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.. currentmodule:: pyarrow .. highlight:: python

.. _io:

======================== Memory and IO Interfaces

This section will introduce you to the major concepts in PyArrow's memory management and IO systems:

Buffers
Memory pools
File-like and stream-like objects

Referencing and Allocating Memory

pyarrow.Buffer

The :class:Buffer object wraps the C++ :cpp:class:arrow::Buffer type which is the primary tool for memory management in Apache Arrow in C++. It permits higher-level array classes to safely interact with memory which they may or may not own. arrow::Buffer can be zero-copy sliced to permit Buffers to cheaply reference other Buffers, while preserving memory lifetime and clean parent-child relationships.

There are many implementations of arrow::Buffer, but they all provide a standard interface: a data pointer and length. This is similar to Python's built-in buffer protocol and memoryview objects.

A :class:Buffer can be created from any Python object implementing the buffer protocol by calling the :func:py_buffer function. Let's consider a bytes object:

.. code-block:: python

import pyarrow as pa data = b'abcdefghijklmnopqrstuvwxyz' buf = pa.py_buffer(data) buf <pyarrow.Buffer address=... size=26 is_cpu=True is_mutable=False> buf.size 26

Creating a Buffer in this way does not allocate any memory; it is a zero-copy view on the memory exported from the data bytes object.

External memory, under the form of a raw pointer and size, can also be referenced using the :func:foreign_buffer function.

Buffers can be used in circumstances where a Python buffer or memoryview is required, and such conversions are zero-copy:

.. code-block:: python

memoryview(buf) <memory at ...>

The Buffer's :meth:~Buffer.to_pybytes method converts the Buffer's data to a Python bytestring (thus making a copy of the data):

.. code-block:: python

buf.to_pybytes() b'abcdefghijklmnopqrstuvwxyz'

Memory Pools

All memory allocations and deallocations (like malloc and free in C) are tracked in an instance of :class:MemoryPool. This means that we can then precisely track amount of memory that has been allocated:

.. code-block:: python

pa.total_allocated_bytes() 0

Let's allocate a resizable :class:Buffer from the default pool:

.. code-block:: python

buf = pa.allocate_buffer(1024, resizable=True) pa.total_allocated_bytes() 1024 buf.resize(2048) pa.total_allocated_bytes() 2048

The default allocator requests memory in a minimum increment of 64 bytes. If the buffer is garbage-collected, all of the memory is freed:

.. code-block:: python

buf = None pa.total_allocated_bytes() 0

Besides the default built-in memory pool, there may be additional memory pools to choose from (such as jemalloc <http://jemalloc.net/>_) depending on how Arrow was built. One can get the backend name for a memory pool::

pa.default_memory_pool().backend_name 'mimalloc'

.. seealso:: :ref:API documentation for memory pools <api.memory_pool>.

.. seealso:: On-GPU buffers using Arrow's optional :doc:CUDA integration <integration/cuda>.

Input and Output

.. _io.native_file:

The Arrow C++ libraries have several abstract interfaces for different kinds of IO objects:

Read-only streams
Read-only files supporting random access
Write-only streams
Write-only files supporting random access
File supporting reads, writes, and random access

In the interest of making these objects behave more like Python's built-in file objects, we have defined a :class:~pyarrow.NativeFile base class which implements the same API as regular Python file objects.

:class:~pyarrow.NativeFile has some important features which make it preferable to using Python files with PyArrow where possible:

Other Arrow classes can access the internal C++ IO objects natively, and do not need to acquire the Python GIL
Native C++ IO may be able to do zero-copy IO, such as with memory maps

There are several kinds of :class:~pyarrow.NativeFile options available:

:class:~pyarrow.OSFile, a native file that uses your operating system's file descriptors
:class:~pyarrow.MemoryMappedFile, for reading (zero-copy) and writing with memory maps
:class:~pyarrow.BufferReader, for reading :class:~pyarrow.Buffer objects as a file
:class:~pyarrow.BufferOutputStream, for writing data in-memory, producing a Buffer at the end
:class:~pyarrow.FixedSizeBufferWriter, for writing data into an already allocated Buffer
:class:~pyarrow.HdfsFile, for reading and writing data to the Hadoop Filesystem
:class:~pyarrow.PythonFile, for interfacing with Python file objects in C++
:class:~pyarrow.CompressedInputStream and :class:~pyarrow.CompressedOutputStream, for on-the-fly compression or decompression to/from another stream

There are also high-level APIs to make instantiating common kinds of streams easier.

