Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-04 18:32 Python dataframe-to-table type_floats 0.011 s -0.010011
2021-10-04 18:54 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.036 s -0.057712
2021-10-04 19:10 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.322 s -1.438122
2021-10-04 18:27 Python csv-read uncompressed, file, fanniemae_2016Q4 1.149 s 1.422334
2021-10-04 19:06 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.952 s 0.412353
2021-10-04 19:09 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.243 s -1.332992
2021-10-04 19:16 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.849 s 1.418757
2021-10-04 19:18 Python wide-dataframe use_legacy_dataset=true 0.396 s -1.332998
2021-10-04 18:50 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.271 s 0.111070
2021-10-04 19:09 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.073 s -1.020778
2021-10-04 19:10 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.052 s -0.912182
2021-10-04 19:11 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s 0.100105
2021-10-04 19:17 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.319 s 0.459591
2021-10-04 18:32 Python dataframe-to-table chi_traffic_2020_Q1 19.607 s 0.509067
2021-10-04 18:28 Python csv-read gzip, file, fanniemae_2016Q4 6.023 s 1.630262
2021-10-04 18:27 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.957 s -0.429169
2021-10-04 18:28 Python csv-read gzip, streaming, fanniemae_2016Q4 14.873 s -0.293118
2021-10-04 18:29 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.524 s 1.367964
2021-10-04 19:07 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.254 s -0.353306
2021-10-04 19:08 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.284 s 1.093817
2021-10-04 19:14 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.293 s 0.329837
2021-10-04 18:30 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.206823
2021-10-04 18:32 Python dataframe-to-table type_dict 0.012 s 0.410312
2021-10-04 18:54 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.072 s -0.480003
2021-10-04 19:09 Python file-read lz4, feather, table, fanniemae_2016Q4 0.605 s -0.378621
2021-10-04 19:13 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.452 s 0.951739
2021-10-04 19:07 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.750 s 0.076225
2021-10-04 19:08 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.796 s -1.375880
2021-10-04 19:10 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.299 s -1.341342
2021-10-04 18:32 Python dataframe-to-table type_nested 2.881 s 0.958590
2021-10-04 19:15 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.828 s 0.769547
2021-10-04 19:18 Python file-write lz4, feather, table, nyctaxi_2010-01 1.800 s 0.609041
2021-10-04 18:33 Python dataset-filter nyctaxi_2010-01 4.353 s 0.505709
2021-10-04 18:29 Python csv-read uncompressed, file, nyctaxi_2010-01 1.018 s -0.446187
2021-10-04 18:32 Python dataframe-to-table type_strings 0.374 s -0.270132
2021-10-04 18:36 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 64.108 s -0.803429
2021-10-04 19:08 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.832 s -0.256580
2021-10-04 19:08 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.883 s -1.664252
2021-10-04 19:14 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.689 s 0.305655
2021-10-04 18:50 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.148 s 0.253283
2021-10-04 19:09 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.944 s -1.442821
2021-10-04 18:32 Python dataframe-to-table type_simple_features 0.914 s -0.114289
2021-10-04 19:06 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.932 s -0.154121
2021-10-04 19:12 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.091 s 0.995514
2021-10-04 19:16 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.831 s 0.494292
2021-10-04 18:54 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.004 s 0.269575
2021-10-04 19:08 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.154 s -0.734485
2021-10-04 19:11 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.944 s -1.318926
2021-10-04 19:18 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.816 s 0.246355
2021-10-04 18:32 Python dataframe-to-table type_integers 0.011 s 1.240398
2021-10-04 18:40 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.531 s 0.943718
2021-10-04 19:11 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.455 s -1.366725
2021-10-04 19:14 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.841 s -0.728270
2021-10-04 19:17 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.353 s -0.092164
2021-10-04 19:07 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.015 s -0.482169
2021-10-04 19:08 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.292 s -0.242363
2021-10-04 19:12 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.348 s 0.213629
2021-10-04 19:15 Python file-write lz4, feather, table, fanniemae_2016Q4 1.160 s 0.240921
2021-10-04 19:15 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.293 s -0.607271
2021-10-04 19:18 Python wide-dataframe use_legacy_dataset=false 0.623 s -0.763401
2021-10-04 19:17 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.820 s 0.807110
2021-10-04 19:57 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.901 s 0.232306
2021-10-04 20:21 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=10, R 0.281 s
2021-10-04 18:30 Python csv-read gzip, streaming, nyctaxi_2010-01 10.487 s 1.636021
2021-10-04 19:10 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.170 s 1.304840
2021-10-04 19:32 R dataframe-to-table type_dict, R 0.051 s -0.059456
2021-10-04 20:02 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.531 s -0.219415
2021-10-04 20:18 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.563 s 1.054037
2021-10-04 20:18 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.519 s -0.345496
2021-10-04 20:20 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=1, R 0.200 s
2021-10-04 19:32 R dataframe-to-table type_strings, R 0.490 s 0.710200
2021-10-04 20:03 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.855 s 0.982756
2021-10-04 20:11 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.820 s 1.660255
2021-10-04 19:58 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.569 s -1.086917
2021-10-04 20:12 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.777 s 2.128192
2021-10-04 20:19 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.891 s 0.903115
2021-10-04 20:05 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.304 s 1.005728
2021-10-04 20:17 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.588 s 0.802803
2021-10-04 20:22 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=10, R 0.900 s
2021-10-04 20:06 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.733 s 1.051564
2021-10-04 20:09 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.399 s 0.351677
2021-10-04 20:15 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.235 s 1.602578
2021-10-04 20:19 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.618 s -1.529048
2021-10-04 19:32 R dataframe-to-table type_integers, R 0.086 s -1.391740
2021-10-04 19:32 R dataframe-to-table type_nested, R 0.539 s -0.621564
2021-10-04 19:55 R dataframe-to-table type_simple_features, R 275.094 s -0.167423
2021-10-04 20:00 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.400565
2021-10-04 19:57 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.291 s -1.614876
2021-10-04 20:10 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.209 s 0.503055
2021-10-04 20:16 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.492 s -0.346632
2021-10-04 20:13 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.455 s 1.722007
2021-10-04 20:23 R tpch arrow, parquet, memory_map=False, query_id=3, scale_factor=1, R 0.465 s
2021-10-04 19:59 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.168 s 0.388856
2021-10-04 20:00 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.227 s 0.879568
2021-10-04 20:15 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.213480
2021-10-04 19:32 R dataframe-to-table type_floats, R 0.106 s 1.223749
2021-10-04 19:58 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.925 s -0.306459
2021-10-04 19:58 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.386 s -0.126887
2021-10-04 20:18 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.379924
2021-10-04 20:20 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.515 s 1.266646
2021-10-04 20:22 R tpch arrow, native, memory_map=False, query_id=3, scale_factor=1, R 0.266 s
2021-10-04 20:23 R tpch arrow, feather, memory_map=False, query_id=3, scale_factor=1, R 0.631 s
2021-10-04 19:59 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.127 s 0.154250
2021-10-04 19:31 R dataframe-to-table chi_traffic_2020_Q1, R 5.353 s 0.831970
2021-10-04 20:04 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.250 s 1.167523
2021-10-04 20:01 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.999 s -1.304583
2021-10-04 20:20 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=1, R 0.413 s
2021-10-04 19:56 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.235 s 0.326284
2021-10-04 19:56 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.248 s 0.030306
2021-10-04 19:58 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.649965
2021-10-04 20:18 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.189 s -1.576846
2021-10-04 20:08 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.552 s 0.938710
2021-10-04 20:17 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.163 s 1.641882
2021-10-04 20:17 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.867 s 1.030437
2021-10-04 19:56 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.905 s 0.322860
2021-10-04 20:01 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.674 s 0.142422
2021-10-04 20:07 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.832 s -0.004303
2021-10-04 20:14 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.635 s 2.332764
2021-10-04 20:18 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 0.920820
2021-10-04 20:20 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=1, R 0.397 s
2021-10-04 20:22 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=10, R 0.665 s
2021-10-04 22:18 Python wide-dataframe use_legacy_dataset=false 0.618 s 0.529730
2021-10-04 22:32 R dataframe-to-table type_nested, R 0.537 s 0.048281
2021-10-04 21:28 Python csv-read gzip, streaming, fanniemae_2016Q4 14.875 s -0.299710
2021-10-04 22:12 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.295 s 0.433980
2021-10-04 22:31 R dataframe-to-table type_dict, R 0.053 s -0.318422
2021-10-04 21:29 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -0.634628
2021-10-04 22:07 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.250 s -0.261182
2021-10-04 21:33 Python dataframe-to-table type_simple_features 0.914 s -0.145137
2021-10-04 22:06 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.925 s 0.610885
2021-10-04 22:07 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.993 s 0.040408
2021-10-04 22:07 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.289 s -0.086264
2021-10-04 22:09 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.243 s -1.340130
2021-10-04 22:17 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.324 s 1.614126
2021-10-04 22:31 R dataframe-to-table type_floats, R 0.107 s 0.764759
2021-10-04 21:28 Python csv-read uncompressed, file, fanniemae_2016Q4 1.182 s -0.468523
2021-10-04 21:50 Python dataset-read async=True, nyctaxi_multi_ipc_s3 193.685 s -0.592885
2021-10-04 21:50 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.225 s 0.425304
2021-10-04 22:06 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.737 s 0.170945
2021-10-04 22:08 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.806 s -1.550126
2021-10-04 22:10 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.181 s -1.137280
2021-10-04 22:11 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.939 s -1.297848
2021-10-04 21:30 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.477338
