Title: feat: tqdm for `to_parquet`
Author: zanussbaum (CONTRIBUTOR)
State: closed
Created: 2023-01-23T22:05:38Z

As described in #5418

I noticed also that the `to_json` function supports multi-workers whereas `to_parquet`, is that not possible/not needed with Parquet or something that hasn't been implemented yet?

Comments:
_The documentation is not available anymore as the PR was closed or merged._

<details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.012395 / 0.011353 (0.001042) | 0.006466 / 0.011008 (-0.004542) | 0.127605 / 0.038508 (0.089097) | 0.044929 / 0.023109 (0.021820) | 0.399856 / 0.275898 (0.123958) | 0.491341 / 0.323480 (0.167861) | 0.009193 / 0.007986 (0.001207) | 0.005419 / 0.004328 (0.001090) | 0.100577 / 0.004250 (0.096327) | 0.045338 / 0.037052 (0.008286) | 0.409970 / 0.258489 (0.151481) | 0.452941 / 0.293841 (0.159100) | 0.054350 / 0.128546 (-0.074197) | 0.019069 / 0.075646 (-0.056578) | 0.427036 / 0.419271 (0.007765) | 0.073616 / 0.043533 (0.030083) | 0.395384 / 0.255139 (0.140245) | 0.442381 / 0.283200 (0.159181) | 0.123185 / 0.141683 (-0.018498) | 1.797640 / 1.452155 (0.345485) | 1.888860 / 1.492716 (0.396143) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.211041 / 0.018006 (0.193035) | 0.539350 / 0.000490 (0.538860) | 0.001683 / 0.000200 (0.001483) | 0.000118 / 0.000054 (0.000064) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.031699 / 0.037411 (-0.005712) | 0.132696 / 0.014526 (0.118170) | 0.133710 / 0.176557 (-0.042846) | 0.190074 / 0.737135 (-0.547061) | 0.142919 / 0.296338 (-0.153420) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.643521 / 0.215209 (0.428312) | 6.137350 / 2.077655 (4.059695) | 2.463894 / 1.504120 (0.959774) | 2.120043 / 1.541195 (0.578848) | 2.121898 / 1.468490 (0.653408) | 1.287319 / 4.584777 (-3.297458) | 5.517864 / 3.745712 (1.772151) | 5.070820 / 5.269862 (-0.199042) | 2.948967 / 4.565676 (-1.616710) | 0.175861 / 0.424275 (-0.248415) | 0.015292 / 0.007607 (0.007685) | 0.843195 / 0.226044 (0.617150) | 7.884275 / 2.268929 (5.615347) | 3.182821 / 55.444624 (-52.261803) | 2.576093 / 6.876477 (-4.300384) | 2.537160 / 2.142072 (0.395088) | 1.510029 / 4.805227 (-3.295198) | 0.249404 / 6.500664 (-6.251260) | 0.080434 / 0.075469 (0.004965) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.618695 / 1.841788 (-0.223093) | 18.879207 / 8.074308 (10.804899) | 21.075272 / 10.191392 (10.883880) | 0.260781 / 0.680424 (-0.419643) | 0.046387 / 0.534201 (-0.487813) | 0.570709 / 0.579283 (-0.008574) | 0.619050 / 0.434364 (0.184686) | 0.642295 / 0.540337 (0.101958) | 0.780070 / 1.386936 (-0.606866) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.010418 / 0.011353 (-0.000935) | 0.006104 / 0.011008 (-0.004905) | 0.133609 / 0.038508 (0.095101) | 0.035101 / 0.023109 (0.011992) | 0.471931 / 0.275898 (0.196033) | 0.504498 / 0.323480 (0.181018) | 0.007388 / 0.007986 (-0.000598) | 0.004852 / 0.004328 (0.000523) | 0.094535 / 0.004250 (0.090284) | 0.056832 / 0.037052 (0.019779) | 0.470513 / 0.258489 (0.212024) | 0.531285 / 0.293841 (0.237444) | 0.058271 / 0.128546 (-0.070276) | 0.020523 / 0.075646 (-0.055123) | 0.437398 / 0.419271 (0.018126) | 0.065390 / 0.043533 (0.021857) | 0.503702 / 0.255139 (0.248563) | 0.515876 / 0.283200 (0.232677) | 0.118615 / 0.141683 (-0.023068) | 1.865380 / 1.452155 (0.413225) | 1.990316 / 1.492716 (0.497600) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.246772 / 0.018006 (0.228766) | 0.560607 / 0.000490 (0.560118) | 0.005675 / 0.000200 (0.005475) | 0.000142 / 0.000054 (0.000088) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.034692 / 0.037411 (-0.002719) | 0.174016 / 0.014526 (0.159490) | 0.179838 / 0.176557 (0.003282) | 0.217118 / 0.737135 (-0.520018) | 0.184811 / 0.296338 (-0.111527) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.675970 / 0.215209 (0.460760) | 6.787039 / 2.077655 (4.709384) | 2.932619 / 1.504120 (1.428499) | 2.545076 / 1.541195 (1.003882) | 2.566705 / 1.468490 (1.098215) | 1.287365 / 4.584777 (-3.297412) | 5.468441 / 3.745712 (1.722729) | 5.227726 / 5.269862 (-0.042136) | 2.868970 / 4.565676 (-1.696706) | 0.153535 / 0.424275 (-0.270740) | 0.020087 / 0.007607 (0.012480) | 0.860562 / 0.226044 (0.634518) | 8.656109 / 2.268929 (6.387180) | 3.749424 / 55.444624 (-51.695200) | 3.011337 / 6.876477 (-3.865139) | 3.119045 / 2.142072 (0.976973) | 1.562174 / 4.805227 (-3.243053) | 0.279161 / 6.500664 (-6.221504) | 0.084905 / 0.075469 (0.009436) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.638684 / 1.841788 (-0.203104) | 18.834760 / 8.074308 (10.760452) | 21.554310 / 10.191392 (11.362918) | 0.274518 / 0.680424 (-0.405906) | 0.030343 / 0.534201 (-0.503858) | 0.539094 / 0.579283 (-0.040189) | 0.627258 / 0.434364 (0.192895) | 0.624638 / 0.540337 (0.084301) | 0.742776 / 1.386936 (-0.644160) |

</details>
</details>

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