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?

從 Python 資料集文件中關於寫入大量資料的部分開始，並在 Windows 上重現 scanner-to-write_dataset 範例；將其與 R 的 open_dataset |> write_dataset 範例進行比較。檢查任一有文件記錄的轉換在輸入大於記憶體時是否會崩潰，並在有文件記錄的操作完成且未耗盡記憶體，或該限制已獲得明確記錄時，認為 issue 已完成。

由索引模型根據 Issue 內容生成。