Fix time type _arrow_to_datasets_dtype conversion
#4628
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Show benchmarks
PyArrow==6.0.0
Show updated benchmarks!
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) | 0.008753 / 0.011353 (-0.002600) | 0.004159 / 0.011008 (-0.006850) | 0.029933 / 0.038508 (-0.008575) | 0.035409 / 0.023109 (0.012300) | 0.307991 / 0.275898 (0.032093) | 0.332821 / 0.323480 (0.009341) | 0.006628 / 0.007986 (-0.001357) | 0.004980 / 0.004328 (0.000652) | 0.007406 / 0.004250 (0.003156) | 0.038529 / 0.037052 (0.001477) | 0.285959 / 0.258489 (0.027470) | 0.343784 / 0.293841 (0.049943) | 0.031949 / 0.128546 (-0.096597) | 0.009837 / 0.075646 (-0.065809) | 0.252360 / 0.419271 (-0.166912) | 0.052521 / 0.043533 (0.008988) | 0.292870 / 0.255139 (0.037731) | 0.314168 / 0.283200 (0.030968) | 0.092384 / 0.141683 (-0.049299) | 1.817181 / 1.452155 (0.365026) | 1.895129 / 1.492716 (0.402413) |
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) | 0.329868 / 0.018006 (0.311862) | 0.578615 / 0.000490 (0.578125) | 0.020492 / 0.000200 (0.020292) | 0.000150 / 0.000054 (0.000095) |
Benchmark: benchmark_indices_mapping.json
| metric | select | shard | shuffle | sort | train_test_split |
|---|---|---|---|---|---|
| new / old (diff) | 0.026803 / 0.037411 (-0.010608) | 0.109268 / 0.014526 (0.094742) | 0.116576 / 0.176557 (-0.059980) | 0.162952 / 0.737135 (-0.574184) | 0.119381 / 0.296338 (-0.176957) |
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) | 0.420224 / 0.215209 (0.205015) | 4.183476 / 2.077655 (2.105822) | 1.795067 / 1.504120 (0.290947) | 1.592892 / 1.541195 (0.051697) | 1.715241 / 1.468490 (0.246751) | 0.438456 / 4.584777 (-4.146321) | 4.822341 / 3.745712 (1.076628) | 2.222622 / 5.269862 (-3.047240) | 0.942113 / 4.565676 (-3.623563) | 0.052940 / 0.424275 (-0.371335) | 0.012035 / 0.007607 (0.004428) | 0.518633 / 0.226044 (0.292589) | 5.202614 / 2.268929 (2.933686) | 2.214123 / 55.444624 (-53.230501) | 1.887806 / 6.876477 (-4.988670) | 2.059426 / 2.142072 (-0.082647) | 0.561196 / 4.805227 (-4.244032) | 0.122998 / 6.500664 (-6.377667) | 0.062548 / 0.075469 (-0.012921) |
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) | 1.613139 / 1.841788 (-0.228648) | 14.902228 / 8.074308 (6.827920) | 26.542624 / 10.191392 (16.351232) | 0.871870 / 0.680424 (0.191447) | 0.528610 / 0.534201 (-0.005591) | 0.494000 / 0.579283 (-0.085283) | 0.515395 / 0.434364 (0.081031) | 0.329894 / 0.540337 (-0.210443) | 0.348506 / 1.386936 (-1.038430) |
Show updated benchmarks!
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) | 0.008817 / 0.011353 (-0.002535) | 0.004320 / 0.011008 (-0.006688) | 0.029670 / 0.038508 (-0.008838) | 0.035356 / 0.023109 (0.012247) | 0.314365 / 0.275898 (0.038467) | 0.329691 / 0.323480 (0.006211) | 0.006897 / 0.007986 (-0.001088) | 0.003889 / 0.004328 (-0.000439) | 0.007736 / 0.004250 (0.003486) | 0.040925 / 0.037052 (0.003873) | 0.293496 / 0.258489 (0.035007) | 0.336601 / 0.293841 (0.042760) | 0.031702 / 0.128546 (-0.096844) | 0.009894 / 0.075646 (-0.065753) | 0.252631 / 0.419271 (-0.166641) | 0.052360 / 0.043533 (0.008827) | 0.298285 / 0.255139 (0.043146) | 0.324213 / 0.283200 (0.041014) | 0.097796 / 0.141683 (-0.043887) | 1.853045 / 1.452155 (0.400890) | 1.894968 / 1.492716 (0.402252) |
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) | 0.347924 / 0.018006 (0.329917) | 0.569546 / 0.000490 (0.569056) | 0.034078 / 0.000200 (0.033879) | 0.000433 / 0.000054 (0.000378) |
Benchmark: benchmark_indices_mapping.json
| metric | select | shard | shuffle | sort | train_test_split |
|---|---|---|---|---|---|
| new / old (diff) | 0.028256 / 0.037411 (-0.009155) | 0.107045 / 0.014526 (0.092519) | 0.114863 / 0.176557 (-0.061694) | 0.156924 / 0.737135 (-0.580211) | 0.116138 / 0.296338 (-0.180201) |
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) | 0.422840 / 0.215209 (0.207631) | 4.227137 / 2.077655 (2.149482) | 1.889461 / 1.504120 (0.385341) | 1.702655 / 1.541195 (0.161460) | 1.853991 / 1.468490 (0.385500) | 0.437951 / 4.584777 (-4.146826) | 4.613988 / 3.745712 (0.868276) | 3.711988 / 5.269862 (-1.557874) | 0.930517 / 4.565676 (-3.635159) | 0.053542 / 0.424275 (-0.370733) | 0.012215 / 0.007607 (0.004608) | 0.534874 / 0.226044 (0.308829) | 5.385805 / 2.268929 (3.116876) | 2.319419 / 55.444624 (-53.125205) | 2.015965 / 6.876477 (-4.860511) | 2.207421 / 2.142072 (0.065349) | 0.560491 / 4.805227 (-4.244736) | 0.124876 / 6.500664 (-6.375788) | 0.061886 / 0.075469 (-0.013583) |
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) | 1.612840 / 1.841788 (-0.228948) | 15.091749 / 8.074308 (7.017441) | 26.719293 / 10.191392 (16.527900) | 0.876356 / 0.680424 (0.195932) | 0.526170 / 0.534201 (-0.008031) | 0.494383 / 0.579283 (-0.084900) | 0.508831 / 0.434364 (0.074467) | 0.323148 / 0.540337 (-0.217189) | 0.333935 / 1.386936 (-1.053001) |
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Fix #4620
The issue stems from the fact that
pa.array([time_data]).typereturnsDataType(time64[unit]), which doesn't expose theunitattribute, instead ofTime64Type(time64[unit]). I believe this is a bug in PyArrow. Luckily, the both types have the samestr(), so in this PR I callpa.type_for_alias(str(type))to convert them both to theTime64Type(time64[unit])format.cc @severo