lakeFS Architecture

lakeFS is distributed as a single binary encapsulating several logical services.

The server itself is stateless, meaning you can easily add more instances to handle a bigger load.

Object Storage

lakeFS stores data in object stores. Those supported include:

• AWS S3
• Google Cloud Storage
• Azure Blob Storage
• MinIO
• NetApp StorageGRID
• Ceph

Metadata Storage

In additional a Key Value storage is used for storing metadata, with supported databases including PostgreSQL, DynamoDB, and CosmosDB Instructions of how to deploy such database on AWS can be found here.

Additional information on the data format can be found in Versioning internals and Internal database structure

Load Balancing

Accessing lakeFS is done using HTTP. lakeFS exposes a frontend UI, an OpenAPI server, as well as an S3-compatible service (see S3 Gateway below). lakeFS uses a single port that serves all three endpoints, so for most use cases a single load balancer pointing to lakeFS server(s) would do.

lakeFS Components

S3 Gateway

The S3 Gateway is the layer in lakeFS responsible for the compatibility with S3. It implements a compatible subset of the S3 API to ensure most data systems can use lakeFS as a drop-in replacement for S3.

See the S3 API Reference section for information on supported API operations.

OpenAPI Server

The Swagger (OpenAPI) server exposes the full set of lakeFS operations (see Reference). This includes basic CRUD operations against repositories and objects, as well as versioning related operations such as branching, merging, committing, and reverting changes to data.

Storage Adapter

The Storage Adapter is an abstraction layer for communicating with any underlying object store. Its implementations allow compatibility with many types of underlying storage such as S3, GCS, Azure Blob Storage, or non-production usages such as the local storage adapter.

See the roadmap for information on the future plans for storage compatibility.

Graveler

The Graveler handles lakeFS versioning by translating lakeFS addresses to the actual stored objects. To learn about the data model used to store lakeFS metadata, see the versioning internals page.

Authentication & Authorization Service

The Auth service handles the creation, management, and validation of user credentials and RBAC policies.

The credential scheme, along with the request signing logic, are compatible with AWS IAM (both SIGv2 and SIGv4).

Currently, the Auth service manages its own database of users and credentials and doesn’t use IAM in any way.

Hooks Engine

The Hooks Engine enables CI/CD for data by triggering user defined Actions that will run during commit/merge.

UI

The UI layer is a simple browser-based client that uses the OpenAPI server. It allows management, exploration, and data access to repositories, branches, commits and objects in the system.

Applications

As a rule of thumb, lakeFS supports any S3-compatible application. This means that many common data applications work with lakeFS out-of-the-box. Check out our integrations to learn more.

lakeFS Clients

Some data applications benefit from deeper integrations with lakeFS to support different use cases or enhanced functionality provided by lakeFS clients.

OpenAPI Generated SDKs

OpenAPI specification can be used to generate lakeFS clients for many programming languages. For example, the Python lakefs-client or the Java client are published with every new lakeFS release.

lakectl

lakectl is a CLI tool that enables lakeFS operations using the lakeFS API from your preferred terminal.

Spark Metadata Client

The lakeFS Spark Metadata Client makes it easy to perform operations related to lakeFS metadata, at scale. Examples include garbage collection or exporting data from lakeFS.

lakeFS Hadoop FileSystem

Thanks to the S3 Gateway, it’s possible to interact with lakeFS using Hadoop’s S3AFIleSystem, but due to limitations of the S3 API, doing so requires reading and writing data objects through the lakeFS server. Using lakeFSFileSystem increases Spark ETL jobs performance by executing the metadata operations on the lakeFS server, and all data operations directly through the same underlying object store that lakeFS uses.

How lakeFS Clients and Gateway Handle Metadata and Data Access

When using the Python client, lakeCTL, or the lakeFS Spark client, these clients communicate with the lakeFS server to retrieve metadata information. For example, they may query lakeFS to understand which version of a file is needed or to track changes in branches and commits. This communication does not include the actual data transfer, but instead involves passing only metadata about data locations and versions. Once the client knows the exact data location from the lakeFS metadata, it directly accesses the data in the underlying object storage (potentially using presigned URLs) without routing through lakeFS. For instance, if data is stored in S3, the Spark client will retrieve the S3 paths from lakeFS, then directly read and write to those paths in S3 without involving lakeFS in the data transfer.