arrow-odbc 7.0.1

Read the data of an ODBC data source as sequence of Apache Arrow record batches.

arrow-odbc-py

Fill Apache Arrow arrays from ODBC data sources. This package is build on top of the pyarrow Python package and arrow-odbc Rust crate and enables you to read the data of an ODBC data source as sequence of Apache Arrow record batches.

• Fast. Makes efficient use of ODBC bulk reads and writes, to lower IO overhead.
• Flexible. Query any ODBC data source you have a driver for. MySQL, MS SQL, Excel, ...
• Portable. Easy to install and update dependencies. No binary dependency to specific implemenations of Python interpreter, Arrow or ODBC driver manager.

About Arrow

Apache Arrow defines a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead.

About ODBC

ODBC (Open DataBase Connectivity) is a standard which enables you to access data from a wide variaty of data sources using SQL.

Usage

Query

from arrow_odbc import read_arrow_batches_from_odbc

connection_string="Driver={ODBC Driver 17 for SQL Server};Server=localhost;"

reader = read_arrow_batches_from_odbc(
    query=f"SELECT * FROM MyTable WHERE a=?",
    connection_string=connection_string,
    parameters=["I'm a positional query parameter"],
    user="SA",
    password="My@Test@Password",
)

# Trade memory for speed. For the price of an additional transit buffer and a native system thread
# we fetch batches now concurrent to our application logic. Just remove this line, if you want to
# fetch sequentially in your main application thread.
reader.fetch_concurrently()

for batch in reader:
    # Process arrow batches
    df = batch.to_pandas()
    # ...

Insert

from arrow_odbc import insert_into_table
import pyarrow as pa
import pandas


def dataframe_to_table(df):
    table = pa.Table.from_pandas(df)
    reader = pa.RecordBatchReader.from_batches(table.schema, table.to_batches())
    insert_into_table(
        connection_string=connection_string,
        user="SA",
        password="My@Test@Password",
        chunk_size=1000,
        table="MyTable",
        reader=reader,
    )

Installation

Installing ODBC driver manager

The provided wheels dynamically link against the driver manager, which must be provided by the system.

Windows

Nothing to do. ODBC driver manager is preinstalled.

Ubuntu

sudo apt-get install unixodbc-dev

OS-X

You can use homebrew to install UnixODBC

brew install unixodbc

Installing the wheel

This package has been designed to be easily deployable, so it provides a prebuild many linux wheel which is independent of the specific version of your Python interpreter and the specific Arrow Version you want to use. It will dynamically link against the ODBC driver manager provided by your system.

Wheels have been uploaded to PyPi and can be installed using pip. The wheel (including the manylinux wheel) will link against the your system ODBC driver manager at runtime. If there are no prebuild wheels for your platform, you can build the wheel from source. For this the rust toolchain must be installed.

pip install arrow-odbc

arrow-odbc utilizes cffi and the Arrow C-Interface to glue Rust and Python code together. Therefore the wheel does not need to be build against the precise version either of Python or Arrow.

Installing with conda

conda install -c conda-forge arrow-odbc

Thanks to @timkpaine for maintaining the recipie!

Building wheel from source

There is no ready made wheel for the platform you want to target? Do not worry, you can probably build it from source.

• To build from source you need to install the Rust toolchain. Installation instruction can be found here: https://www.rust-lang.org/tools/install

• Install ODBC driver manager. See above.

• Build wheel

  python -m pip install build
  python -m build
  

Matching of ODBC to Arrow types then querying

ODBC	Arrow
Numeric(p <= 38)	Decimal128
Decimal(p <= 38, s >= 0)	Decimal128
Integer	Int32
SmallInt	Int16
Real	Float32
Float(p <=24)	Float32
Double	Float64
Float(p > 24)	Float64
Date	Date32
LongVarbinary	Binary
Timestamp(p = 0)	TimestampSecond
Timestamp(p: 1..3)	TimestampMilliSecond
Timestamp(p: 4..6)	TimestampMicroSecond
Timestamp(p >= 7 )	TimestampNanoSecond
BigInt	Int64
TinyInt Signed	Int8
TinyInt Unsigned	UInt8
Bit	Boolean
Varbinary	Binary
Binary	FixedSizedBinary
All others	Utf8

Matching of Arrow to ODBC types then inserting

Arrow	ODBC
Utf8	VarChar
Decimal128(p, s = 0)	VarChar(p + 1)
Decimal128(p, s != 0)	VarChar(p + 2)
Decimal128(p, s < 0)	VarChar(p - s + 1)
Decimal256(p, s = 0)	VarChar(p + 1)
Decimal256(p, s != 0)	VarChar(p + 2)
Decimal256(p, s < 0)	VarChar(p - s + 1)
Int8	TinyInt
Int16	SmallInt
Int32	Integer
Int64	BigInt
Float16	Real
Float32	Real
Float64	Double
Timestamp s	Timestamp(7)
Timestamp ms	Timestamp(7)
Timestamp us	Timestamp(7)
Timestamp ns	Timestamp(7)
Date32	Date
Date64	Date
Time32 s	Time
Time32 ms	VarChar(12)
Time64 us	VarChar(15)
Time64 ns	VarChar(16)
Binary	Varbinary
FixedBinary(l)	Varbinary(l)
All others	Unsupported

Comparision to other Python ODBC bindings

• pyodbc - General purpose ODBC python bindings. In contrast arrow-odbc is specifically concerned with bulk reads and writes to arrow arrays.
• turbodbc - Complies with the Python Database API Specification 2.0 (PEP 249) which arrow-odbc does not aim to do. Like arrow-odbc bulk read and writes is the strong point of turbodbc. turbodbc has more system dependencies, which can make it cumbersome to install if not using conda. turbodbc is build against the C++ implementation of Arrow, which implies it is only compatible with matching version of pyarrow.