Outliers: 9


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 22:48 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.876 s 0.476987
2021-10-13 22:48 Python csv-read uncompressed, file, fanniemae_2016Q4 1.170 s 0.102969
2021-10-13 22:48 Python csv-read gzip, streaming, fanniemae_2016Q4 14.811 s 0.458492
2021-10-13 23:37 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.758 s 2.162447
2021-10-13 22:49 Python csv-read gzip, file, fanniemae_2016Q4 6.026 s 0.822658
2021-10-13 22:49 Python csv-read uncompressed, file, nyctaxi_2010-01 1.029 s -1.707235
2021-10-13 22:50 Python csv-read gzip, streaming, nyctaxi_2010-01 10.674 s -0.786685
2021-10-13 22:49 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.687 s -0.513056
2021-10-13 22:52 Python dataframe-to-table chi_traffic_2020_Q1 19.216 s 1.020684
2021-10-13 22:51 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.439816
2021-10-13 22:52 Python dataframe-to-table type_strings 0.364 s 0.707961
2021-10-13 23:27 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.890 s -2.162604
2021-10-13 23:29 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.034 s 0.054493
2021-10-13 23:33 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.740 s 0.325290
2021-10-13 23:52 R dataframe-to-table type_floats, R 0.013 s 1.001103
2021-10-13 22:52 Python dataframe-to-table type_dict 0.012 s 0.326571
2021-10-13 23:26 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.920 s 0.540823
2021-10-13 23:32 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.489 s -0.482095
2021-10-13 23:34 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.341 s -0.055504
2021-10-13 23:35 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.829 s 0.387649
2021-10-13 23:52 R dataframe-to-table type_dict, R 0.062 s -1.966872
2021-10-13 23:54 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.211 s 0.947696
2021-10-13 22:53 Python dataframe-to-table type_nested 2.881 s -0.138203
2021-10-13 22:52 Python dataframe-to-table type_integers 0.011 s 0.151734
2021-10-13 23:28 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.621 s 1.756465
2021-10-13 23:30 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.565274
2021-10-13 22:52 Python dataframe-to-table type_floats 0.011 s 0.669130
2021-10-13 23:52 R dataframe-to-table type_strings, R 0.489 s 0.230984
2021-10-13 23:52 R dataframe-to-table type_integers, R 0.010 s 0.990291
2021-10-13 23:53 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.319 s -1.659092
2021-10-13 23:15 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.076 s -0.330792
2021-10-13 23:27 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.306 s -2.164042
2021-10-13 23:33 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.216 s 0.522165
2021-10-13 23:53 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.220 s 0.367007
2021-10-13 22:53 Python dataset-filter nyctaxi_2010-01 4.372 s -1.582763
2021-10-13 23:29 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.027 s 0.711985
2021-10-13 23:30 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.125 s 1.948573
2021-10-13 23:31 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.075 s 0.598490
2021-10-13 23:32 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.443 s 0.509300
2021-10-13 23:36 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.921 s -0.108529
2021-10-13 23:37 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.323 s 0.763794
2021-10-13 23:30 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.500338
2021-10-13 23:37 Python wide-dataframe use_legacy_dataset=false 0.619 s 0.240191
2021-10-13 22:56 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.525 s -0.056025
2021-10-13 23:26 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.843 s 0.239252
2021-10-13 23:28 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.135 s 0.132509
2021-10-13 23:35 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.854 s -0.034360
2021-10-13 23:37 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.170727
2021-10-13 23:27 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.307 s -1.281933
2021-10-13 23:28 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.291 s -0.254362
2021-10-13 23:29 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.146 s 1.753133
2021-10-13 23:34 Python file-write lz4, feather, table, fanniemae_2016Q4 1.142 s 1.102640
2021-10-13 23:52 R dataframe-to-table chi_traffic_2020_Q1, R 3.394 s 0.266210
2021-10-13 23:36 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.863 s 0.378639
2021-10-13 23:28 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.687 s 1.864513
2021-10-13 23:34 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.827 s 0.213542
2021-10-13 23:56 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.833 s 0.588858
