connectorx
Decision gist · record as of 2026-08-14
Yes. ConnectorX is actively maintained, has no known vulnerabilities, supports current Python versions (3.10+), and offers measurable performance and memory gains over standard SQL connectors—especially for large datasets. The permissive MIT license and zero runtime dependencies make adoption low-risk. Install it if you regularly load data from databases; the one-line API and optional parallelism justify the medium wheel size.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; precompiled wheels available for macOS, Linux, and Windows—building from source requires Rust toolchain.
- Medium install friction: precompiled wheels available for Python 3.10–3.13 on macOS (Intel and ARM), Linux (x86_64 and aarch64), and Windows, but no runtime dependencies means no secondary build burden once the wheel installs.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute ConnectorX freely in commercial and private projects with minimal restrictions.
last release 2026-01-18 (208 days) · last repo commit 2026-07-20 · 2,643 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,807,317 downloads/mo, #3,533 on PyPI
Alternatives
Verify before relying
pip install connectorx
import connectorx as cx
# Single-threaded load
df = cx.read_sql("postgresql://user:pass@localhost/mydb", "SELECT * FROM table")
# Parallel load with partitioning
df = cx.read_sql(
"postgresql://user:pass@localhost/mydb",
"SELECT * FROM table",
partition_on="id",
partition_num=4
)- Whether federated query support (joining across multiple databases) is production-ready or remains experimental.
- Specific performance gains on your hardware and database size relative to the 10x TPC-H lineitem benchmark cited.
What it is and what it does
ConnectorX is a Rust-based database connector that executes SQL queries and streams results directly into Python data structures (Pandas, PyArrow, Polars, Modin, Dask) with minimal memory overhead. It follows a zero-copy architecture, meaning data moves exactly once from the database to your destination, avoiding the multiple intermediate copies that traditional Python connectors perform. The library supports Postgres, MySQL, MariaDB, SQLite, Redshift, Clickhouse, SQL Server, Azure SQL Database, Oracle, Big Query, and Trino.
You use it by calling `cx.read_sql()` with a connection string and SQL query. For large tables, you can specify a partition column and partition count to split the query across multiple threads, each loading its partition in parallel. The library automatically determines the partition range, allocates memory based on row counts, and streams data row-wise or column-wise depending on the source. No runtime dependencies means installation is a single pip command on supported platforms.
Use it for
- Load large tables (gigabytes+) from production databases into Pandas or Polars for analysis without exhausting system memory.
- Parallelize SQL query execution by partitioning on a numerical column to reduce total load time on multi-core systems.
- Replace pandas.read_sql() in existing data pipelines to cut memory usage and execution time without changing application logic.
- Join tables across two or more databases in a single query (experimental federated mode) without staging intermediate results.
- Stream query results into PyArrow or Polars for downstream ML or analytics workflows that expect columnar data.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
ConnectorX is actively maintained, has no known vulnerabilities, supports current Python versions (3.10+), and offers measurable performance and memory gains over standard SQL connectors—especially for large datasets. The permissive MIT license and zero runtime dependencies make adoption low-risk. Install it if you regularly load data from databases; the one-line API and optional parallelism justify the medium wheel size.
Install
connectorx on PyPI
Before you install
Medium install friction: precompiled wheels available for Python 3.10–3.13 on macOS (Intel and ARM), Linux (x86_64 and aarch64), and Windows, but no runtime dependencies means no secondary build burden once the wheel installs.
Requires Python 3.10 or later; precompiled wheels available for macOS, Linux, and Windows—building from source requires Rust toolchain.
License in practice
MIT license is permissive; you can use, modify, and distribute ConnectorX freely in commercial and private projects with minimal restrictions.
Quickstart
pip install connectorx
import connectorx as cx
# Single-threaded load
df = cx.read_sql("postgresql://user:pass@localhost/mydb", "SELECT * FROM table")
# Parallel load with partitioning
df = cx.read_sql(
"postgresql://user:pass@localhost/mydb",
"SELECT * FROM table",
partition_on="id",
partition_num=4
)Verify before relying
- Whether federated query support (joining across multiple databases) is production-ready or remains experimental.
- Specific performance gains on your hardware and database size relative to the 10x TPC-H lineitem benchmark cited.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 208 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,807,317 / month, #3,533 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: connectorx-0.4.5-cp310-cp310-macosx_10_7_x86_64.whl; connectorx-0.4.5-cp310-cp310-macosx_11_0_arm64.whl; connectorx-0.4.5-cp310-cp310-manylinux_2_28_aarch64.whl; connectorx-0.4.5-cp310-cp310-manylinux_2_28_x86_64.whl; connectorx-0.4.5-cp310-none-win_amd64.whl; connectorx-0.4.5-cp311-cp311-macosx_10_7_x86_64.whl; connectorx-0.4.5-cp311-cp311-macosx_11_0_arm64.whl; connectorx-0.4.5-cp311-cp311-manylinux_2_28_aarch64.whl; connectorx-0.4.5-cp311-cp311-manylinux_2_28_x86_64.whl; connectorx-0.4.5-cp311-none-win_amd64.whl; connectorx-0.4.5-cp312-cp312-macosx_10_7_x86_64.whl; connectorx-0.4.5-cp312-cp312-macosx_11_0_arm64.whl; connectorx-0.4.5-cp312-cp312-manylinux_2_28_aarch64.whl; connectorx-0.4.5-cp312-cp312-manylinux_2_28_x86_64.whl; connectorx-0.4.5-cp312-none-win_amd64.whl; connectorx-0.4.5-cp313-cp313-macosx_10_7_x86_64.whl; connectorx-0.4.5-cp313-cp313-macosx_11_0_arm64.whl; connectorx-0.4.5-cp313-cp313-manylinux_2_28_aarch64.whl; connectorx-0.4.5-cp313-cp313-manylinux_2_28_x86_64.whl; connectorx-0.4.5-cp313-none-win_amd64.whl
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