2021-10-04 21:32 Python dataframe-to-table type_integers 0.011 s 1.322859
2021-10-04 21:41 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.706 s 0.941945
2021-10-04 22:14 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.744 s -0.077997
2021-10-04 22:17 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.809 s 0.881529
2021-10-04 22:31 R dataframe-to-table type_integers, R 0.085 s -0.189234
2021-10-04 21:32 Python dataframe-to-table type_dict 0.012 s -1.093215
2021-10-04 21:33 Python dataset-filter nyctaxi_2010-01 4.355 s 0.444485
2021-10-04 22:08 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.152 s -0.633235
2021-10-04 22:10 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.326 s -1.465340
2021-10-04 22:31 R dataframe-to-table chi_traffic_2020_Q1, R 5.371 s 0.515150
2021-10-04 21:29 Python csv-read uncompressed, file, nyctaxi_2010-01 1.011 s 0.193159
2021-10-04 21:32 Python dataframe-to-table type_strings 0.370 s 0.179495
2021-10-04 22:13 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.642 s 0.504023
2021-10-04 21:36 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.387 s -0.647108
2021-10-04 22:07 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.825 s -0.097302
2021-10-04 22:16 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.741 s 1.089610
2021-10-04 22:16 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.832 s 1.688017
2021-10-04 21:27 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.982 s -0.583719
2021-10-04 22:15 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.763 s 1.781947
2021-10-04 21:29 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.521 s 1.391213
2021-10-04 22:14 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.334 s 0.042756
2021-10-04 22:17 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.351425
2021-10-04 21:30 Python csv-read gzip, streaming, nyctaxi_2010-01 10.483 s 1.673577
2021-10-04 21:32 Python dataframe-to-table type_floats 0.011 s 0.722736
2021-10-04 21:55 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.012 s 0.146824
2021-10-04 22:09 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.226 s -0.993375
2021-10-04 22:18 Python wide-dataframe use_legacy_dataset=true 0.396 s -1.178557
2021-10-04 21:32 Python dataframe-to-table chi_traffic_2020_Q1 19.462 s 1.174693
2021-10-04 21:54 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.011 s 0.331390
2021-10-04 22:09 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.932 s -1.251824
2021-10-04 22:10 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.473 s -1.452027
2021-10-04 22:08 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.297 s -1.160764
2021-10-04 22:14 Python file-write lz4, feather, table, fanniemae_2016Q4 1.152 s 0.761558
2021-10-04 22:06 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.828 s 0.382084
2021-10-04 22:09 Python file-read lz4, feather, table, fanniemae_2016Q4 0.604 s -0.360663
2021-10-04 22:09 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.992 s 2.831719
2021-10-04 22:15 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.299 s -0.660003
2021-10-04 21:33 Python dataframe-to-table type_nested 2.878 s 1.010238
2021-10-04 21:54 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.054 s -0.223164
2021-10-04 22:08 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.879 s -1.602002
2021-10-04 22:11 Python file-read lz4, feather, table, nyctaxi_2010-01 0.662 s 1.522801
2021-10-04 22:17 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.307 s 0.560129
2021-10-04 22:09 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.014 s 1.124128
2021-10-04 22:11 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.082 s 1.059973
2021-10-04 22:13 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.443 s 1.013587
2021-10-04 22:31 R dataframe-to-table type_strings, R 0.498 s -2.668262
2021-10-04 22:18 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.803 s 0.359153
2021-10-04 22:55 R dataframe-to-table type_simple_features, R 275.218 s -0.403799
2021-10-04 22:55 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.244 s 0.251313
2021-10-04 22:56 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.910 s 0.279918
2021-10-04 22:56 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.250 s 0.011874
2021-10-04 23:01 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.663 s 0.281188
2021-10-04 22:56 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.904 s 0.191627
2021-10-04 22:56 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.764944
2021-10-04 22:57 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.911 s 0.552406
2021-10-04 22:59 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.108 s 1.539062
2021-10-04 22:57 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.374554
2021-10-04 22:58 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.551240
2021-10-04 22:59 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.171 s 0.204690
2021-10-04 22:58 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.366 s 0.952536
2021-10-04 23:00 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.221 s 1.236306
2021-10-04 23:00 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.194 s 2.789996
2021-10-04 23:00 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.994 s -1.009970
2021-10-04 23:01 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.535 s -0.431874
2021-10-04 23:02 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.856 s 0.977595
2021-10-04 23:04 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.298 s 1.042436
2021-10-04 23:04 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.249 s 1.175847
2021-10-04 23:06 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.699 s 1.244190
2021-10-04 23:20 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=10, R 0.285 s
2021-10-04 23:21 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=10, R 0.897 s
2021-10-05 00:33 Python dataframe-to-table type_dict 0.012 s 0.363846
2021-10-05 00:34 Python dataset-filter nyctaxi_2010-01 4.355 s 0.450897
2021-10-04 23:07 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.517465
2021-10-04 23:17 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.277211
2021-10-04 23:19 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.544 s 0.812291
2021-10-04 23:20 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=1, R 0.449 s
2021-10-04 23:13 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.644 s 2.098032
2021-10-04 23:21 R tpch arrow, feather, memory_map=False, query_id=3, scale_factor=1, R 0.622 s
2021-10-05 00:37 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.550 s -0.248618
2021-10-04 23:14 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 0.879492
2021-10-04 23:18 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.617 s -1.400162
2021-10-04 23:21 R tpch arrow, parquet, memory_map=False, query_id=3, scale_factor=1, R 0.520 s
2021-10-04 23:17 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 0.843752
2021-10-04 23:17 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.187 s -1.279620
2021-10-04 23:18 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.607 s 0.850332
2021-10-05 00:34 Python dataframe-to-table type_simple_features 0.909 s 0.392545
2021-10-04 23:19 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.892 s 0.902806
2021-10-04 23:11 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.781 s 2.036563
2021-10-04 23:08 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.414 s -2.419061
2021-10-04 23:09 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.204 s 0.641604
2021-10-04 23:16 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.486 s 0.768166
2021-10-05 00:28 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.945 s -0.358541
2021-10-05 00:30 Python csv-read uncompressed, file, nyctaxi_2010-01 1.004 s 0.933445
2021-10-05 00:31 Python csv-read gzip, streaming, nyctaxi_2010-01 10.506 s 1.466321
2021-10-04 23:17 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 1.371031
2021-10-04 23:20 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=10, R 0.736 s
2021-10-04 23:21 R tpch arrow, native, memory_map=False, query_id=3, scale_factor=1, R 0.261 s
2021-10-05 00:29 Python csv-read uncompressed, file, fanniemae_2016Q4 1.163 s 0.598402
2021-10-05 00:30 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.486 s 1.671184
2021-10-05 00:34 Python dataframe-to-table type_nested 2.862 s 1.365629
2021-10-04 23:08 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.534 s 1.334544
2021-10-04 23:20 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=1, R 0.411 s
2021-10-04 23:15 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.244 s 0.972896
2021-10-05 00:32 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.225134
2021-10-05 00:42 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.944 s 0.949663
2021-10-04 23:10 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.822 s 1.622585
2021-10-04 23:17 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.865 s 1.035709
2021-10-05 00:29 Python csv-read gzip, streaming, fanniemae_2016Q4 14.878 s -0.318731
2021-10-05 00:34 Python dataframe-to-table type_floats 0.012 s -1.259991
2021-10-04 23:17 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.566 s 1.030450
2021-10-04 23:19 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=1, R 0.201 s
2021-10-04 23:12 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.454 s 1.743208
2021-10-04 23:18 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.514 s 0.283936
2021-10-05 00:33 Python dataframe-to-table type_strings 0.374 s -0.239868
2021-10-05 00:34 Python dataframe-to-table type_integers 0.011 s -0.239036
2021-10-05 00:30 Python csv-read gzip, file, fanniemae_2016Q4 6.041 s -2.239073
2021-10-05 00:33 Python dataframe-to-table chi_traffic_2020_Q1 19.459 s 1.189036
2021-10-05 00:51 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.178 s 0.249914
2021-10-05 00:51 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.296 s -0.055463
2021-10-05 01:07 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.016 s -0.043082
2021-10-05 01:11 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.189 s -2.774807
2021-10-05 01:17 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.805 s 0.909431
2021-10-05 01:59 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.361 s 1.271714
2021-10-05 02:18 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.866 s 1.034584
2021-10-05 02:19 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.603 s 0.866713
2021-10-05 02:22 R tpch arrow, native, memory_map=False, query_id=3, scale_factor=1, R 0.262 s
2021-10-05 01:08 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.249 s -0.240909
2021-10-05 01:33 R dataframe-to-table type_nested, R 0.538 s -0.386261
2021-10-05 01:56 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.245 s 0.247840
2021-10-05 01:15 Python file-write lz4, feather, table, fanniemae_2016Q4 1.161 s 0.187218
2021-10-05 01:32 R dataframe-to-table type_dict, R 0.052 s -0.202033
2021-10-05 01:57 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.240 s 0.123447
2021-10-05 02:21 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=10, R 0.286 s
2021-10-05 01:08 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.159 s -0.993599
2021-10-05 01:18 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.620375
2021-10-05 02:21 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=10, R 0.715 s
2021-10-05 00:55 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.020 s 0.250742
2021-10-05 01:08 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.327 s -1.667811
2021-10-05 01:09 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.297 s -1.171129
2021-10-05 01:33 R dataframe-to-table type_floats, R 0.106 s 1.166327
2021-10-05 02:07 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.706 s 1.200795
2021-10-05 02:20 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=1, R 0.200 s
2021-10-05 02:22 R tpch arrow, feather, memory_map=False, query_id=3, scale_factor=1, R 0.625 s
2021-10-05 00:55 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.027 s 0.084898
2021-10-05 01:12 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.952 s -1.358158
