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See the License for the .. specific language governing permissions and limitations .. under the License. .. currentmodule:: pyarrow .. _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: .. ipython:: python import pyarrow as pa data = b'abcdefghijklmnopqrstuvwxyz' buf = pa.py_buffer(data) buf buf.size 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: .. ipython:: python memoryview(buf) The Buffer's :meth:`~Buffer.to_pybytes` method converts the Buffer's data to a Python bytestring (thus making a copy of the data): .. ipython:: python buf.to_pybytes() 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: .. ipython:: python pa.total_allocated_bytes() 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: .. ipython:: python buf = pa.allocate_buffer(1024, resizable=True) pa.total_allocated_bytes() buf.resize(2048) pa.total_allocated_bytes() The default allocator requests memory in a minimum increment of 64 bytes. If the buffer is garbaged-collected, all of the memory is freed: .. ipython:: python buf = None pa.total_allocated_bytes() .. seealso:: On-GPU buffers using Arrow's optional :doc:`CUDA integration `. 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: .. ipython:: python buf = memoryview(b"some data") stream = pa.input_stream(buf) stream.read(4) * 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. .. ipython:: python import gzip with gzip.open('example.gz', 'wb') as f: f.write(b'some data\n' * 3) stream = pa.input_stream('example.gz') stream.read() * 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. .. ipython:: python with pa.output_stream('example1.dat') as stream: stream.write(b'some data') f = open('example1.dat', 'rb') f.read() 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: .. ipython:: python with open('example2.dat', 'wb') as f: f.write(b'some example data') Using pyarrow's :class:`~pyarrow.OSFile` class, you can write: .. ipython:: python with pa.OSFile('example3.dat', 'wb') as f: f.write(b'some example data') 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: .. ipython:: python file_obj = pa.OSFile('example2.dat') mmap = pa.memory_map('example3.dat') file_obj.read(4) mmap.read(4) The ``read`` method implements the standard Python file ``read`` API. To read into Arrow Buffer objects, use ``read_buffer``: .. ipython:: python mmap.seek(0) buf = mmap.read_buffer(4) print(buf) buf.to_pybytes() 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. .. ipython:: python :suppress: buf = mmap = file_obj = None !rm example.dat !rm example2.dat 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. .. ipython:: python writer = pa.BufferOutputStream() writer.write(b'hello, friends') buf = writer.getvalue() buf buf.size reader = pa.BufferReader(buf) reader.seek(7) reader.read(7) These have similar semantics to Python's built-in ``io.BytesIO``.