Fast SQL Server driver for Python with a Rust core. Drop-in for mssql-python, Arrow-native.
- 2-7x faster than mssql-python, up to 10x faster than pyodbc on large reads.
- Arrow out of the box: results go straight to DuckDB, polars, pyarrow and Delta, no pyarrow required.
- Parallel reads and bulk loads over several connections.
- No ODBC driver: native TDS in Rust, zero required dependencies, wheels for Linux, macOS and Windows.
- Drop-in: same DB-API, same connection strings;
import mssql_pythonkeeps working.
Install
pip install arrowsql
Extras: arrowsql[arrow] (pyarrow for cursor.arrow()), arrowsql[azure] (Entra ID login).
Do not install it next to Microsoft's mssql-python: both provide the mssql_python package.
Quick start
import arrowsql
conn = arrowsql.connect("Server=myserver;Database=sales;Uid=app;Pwd=...;Encrypt=yes")
cur = conn.cursor()
cur.execute("SELECT id, total FROM dbo.orders WHERE customer_id = ?", (42,))
for row in cur.fetchall():
print(row.id, row.total)
Arrow, DuckDB, polars
import duckdb
t = cur.execute("SELECT * FROM dbo.orders").fetchall_arrow() # native Arrow table
duckdb.sql("SELECT customer_id, sum(total) FROM t GROUP BY 1").show()
# Large tables: read over 8 connections and stream into DuckDB, deltalake or polars
with arrowsql.read_arrow_stream(conn_str, "SELECT * FROM dbo.orders",
partition_on="id", partitions=8) as stream:
duckdb.sql("SELECT avg(total) FROM stream").show()
String columns travel as UTF-8 on SQL Server 2019+ (about half the bytes). More in examples/.
Bulk loads
cur.bulkcopy_arrow("dbo.orders", arrow_table) # Arrow -> TDS bulk load
arrowsql.write_arrow(conn_str, "dbo.orders", arrow_table, workers=8) # in parallel
Async and agent mode
arrowsql.aio: native asyncio with a connection pool; cancelling a task cancels the query (docs/ASYNC.md).arrowsql-agent -c "SELECT ...": bounded, typed output and JSON errors for LLM agents (docs/AGENT_MODE.md).
Benchmarks
NYC taxi data (2.96M rows x 20 columns), SQL Server 2022 on the same host. Compare ratios: the box was shared.
| mssql-python | arrowsql | pyodbc | |
|---|---|---|---|
fetchall(), 100k rows |
0.351 s | 0.117 s | 0.865 s |
fetchmany, 3M rows |
9.74 s | 2.75 s | 27.0 s |
| Arrow, 3M rows over 8 connections | 4.25 s | 0.59 s | – |
| Bulk load, 3M rows (8 workers) | 12.35 s | 1.82 s | – |
executemany, 100k rows |
1.26 s | 0.65 s | 0.76 s |
| 16 threads x point lookups | 2.40 s | 0.43 s | – |
Reproduce: scripts/bench_reproduce.sh. Full matrix: benchmarks/RESULTS.md.
Docs
Compatibility · Platforms and connection strings · Features · Architecture · Performance notes · Contributing
License
MIT. Builds on microsoft/mssql-python and mssql-tds (both MIT).
1 maintainer
Metadata
1 maintainer
Release files for arrowsql 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| arrowsql-0.1.0.tar.gz | 1.9 MB | With provenance | Details for arrowsql-0.1.0.tar.gz |
Built distributions (wheels)
Total release size: 206.0 MB
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