---
title: "connectorx"
description: "connectorx is a Rust-based Python library for loading data from SQL databases into DataFrames (pandas, Arrow, Polars) as fast as possible by parallelizing extraction and avoiding unnecessary data copies."
canonical: "https://motherduck.com/glossary/connectorx/"
related:
  - title: "Why Use DuckDB for Analytics?"
    url: "https://motherduck.com/blog/six-reasons-duckdb-slaps/"
  - title: "Multithreading and parallelism | MotherDuck Docs"
    url: "https://motherduck.com/docs/key-tasks/authenticating-and-connecting-to-motherduck/multithreading-and-parallelism/"
  - title: "Devin + MotherDuck Integration | DuckDB Analytics"
    url: "https://motherduck.com/ecosystem/devin/"
---

# connectorx

> connectorx is a Rust-based Python library for loading data from SQL databases into DataFrames (pandas, Arrow, Polars) as fast as possible by parallelizing extraction and avoiding unnecessary data copies.

## Overview

connectorx (imported as `connectorx`, commonly aliased `cx`) is a library designed to solve one specific problem well: getting data out of a SQL database and into a Python DataFrame as quickly as possible. Traditional approaches using drivers like SQLAlchemy or `pandas.read_sql` extract rows one at a time in Python, which is slow for large result sets. connectorx instead partitions a query, extracts data in parallel using a Rust core, and writes results directly into the destination format (pandas, Polars, Arrow, or Dask) with as few copies as possible, following a "load once, copy never" design.

## Basic usage

```python
import connectorx as cx

df = cx.read_sql(
    "postgresql://user:pass@host:5432/db",
    "SELECT * FROM orders WHERE order_date >= '2026-01-01'",
    partition_on="order_id",
    partition_num=4,
)
```

Specifying a `partition_on` column lets connectorx split the query into ranges and pull them concurrently, which is where most of its speedup comes from on large tables.

## Where it fits with DuckDB

connectorx and DuckDB solve adjacent but different problems: connectorx is focused purely on fast extraction from source databases (Postgres, MySQL, SQL Server, and others) into a DataFrame, while DuckDB is a fast in-process analytical query engine for transforming and analyzing that data once it's local. A common pattern is to use connectorx to pull data out of an OLTP database quickly, land it as a Polars or pandas DataFrame (or write it to Parquet), and then run DuckDB SQL directly against that in-memory DataFrame or file for aggregation, joins, and analysis — combining connectorx's fast extraction with DuckDB's fast local querying rather than relying on the source database's own (often single-threaded) query execution for analytical workloads.