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.. to you under the Apache License, Version 2.0 (the
.. "License"); you may not use this file except in compliance
.. with the License.  You may obtain a copy of the License at

..   http://www.apache.org/licenses/LICENSE-2.0

.. Unless required by applicable law or agreed to in writing,
.. software distributed under the License is distributed on an
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.. default-domain:: cpp
.. highlight:: cpp

=================
Memory Management
=================

Buffers
=======

To avoid passing around raw data pointers with varying and non-obvious
lifetime rules, Arrow provides a generic abstraction called :class:`arrow::Buffer`.
A Buffer encapsulates a pointer and data size, and generally also ties its
lifetime to that of an underlying provider (in other words, a Buffer should
*always* point to valid memory till its destruction).  Buffers are untyped:
they simply denote a physical memory area regardless of its intended meaning
or interpretation.

Buffers may be allocated by Arrow itself , or by third-party routines.
For example, it is possible to pass the data of a Python bytestring as a Arrow
buffer, keeping the Python object alive as necessary.

In addition, buffers come in various flavours: mutable or not, resizable or
not.  Generally, you will hold a mutable buffer when building up a piece
of data, then it will be frozen as an immutable container such as an
:doc:`array <arrays>`.

.. note::
   Some buffers may point to non-CPU memory, such as GPU-backed memory
   provided by a CUDA context.  If you're writing a GPU-aware application,
   you will need to be careful not to interpret a GPU memory pointer as
   a CPU-reachable pointer, or vice-versa.

Accessing Buffer Memory
-----------------------

Buffers provide fast access to the underlying memory using the
:func:`~arrow::Buffer::size` and :func:`~arrow::Buffer::data` accessors
(or :func:`~arrow::Buffer::mutable_data` for writable access to a mutable
buffer).

Slicing
-------

It is possible to make zero-copy slices of buffers, to obtain a buffer
referring to some contiguous subset of the underlying data.  This is done
by calling the :func:`arrow::SliceBuffer` and :func:`arrow::SliceMutableBuffer`
functions.

Allocating a Buffer
-------------------

You can allocate a buffer yourself by calling one of the
:func:`arrow::AllocateBuffer` or :func:`arrow::AllocateResizableBuffer`
overloads::

   std::shared_ptr<arrow::Buffer> buffer;

   if (!arrow::AllocateBuffer(4096, &buffer).ok()) {
      // ... handle allocation error
   }
   uint8_t* buffer_data = buffer->mutable_data();
   memcpy(buffer_data, "hello world", 11);

Allocating a buffer this way ensures it is 64-bytes aligned and padded
as recommended by the :doc:`Arrow memory specification <../format/Layout>`.

Building a Buffer
-----------------

You can also allocate *and* build a Buffer incrementally, using the
:class:`arrow::BufferBuilder` API::

   BufferBuilder builder;
   builder.Resize(11);
   builder.Append("hello ", 6);
   builder.Append("world", 5);

   std::shared_ptr<arrow::Buffer> buffer;
   if (!builder.Finish(&buffer).ok()) {
      // ... handle buffer allocation error
   }

Memory Pools
============

When allocating a Buffer using the Arrow C++ API, the buffer's underlying
memory is allocated by a :class:`arrow::MemoryPool` instance.  Usually this
will be the process-wide *default memory pool*, but many Arrow APIs allow
you to pass another MemoryPool instance for their internal allocations.

Memory pools are used for large long-lived data such as array buffers.
Other data, such as small C++ objects and temporary workspaces, usually
goes through the regular C++ allocators.

Default Memory Pool
-------------------

Depending on how Arrow was compiled, the default memory pool may use the
standard C ``malloc`` allocator, or a `jemalloc <http://jemalloc.net/>`_ heap.

STL Integration
---------------

If you wish to use a Arrow memory pool to allocate the data of STL containers,
you can do so using the :class:`arrow::stl::allocator` wrapper.

Conversely, you can also use a STL allocator to allocate Arrow memory,
using the :class:`arrow::stl::STLMemoryPool` class.  However, this may be less
performant, as STL allocators don't provide a resizing operation.

Devices
=======

Many Arrow applications only access host (CPU) memory.  However, in some cases
it is desirable to handle on-device memory (such as on-board memory on a GPU)
as well as host memory.

Arrow represents the CPU and other devices using the
:class:`arrow::Device` abstraction.  The associated class :class:`arrow::MemoryManager`
specifies how to allocate on a given device.  Each device has a default memory manager, but
additional instances may be constructed (for example, wrapping a custom
:class:`arrow::MemoryPool` the CPU).
:class:`arrow::MemoryManager` instances which specify how to allocate
memory on a given device (for example, using a particular
:class:`arrow::MemoryPool` on the CPU).

Device-Agnostic Programming
---------------------------

If you receive a Buffer from third-party code, you can query whether it is
CPU-readable by calling its :func:`~arrow::Buffer::is_cpu` method.

You can also view the Buffer on a given device, in a generic way, by calling
:func:`arrow::Buffer::View` or :func:`arrow::Buffer::ViewOrCopy`.  This will
be a no-operation if the source and destination devices are identical.
Otherwise, a device-dependent mechanism will attempt to construct a memory
address for the destination device that gives access to the buffer contents.
Actual device-to-device transfer may happen lazily, when reading the buffer
contents.

Similarly, if you want to do I/O on a buffer without assuming a CPU-readable
buffer, you can call :func:`arrow::Buffer::GetReader` and
:func:`arrow::Buffer::GetWriter`.

For example, to get an on-CPU view or copy of an arbitrary buffer, you can
simply do::

   std::shared_ptr<arrow::Buffer> arbitrary_buffer = ... ;
   std::shared_ptr<arrow::Buffer> cpu_buffer = arrow::Buffer::ViewOrCopy(
      arbitrary_buffer, arrow::default_cpu_memory_manager());