arrow-odbc 0.2.4

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 crate 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.

This package can also be used to insert data in Arrow record batches to database tables.

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.

Users looking for more features than just bulk fetching/inserting data from/into ODBC data sources in Python should also take a look at turbodbc which has a helpful community and seen a lot of battle testing. This Python package is more narrow in Scope (which is a fancy way of saying it has less features), as it is only concerned with bulk fetching Arrow Arrays. turbodbc may be harder to install using pip though, due to it's reliance on C++ API and external dependencies like boost.

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,
    batch_size=1000,
    parameters=["I'm a positional query parameter"],
    user="SA",
    password="My@Test@Password",
)

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

Insert

from arrow_odbc import insert_into_table

# Arrow Field name must match column name of database table.
schema = pa.schema([("a", pa.int64())])
def iter_record_batches():
    for i in range(2):
        yield pa.RecordBatch.from_arrays([pa.array([1, 2, 3])], schema=schema)
# Reader must be ablet to iterate over record batches, exposing a `schema` attribute.
# RecordBatchReader implements this protocol.
reader = pa.RecordBatchReader.from_batches(schema, iter_record_batches())

insert_into_table(
    connection_string="Driver={ODBC Driver 17 for SQL Server};Server=localhost;",
    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 Rust toolchain

Note: Only required if building 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

Installing the wheel

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.

Matching of ODBC to Arrow types then querying

ODBC	Arrow
Numeric(p <= 38)	Decimal
Decimal(p <= 38)	Decimal
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	Int8
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)
Decimal256(p, s = 0)	VarChar(p + 1)
Decimal256(p, s != 0)	VarChar(p + 2)
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