2021-10-05 01:13 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.311 s 0.365040
2021-10-05 01:14 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.707 s 0.231621
2021-10-05 01:18 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.800 s 0.383932
2021-10-05 02:00 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.189 s -0.857042
2021-10-05 02:16 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.240 s 1.225739
2021-10-05 02:19 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -1.066918
2021-10-05 02:21 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=1, R 0.447 s
2021-10-05 02:22 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=10, R 0.847 s
2021-10-05 00:55 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.018 s 0.061380
2021-10-05 01:09 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.238 s -1.259798
2021-10-05 01:11 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.475 s -1.460599
2021-10-05 01:17 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.847 s 1.448685
2021-10-05 02:18 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 0.953936
2021-10-05 02:19 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.517 s -0.072299
2021-10-05 01:08 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.892 s -1.816372
2021-10-05 01:18 Python file-write lz4, feather, table, nyctaxi_2010-01 1.800 s 0.628600
2021-10-05 01:14 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.307 s 0.228832
2021-10-05 01:15 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.787 s -0.365464
2021-10-05 02:00 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.135 s -0.437792
2021-10-05 02:06 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.302 s 1.018129
2021-10-05 02:09 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.402 s -0.226608
2021-10-05 02:18 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.200701
2021-10-05 01:07 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.827 s 0.382892
2021-10-05 01:08 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.850 s -0.680344
2021-10-05 01:18 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.334 s 0.337729
2021-10-05 02:15 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 0.891169
2021-10-05 01:13 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.449 s 0.976333
2021-10-05 01:58 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.914 s 0.366337
2021-10-05 02:17 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.495 s -0.956321
2021-10-05 02:20 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.886 s 0.905207
2021-10-05 01:07 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.017 s -0.546737
2021-10-05 01:09 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.052 s -0.256445
2021-10-05 02:01 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.088492
2021-10-05 02:13 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.451 s 1.823850
2021-10-05 01:09 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.945 s -1.453809
2021-10-05 01:10 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.289 s -1.293348
2021-10-05 01:12 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.090 s 1.001051
2021-10-05 01:32 R dataframe-to-table type_integers, R 0.084 s 0.446687
2021-10-05 01:56 R dataframe-to-table type_simple_features, R 275.293 s -0.547166
2021-10-05 01:58 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.948434
2021-10-05 02:01 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.260 s -0.969772
2021-10-05 02:11 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.814 s 1.808012
2021-10-05 02:20 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.694 s -1.556522
2021-10-05 02:22 R tpch arrow, parquet, memory_map=False, query_id=3, scale_factor=1, R 0.519 s
2021-10-05 01:07 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.766 s -0.031226
2021-10-05 01:17 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.361 s -0.554862
2021-10-05 02:05 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.240 s 1.223088
2021-10-05 02:20 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -1.142858
2021-10-05 02:21 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=1, R 0.407 s
2021-10-05 01:09 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s -0.146221
2021-10-05 01:10 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.032 s 0.314970
2021-10-05 01:11 Python file-read lz4, feather, table, nyctaxi_2010-01 0.662 s 1.397212
2021-10-05 02:02 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.670 s 0.194534
2021-10-05 01:32 R dataframe-to-table chi_traffic_2020_Q1, R 5.384 s 0.277857
2021-10-05 01:57 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.911 s 0.275437
2021-10-05 01:57 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.898 s 0.257482
2021-10-05 01:59 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.050 s 1.106779
2021-10-05 02:03 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.845 s 1.045862
2021-10-05 02:10 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.182 s 1.250961
2021-10-05 01:09 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.810 s -1.611788
2021-10-05 01:16 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.783 s 1.461649
2021-10-05 02:08 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.836 s -0.968731
2021-10-05 02:09 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.546 s 1.059737
2021-10-05 01:10 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.318 s -1.418454
2021-10-05 01:16 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.779 s 0.834750
2021-10-05 02:02 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.559 s -1.574047
2021-10-05 02:15 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.644 s 2.101553
2021-10-05 02:18 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 1.384523
2021-10-05 02:18 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.583 s 0.868980
2021-10-05 01:15 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.311 s -0.764303
2021-10-05 01:18 Python wide-dataframe use_legacy_dataset=false 0.612 s 1.745384
2021-10-05 01:32 R dataframe-to-table type_strings, R 0.493 s -0.528795
2021-10-05 01:58 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.609045
2021-10-05 02:02 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.003 s -1.513408
2021-10-05 02:12 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.772 s 2.244253
2021-10-05 03:35 Python dataframe-to-table type_strings 0.373 s -0.114592
2021-10-05 03:30 Python csv-read uncompressed, file, fanniemae_2016Q4 1.177 s -0.222032
2021-10-05 03:35 Python dataframe-to-table type_integers 0.011 s -0.292392
2021-10-05 03:56 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.032 s 0.008477
2021-10-05 03:35 Python dataframe-to-table type_nested 2.872 s 1.139878
2021-10-05 04:10 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.834 s -2.018333
2021-10-05 04:16 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.239 s -0.150011
2021-10-05 03:32 Python csv-read uncompressed, file, nyctaxi_2010-01 1.022 s -0.783658
2021-10-05 03:56 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.007 s 0.219914
2021-10-05 03:30 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.957 s -0.429272
2021-10-05 03:35 Python dataset-filter nyctaxi_2010-01 4.354 s 0.473217
2021-10-05 04:10 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.155 s -0.793648
2021-10-05 04:12 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.478 s -1.471334
2021-10-05 04:18 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.834 s 1.661203
2021-10-05 04:10 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.291 s -0.205985
2021-10-05 03:34 Python dataframe-to-table chi_traffic_2020_Q1 19.538 s 0.823055
2021-10-05 03:56 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.029 s 0.126596
2021-10-05 04:19 Python file-write lz4, feather, table, nyctaxi_2010-01 1.796 s 0.847855
2021-10-05 04:09 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.252 s -0.298239
2021-10-05 04:09 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.293 s -0.269406
2021-10-05 04:10 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.944 s -1.441332
2021-10-05 04:16 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.782 s -0.329454
2021-10-05 04:08 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.887 s 0.075420
2021-10-05 03:31 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.545 s 1.195547
2021-10-05 03:32 Python csv-read gzip, streaming, nyctaxi_2010-01 10.490 s 1.609759
2021-10-05 03:38 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 56.140 s 0.924981
2021-10-05 04:11 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.026 s 0.701481
2021-10-05 03:31 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.709654
2021-10-05 04:17 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.763 s 0.942513
2021-10-05 04:14 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.314 s 0.354795
2021-10-05 04:16 Python file-write lz4, feather, table, fanniemae_2016Q4 1.146 s 1.186403
2021-10-05 03:33 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.222269
2021-10-05 04:10 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.892 s -1.809475
2021-10-05 04:12 Python file-read lz4, feather, table, nyctaxi_2010-01 0.665 s 0.839753
2021-10-05 04:19 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.800 s 0.384185
2021-10-05 03:31 Python csv-read gzip, streaming, fanniemae_2016Q4 14.880 s -0.333063
2021-10-05 03:35 Python dataframe-to-table type_dict 0.012 s 1.054207
2021-10-05 03:52 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.255 s 0.223720
2021-10-05 04:11 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.267 s -1.724944
2021-10-05 04:13 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.090 s 1.006296
2021-10-05 03:52 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.551 s 0.208065
2021-10-05 04:14 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.439 s 1.040077
2021-10-05 04:19 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.759433
2021-10-05 03:35 Python dataframe-to-table type_floats 0.012 s -0.288742
2021-10-05 04:09 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.832 s -0.254766
2021-10-05 04:10 Python file-read lz4, feather, table, fanniemae_2016Q4 0.607 s -0.752574
2021-10-05 04:11 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.362 s -1.619630
2021-10-05 04:13 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.982 s -1.495523
2021-10-05 03:35 Python dataframe-to-table type_simple_features 0.908 s 0.518385
2021-10-05 03:42 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.887 s 0.950241
2021-10-05 04:12 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.361 s -1.627341
2021-10-05 04:15 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.650 s 0.466969
2021-10-05 04:08 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.013 s -0.433887
2021-10-05 04:18 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.824 s 0.777517
2021-10-05 04:11 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.089 s -1.613219
2021-10-05 04:15 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.273 s 0.468117
2021-10-05 04:17 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.776 s 1.583756
2021-10-05 04:19 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.303 s 0.589843
2021-10-05 04:08 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.047 s -0.269524
2021-10-05 04:08 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.743 s 0.128530
2021-10-05 04:12 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.189 s -2.964332
2021-10-05 04:19 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.314 s 2.197145
2021-10-05 04:20 Python wide-dataframe use_legacy_dataset=false 0.624 s -0.869493
2021-10-05 05:19 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.588 s 0.798192
2021-10-05 05:23 R tpch arrow, feather, memory_map=False, query_id=3, scale_factor=1, R 0.624 s
2021-10-05 05:14 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.459 s 1.640690
2021-10-05 05:20 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s -0.802755
2021-10-05 05:21 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=1, R 0.445 s
