Fix CI: pyarrow 15, pandas 2.2 and sqlachemy
State: closed | Author: lhoestq (MEMBER)

this should fix the CI failures on `main`

close https://github.com/huggingface/datasets/issues/5477

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6617). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

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

PyArrow==8.0.0

<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) | 0.004774 / 0.011353 (-0.006579) | 0.003397 / 0.011008 (-0.007611) | 0.063862 / 0.038508 (0.025354) | 0.029353 / 0.023109 (0.006244) | 0.245921 / 0.275898 (-0.029977) | 0.268414 / 0.323480 (-0.055066) | 0.002834 / 0.007986 (-0.005152) | 0.002606 / 0.004328 (-0.001723) | 0.049690 / 0.004250 (0.045439) | 0.041637 / 0.037052 (0.004585) | 0.262526 / 0.258489 (0.004037) | 0.288200 / 0.293841 (-0.005641) | 0.027233 / 0.128546 (-0.101313) | 0.010322 / 0.075646 (-0.065324) | 0.213860 / 0.419271 (-0.205411) | 0.034930 / 0.043533 (-0.008602) | 0.249256 / 0.255139 (-0.005883) | 0.270016 / 0.283200 (-0.013184) | 0.019413 / 0.141683 (-0.122270) | 1.124801 / 1.452155 (-0.327354) | 1.166224 / 1.492716 (-0.326492) |

### 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) | 0.091641 / 0.018006 (0.073635) | 0.299679 / 0.000490 (0.299189) | 0.000209 / 0.000200 (0.000009) | 0.000051 / 0.000054 (-0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018084 / 0.037411 (-0.019327) | 0.060143 / 0.014526 (0.045617) | 0.072556 / 0.176557 (-0.104001) | 0.118555 / 0.737135 (-0.618580) | 0.073786 / 0.296338 (-0.222553) |

### 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) | 0.278193 / 0.215209 (0.062984) | 2.707954 / 2.077655 (0.630300) | 1.483575 / 1.504120 (-0.020545) | 1.371939 / 1.541195 (-0.169256) | 1.395009 / 1.468490 (-0.073481) | 0.559949 / 4.584777 (-4.024828) | 2.372529 / 3.745712 (-1.373183) | 2.823641 / 5.269862 (-2.446221) | 1.722999 / 4.565676 (-2.842678) | 0.062535 / 0.424275 (-0.361741) | 0.004970 / 0.007607 (-0.002637) | 0.338625 / 0.226044 (0.112580) | 3.317576 / 2.268929 (1.048648) | 1.854552 / 55.444624 (-53.590073) | 1.589323 / 6.876477 (-5.287154) | 1.624630 / 2.142072 (-0.517442) | 0.638388 / 4.805227 (-4.166839) | 0.116675 / 6.500664 (-6.383989) | 0.041850 / 0.075469 (-0.033619) |

### 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) | 0.938025 / 1.841788 (-0.903763) | 11.450072 / 8.074308 (3.375764) | 10.414943 / 10.191392 (0.223551) | 0.128416 / 0.680424 (-0.552007) | 0.013798 / 0.534201 (-0.520403) | 0.287997 / 0.579283 (-0.291286) | 0.259976 / 0.434364 (-0.174387) | 0.320737 / 0.540337 (-0.219601) | 0.424292 / 1.386936 (-0.962644) |

</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) | 0.005107 / 0.011353 (-0.006246) | 0.003374 / 0.011008 (-0.007634) | 0.050067 / 0.038508 (0.011559) | 0.031419 / 0.023109 (0.008310) | 0.275303 / 0.275898 (-0.000595) | 0.286736 / 0.323480 (-0.036744) | 0.004177 / 0.007986 (-0.003808) | 0.002742 / 0.004328 (-0.001586) | 0.049011 / 0.004250 (0.044761) | 0.044373 / 0.037052 (0.007321) | 0.289189 / 0.258489 (0.030700) | 0.320117 / 0.293841 (0.026276) | 0.050154 / 0.128546 (-0.078392) | 0.010541 / 0.075646 (-0.065106) | 0.058318 / 0.419271 (-0.360954) | 0.033090 / 0.043533 (-0.010443) | 0.276820 / 0.255139 (0.021681) | 0.290854 / 0.283200 (0.007654) | 0.017268 / 0.141683 (-0.124415) | 1.159345 / 1.452155 (-0.292809) | 1.224829 / 1.492716 (-0.267887) |

### 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) | 0.092468 / 0.018006 (0.074462) | 0.301176 / 0.000490 (0.300686) | 0.000216 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000012) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021858 / 0.037411 (-0.015553) | 0.074873 / 0.014526 (0.060347) | 0.086238 / 0.176557 (-0.090318) | 0.125555 / 0.737135 (-0.611580) | 0.087791 / 0.296338 (-0.208547) |

### 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) | 0.292283 / 0.215209 (0.077073) | 2.847306 / 2.077655 (0.769651) | 1.600833 / 1.504120 (0.096713) | 1.474253 / 1.541195 (-0.066942) | 1.474871 / 1.468490 (0.006381) | 0.576427 / 4.584777 (-4.008350) | 2.380116 / 3.745712 (-1.365596) | 2.782059 / 5.269862 (-2.487803) | 1.730642 / 4.565676 (-2.835035) | 0.063860 / 0.424275 (-0.360415) | 0.005019 / 0.007607 (-0.002588) | 0.343247 / 0.226044 (0.117202) | 3.393427 / 2.268929 (1.124498) | 1.935346 / 55.444624 (-53.509278) | 1.680124 / 6.876477 (-5.196353) | 1.665788 / 2.142072 (-0.476285) | 0.648767 / 4.805227 (-4.156460) | 0.121962 / 6.500664 (-6.378702) | 0.040669 / 0.075469 (-0.034800) |

### 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) | 0.996535 / 1.841788 (-0.845252) | 12.074553 / 8.074308 (4.000245) | 10.812740 / 10.191392 (0.621348) | 0.142690 / 0.680424 (-0.537734) | 0.014977 / 0.534201 (-0.519224) | 0.285619 / 0.579283 (-0.293664) | 0.269401 / 0.434364 (-0.164963) | 0.329882 / 0.540337 (-0.210456) | 0.416169 / 1.386936 (-0.970767) |

</details>
</details>

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