pitrou commented on a change in pull request #10266:
URL: https://github.com/apache/arrow/pull/10266#discussion_r657126453
##########
File path: docs/source/python/memory.rst
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@@ -277,6 +277,95 @@ types than with normal Python file objects.
!rm example.dat
!rm example2.dat
+Efficiently Writing and Reading Arrow Arrays
+--------------------------------------------
+
+Being optimized for zero copy and memory mapped data, Arrow allows to easily
+read and write arrays consuming the minimum amount of resident memory.
+
+When writing and reading raw arrow data, we can use the Arrow File Format
+or the Arrow Streaming Format.
+
+To dump an array to file, you can use the :meth:`~pyarrow.ipc.new_file`
+which will provide a new :class:`~pyarrow.ipc.RecordBatchFileWriter` instance
+that can be used to write batches of data to that file.
+
+For example to write an array of 100M integers, we could write it in 1000
chunks
+of 100000 entries:
+
+.. ipython:: python
Review comment:
In this case in particular, we're creating and serializing a large
amount of data, so that would really seem to add some cost to building the docs.
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