Fast dataset iter
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2022-09-27T16:52:43Z
https://github.com/huggingface/datasets/pull/5030#issuecomment-1259786096
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2022-09-29T15:50:44Z
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I ran some benchmarks (focused on the data fetching part of `__iter__`) and it seems like the combination `table.to_reader(batch_size)` + `RecordBatch.slice` performs the best ([script](https://gist.github.com/mariosasko/0248288a2e3a7556873969717c1fe52b) with the results). I think we can choose (implicit) `batch_size=10` in the final implementation to avoid having problems with fetching large examples.
2022-09-28T17:56:20Z
https://github.com/huggingface/datasets/pull/5030#issuecomment-1261273236
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2022-09-28T17:56:20Z
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Use `pa.Table.to_reader` to make iteration over examples/batches faster in `Dataset.{__iter__, map}`

TODO:
* [x] benchmarking (the only benchmark for now - iterating over (single) examples of `bookcorpus` (75 mil examples) in Colab is approx. 2.3x faster)
* [x] check if iterating over bigger chunks + slicing to fetch individual examples in `_iter` yields better performance