2021-10-13 23:26 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 2.350 s -5.818111
2021-10-13 23:00 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.907 s -0.381924
2021-10-13 23:37 Python file-write lz4, feather, table, nyctaxi_2010-01 1.791 s 0.784568
2021-10-13 23:28 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s -0.061137
2021-10-13 23:54 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.767 s 420.054046
2021-10-13 23:55 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.683 s 0.085110
2021-10-13 23:10 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.482 s 0.056342
2021-10-13 23:14 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.068 s -1.190205
2021-10-13 23:31 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.812 s 1.488396
2021-10-13 23:54 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 1.114 s 135.803307
2021-10-13 23:54 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.112 s 0.687187
2021-10-13 23:53 R dataframe-to-table type_nested, R 0.531 s 0.233664
2021-10-13 23:54 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.051 s 0.434264
2021-10-13 23:30 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.273 s 1.932545
2021-10-13 23:15 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.052 s -0.335125
2021-10-13 23:53 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.208 s 0.937472
2021-10-13 23:53 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.450 s 0.948983
2021-10-13 23:54 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.574 s -2.356751
2021-10-13 23:10 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.320 s 0.100130
2021-10-13 23:26 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.050 s -1.960441
2021-10-13 23:29 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.007 s 1.809213
2021-10-13 23:36 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.342 s 0.460945
2021-10-13 23:55 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.611 s 160.590679
2021-10-13 23:28 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.544 s 1.721382
2021-10-13 23:54 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.161 s 0.954915
2021-10-13 23:55 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.770 s 126.565725
2021-10-13 23:54 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -1.704176
2021-10-13 23:53 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.454 s 0.971727
2021-10-14 08:48 Python csv-read uncompressed, file, fanniemae_2016Q4 1.184 s -0.786656
2021-10-14 09:11 Python dataset-read async=True, nyctaxi_multi_ipc_s3 204.113 s -2.778806
2021-10-14 08:49 Python csv-read gzip, file, fanniemae_2016Q4 6.023 s 1.596766
2021-10-14 09:37 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.866 s 0.332907
2021-10-14 08:51 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s -0.133546
2021-10-14 08:53 Python dataframe-to-table type_strings 0.366 s 0.526607
2021-10-14 08:49 Python csv-read gzip, streaming, fanniemae_2016Q4 14.802 s 0.555358
2021-10-14 09:53 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.215 s -1.445507
2021-10-14 08:53 Python dataframe-to-table type_nested 2.856 s 1.330840
2021-10-14 08:48 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.853 s 0.744175
2021-10-14 08:53 Python dataframe-to-table type_integers 0.011 s -0.023188
2021-10-14 09:11 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.174 s 0.253947
2021-10-14 08:53 Python dataframe-to-table type_dict 0.012 s 0.315671
2021-10-14 08:53 Python dataset-filter nyctaxi_2010-01 4.355 s -0.615150
2021-10-14 08:50 Python csv-read uncompressed, file, nyctaxi_2010-01 1.021 s -0.972329
2021-10-14 09:00 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.028 s 0.317199
2021-10-14 08:50 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.683 s -0.485785
2021-10-14 08:50 Python csv-read gzip, streaming, nyctaxi_2010-01 10.669 s -0.742856
2021-10-14 08:53 Python dataframe-to-table type_floats 0.011 s 0.318560
2021-10-14 08:52 Python dataframe-to-table chi_traffic_2020_Q1 19.258 s 0.903490
2021-10-14 08:56 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 56.853 s 1.308825
2021-10-14 09:15 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.063 s -1.006980
2021-10-14 09:15 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.042 s -0.189342
2021-10-14 09:15 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.084 s -0.422387
2021-10-14 09:30 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.033 s 0.130550
2021-10-14 09:30 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.171 s 0.674080
2021-10-14 09:35 Python file-write lz4, feather, table, fanniemae_2016Q4 1.145 s 0.951840
2021-10-14 09:52 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.221 s 0.359025
2021-10-14 09:27 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.046 s -1.864635
