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.
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 ensure 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 realtime models, and from one data infra system to another.
Note: Feast today primarily addresses timestamped structured data.
Note: Feast uses a push model for online serving. This means that the feature store pushes feature values to the online store, which reduces the latency of feature retrieval. This is more efficient than a pull model, where the model serving system must make a request to the feature store to retrieve feature values. See this document for a more detailed discussion.
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.
Feast is likely not the right tool if you
are in an organization that’s just getting started with ML and is not yet sure what the business impact of ML is
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 light-weight 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
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.
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.
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 probability of default
The best way to learn Feast is to use it. Head over to our Quickstart and try it out!
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.
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.
GitHub Issues: Found a bug or need a feature? Create an issue on GitHub.
The list below contains the functionality that contributors are planning to develop for Feast.
We welcome contribution to all items in the roadmap!
Data Sources
Offline Stores
Online Stores
Feature Engineering
Streaming
Deployments
Feature Serving
Data Quality Management (See RFC)
Feature Discovery and Governance
Natural Language Processing
In this tutorial we will
Deploy a local feature store with a Parquet file offline store and Sqlite online store.
Build a training dataset using our time series features from our Parquet files.
Ingest batch features ("materialization") and streaming features (via a Push API) into the online store.
Read the latest features from the offline store for batch scoring
Read the latest features from the online store for real-time inference.
Explore the (experimental) Feast UI
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:
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.
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.
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.
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
Bootstrap a new feature repository using feast init from the command line.
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.
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.
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.
There's an included test_workflow.py file which runs through a full sample workflow:
Register feature definitions through feast apply
Generate a training dataset (using get_historical_features)
Generate features for batch scoring (using get_historical_features)
Ingest batch features into an online store (using materialize_incremental)
Fetch online features to power real time inference (using get_online_features)
Ingest streaming features into offline / online stores (using push)
Verify online features are updated / fresher
We'll walk through some snippets of code below and explain
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.
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:
The user can query that table of labels with timestamps and pass that into Feast as an entity dataframe for training data generation.
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.
To power a batch model, we primarily need to generate features with the get_historical_features call, but using the current timestamp
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).
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.
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:
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.
test_workflow.pyTake 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.
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.
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.
Use Python to serve your features.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
There are two ways a client (or Data Producer) can send data to the online store:
Synchronously
Asynchronously
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.
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.
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:
Precomputing transformations
Computing transformations On Demand
Hybrid (Precomputed + On Demand)
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.
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.
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).
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.
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 transformations can be executed by three types of "transformation engines":
The Feast Feature Server
An Offline Store (e.g., Snowflake, BigQuery, DuckDB, Spark, etc.)
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.
Note: this ML Infrastructure diagram highlights an orchestration pattern that is driven by a client application. This is not the only approach that can be taken and different patterns will result in different trade-offs.
Production machine learning systems can choose from four approaches to serving machine learning predictions (the output of model inference):
Online model inference with online features
Offline mode inference without online features
Online model inference with online features and cached predictions
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.
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.
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.
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.
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.
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.
While this requires additional writes for every data producer, this approach will result in the lowest latency for model inference.
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").
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.
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.
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.
The primary business goals of implementing RBAC in the Feast are:
Feature Sharing: Enable multiple teams to share the feature store while ensuring controlled access. This allows for collaborative work without compromising data security.
Access Control Management: Prevent unauthorized access to team-specific resources and spaces, governing the operations that each user or group can perform.
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.
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.
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.
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).
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.
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.
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.
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:
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.
Ingesting from stream sources happens either via a Push API or via a contrib processor that leverages an existing Spark context.
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.
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:
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.
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).
Q: Can I retrieve features for all entities?
Kind of.
In practice, this is most relevant for batch scoring models (e.g. predict user churn for all existing users) that are offline only. For these use cases, Feast supports generating features for a SQL-backed list of entities. There is an open GitHub issue that welcomes contribution to make this a more intuitive API.
For real-time feature retrieval, there is no out of the box support for this because it would promote expensive and slow scan operations which can affect the performance of other operations on your data sources. Users can still pass in a large list of entities for retrieval, but this does not scale well.
Note: Feature views do not work with non-timestamped data. A workaround is to insert dummy timestamps.
