will support Batch transformations in the future. For Streaming and Batch data sources, Feast requires a separate Feature Transformation Engine (in the batch case, this is typically your Offline Store). We are exploring adding a default streaming engine to Feast.

• Domain expertise is recommended when integrating a data source with Feast understand the tradeoffs from different write patterns to your application

• We recommend using Python for your Feature Store microservice. As mentioned in the document, precomputing features is the recommended optimal path to ensure low latency performance. Reducing feature serving to a lightweight database lookup is the ideal pattern, which means the marginal overhead of Python should be tolerable. Because of this we believe the pros of Python outweigh the costs, as reimplementing feature logic is undesirable. Java and Go Clients are also available for online feature retrieval.

• Role-Based Access Control (RBAC) is a security mechanism that restricts access to resources based on the roles of individual users within an organization. In the context of the Feast, RBAC ensures that only authorized users or groups can access or modify specific resources, thereby maintaining data security and operational integrity.

Production machine learning systems can choose from four approaches to serving machine learning predictions (the output of model inference):

1. Online model inference with online features

2. Offline mode inference without online features

3. Online model inference with online features and cached predictions

4. Online model inference without features

Note: online features can be sourced from batch, streaming, or request data sources.

These three approaches have different tradeoffs but, in general, have significant implementation differences.

1. Online Model Inference with Online Features

Online model inference with online features is a powerful approach to serving data-driven machine learning applications. This requires a feature store to serve online features and a model server to serve model predictions (e.g., KServe). This approach is particularly useful for applications where request-time data is required to run inference.

features = store.get_online_features(
    feature_refs=[
        "user_data:click_through_rate",
        "user_data:number_of_clicks",
        "user_data:average_page_duration",
    ],
    entity_rows=[{"user_id": 1}],
)
model_predictions = model_server.predict(features)

2. Offline Model Inference without Online Features

Typically, Machine Learning teams find serving precomputed model predictions to be the most straightforward to implement. This approach simply treats the model predictions as a feature and serves them from the feature store using the standard Feast sdk. These model predictions are typically generated through some batch process where the model scores are precomputed. As a concrete example, the batch process can be as simple as a script that runs model inference locally for a set of users that can output a CSV. This output file could be used for materialization so that the model could be served online as shown in the code below.

model_predictions = store.get_online_features(
    feature_refs=[
        "user_data:model_predictions",
    ],
    entity_rows=[{"user_id": 1}],
)

Notice that the model server is not involved in this approach. Instead, the model predictions are precomputed and materialized to the online store.

While this approach can lead to quick impact for different business use cases, it suffers from stale data as well as only serving users/entities that were available at the time of the batch computation. In some cases, this tradeoff may be tolerable.

3. Online Model Inference with Online Features and Cached Predictions

This approach is the most sophisticated where inference is optimized for low-latency by caching predictions and running model inference when data producers write features to the online store. This approach is particularly useful for applications where features are coming from multiple data sources, the model is computationally expensive to run, or latency is a significant constraint.

# Client Reads
features = store.get_online_features(
    feature_refs=[
        "user_data:click_through_rate",
        "user_data:number_of_clicks",
        "user_data:average_page_duration",
        "user_data:model_predictions",
    ],
    entity_rows=[{"user_id": 1}],
)
if features.to_dict().get('user_data:model_predictions') is None:
    model_predictions = model_server.predict(features)
    store.write_to_online_store(feature_view_name="user_data", df=pd.DataFrame(model_predictions))

Note that in this case a seperate call to write_to_online_store is required when the underlying data changes and predictions change along with it.

# Client Writes from the Data Producer
user_data = request.POST.get('user_data')
model_predictions = model_server.predict(user_data) # assume this includes `user_data` in the Data Frame
store.write_to_online_store(feature_view_name="user_data", df=pd.DataFrame(model_predictions))

While this requires additional writes for every data producer, this approach will result in the lowest latency for model inference.

4. Online Model Inference without Features

This approach does not require Feast. The model server can directly serve predictions without any features. This approach is common in Large Language Models (LLMs) and other models that do not require features to make predictions.