2021-10-14 09:29 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.009 s 1.796229
2021-10-14 09:51 R dataframe-to-table type_integers, R 0.010 s 0.989759
2021-10-14 09:53 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.764 s 420.206690
2021-10-14 09:30 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.130 s 1.886091
2021-10-14 09:52 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.457 s 0.970597
2021-10-14 09:53 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.581027
2021-10-14 09:53 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.040 s 2.020622
2021-10-14 09:29 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.302 s -1.734691
2021-10-14 09:34 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.828 s 0.205752
2021-10-14 09:38 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.773 s 1.634967
2021-10-14 09:52 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.218 s 0.396722
2021-10-14 09:27 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.701 s 0.440533
2021-10-14 09:29 Python file-read lz4, feather, table, fanniemae_2016Q4 0.618 s -2.514492
2021-10-14 09:37 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.290 s 1.674060
2021-10-14 09:28 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.124 s 0.710778
2021-10-14 09:30 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.115 s 2.171126
2021-10-14 09:36 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.834 s 0.327767
2021-10-14 09:36 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.928 s -0.207041
2021-10-14 09:55 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.843 s 0.518047
2021-10-14 09:27 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.300 s -1.997539
2021-10-14 09:28 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.882 s -1.932332
2021-10-14 09:28 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.530 s 1.848255
2021-10-14 09:51 R dataframe-to-table type_dict, R 0.063 s -2.235646
2021-10-14 09:52 R dataframe-to-table type_floats, R 0.013 s 0.998837
2021-10-14 09:53 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.105 s 1.190282
2021-10-14 09:29 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.703 s 1.711004
2021-10-14 09:54 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.692 s -0.004678
2021-10-14 09:27 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.932 s 0.483811
2021-10-14 09:38 Python wide-dataframe use_legacy_dataset=false 0.612 s 1.546774
2021-10-14 09:28 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.622 s 1.750998
2021-10-14 09:31 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.781 s 1.874842
2021-10-14 09:33 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.434 s 0.591200
2021-10-14 09:52 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.321 s -1.817082
2021-10-14 09:38 Python wide-dataframe use_legacy_dataset=true 0.390 s 1.458437
2021-10-14 09:54 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.772 s 126.554167
2021-10-14 09:32 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.075 s 0.598064
2021-10-14 09:34 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.720 s 0.472451
2021-10-14 09:52 R dataframe-to-table type_nested, R 0.527 s 0.234630
2021-10-14 09:53 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 1.105 s 135.938839
2021-10-14 09:31 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.280 s 1.848655
2021-10-14 09:52 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.451 s 0.948543
2021-10-14 09:53 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.208 s 0.948174
2021-10-14 09:54 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.607 s 160.634655
2021-10-14 09:27 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.783 s 0.577963
2021-10-14 09:34 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.349 s -0.204211
2021-10-14 09:29 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.015 s 1.165384
2021-10-14 09:35 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.308 s 0.256768
2021-10-14 09:51 R dataframe-to-table chi_traffic_2020_Q1, R 3.400 s 0.266134
2021-10-14 09:28 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.268 s 1.100074
2021-10-14 09:32 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.354 s 0.468946
2021-10-14 09:37 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.342 s 0.443372
2021-10-14 09:37 Python file-write lz4, feather, table, nyctaxi_2010-01 1.798 s 0.437571
2021-10-14 09:53 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.160 s 0.955002
2021-10-14 09:31 Python file-read lz4, feather, table, nyctaxi_2010-01 0.673 s -0.383357
2021-10-14 09:35 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.860 s -0.131048
2021-10-14 09:51 R dataframe-to-table type_strings, R 0.492 s 0.230280