2021-10-05 04:33 R dataframe-to-table type_integers, R 0.085 s -0.347719
2021-10-05 05:09 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.550 s 0.986775
2021-10-05 05:13 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.781 s 2.024981
2021-10-05 05:17 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.242 s 1.129600
2021-10-05 05:19 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.866489
2021-10-05 05:21 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.900 s 0.899575
2021-10-05 05:02 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.115463
2021-10-05 05:08 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.718 s 1.137088
2021-10-05 05:20 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.600 s 0.885837
2021-10-05 05:00 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.171 s 0.258867
2021-10-05 05:02 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.013 s -2.091111
2021-10-05 05:04 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.843 s 1.058658
2021-10-05 04:57 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.237 s 0.309478
2021-10-05 05:08 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.828 s 0.755955
2021-10-05 05:18 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.486 s 0.781167
2021-10-05 04:58 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.895 s 0.292883
2021-10-05 05:01 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.127 s 0.162508
2021-10-05 05:01 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.266 s -1.283452
2021-10-05 05:23 R tpch arrow, parquet, memory_map=False, query_id=3, scale_factor=1, R 0.521 s
2021-10-05 04:33 R dataframe-to-table type_nested, R 0.535 s 1.354105
2021-10-05 04:58 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.150496
2021-10-05 04:59 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.311601
2021-10-05 05:00 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.071 s -2.565178
2021-10-05 04:33 R dataframe-to-table chi_traffic_2020_Q1, R 5.360 s 0.703439
2021-10-05 05:01 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -2.152468
2021-10-05 05:03 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.533 s -0.334713
2021-10-05 04:33 R dataframe-to-table type_floats, R 0.106 s 1.301210
2021-10-05 05:15 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.637 s 2.282766
2021-10-05 05:20 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.516 s 0.041740
2021-10-05 05:12 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.814 s 1.810322
2021-10-05 05:21 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.692 s -1.531563
2021-10-05 04:33 R dataframe-to-table type_strings, R 0.490 s 0.684472
2021-10-05 04:57 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.239 s 0.135437
2021-10-05 05:22 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=10, R 0.846 s
2021-10-05 05:05 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.250 s 1.166604
2021-10-05 05:19 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.867 s 1.031890
2021-10-05 04:33 R dataframe-to-table type_dict, R 0.048 s 0.196808
2021-10-05 04:59 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.900 s 1.181744
2021-10-05 05:11 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.195 s 0.870705
2021-10-05 05:23 R tpch arrow, native, memory_map=False, query_id=3, scale_factor=1, R 0.265 s
2021-10-05 04:57 R dataframe-to-table type_simple_features, R 274.599 s 0.778820
2021-10-05 04:57 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.910 s 0.281936
2021-10-05 05:06 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.300 s 1.031980
2021-10-05 05:18 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.163 s 1.704088
2021-10-05 04:59 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.376 s 0.401932
2021-10-05 05:21 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=1, R 0.201 s
2021-10-05 05:10 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.414 s -2.446625
2021-10-05 05:16 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.184707
2021-10-05 05:22 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=1, R 0.405 s
2021-10-05 05:22 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=10, R 0.732 s
2021-10-05 05:19 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 0.953238
2021-10-05 05:19 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.437569
2021-10-05 05:22 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=10, R 0.285 s
2021-10-05 06:36 Python dataframe-to-table type_integers 0.011 s 1.444124
2021-10-05 07:08 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.738 s 0.158702
2021-10-05 07:08 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.250 s -0.263020
2021-10-05 07:56 R dataframe-to-table type_simple_features, R 275.361 s -0.676963
2021-10-05 06:35 Python dataframe-to-table chi_traffic_2020_Q1 19.436 s 1.292850
2021-10-05 06:58 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.036 s 0.028356
2021-10-05 06:31 Python csv-read uncompressed, file, fanniemae_2016Q4 1.203 s -1.681371
2021-10-05 06:32 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -0.474206
2021-10-05 07:12 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.970 s -1.440608
2021-10-05 07:14 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.461 s 0.895072
2021-10-05 07:19 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.333 s 0.342841
2021-10-05 07:33 R dataframe-to-table type_floats, R 0.107 s 0.957835
2021-10-05 06:44 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.225 s 0.946823
2021-10-05 07:18 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.353 s -0.105449
2021-10-05 06:33 Python csv-read uncompressed, file, nyctaxi_2010-01 1.016 s -0.227500
2021-10-05 06:34 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s -0.124497
2021-10-05 06:35 Python dataframe-to-table type_strings 0.369 s 0.325112
2021-10-05 07:11 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.342 s -1.540658
2021-10-05 06:39 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 64.118 s -0.805590
2021-10-05 07:08 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.986 s 0.197025
2021-10-05 07:32 R dataframe-to-table chi_traffic_2020_Q1, R 5.358 s 0.734859
2021-10-05 07:57 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.916 s 0.242287
2021-10-05 06:53 Python dataset-read async=True, nyctaxi_multi_ipc_s3 184.968 s 0.385691
2021-10-05 07:08 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.005 s 0.030709
2021-10-05 06:57 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.006 s 0.405569
2021-10-05 07:09 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.866 s -1.390067
2021-10-05 07:13 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.089 s 1.012336
2021-10-05 07:15 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.668 s 0.393738
2021-10-05 07:17 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.776 s 0.860005
2021-10-05 07:19 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.826493
2021-10-05 07:32 R dataframe-to-table type_dict, R 0.053 s -0.238716
2021-10-05 06:33 Python csv-read gzip, streaming, nyctaxi_2010-01 10.489 s 1.619686
2021-10-05 07:10 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.485378
2021-10-05 07:10 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.248 s -1.411781
2021-10-05 07:14 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.316 s 0.347878
2021-10-05 07:32 R dataframe-to-table type_strings, R 0.489 s 1.290654
2021-10-05 07:33 R dataframe-to-table type_nested, R 0.540 s -0.897771
2021-10-05 07:56 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.270 s 0.053021
2021-10-05 06:32 Python csv-read gzip, streaming, fanniemae_2016Q4 14.868 s -0.262666
2021-10-05 07:09 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.301 s -0.577647
2021-10-05 07:16 Python file-write lz4, feather, table, fanniemae_2016Q4 1.161 s 0.139732
2021-10-05 07:17 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.851 s 1.393954
2021-10-05 07:19 Python wide-dataframe use_legacy_dataset=false 0.625 s -1.111832
2021-10-05 07:57 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.916 s 0.073265
2021-10-05 07:58 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.880 s 2.377332
2021-10-05 07:10 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.079 s -1.244512
2021-10-05 07:19 Python file-write lz4, feather, table, nyctaxi_2010-01 1.820 s -0.426882
2021-10-05 07:32 R dataframe-to-table type_integers, R 0.084 s 0.130708
2021-10-05 07:09 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.154 s -0.749060
2021-10-05 07:10 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.929 s -1.193661
2021-10-05 07:11 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.039 s -0.141652
2021-10-05 07:15 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.262 s 0.544430
2021-10-05 07:16 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.790 s 1.360708
2021-10-05 06:36 Python dataframe-to-table type_floats 0.011 s 1.912371
2021-10-05 07:12 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.433 s -1.268639
2021-10-05 07:12 Python file-read lz4, feather, table, nyctaxi_2010-01 0.675 s -1.220924
2021-10-05 06:31 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.017 s -0.792394
2021-10-05 06:36 Python dataset-filter nyctaxi_2010-01 4.356 s 0.384014
2021-10-05 07:11 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.175 s 0.220167
2021-10-05 06:36 Python dataframe-to-table type_simple_features 0.910 s 0.304599
2021-10-05 07:09 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.800 s -1.455879
2021-10-05 07:11 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.331 s -1.476321
2021-10-05 07:16 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.789 s -0.377153
2021-10-05 06:58 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.009 s 0.186472
2021-10-05 07:10 Python file-read lz4, feather, table, fanniemae_2016Q4 0.608 s -0.997650
2021-10-05 06:36 Python dataframe-to-table type_dict 0.012 s 0.264274
2021-10-05 07:09 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.829 s -0.175381
2021-10-05 07:16 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.273 s -0.440224
2021-10-05 07:18 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.815 s 0.843457
2021-10-05 07:19 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.798 s 0.393767
2021-10-05 06:32 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.501 s 1.549398
2021-10-05 06:36 Python dataframe-to-table type_nested 2.889 s 0.779298
2021-10-05 06:53 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.380 s -0.634117
2021-10-05 07:07 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.879 s 0.117938
2021-10-05 07:57 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.244 s 0.076442
2021-10-05 07:59 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.364 s 1.082976
2021-10-05 07:58 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.921733
2021-10-05 07:58 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.225953
2021-10-05 08:01 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.018296
2021-10-05 07:59 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.051 s 0.960563
2021-10-05 08:00 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.121 s 0.626116
2021-10-05 08:00 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.161 s 0.840377
2021-10-05 08:01 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.217 s 1.440778
2021-10-05 08:02 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.679 s 0.072028
2021-10-05 08:02 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.982 s -0.268082
2021-10-05 08:02 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.553 s -1.283745
2021-10-05 08:07 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.697 s 1.254981
2021-10-05 08:10 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.194 s 0.904791
2021-10-05 08:20 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.548 s 0.752331
2021-10-05 08:19 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.518 s -0.244881
2021-10-05 09:33 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.628450
2021-10-05 08:19 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 0.903111