High-Level API

Input Streams


The :func:`~pyarrow.input_stream` function allows creating a readable
:class:`~pyarrow.NativeFile` from various kinds of sources.

* If passed a :class:`~pyarrow.Buffer` or a ``memoryview`` object, a
  :class:`~pyarrow.BufferReader` will be returned:

   .. code-block:: python

      >>> buf = memoryview(b"some data")
      >>> stream = pa.input_stream(buf)
      >>> stream.read(4)
      b'some'

* If passed a string or file path, it will open the given file on disk
  for reading, creating a :class:`~pyarrow.OSFile`.  Optionally, the file
  can be compressed: if its filename ends with a recognized extension
  such as ``.gz``, its contents will automatically be decompressed on
  reading.

  .. code-block:: python

     >>> import gzip
     >>> with gzip.open('example.gz', 'wb') as f:
     ...     f.write(b'some data\n' * 3)
     30
     >>> stream = pa.input_stream('example.gz')
     >>> stream.read()
     b'some data\nsome data\nsome data\n'

* If passed a Python file object, it will wrapped in a :class:`PythonFile`
  such that the Arrow C++ libraries can read data from it (at the expense
  of a slight overhead).

Output Streams

:func:~pyarrow.output_stream is the equivalent function for output streams and allows creating a writable :class:~pyarrow.NativeFile. It has the same features as explained above for :func:~pyarrow.input_stream, such as being able to write to buffers or do on-the-fly compression.

.. code-block:: python

with pa.output_stream('example1.dat') as stream: ... stream.write(b'some data') 9 with open('example1.dat', 'rb') as f: ... f.read() b'some data'

On-Disk and Memory Mapped Files

PyArrow includes two ways to interact with data on disk: standard operating system-level file APIs, and memory-mapped files. In regular Python we can write:

.. code-block:: python

with open('example2.dat', 'wb') as f: ... f.write(b'some example data') 17

Using pyarrow's :class:~pyarrow.OSFile class, you can write:

.. code-block:: python

with pa.OSFile('example3.dat', 'wb') as f: ... f.write(b'some example data') 17

For reading files, you can use :class:~pyarrow.OSFile or :class:~pyarrow.MemoryMappedFile. The difference between these is that :class:~pyarrow.OSFile allocates new memory on each read, like Python file objects. In reads from memory maps, the library constructs a buffer referencing the mapped memory without any memory allocation or copying:

.. code-block:: python

file_obj = pa.OSFile('example2.dat') mmap = pa.memory_map('example3.dat') file_obj.read(4) b'some' mmap.read(4) b'some'

The read method implements the standard Python file read API. To read into Arrow Buffer objects, use read_buffer:

.. code-block:: python

mmap.seek(0) 0 buf = mmap.read_buffer(4) buf <pyarrow.Buffer address=... size=4 is_cpu=True is_mutable=False> buf.to_pybytes() b'some'

Many tools in PyArrow, particular the Apache Parquet interface and the file and stream messaging tools, are more efficient when used with these NativeFile types than with normal Python file objects.

In-Memory Reading and Writing

To assist with serialization and deserialization of in-memory data, we have file interfaces that can read and write to Arrow Buffers.

.. code-block:: python

writer = pa.BufferOutputStream() writer.write(b'hello, friends') 14 buf = writer.getvalue() buf <pyarrow.Buffer address=... size=14 is_cpu=True is_mutable=True> buf.size 14 reader = pa.BufferReader(buf) reader.seek(7) 7 reader.read(7) b'friends'

These have similar semantics to Python's built-in io.BytesIO.