Note that generative models using Retrieval Augmented Generation (RAG) do require features where the document embeddings are treated as features, which Feast supports (this would fall under "Online Model Inference with Online Features").

Client Orchestration

Implicit in the code examples above is a design choice about how clients orchestrate calls to get features and run model inference. The examples had a Feast-centric pattern because they are inputs to the model, so the sequencing is fairly obvious. An alternative approach can be Inference-centric where a client would call an inference endpoint and the inference service would be responsible for orchestration.

Data source

A data source in Feast refers to raw underlying data that users own (e.g. in a table in BigQuery). Feast does not manage any of the raw underlying data but instead, is in charge of loading this data and performing different operations on the data to retrieve or serve features.

Feast uses a time-series data model to represent data. This data model is used to interpret feature data in data sources in order to build training datasets or materialize features into an online store.

Below is an example data source with a single entity column (driver) and two feature columns (trips_today, and rating).

Feast supports primarily time-stamped tabular data as data sources. There are many kinds of possible data sources:

• Batch data sources: ideally, these live in data warehouses (BigQuery, Snowflake, Redshift), but can be in data lakes (S3, GCS, etc). Feast supports ingesting and querying data across both.

• Stream data sources: Feast does not have native streaming integrations. It does however facilitate making streaming features available in different environments. There are two kinds of sources:

  • Push sources allow users to push features into Feast, and make it available for training / batch scoring ("offline"), for realtime feature serving ("online") or both.

  • [Alpha] Stream sources allow users to register metadata from Kafka or Kinesis sources. The onus is on the user to ingest from these sources, though Feast provides some limited helper methods to ingest directly from Kafka / Kinesis topics.

• (Experimental) Request data sources: This is data that is only available at request time (e.g. from a user action that needs an immediate model prediction response). This is primarily relevant as an input into on-demand feature views, which allow light-weight feature engineering and combining features across sources.

Batch data ingestion

Ingesting from batch sources is only necessary to power real-time models. This is done through materialization. Under the hood, Feast manages an offline store (to scalably generate training data from batch sources) and an online store (to provide low-latency access to features for real-time models).

A key command to use in Feast is the materialize_incremental command, which fetches the latest values for all entities in the batch source and ingests these values into the online store.

Materialization can be called programmatically or through the CLI:

# Define Python callable
def materialize():
  repo_config = RepoConfig(
    registry=RegistryConfig(path="s3://[YOUR BUCKET]/registry.pb"),
    project="feast_demo_aws",
    provider="aws",
    offline_store="file",
    online_store=DynamoDBOnlineStoreConfig(region="us-west-2")
  )
  store = FeatureStore(config=repo_config)
  store.materialize_incremental(datetime.datetime.now())

# (In production) Use Airflow PythonOperator
materialize_python = PythonOperator(
    task_id='materialize_python',
    python_callable=materialize,
)

CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize-incremental $CURRENT_TIME

# Use BashOperator
materialize_bash = BashOperator(
    task_id='materialize',
    bash_command=f'feast materialize-incremental {datetime.datetime.now().replace(microsecond=0).isoformat()}',
)

Batch data schema inference

If the schema parameter is not specified when defining a data source, Feast attempts to infer the schema of the data source during feast apply. The way it does this depends on the implementation of the offline store. For the offline stores that ship with Feast out of the box this inference is performed by inspecting the schema of the table in the cloud data warehouse, or if a query is provided to the source, by running the query with a LIMIT clause and inspecting the result.

Stream data ingestion

Ingesting from stream sources happens either via a Push API or via a contrib processor that leverages an existing Spark context.

• To push data into the offline or online stores: see push sources for details.

• (experimental) To use a contrib Spark processor to ingest from a topic, see Tutorial: Building streaming features

When individuals apply for loans from banks and other credit providers, the decision to approve a loan application is often made through a statistical model. This model uses information about a customer to determine the likelihood that they will repay or default on a loan, in a process called credit scoring.

In this example, we will demonstrate how a real-time credit scoring system can be built using Feast and Scikit-Learn on AWS, using feature data from S3.

This real-time system accepts a loan request from a customer and responds within 100ms with a decision on whether their loan has been approved or rejected.

