amol- commented on a change in pull request #10266:
URL: https://github.com/apache/arrow/pull/10266#discussion_r636905643
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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:
I don't have a strong opinion about using `ipython` blocks or just code
blocks. I used them just for consistency with the rest of the document.
I think that they have a value in at least verifying that the code you
provided as an example can actually execute (even thought it might lead to
different results) which makes more easy to catch examples that became invalid
due to api changes.
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