2021-10-05 09:37 Python dataframe-to-table type_dict 0.012 s 0.144789
2021-10-05 09:41 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.946 s -0.551467
2021-10-05 08:18 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.588 s 0.799023
2021-10-05 08:05 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.252 s 1.159319
2021-10-05 08:22 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=10, R 0.879 s
2021-10-05 08:03 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.857 s 0.967135
2021-10-05 08:18 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.870 s 1.023468
2021-10-05 09:37 Python dataframe-to-table type_floats 0.011 s 1.682490
2021-10-05 08:08 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.837 s -1.048809
2021-10-05 09:35 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.319630
2021-10-05 09:37 Python dataframe-to-table type_integers 0.011 s 1.448974
2021-10-05 09:34 Python csv-read uncompressed, file, nyctaxi_2010-01 1.001 s 1.169411
2021-10-05 08:13 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.461 s 1.590030
2021-10-05 08:19 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.187 s -1.286176
2021-10-05 09:33 Python csv-read gzip, streaming, fanniemae_2016Q4 14.883 s -0.348874
2021-10-05 09:34 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.478 s 1.733098
2021-10-05 09:45 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.880 s 0.940185
2021-10-05 08:22 R tpch arrow, feather, memory_map=False, query_id=3, scale_factor=1, R 0.625 s
2021-10-05 09:37 Python dataframe-to-table type_nested 2.877 s 1.041661
2021-10-05 08:18 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.415559
2021-10-05 08:20 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s -0.840249
2021-10-05 08:21 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=1, R 0.410 s
2021-10-05 08:21 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=10, R 0.284 s
2021-10-05 08:15 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.346505
2021-10-05 08:18 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.169 s 1.217447
2021-10-05 08:22 R tpch arrow, parquet, memory_map=False, query_id=3, scale_factor=1, R 0.519 s
2021-10-05 08:09 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.536 s 1.295833
2021-10-05 08:09 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s -0.073702
2021-10-05 08:11 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.819 s 1.682334
2021-10-05 09:38 Python dataset-filter nyctaxi_2010-01 4.358 s 0.308187
2021-10-05 08:17 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.490 s -0.041310
2021-10-05 08:20 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.901 s 0.899184
2021-10-05 08:12 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.771 s 2.255788
2021-10-05 08:16 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.240 s 1.237940
2021-10-05 08:22 R tpch arrow, native, memory_map=False, query_id=3, scale_factor=1, R 0.260 s
2021-10-05 08:14 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.631 s 2.434134
2021-10-05 08:20 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=1, R 0.199 s
2021-10-05 09:32 Python csv-read uncompressed, file, fanniemae_2016Q4 1.170 s 0.213041
2021-10-05 08:21 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=10, R 0.724 s
2021-10-05 09:34 Python csv-read gzip, streaming, nyctaxi_2010-01 10.467 s 1.808522
2021-10-05 09:37 Python dataframe-to-table type_simple_features 0.909 s 0.377632
2021-10-05 08:06 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.294 s 1.066937
2021-10-05 08:18 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.589 s 0.783520
2021-10-05 08:21 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=1, R 0.444 s
2021-10-05 08:23 R tpch arrow, native, memory_map=False, query_id=3, scale_factor=10, R 1.761 s
2021-10-05 09:32 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.946 s -0.360610
2021-10-05 09:37 Python dataframe-to-table type_strings 0.370 s 0.161013
2021-10-05 09:37 Python dataframe-to-table chi_traffic_2020_Q1 19.287 s 1.977341
2021-10-05 10:11 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.013 s -0.440348
2021-10-05 10:19 Python file-write lz4, feather, table, fanniemae_2016Q4 1.155 s 0.588627
2021-10-05 10:35 R dataframe-to-table type_strings, R 0.492 s 0.025029
2021-10-05 10:19 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.785 s -0.346745
2021-10-05 10:21 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.817 s 0.827777
2021-10-05 10:10 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.836 s 0.336395
2021-10-05 10:14 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.169 s 1.635622
2021-10-05 10:22 Python wide-dataframe use_legacy_dataset=false 0.618 s 0.429801
2021-10-05 10:35 R dataframe-to-table type_floats, R 0.107 s 0.935098
2021-10-05 09:59 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.014 s 0.288783
2021-10-05 10:17 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.306 s 0.387595
2021-10-05 10:20 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.833 s 1.679744
2021-10-05 10:22 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.178755
2021-10-05 11:00 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.266 s -0.165592
2021-10-05 11:01 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.939 s -0.170892
2021-10-05 10:11 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.966 s 0.316066
2021-10-05 10:13 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.241 s -1.308821
2021-10-05 11:02 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.911 s 0.535555
2021-10-05 09:59 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.007 s 0.217458
2021-10-05 10:14 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.327 s -1.472597
2021-10-05 10:22 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.427985
2021-10-05 11:02 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.785887
2021-10-05 11:04 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.254 s -0.621009
2021-10-05 11:07 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.849 s 1.021144
2021-10-05 11:17 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.772 s 2.238250
2021-10-05 11:24 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.514 s 0.319089
2021-10-05 09:55 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.193 s 0.648834
2021-10-05 10:11 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.250 s -0.261842
2021-10-05 10:20 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.781 s 0.822202
2021-10-05 10:35 R dataframe-to-table chi_traffic_2020_Q1, R 5.370 s 0.532876
2021-10-05 10:13 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.930 s -1.214197
2021-10-05 10:14 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.329 s -1.470485
2021-10-05 10:36 R dataframe-to-table type_nested, R 0.542 s -1.882087
2021-10-05 10:13 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s -0.164355
2021-10-05 10:14 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.039 s -0.120151
2021-10-05 10:16 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.080 s 1.071358
2021-10-05 10:19 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.269 s -0.404836
2021-10-05 11:12 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.458134
2021-10-05 10:13 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.280 s 1.610283
2021-10-05 10:35 R dataframe-to-table type_dict, R 0.053 s -0.308342
2021-10-05 11:04 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.154 s 1.311115
2021-10-05 10:18 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.280 s 0.420973
2021-10-05 11:09 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.247 s 1.187323
2021-10-05 11:10 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.307 s 0.987766
2021-10-05 11:15 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.183 s 1.214860
2021-10-05 11:27 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=10, R 0.740 s
2021-10-05 09:59 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.015 s 0.313938
2021-10-05 10:21 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.351 s 0.196667
2021-10-05 10:22 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.806 s 0.333328
2021-10-05 10:12 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.292 s -0.238447
2021-10-05 10:15 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.455 s -1.370004
2021-10-05 10:18 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.699 s 0.266279
2021-10-05 10:19 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.780 s 1.520322
2021-10-05 11:01 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.440725
2021-10-05 11:06 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.674 s 0.136161
2021-10-05 11:23 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.868 s 1.029383
2021-10-05 11:24 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.597 s 0.900003
2021-10-05 10:11 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.730 s 0.213047
2021-10-05 10:12 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.863 s -1.342276
2021-10-05 10:13 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.008 s 1.335455
2021-10-05 10:15 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.420831
2021-10-05 10:15 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.968 s -1.431120
2021-10-05 10:17 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.441 s 1.023789
2021-10-05 09:55 Python dataset-read async=True, nyctaxi_multi_ipc_s3 190.061 s -0.185993
2021-10-05 10:12 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.838 s -0.398302
2021-10-05 10:12 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.155 s -0.808896
2021-10-05 10:35 R dataframe-to-table type_integers, R 0.085 s -0.405170
2021-10-05 10:59 R dataframe-to-table type_simple_features, R 275.487 s -0.918010
2021-10-05 10:12 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.800 s -1.445238
2021-10-05 10:21 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.348 s 0.172019
2021-10-05 11:24 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.185503
2021-10-05 11:26 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=1, R 0.450 s
2021-10-05 11:06 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.536 s -0.484068
2021-10-05 11:26 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=1, R 0.413 s
2021-10-05 11:27 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=10, R 0.879 s
2021-10-05 11:04 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.124 s 0.379752
2021-10-05 11:00 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.901 s 0.349591
2021-10-05 11:05 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.999 s -1.258663
2021-10-05 11:11 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.710 s 1.179519
2021-10-05 11:20 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.703045
2021-10-05 11:28 R tpch arrow, feather, memory_map=False, query_id=3, scale_factor=1, R 0.627 s
2021-10-05 10:59 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.251 s 0.200498
2021-10-05 11:03 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.380 s 0.218544
2021-10-05 11:13 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.535 s 1.332280
2021-10-05 11:16 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.815 s 1.771332
2021-10-05 11:19 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.634 s 2.349472
2021-10-05 11:23 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.584 s 0.862952
2021-10-05 11:27 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=10, R 0.284 s
2021-10-05 11:27 R tpch arrow, native, memory_map=False, query_id=3, scale_factor=1, R 0.262 s
2021-10-05 11:27 R tpch arrow, parquet, memory_map=False, query_id=3, scale_factor=1, R 0.525 s
2021-10-05 11:03 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.061 s -0.776770
2021-10-05 11:05 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.347253
2021-10-05 11:14 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.409 s -1.476485
2021-10-05 11:18 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.458 s 1.652216
2021-10-05 11:21 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.240 s 1.283658
2021-10-05 11:23 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.577 s 0.913595
