Conversion from one dataset to another that will not fit in memory?

Having found the following description in the documentation, I tried the operation of scanning a dataset larger than memory and writing it to another dataset.

https://arrow.apache.org/docs/python/dataset.html#writing-large-amounts-of-data

> The above examples wrote data from a table. If you are writing a large amount of data you may not be able to load everything into a single in-memory table. Fortunately, the write_dataset() method also accepts an iterable of record batches. This makes it really simple, for example, to repartition a large dataset without loading the entire dataset into memory:

```python
import pyarrow.dataset as ds

input_dataset = ds.dataset("input")
ds.write_dataset(inpute_dataset.scanner(), "output", format="parquet")
```

```r
arrow::open_dataset("input") |>
arrow::write_dataset("output")
```

But both Python and R on Windows crashed due to lack of memory. Am I missing something?
Is there a recommended way to convert one dataset to another without running out of computer memory?

Python のデータセットドキュメントにある大量のデータの書き込みに関するセクションから始め、Windows で scanner-to-write_dataset の例を再現します。R の open_dataset |> write_dataset の例と比較してください。文書化されているどちらかの変換が、メモリより大きい入力に対してクラッシュするかを確認し、文書化された操作がメモリを使い果たさずに完了するか、制限が明確に文書化されていれば、issue は完了したものとします。

索引モデルが issue の本文から書いたものです。