Real-time Credit Scoring Example

This end-to-end tutorial will take you through the following steps:

• Deploying S3 with Parquet as your primary data source, containing both loan features and zip code features

• Deploying Redshift as the interface Feast uses to build training datasets

• Registering your features with Feast and configuring DynamoDB for online serving

• Building a training dataset with Feast to train your credit scoring model

• Loading feature values from S3 into DynamoDB

• Making online predictions with your credit scoring model using features from DynamoDB

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 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

Prerequisites

• Ensure that you have Python (3.9 or above) installed.

• It is recommended to create and work in a virtual environment:

  Copy

  # 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 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

pip install feast

Step 2: Create a feature repository

Bootstrap a new feature repository using feast init from the command line.

feast init my_project
cd my_project/feature_repo

Creating a new Feast repository in /home/Jovyan/my_project.

Let's take a look at the resulting demo repo itself. It breaks down into

• data/ contains raw demo parquet data

• example_repo.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.

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: 2

# 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
    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],
)

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 for all supported data sources.

A custom setup can also be made by following 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.

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 example_repo.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.

feast apply

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

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 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

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
        "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())

----- Feature schema -----

<class 'pandas.core.frame.DataFrame'>
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  

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

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())

----- 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  

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_repo/feature_repo/example_repo.py).

CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")

feast materialize-incremental $CURRENT_TIME

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<00:00, 370.32it/s]
driver_hourly_stats_fresh from 2024-04-18 15:00:46-04:00 to 2024-04-19 10:59:58-04:00:
100%|███████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1046.64it/s]
Materializing 2 feature views to 2024-04-19 10:59:58-04:00 into the sqlite online store.

Step 7: Fetching feature vectors for inference

At inference time, we need to quickly read the latest feature values for different drivers (which otherwise might have existed only in batch sources) from the online feature store using get_online_features(). These feature vectors can then be fed to the model.

from pprint import pprint
from feast import FeatureStore

store = FeatureStore(repo_path=".")

feature_vector = store.get_online_features(
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_hourly_stats:avg_daily_trips",
    ],
    entity_rows=[
        # {join_key: entity_value}
        {"driver_id": 1004},
        {"driver_id": 1005},
    ],
).to_dict()

pprint(feature_vector)

{
 'acc_rate': [0.25351759791374207, 0.8949751853942871],
 'avg_daily_trips': [712, 791],
 'conv_rate': [0.5038306713104248, 0.9839504361152649],
 'driver_id': [1004, 1005]
 }

Step 8: Using a feature service to fetch online features instead.

You can also use feature services to manage multiple features, and decouple feature view definitions and the features needed by end applications. The feature store can also be used to fetch either online or historical features using the same API below. More information can be found here.

The driver_activity_v1 feature service pulls all features from the driver_hourly_stats feature view:

from feast import FeatureService
driver_stats_fs = FeatureService(
    name="driver_activity_v1", features=[driver_stats_fv]
)

from pprint import pprint
from feast import FeatureStore
feature_store = FeatureStore('.')  # Initialize the feature store

feature_service = feature_store.get_feature_service("driver_activity_v1")
feature_vector = feature_store.get_online_features(
    features=feature_service,
    entity_rows=[
        # {join_key: entity_value}
        {"driver_id": 1004},
        {"driver_id": 1005},
    ],
).to_dict()
pprint(feature_vector)

{
 'acc_rate': [0.5732735991477966, 0.7828438878059387],
 'avg_daily_trips': [33, 984],
 'conv_rate': [0.15498852729797363, 0.6263588070869446],
 'driver_id': [1004, 1005]
}

Step 9: Browse your features with the Web UI (experimental)

View all registered features, data sources, entities, and feature services with the Web UI.

