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?

从 Python 数据集文档中关于写入大量数据的部分开始，并在 Windows 上复现 scanner-to-write_dataset 示例；将其与 R 的 open_dataset |> write_dataset 示例进行比较。检查任一有文档记录的转换在输入大于内存时是否会崩溃，并在有文档记录的操作完成且未耗尽内存，或该限制已得到明确记录时，认为 issue 已完成。

由索引模型根据 Issue 内容生成。