Boost SQL Server Data Processing: mssql-python Now Supports Apache Arrow

Speed Up Data Workloads with Zero-Copy Arrow Integration

Fetching a million rows from SQL Server into a Python DataFrame used to be a heavy operation. Every row required creating a Python object, allocating memory in the garbage collector, and then converting that data into a columnar format like Polars or Pandas. This overhead added significant delays and memory bloat. Today, that bottleneck is gone. The mssql-python library now offers native support for Apache Arrow, enabling direct transfer of SQL Server data as Arrow structures. This change—contributed by community developer Felix Graßl (@ffelixg)—provides a faster and more memory-efficient path for anyone working with SQL Server data in Arrow-native tools such as Polars, Pandas (with ArrowDtype), DuckDB, and Hugging Face datasets.

Understanding Apache Arrow: The Foundation for Zero-Copy

Apache Arrow is more than just a columnar memory format—it’s a cross-language application binary interface (ABI) that defines a stable shared-memory layout. The key to its power is the Arrow C Data Interface, a specification that allows any language to exchange data by simply sharing a pointer to memory. No serialization, no copying, no reparsing—just direct access to the same bytes. A C++ database driver and a Python DataFrame library can operate on identical memory without knowing about each other’s internals.

Arrow stores table data in a columnar fashion: instead of representing rows as collections of Python objects, all values of a column are placed contiguously in a typed buffer. Null values are tracked via a compact bitmap rather than per-cell Python None objects. This layout is inherently cache-friendly and enables vectorized operations.

Key Terms: API vs. ABI vs. Arrow C Data Interface

To appreciate Arrow’s impact, it helps to understand a few terms:

• API (Application Programming Interface): A source-code contract that defines how functions or libraries are called. APIs are language-specific.
• ABI (Application Binary Interface): A binary-level contract that specifies how compiled code is laid out in memory. Two programs built in different languages can share an ABI and exchange data directly—no serialization needed.
• Arrow C Data Interface: Apache Arrow’s ABI specification—the standard that makes zero-copy data exchange between languages possible.

How mssql-python Leverages Arrow for High Performance

In traditional Python database drivers, fetching data involves a loop: for each row of the result set, the driver creates Python objects (integers, strings, datetimes) and appends them to a list. This generates many small object allocations and puts heavy pressure on the garbage collector. When the data is later converted to a DataFrame, those objects are often thrown away and rebuilt into columnar arrays—a double waste.

With Arrow support in mssql-python, the entire fetch loop can run in C++. Values are written directly into Arrow buffers—typed, contiguous arrays—without ever creating Python objects per row. The DataFrame library receives a pointer to that memory and can start operating immediately. Crucially, subsequent operations (filters, joins, aggregations) also work in-place on those same buffers. A Polars pipeline reading from mssql-python never needs to materialize intermediate Python objects at any stage.

Four Concrete Benefits for Data Practitioners

For users of mssql-python, Arrow support translates into four clear advantages:

1. Speed: The columnar fetch path eliminates per-row Python object creation. The improvement is especially noticeable for temporal types like DATETIME and DATETIMEOFFSET, where Python-side per-value conversions are completely avoided.
2. Lower memory usage: A column of one million integers is a single contiguous C array, not a million individual Python objects. Memory overhead drops dramatically.
3. Seamless interoperability: Polars, Pandas (via ArrowDtype), DuckDB, Hugging Face datasets, and many other tools can consume Arrow data natively. No format conversion or serialization step is necessary—just pass the Arrow object.
4. Future-proof scalability: Arrow is becoming the de facto standard for high-performance data exchange. Adopting it now positions your workflow for continued optimizations.

Getting Started with Arrow in mssql-python

To use Arrow support, ensure you have mssql-python installed (version 2.2.0 or later). When fetching query results, specify the cursor type that returns Arrow data. For example, cursor.fetch_arrow() returns a PyArrow Table. This Table can be zero-copy converted to a Polars DataFrame via pl.from_arrow() or to a Pandas DataFrame with ArrowDtype. The exact API may evolve; consult the official documentation for the latest usage.

As always, benchmark your specific workloads to measure the improvements. For many common SQL Server data types and large result sets, the speed and memory gains speak for themselves.

A Community-Driven Leap Forward

This feature was contributed by Felix Graßl (@ffelixg), a member of the open-source community. His work demonstrates how collaborative development can dramatically improve the efficiency of data pipelines. We encourage you to give Arrow a try, share feedback, and contribute to the ongoing evolution of mssql-python. The era of wasteful Python object allocation for database fetching is fading—embrace zero-copy Arrow today.