One of the ways to view this is with the feast ui command.

feast ui

INFO:     Started server process [66664]
08/17/2022 01:25:49 PM uvicorn.error INFO: Started server process [66664]
INFO:     Waiting for application startup.
08/17/2022 01:25:49 PM uvicorn.error INFO: Waiting for application startup.
INFO:     Application startup complete.
08/17/2022 01:25:49 PM uvicorn.error INFO: Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8888 (Press CTRL+C to quit)
08/17/2022 01:25:49 PM uvicorn.error INFO: Uvicorn running on http://0.0.0.0:8888 (Press CTRL+C to quit)

Step 10: Re-examine test_workflow.py

Take a look at test_workflow.py again. It showcases many sample flows on how to interact with Feast. You'll see these show up in the upcoming concepts + architecture + tutorial pages as well.

Next steps

• Read the Concepts page to understand the Feast data model.

• Read the Architecture page.

• Check out our Tutorials section for more examples on how to use Feast.

• Follow our Running Feast with Snowflake/GCP/AWS guide for a more in-depth tutorial on using Feast.

These Feast tutorials showcase how to use Feast to simplify end to end model training / serving.

In this example, you'll learn how to use some of the key functionality in Feast. The tutorial runs in both local mode and on the Google Cloud Platform (GCP). For GCP, you must have access to a GCP project already, including read and write permissions to BigQuery.

Driver Ranking Example

This tutorial guides you on how to use Feast with Scikit-learn. You will learn how to:

• Train a model locally (on your laptop) using data from BigQuery

• Test the model for online inference using SQLite (for fast iteration)

• Test the model for online inference using Firestore (for production use)

Try it and let us know what you think!

Throughout this tutorial, we’ll walk through the creation of a production-ready fraud prediction system. A prediction is made in real-time as the user makes the transaction, so we need to be able to generate a prediction at low latency.

Fraud Detection Example

Our end-to-end example will perform the following workflows:

• Computing and backfilling feature data from raw data

• Building point-in-time correct training datasets from feature data and training a model

• Making online predictions from feature data

Here's a high-level picture of our system architecture on Google Cloud Platform (GCP):

Feast uses online stores to serve features at low latency. Feature values are loaded from data sources into the online store through materialization, which can be triggered through the materialize command.

The storage schema of features within the online store mirrors that of the original data source. One key difference is that for each entity key, only the latest feature values are stored. No historical values are stored.

Here is an example batch data source:

Once the above data source is materialized into Feast (using feast materialize), the feature values will be stored as follows:

Features can also be written directly to the online store via push sources .

The easiest way to create a new feature repository to use feast init command:

The init command creates a Python file with feature definitions, sample data, and a Feast configuration file for local development:

Enter the directory:

You can now use this feature repository for development. You can try the following:

• Run feast apply to apply these definitions to Feast.

• Edit the example feature definitions in example.py and run feast apply again to change feature definitions.

• Initialize a git repository in the same directory and checking the feature repository into version control.

Install Feast with Snowflake dependencies (required when using Snowflake):

Install Feast with GCP dependencies (required when using BigQuery or Firestore):

Install Feast with AWS dependencies (required when using Redshift or DynamoDB):

Install Feast with Redis dependencies (required when using Redis, either through AWS Elasticache or independently):

In the steps below, we will set up a sample Feast project that leverages Snowflake as an offline store + materialization engine + online store.

Starting with data in a Snowflake table, we will register that table to the feature store and define features associated with the columns in that table. From there, we will generate historical training data based on those feature definitions and then materialize the latest feature values into the online store. Lastly, we will retrieve the materialized feature values.

Our template will generate new data containing driver statistics. From there, we will show you code snippets that will call to the offline store for generating training datasets, and then the code for calling the online store to serve you the latest feature values to serve models in production.

Snowflake Offline Store Example

Install feast-snowflake

Get a Snowflake Trial Account (Optional)

Create a feature repository

The following files will automatically be created in your project folder:

• feature_store.yaml -- This is your main configuration file

• driver_repo.py -- This is your main feature definition file

• test.py -- This is a file to test your feature store configuration

Inspect feature_store.yaml

Here you will see the information that you entered. This template will use Snowflake as the offline store, materialization engine, and the online store. The main thing to remember is by default, Snowflake objects have ALL CAPS names unless lower case was specified.

Run our test python script test.py

What we did in test.py

Initialize our Feature Store

Create a dummy training dataframe, then call our offline store to add additional columns