Getting started

FeatureMesh has three entry points, depending on how deep you want to go.

Step 1: Try it in the browser

Every code example on this site is interactive. Click any playground to edit the query, run it, and see results — no installation needed.

Here's a taste: a FeatureQL query that defines a greeting feature and evaluates it for a given name.

WITH
    -- Define features
    YOUR_NAME := INPUT(VARCHAR),
    A_MESSAGE_TO_YOU := 'Hello, ' || YOUR_NAME || '!'
SELECT
    -- Return features
    A_MESSAGE_TO_YOU
FOR
    -- Bind values to features for evaluation
    YOUR_NAME := BIND_VALUE('FeatureMesh')  -- <-- insert your name here

Under the hood, the playground sends your FeatureQL query to a remote FeatureMesh registry, which translates it to SQL. The SQL then runs in a DuckDB WASM module directly in your browser.

This is the fastest way to learn the language. Start with the FeatureQL foundations or jump to FeatureQL for the Impatient if you already know SQL.

Step 2: Install the Python library

pip install featuremesh

The featuremesh package bundles the FeatureQL transpilation engine and runs locally — no server, no account needed. Local mode executes with DuckDB only; Trino, BigQuery, and DataFusion run through managed mode with your own sql_executor.

A minimal example (works immediately after install):

from featuremesh import BatchClient

client = BatchClient()

result = client.query("""
    SELECT
        F1 := 1,
        F2 := 2,
        F3 := F1 + F2;
""")

print(result.dataframe)

For team collaboration with shared feature definitions, switch to managed mode with an access token. See the Python library reference for all modes, backends, and configuration options.

Step 3: Run the demos Docker container

The featuremesh-demos container is a self-contained environment with everything pre-configured: the FeatureMesh registry, analytics proxy, online serving, and sample data.

It includes:

• Jupyter notebooks walking through batch analytics, real-time serving, and business functions
• Pre-configured backends: DuckDB, Redis, PostgreSQL, and an HTTP server
• Sample datasets for an e-commerce scenario

This is the best way to experience the full platform — batch analytics with BigQuery/Trino, real-time serving with Redis and JDBC, and feature persistence — without setting up infrastructure.

Get started: github.com/featuremesh/demos

Which path should I choose?