2021-10-05 11:25 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -1.095522
2021-10-05 11:22 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.495 s -0.860537
2021-10-05 11:25 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.892 s 0.902603
2021-10-05 11:26 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.655 s -0.949796
2021-10-05 11:23 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.166 s 1.477140
2021-10-05 11:26 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=1, R 0.201 s
2021-10-05 11:24 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.848217
2021-10-05 12:35 Python csv-read gzip, streaming, nyctaxi_2010-01 10.497 s 1.550449
2021-10-05 13:15 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.230 s -1.118954
2021-10-05 13:17 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.958 s -1.387001
2021-10-05 12:38 Python dataframe-to-table type_floats 0.011 s 1.771569
2021-10-05 12:38 Python dataframe-to-table type_simple_features 0.909 s 0.420736
2021-10-05 12:55 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.675 s -0.030390
2021-10-05 12:38 Python dataframe-to-table type_strings 0.373 s -0.134190
2021-10-05 12:55 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.154 s 0.912280
2021-10-05 12:33 Python csv-read uncompressed, file, fanniemae_2016Q4 1.186 s -0.715302
2021-10-05 12:37 Python dataframe-to-table chi_traffic_2020_Q1 19.610 s 0.492243
2021-10-05 13:16 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.289 s -1.292410
2021-10-05 13:37 R dataframe-to-table type_nested, R 0.539 s -0.774181
2021-10-05 12:34 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s -0.161305
2021-10-05 12:38 Python dataframe-to-table type_dict 0.012 s -1.913019
2021-10-05 13:00 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.039 s -0.101215
2021-10-05 13:00 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.002 s 0.295751
2021-10-05 13:14 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.949 s -1.526748
2021-10-05 13:17 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.101 s 0.932684
2021-10-05 13:22 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.792 s 0.752414
2021-10-05 13:37 R dataframe-to-table type_strings, R 0.488 s 1.579527
2021-10-05 13:12 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.866 s 0.182891
2021-10-05 13:12 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.719 s 0.290899
2021-10-05 13:14 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.810 s -1.608809
2021-10-05 13:16 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.460 s -1.390660
2021-10-05 13:20 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.338489
2021-10-05 13:21 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.790 s 1.352851
2021-10-05 12:35 Python csv-read uncompressed, file, nyctaxi_2010-01 0.986 s 2.578502
2021-10-05 13:37 R dataframe-to-table type_integers, R 0.085 s -0.369511
2021-10-05 12:36 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.625896
2021-10-05 12:42 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.847 s 0.337658
2021-10-05 13:23 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.337 s 0.308742
2021-10-05 13:24 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.813 s 0.272974
2021-10-05 13:24 Python wide-dataframe use_legacy_dataset=true 0.391 s 1.303675
2021-10-05 12:35 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.522 s 1.384284
2021-10-05 12:38 Python dataset-filter nyctaxi_2010-01 4.354 s 0.479431
2021-10-05 13:00 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.045 s -0.103916
2021-10-05 13:13 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.251 s -0.287089
2021-10-05 13:13 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.834 s -0.305037
2021-10-05 13:14 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.871 s -1.465591
2021-10-05 13:14 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.150 s -0.566617
2021-10-05 13:15 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.025 s 0.714644
2021-10-05 12:33 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.066 s -1.093542
2021-10-05 12:34 Python csv-read gzip, streaming, fanniemae_2016Q4 14.866 s -0.249803
2021-10-05 12:46 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.314 s 0.935791
2021-10-05 13:13 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.298 s -0.492765
2021-10-05 13:15 Python file-read lz4, feather, table, fanniemae_2016Q4 0.602 s 0.130018
2021-10-05 13:16 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.179 s -0.696016
2021-10-05 13:19 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.669 s 0.390778
2021-10-05 13:24 Python wide-dataframe use_legacy_dataset=false 0.625 s -1.052623
2021-10-05 12:38 Python dataframe-to-table type_nested 2.895 s 0.645334
2021-10-05 13:12 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.962 s 0.344859
2021-10-05 13:15 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.041 s -0.264111
2021-10-05 13:16 Python file-read lz4, feather, table, nyctaxi_2010-01 0.666 s 0.595625
2021-10-05 13:21 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.330 s -0.923448
2021-10-05 13:37 R dataframe-to-table type_dict, R 0.050 s 0.005909
2021-10-05 13:14 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.304 s -2.331409
2021-10-05 13:22 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.861 s 1.223791
2021-10-05 13:23 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.350 s 0.051930
2021-10-05 13:23 Python file-write lz4, feather, table, nyctaxi_2010-01 1.802 s 0.500548
2021-10-05 12:38 Python dataframe-to-table type_integers 0.011 s 1.206444
2021-10-05 13:13 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.004 s -0.222910
2021-10-05 13:18 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.321 s 0.324898
2021-10-05 13:20 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.772 s -0.266065
2021-10-05 13:37 R dataframe-to-table chi_traffic_2020_Q1, R 5.354 s 0.821198
2021-10-05 13:37 R dataframe-to-table type_floats, R 0.107 s 1.015257
2021-10-05 13:15 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.290 s -1.286381
2021-10-05 13:19 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.455 s 0.936529
2021-10-05 13:20 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.390 s -0.341614
2021-10-05 13:23 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.836 s 0.699708
2021-10-05 14:01 R dataframe-to-table type_simple_features, R 274.078 s 1.773502
2021-10-05 14:19 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.640 s 2.192309
2021-10-05 14:24 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.994287
2021-10-05 14:12 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.843 s -2.202284
2021-10-05 14:03 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.909 s 0.661849
2021-10-05 14:01 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.906 s 0.309578
2021-10-05 14:04 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.147 s 1.719988
2021-10-05 14:10 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.290 s 1.093659
2021-10-05 14:15 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.185 s 1.167651
2021-10-05 14:04 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.107 s 1.590058
2021-10-05 14:05 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.280813
2021-10-05 14:14 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.395 s 1.089572
2021-10-05 14:02 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.244 s 0.080465
2021-10-05 14:01 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.246 s 0.241467
2021-10-05 14:02 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.206061
2021-10-05 14:26 R tpch arrow, native, memory_map=False, query_id=3, scale_factor=1, R 0.266 s
2021-10-05 14:24 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.518 s -0.271315
2021-10-05 14:06 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.670 s 0.194196
2021-10-05 14:09 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.264 s 1.092343
2021-10-05 14:23 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.866 s 1.035218
2021-10-05 14:24 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.903 s 0.898201
2021-10-05 14:25 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=1, R 0.403 s
2021-10-05 14:27 R tpch arrow, feather, memory_map=False, query_id=3, scale_factor=1, R 0.627 s
2021-10-05 14:26 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=10, R 0.283 s
2021-10-05 14:22 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 1.374750
2021-10-05 14:25 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=1, R 0.447 s
2021-10-05 14:27 R tpch arrow, parquet, memory_map=False, query_id=3, scale_factor=1, R 0.519 s
2021-10-05 14:17 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.788 s 1.874853
2021-10-05 14:23 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.189 s -1.613250
2021-10-05 14:25 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=1, R 0.201 s
2021-10-05 14:26 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=10, R 0.740 s
2021-10-05 14:03 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.377 s 0.356319
2021-10-05 14:25 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.554 s 0.659556
2021-10-05 14:02 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.941 s -0.185563
2021-10-05 14:13 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.551 s 0.944577
2021-10-05 14:20 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.612138
2021-10-05 14:05 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.250 s -0.427079
2021-10-05 14:06 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.999 s -1.312906
2021-10-05 14:21 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.495 s -0.982513
2021-10-05 14:23 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.601 s 0.877124
2021-10-05 14:26 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=10, R 0.856 s
2021-10-05 14:07 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.547 s -0.998025
2021-10-05 14:12 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.710 s 1.182378
2021-10-05 14:18 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.458 s 1.659014
2021-10-05 14:22 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.582 s 0.889068
2021-10-05 14:23 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 0.939881
2021-10-05 14:08 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.851 s 1.008203
2021-10-05 14:16 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.820 s 1.667226
2021-10-05 14:21 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.246 s 0.840154
2021-10-05 14:03 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.557 s 1.293799
2021-10-05 14:04 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.568505
2021-10-05 14:23 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.335904
2021-10-05 15:34 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.944 s -0.352410
2021-10-05 15:34 Python csv-read uncompressed, file, fanniemae_2016Q4 1.191 s -0.986677
2021-10-05 15:35 Python csv-read gzip, streaming, fanniemae_2016Q4 14.887 s -0.374796
2021-10-05 15:56 Python dataset-read async=True, nyctaxi_multi_ipc_s3 187.078 s 0.148835
2021-10-05 15:56 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.744 s -3.131909
2021-10-05 16:12 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.850 s 0.267756
2021-10-05 16:15 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.244 s -1.350556
2021-10-05 16:20 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.280 s -0.499686
2021-10-05 16:38 R dataframe-to-table type_nested, R 0.543 s -2.506630
2021-10-05 17:04 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.051 s 0.940420
2021-10-05 16:14 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.882 s -1.647294
2021-10-05 17:06 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.994 s -1.001996
2021-10-05 15:39 Python dataframe-to-table type_nested 2.873 s 1.128103
2021-10-05 16:01 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.061 s -0.445346
2021-10-05 16:20 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.284 s 0.392705
2021-10-05 16:21 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.780 s 1.522271
