LogoLogo
v0.45-branch
v0.45-branch
  • Introduction
  • Community & getting help
  • Roadmap
  • Changelog
  • Getting started
    • Quickstart
    • Architecture
      • Overview
      • Language
      • Push vs Pull Model
      • Write Patterns
      • Feature Transformation
      • Feature Serving and Model Inference
      • Role-Based Access Control (RBAC)
    • Concepts
      • Overview
      • Project
      • Data ingestion
      • Entity
      • Feature view
      • Feature retrieval
      • Point-in-time joins
      • [Alpha] Saved dataset
      • Permission
      • Tags
    • Components
      • Overview
      • Registry
      • Offline store
      • Online store
      • Feature server
      • Batch Materialization Engine
      • Provider
      • Authorization Manager
    • Third party integrations
    • FAQ
  • Tutorials
    • Sample use-case tutorials
      • Driver ranking
      • Fraud detection on GCP
      • Real-time credit scoring on AWS
      • Driver stats on Snowflake
    • Validating historical features with Great Expectations
    • Building streaming features
  • How-to Guides
    • Running Feast with Snowflake/GCP/AWS
      • Install Feast
      • Create a feature repository
      • Deploy a feature store
      • Build a training dataset
      • Load data into the online store
      • Read features from the online store
      • Scaling Feast
      • Structuring Feature Repos
    • Running Feast in production (e.g. on Kubernetes)
    • Customizing Feast
      • Adding a custom batch materialization engine
      • Adding a new offline store
      • Adding a new online store
      • Adding a custom provider
    • Adding or reusing tests
    • Starting Feast servers in TLS(SSL) Mode
  • Reference
    • Codebase Structure
    • Type System
    • Data sources
      • Overview
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • Push
      • Kafka
      • Kinesis
      • Spark (contrib)
      • PostgreSQL (contrib)
      • Trino (contrib)
      • Azure Synapse + Azure SQL (contrib)
    • Offline stores
      • Overview
      • Dask
      • Snowflake
      • BigQuery
      • Redshift
      • DuckDB
      • Spark (contrib)
      • PostgreSQL (contrib)
      • Trino (contrib)
      • Azure Synapse + Azure SQL (contrib)
      • Remote Offline
    • Online stores
      • Overview
      • SQLite
      • Snowflake
      • Redis
      • Dragonfly
      • IKV
      • Datastore
      • DynamoDB
      • Bigtable
      • Remote
      • PostgreSQL
      • Cassandra + Astra DB
      • Couchbase
      • MySQL
      • Hazelcast
      • ScyllaDB
      • SingleStore
      • Milvus
    • Registries
      • Local
      • S3
      • GCS
      • SQL
      • Snowflake
    • Providers
      • Local
      • Google Cloud Platform
      • Amazon Web Services
      • Azure
    • Batch Materialization Engines
      • Snowflake
      • AWS Lambda (alpha)
      • Spark (contrib)
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feature servers
      • Python feature server
      • [Alpha] Go feature server
      • Offline Feature Server
    • [Beta] Web UI
    • [Beta] On demand feature view
    • [Alpha] Vector Database
    • [Alpha] Data quality monitoring
    • [Alpha] Streaming feature computation with Denormalized
    • Feast CLI reference
    • Python API reference
    • Usage
  • Project
    • Contribution process
    • Development guide
    • Backwards Compatibility Policy
      • Maintainer Docs
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
Powered by GitBook
On this page
  • Description
  • Example
  • Functionality Matrix

Was this helpful?

Edit on GitHub
Export as PDF
  1. Reference
  2. Offline stores

Dask

PreviousOverviewNextSnowflake

Last updated 3 months ago

Was this helpful?

Description

The Dask offline store provides support for reading .

All data is downloaded and joined using Python and therefore may not scale to production workloads.

Example

feature_store.yaml
project: my_feature_repo
registry: data/registry.db
provider: local
offline_store:
  type: dask

The full set of configuration options is available in .

Functionality Matrix

The set of functionality supported by offline stores is described in detail . Below is a matrix indicating which functionality is supported by the dask offline store.

Dask

get_historical_features (point-in-time correct join)

yes

pull_latest_from_table_or_query (retrieve latest feature values)

yes

pull_all_from_table_or_query (retrieve a saved dataset)

yes

offline_write_batch (persist dataframes to offline store)

yes

write_logged_features (persist logged features to offline store)

yes

Below is a matrix indicating which functionality is supported by DaskRetrievalJob.

Dask

export to dataframe

yes

export to arrow table

yes

export to arrow batches

no

export to SQL

no

export to data lake (S3, GCS, etc.)

no

export to data warehouse

no

export as Spark dataframe

no

local execution of Python-based on-demand transforms

yes

remote execution of Python-based on-demand transforms

no

persist results in the offline store

yes

preview the query plan before execution

yes

read partitioned data

yes

To compare this set of functionality against other offline stores, please see the full .

FileSources
DaskOfflineStoreConfig
here
functionality matrix