Feature views consist of:
a name to uniquely identify this feature view in the project.
(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.
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.
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.
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).
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 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.
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:
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.
This enables data scientists to easily impact the online feature retrieval path. For example, a data scientist could
Call get_historical_features to generate a training dataframe
Iterate in notebook on feature engineering in Pandas
Copy transformation logic into on demand feature views and commit to a dev branch of the feature repository
Verify with get_historical_features (on a small dataset) that the transformation gives expected output over historical data
Verify with get_online_features on dev branch that the transformation correctly outputs online features
Submit a pull request to the staging / prod branches which impact production traffic
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:
Generally, Feast supports several patterns of feature retrieval:
Training data generation (via feature_store.get_historical_features(...))
Offline feature retrieval for batch scoring (via feature_store.get_historical_features(...))
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)
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).
Before diving into how to retrieve features, we need to understand some high level concepts in Feast.
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.
Applying a feature service does not result in an actual service being deployed.
Feature services enable referencing all or some features from a feature view.
Retrieving from the online store with a feature service
Retrieving from the offline store with a feature service
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:
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.
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.
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.
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.
Feast accepts either:
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.
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.
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
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:
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:
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.
Please note that the TTL time is relative to each timestamp within the entity dataframe. TTL is not relative to the current point in time (when you run the query).
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.
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.
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_pattern: A regex to match the resource name. Defaults to None, meaning that 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.
Note: Feast resources that do not match any of the configured permissions are not secured by any authorization policy, meaning any user can execute any action on such resources.
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:
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:
Note: When using multiple roles in a role-based policy, the user must be granted at least one of the specified roles.
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:
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.
Dataset can be created from:
Results of historical retrieval
[planned] Logging features during writing to online store (from batch source or stream)
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.
Saved dataset can be retrieved later using the get_saved_dataset method in the feature store:
Create Batch Features: ELT/ETL systems like Spark and SQL are used to transform data in the batch store.
Feast Apply: The user (or CI) publishes versioned controlled feature definitions using feast apply. This CLI command updates infrastructure and persists definitions in the object store registry.
Feast Materialize: The user (or scheduler) executes feast materialize which loads features from the offline store into the online store.
Model Training: A model training pipeline is launched. It uses the Feast Python SDK to retrieve a training dataset that can be used for training models.
Get Historical Features: Feast exports a point-in-time correct training dataset based on the list of features and entity dataframe provided by the model training pipeline.
Deploy Model: The trained model binary (and list of features) are deployed into a model serving system. This step is not executed by Feast.
Prediction: A backend system makes a request for a prediction from the model serving service.
Get Online Features: The model serving service makes a request to the Feast Online Serving service for online features using a Feast SDK.
A complete Feast deployment contains the following components:
Feast Registry: An object store (GCS, S3) based registry used to persist feature definitions that are registered with the feature store. Systems can discover feature data by interacting with the registry through the Feast SDK.
Feast Python SDK/CLI: The primary user facing SDK. Used to:
Manage version controlled feature definitions.
Materialize (load) feature values into the online store.
Build and retrieve training datasets from the offline store.
Retrieve online features.
Stream Processor: The Stream Processor can be used to ingest feature data from streams and write it into the online or offline stores. Currently, there's an experimental Spark processor that's able to consume data from Kafka.
Offline Store: The offline store persists batch data that has been ingested into Feast. This data is used for producing training datasets. For feature retrieval and materialization, Feast does not manage the offline store directly, but runs queries against it. However, offline stores can be configured to support writes if Feast configures logging functionality of served features.
Authorization Manager: The authorization manager detects authentication tokens from client requests to Feast servers and uses this information to enforce permission policies on the requested services.
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.
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:
feature_store.yaml fileInstantiating a FeatureStore object can then point to this:
Offline stores are primarily used for two reasons:
Building training datasets from time-series features.
Materializing (loading) features into an online store to serve those features at low-latency in a production setting.
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.
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.
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:
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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).
Note: Feast does not provide authentication capabilities; it is the client's responsibility to manage the authentication token and pass it to the Feast server, which then validates the token and extracts user details from the configured authentication server.
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.
The authorization is configured using a dedicated auth section in the feature_store.yaml configuration.