2021-10-05 16:37 R dataframe-to-table type_integers, R 0.085 s -0.206073
2021-10-05 17:02 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.243 s 0.088232
2021-10-05 17:05 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.215 s 1.528345
2021-10-05 16:20 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.764 s -0.207441
2021-10-05 16:17 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.939 s -1.299225
2021-10-05 16:18 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.285 s 0.473822
2021-10-05 16:37 R dataframe-to-table type_dict, R 0.052 s -0.218764
2021-10-05 17:12 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.809 s 0.629547
2021-10-05 15:35 Python csv-read gzip, file, fanniemae_2016Q4 6.035 s -0.975774
2021-10-05 15:36 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.504 s 1.524488
2021-10-05 16:16 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.173152
2021-10-05 16:23 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.334 s 0.338522
2021-10-05 16:23 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.811 s 0.289132
2021-10-05 17:14 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.548 s 1.031375
2021-10-05 15:39 Python dataframe-to-table type_strings 0.371 s 0.032265
2021-10-05 16:15 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.045 s -0.010181
2021-10-05 16:17 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.085 s 1.036794
2021-10-05 16:18 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.443 s 1.016679
2021-10-05 17:15 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.185 s 1.161961
2021-10-05 15:39 Python dataframe-to-table chi_traffic_2020_Q1 19.367 s 1.606986
2021-10-05 16:14 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.296 s -0.932558
2021-10-05 16:14 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.302909
2021-10-05 16:15 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.319 s -1.422190
2021-10-05 16:20 Python file-write lz4, feather, table, fanniemae_2016Q4 1.156 s 0.489624
2021-10-05 16:24 Python wide-dataframe use_legacy_dataset=false 0.626 s -1.257763
2021-10-05 15:36 Python csv-read gzip, streaming, nyctaxi_2010-01 10.482 s 1.681228
2021-10-05 15:39 Python dataframe-to-table type_simple_features 0.909 s 0.349747
2021-10-05 16:14 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.814 s -1.674178
2021-10-05 17:04 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.367 s 0.924463
2021-10-05 15:39 Python dataset-filter nyctaxi_2010-01 4.382 s -0.607989
2021-10-05 15:43 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.363 s 0.225852
2021-10-05 16:01 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.023 s -0.010954
2021-10-05 16:12 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.962 s 0.342861
2021-10-05 17:05 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.174 s 0.062799
2021-10-05 17:07 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.523 s 0.151847
2021-10-05 15:39 Python dataframe-to-table type_floats 0.011 s 1.742834
2021-10-05 16:16 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.285 s -1.276643
2021-10-05 16:19 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.720 s 0.179090
2021-10-05 15:37 Python csv-read gzip, file, nyctaxi_2010-01 9.050 s -1.686780
2021-10-05 16:01 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 0.997 s 0.564897
2021-10-05 17:02 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.898 s 0.369007
2021-10-05 17:06 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.468789
2021-10-05 17:06 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.670 s 0.191120
2021-10-05 15:39 Python dataframe-to-table type_integers 0.011 s 1.351962
2021-10-05 16:13 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.005 s -0.247796
2021-10-05 17:02 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.900 s 0.239782
2021-10-05 17:03 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.895 s 1.478497
2021-10-05 15:47 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.039 s 0.938573
2021-10-05 16:14 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.153 s -0.709185
2021-10-05 16:23 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.348 s 0.168522
2021-10-05 17:09 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.261 s 1.111900
2021-10-05 17:16 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.820 s 1.671624
2021-10-05 16:13 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.842 s -0.503052
2021-10-05 16:16 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.180 s -0.879341
2021-10-05 16:21 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.786 s 0.791622
2021-10-05 16:22 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.818 s 0.817810
2021-10-05 16:37 R dataframe-to-table type_strings, R 0.494 s -0.876797
2021-10-05 15:39 Python dataframe-to-table type_dict 0.012 s 0.337293
2021-10-05 16:13 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.298 s -0.463357
2021-10-05 16:23 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.416207
2021-10-05 17:02 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.529441
2021-10-05 17:12 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.831 s 0.102145
2021-10-05 16:14 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.976 s -1.961678
2021-10-05 16:15 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.044 s -0.415176
2021-10-05 17:10 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.299 s 1.038927
2021-10-05 15:36 Python csv-read uncompressed, file, nyctaxi_2010-01 1.017 s -0.316353
2021-10-05 16:12 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.716 s 0.315888
2021-10-05 16:13 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.259 s -0.470536
2021-10-05 16:16 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.493 s -1.541922
2021-10-05 16:23 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.607024
2021-10-05 17:03 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.813953
2021-10-05 16:22 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.847 s 1.444987
2021-10-05 16:37 R dataframe-to-table chi_traffic_2020_Q1, R 5.344 s 0.996403
2021-10-05 17:05 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.123 s 0.436037
2021-10-05 16:37 R dataframe-to-table type_floats, R 0.108 s 0.698473
2021-10-05 17:01 R dataframe-to-table type_simple_features, R 275.406 s -0.761803
2021-10-05 17:01 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.248 s 0.223524
2021-10-05 17:08 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.852 s 1.002603
2021-10-05 17:14 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.399 s 0.301019
2021-10-05 17:17 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.768 s 2.332670
2021-10-05 17:18 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.453 s 1.761489
2021-10-05 17:19 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.652 s 1.889547
2021-10-05 17:20 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.286 s -1.485348
2021-10-05 17:22 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.497 s -1.331999
2021-10-05 17:21 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.240 s 1.224784
2021-10-05 17:23 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.165 s 1.564579
2021-10-05 17:24 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.528 s -1.632316
2021-10-05 18:44 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.820 s -0.307024
2021-10-05 17:23 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.864 s 1.037950
2021-10-05 17:25 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=1, R 0.200 s
2021-10-05 17:27 R tpch arrow, parquet, memory_map=False, query_id=3, scale_factor=1, R 0.520 s
2021-10-05 17:24 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 0.918699
2021-10-05 18:40 Python dataframe-to-table type_integers 0.011 s 0.823246
2021-10-05 17:27 R tpch arrow, feather, memory_map=False, query_id=3, scale_factor=1, R 0.629 s
2021-10-05 18:40 Python dataframe-to-table type_floats 0.012 s -0.449659
2021-10-05 18:57 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.373 s -0.584021
2021-10-05 18:38 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 1.210802
2021-10-05 17:26 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=10, R 0.875 s
2021-10-05 18:48 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.320 s 0.945861
2021-10-05 17:23 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -1.087560
2021-10-05 18:40 Python dataframe-to-table type_nested 2.877 s 1.047294
2021-10-05 19:02 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.033 s -0.155303
2021-10-05 17:26 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=1, R 0.403 s
2021-10-05 17:26 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=10, R 0.724 s
2021-10-05 18:40 Python dataframe-to-table type_dict 0.011 s 1.565340
2021-10-05 18:57 Python dataset-read async=True, nyctaxi_multi_ipc_s3 180.982 s 0.833218
2021-10-05 17:23 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.583 s 0.847157
2021-10-05 17:25 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.655 s -0.948418
2021-10-05 18:40 Python dataset-filter nyctaxi_2010-01 4.359 s 0.274983
2021-10-05 17:23 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 0.839027
2021-10-05 18:37 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.482 s 1.696165
2021-10-05 18:39 Python dataframe-to-table chi_traffic_2020_Q1 19.366 s 1.611548
2021-10-05 17:25 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=1, R 0.443 s
2021-10-05 17:26 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=10, R 0.283 s
2021-10-05 18:35 Python csv-read uncompressed, file, fanniemae_2016Q4 1.160 s 0.763131
2021-10-05 18:37 Python csv-read uncompressed, file, nyctaxi_2010-01 1.009 s 0.434885
2021-10-05 18:40 Python dataframe-to-table type_strings 0.377 s -0.693339
2021-10-05 17:24 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.613 s -0.663401
2021-10-05 17:27 R tpch arrow, native, memory_map=False, query_id=3, scale_factor=1, R 0.260 s
2021-10-05 18:35 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.932 s -0.278346
2021-10-05 18:36 Python csv-read gzip, streaming, fanniemae_2016Q4 14.866 s -0.247847
2021-10-05 18:36 Python csv-read gzip, file, fanniemae_2016Q4 6.021 s 2.062542
2021-10-05 18:40 Python dataframe-to-table type_simple_features 0.909 s 0.407560
2021-10-05 17:23 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.323327
2021-10-05 17:25 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.880 s 0.907561
2021-10-05 19:01 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.010 s 0.352361
2021-10-05 18:37 Python csv-read gzip, streaming, nyctaxi_2010-01 10.473 s 1.758256
2021-10-05 19:02 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.033 s 0.060691
2021-10-05 19:20 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.379 s -0.264740
2021-10-05 19:13 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.988 s 0.151385
2021-10-05 19:16 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.466 s -1.416292
2021-10-05 19:23 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.311 s 2.320788
2021-10-05 19:15 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.249 s -1.430391
2021-10-05 19:17 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.968 s -1.431309
2021-10-05 19:19 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.437 s 1.054317
2021-10-05 19:19 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.630 s 0.551934
2021-10-05 19:20 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.230 s -0.066056
2021-10-05 20:02 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.902 s 0.213586
2021-10-05 20:01 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.267 s 0.076139
2021-10-05 19:12 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.769 s -0.058152
2021-10-05 19:21 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.752 s 1.017000
2021-10-05 20:06 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.640560
2021-10-05 20:21 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.239 s 1.311588
2021-10-05 20:25 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.566 s 0.460861
