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 Buffer object wraps the C++ 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 Buffer can be created from any Python object implementing the buffer protocol by calling the py_buffer() function. Let’s consider a bytes object:

In [1]: import pyarrow as pa

In [2]: data = b'abcdefghijklmnopqrstuvwxyz'

In [3]: buf = pa.py_buffer(data)

In [4]: buf
Out[4]: <pyarrow.lib.Buffer at 0x7fde442bfc70>

In [5]: buf.size
Out[5]: 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 foreign_buffer() function.

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

In [6]: memoryview(buf)
Out[6]: <memory at 0x7fde4434ab88>

The Buffer’s to_pybytes() method converts the Buffer’s data to a Python bytestring (thus making a copy of the data):

In [7]: buf.to_pybytes()
Out[7]: b'abcdefghijklmnopqrstuvwxyz'

Memory Pools

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

In [8]: pa.total_allocated_bytes()
Out[8]: 200050432

PyArrow uses a default built-in memory pool, but in the future there may be additional memory pools (and subpools) to choose from. Let’s allocate a resizable Buffer from the default pool:

In [9]: buf = pa.allocate_buffer(1024, resizable=True)

In [10]: pa.total_allocated_bytes()
Out[10]: 200051456

In [11]: buf.resize(2048)

In [12]: pa.total_allocated_bytes()
Out[12]: 200052480

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

In [13]: buf = None

In [14]: pa.total_allocated_bytes()
Out[14]: 200050432

See also

On-GPU buffers using Arrow’s optional CUDA integration.

Input and Output

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 NativeFile base class which implements the same API as regular Python file objects.

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 NativeFile options available:

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

High-Level API

Input Streams

The input_stream() function allows creating a readable NativeFile from various kinds of sources.

  • If passed a Buffer or a memoryview object, a BufferReader will be returned:

    In [15]: buf = memoryview(b"some data")

In [16]: stream = pa.input_stream(buf)

In [17]: stream.read(4)
Out[17]: b'some'

  • If passed a string or file path, it will open the given file on disk for reading, creating a 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.

    In [18]: import gzip

In [19]: with gzip.open('example.gz', 'wb') as f:
   ....:     f.write(b'some data\n' * 3)
   ....: 

In [20]: stream = pa.input_stream('example.gz')

In [21]: stream.read()
Out[21]: b'some data\nsome data\nsome data\n'

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

Output Streams

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

In [22]: with pa.output_stream('example1.dat') as stream:
   ....:     stream.write(b'some data')
   ....: 

In [23]: f = open('example1.dat', 'rb')

In [24]: f.read()
Out[24]: 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:

In [25]: with open('example2.dat', 'wb') as f:
   ....:     f.write(b'some example data')
   ....:

Using pyarrow’s OSFile class, you can write:

In [26]: with pa.OSFile('example3.dat', 'wb') as f:
   ....:     f.write(b'some example data')
   ....:

For reading files, you can use OSFile or MemoryMappedFile. The difference between these is that 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:

In [27]: file_obj = pa.OSFile('example2.dat')

In [28]: mmap = pa.memory_map('example3.dat')

In [29]: file_obj.read(4)
Out[29]: b'some'

In [30]: mmap.read(4)
Out[30]: b'some'

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

In [31]: mmap.seek(0)
Out[31]: 0

In [32]: buf = mmap.read_buffer(4)

In [33]: print(buf)
<pyarrow.lib.Buffer object at 0x7fde3ff455a8>

In [34]: buf.to_pybytes()
Out[34]: 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.

In [35]: writer = pa.BufferOutputStream()

In [36]: writer.write(b'hello, friends')
Out[36]: 14

In [37]: buf = writer.getvalue()

In [38]: buf
Out[38]: <pyarrow.lib.Buffer at 0x7fde3ff45810>

In [39]: buf.size
Out[39]: 14

In [40]: reader = pa.BufferReader(buf)

In [41]: reader.seek(7)
Out[41]: 7

In [42]: reader.read(7)
Out[42]: b'friends'

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