2021-10-05 19:16 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.290 s -1.296445
2021-10-05 19:12 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.814 s 0.453797
2021-10-05 19:18 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.333 s 0.274837
2021-10-05 19:23 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.294 s 0.666407
2021-10-05 20:03 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.914 s 0.389632
2021-10-05 20:14 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.409 s -1.507587
2021-10-05 20:26 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=1, R 0.447 s
2021-10-05 19:14 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.806 s -1.554131
2021-10-05 19:24 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.385523
2021-10-05 20:04 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.385 s -0.050774
2021-10-05 19:15 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.316 s -1.410393
2021-10-05 19:22 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.839 s 1.576251
2021-10-05 19:37 R dataframe-to-table type_dict, R 0.052 s -0.149866
2021-10-05 19:38 R dataframe-to-table type_nested, R 0.540 s -1.014763
2021-10-05 20:10 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.274 s 1.040830
2021-10-05 20:19 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.653 s 1.881681
2021-10-05 20:27 R tpch arrow, parquet, memory_map=False, query_id=3, scale_factor=1, R 0.521 s
2021-10-05 19:13 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.258 s -0.445972
2021-10-05 19:16 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.178 s -0.526861
2021-10-05 19:16 Python file-read lz4, feather, table, nyctaxi_2010-01 0.674 s -1.016308
2021-10-05 19:20 Python file-write lz4, feather, table, fanniemae_2016Q4 1.156 s 0.499941
2021-10-05 19:23 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.822 s 0.795968
2021-10-05 19:37 R dataframe-to-table type_integers, R 0.084 s 0.120802
2021-10-05 20:02 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.234 s 0.188906
2021-10-05 20:23 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.496000
2021-10-05 20:26 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=10, R 0.283 s
2021-10-05 19:15 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.019 s 1.140155
2021-10-05 19:21 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.765 s 1.751054
2021-10-05 19:23 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.796 s 0.410567
2021-10-05 19:37 R dataframe-to-table chi_traffic_2020_Q1, R 5.337 s 1.125920
2021-10-05 20:06 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.129887
2021-10-05 20:08 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.841 s 1.067969
2021-10-05 20:10 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.297 s 1.051121
2021-10-05 20:23 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.164 s 1.621462
2021-10-05 20:24 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.602 s 0.873326
2021-10-05 20:02 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.909 s 0.289936
2021-10-05 19:14 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.935 s -1.298546
2021-10-05 20:02 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.461456
2021-10-05 20:15 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.198 s 0.792459
2021-10-05 19:12 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.994 s 0.115406
2021-10-05 19:13 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.301 s -0.591964
2021-10-05 20:01 R dataframe-to-table type_simple_features, R 274.441 s 1.080969
2021-10-05 20:07 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.544 s -0.866147
2021-10-05 20:25 R tpch arrow, native, memory_map=False, query_id=2, scale_factor=1, R 0.201 s
2021-10-05 19:14 Python file-read lz4, feather, table, fanniemae_2016Q4 0.607 s -0.808913
2021-10-05 19:23 Python file-write lz4, feather, table, nyctaxi_2010-01 1.782 s 1.584936
2021-10-05 20:12 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.730 s 1.067256
2021-10-05 19:13 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.839 s -0.428407
2021-10-05 20:14 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.546 s 1.079508
2021-10-05 20:25 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.880 s 0.907542
2021-10-05 19:14 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.280 s 1.666059
2021-10-05 19:17 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.090 s 1.004481
2021-10-05 20:03 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.571 s -1.537333
2021-10-05 20:22 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.492 s -0.434577
2021-10-05 19:14 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.901 s -1.960686
2021-10-05 19:15 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.061 s -0.584664
2021-10-05 19:37 R dataframe-to-table type_strings, R 0.489 s 1.215728
2021-10-05 20:17 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.780 s 2.042894
2021-10-05 20:26 R tpch arrow, parquet, memory_map=False, query_id=2, scale_factor=10, R 0.725 s
2021-10-05 19:14 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.152 s -0.661855
2021-10-05 19:20 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.732 s 0.007013
2021-10-05 19:24 Python wide-dataframe use_legacy_dataset=false 0.623 s -0.683282
2021-10-05 19:37 R dataframe-to-table type_floats, R 0.107 s 0.883457
2021-10-05 20:04 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.044 s 2.304567
2021-10-05 20:05 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.138 s -0.660831
2021-10-05 20:06 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.994 s -1.018875
2021-10-05 20:24 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.522 s -0.746572
2021-10-05 20:28 R tpch arrow, parquet, memory_map=False, query_id=3, scale_factor=10, R 2.087 s
2021-10-05 20:12 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.504668
2021-10-05 20:23 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.865 s 1.036520
2021-10-05 20:27 R tpch arrow, native, memory_map=False, query_id=3, scale_factor=1, R 0.261 s
2021-10-05 20:27 R tpch arrow, feather, memory_map=False, query_id=3, scale_factor=1, R 0.625 s
2021-10-05 20:20 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 1.042959
2021-10-05 20:23 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 0.825153
2021-10-05 20:23 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 0.950414
2021-10-05 20:23 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.992297
2021-10-05 20:26 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=10, R 0.876 s
2021-10-05 20:05 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.176 s -0.059131
2021-10-05 20:05 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.250 s -0.379670
2021-10-05 20:16 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.818 s 1.714221
2021-10-05 20:18 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.507 s 0.585764
2021-10-05 20:26 R tpch arrow, feather, memory_map=False, query_id=2, scale_factor=1, R 0.404 s
2021-10-05 20:24 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -1.145201
2021-10-05 20:28 R tpch arrow, native, memory_map=False, query_id=3, scale_factor=10, R 1.768 s
2021-10-05 21:37 Python csv-read gzip, streaming, fanniemae_2016Q4 14.875 s -0.305297
2021-10-05 21:41 Python dataframe-to-table type_integers 0.011 s 1.235548
2021-10-05 21:37 Python csv-read gzip, file, fanniemae_2016Q4 6.034 s -0.787769
2021-10-05 21:39 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s -0.143153
2021-10-05 22:16 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.000 s -0.126538
2021-10-05 22:41 R dataframe-to-table type_nested, R 0.539 s -0.696773
2021-10-05 22:17 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.290 s 0.016956
2021-10-05 22:18 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.054 s -0.335026
2021-10-05 22:41 R dataframe-to-table type_dict, R 0.055 s -0.458401
2021-10-05 22:21 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.299 s 0.415317
2021-10-05 21:36 Python csv-read uncompressed, file, fanniemae_2016Q4 1.177 s -0.194052
2021-10-05 22:03 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.043 s -0.158009
2021-10-05 22:27 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.825 s 0.172484
2021-10-05 21:41 Python dataframe-to-table type_simple_features 0.914 s -0.068019
2021-10-05 21:41 Python dataframe-to-table type_strings 0.374 s -0.237759
2021-10-05 22:15 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.009 s 0.000686
2021-10-05 22:18 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.249 s -1.437114
2021-10-05 22:41 R dataframe-to-table type_integers, R 0.084 s -0.051550
2021-10-05 21:58 Python dataset-read async=True, nyctaxi_multi_ipc_s3 187.850 s 0.062217
2021-10-05 22:20 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.499 s -1.570641
2021-10-05 22:24 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.256 s -0.288665
2021-10-05 22:26 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.397 s -0.178903
2021-10-05 21:45 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 64.391 s -0.864702
2021-10-05 22:23 Python file-write lz4, feather, table, fanniemae_2016Q4 1.160 s 0.240033
2021-10-05 22:24 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.784 s 1.450383
2021-10-05 21:37 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.507 s 1.501771
2021-10-05 21:38 Python csv-read gzip, streaming, nyctaxi_2010-01 10.513 s 1.403580
2021-10-05 21:41 Python dataframe-to-table type_dict 0.012 s -2.102204
2021-10-05 22:15 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.752 s 0.062247
2021-10-05 22:16 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.244 s -0.116961
2021-10-05 22:17 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.306 s -0.810388
2021-10-05 21:41 Python dataframe-to-table type_nested 2.885 s 0.866317
2021-10-05 22:22 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.676 s 0.360959
2021-10-05 22:23 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.335 s 0.038550
2021-10-05 22:26 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.356 s -0.269878
2021-10-05 22:40 R dataframe-to-table type_strings, R 0.489 s 1.048272
2021-10-05 21:38 Python csv-read uncompressed, file, nyctaxi_2010-01 1.032 s -1.732149
2021-10-05 21:36 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.933 s -0.285618
2021-10-05 22:17 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.889 s -1.756881
2021-10-05 22:18 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.355 s -1.591244
2021-10-05 22:21 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.082 s 1.055331
2021-10-05 22:22 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.440 s 1.030483
2021-10-05 22:15 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.879 s 0.117907
2021-10-05 22:18 Python file-read lz4, feather, table, fanniemae_2016Q4 0.608 s -0.981629
2021-10-05 22:25 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.795 s 0.730241
2021-10-05 22:19 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.334 s -1.501815
2021-10-05 22:26 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.841 s 0.661948
2021-10-05 21:40 Python dataframe-to-table chi_traffic_2020_Q1 19.449 s 1.233458
2021-10-05 21:49 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.029 s 0.938672
2021-10-05 22:03 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.014 s 0.331088
2021-10-05 22:17 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.796 s -1.377105
2021-10-05 22:18 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.940 s -1.372617
2021-10-05 22:20 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.991 s -1.539314
2021-10-05 22:25 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.845 s 1.477819
2021-10-05 21:41 Python dataframe-to-table type_floats 0.011 s 1.780190
2021-10-05 21:41 Python dataset-filter nyctaxi_2010-01 4.359 s 0.267661
2021-10-05 22:17 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.154 s -0.738862
2021-10-05 22:19 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.043 s -0.390435
2021-10-05 22:40 R dataframe-to-table chi_traffic_2020_Q1, R 5.353 s 0.840484
2021-10-05 22:41 R dataframe-to-table type_floats, R 0.107 s 0.866114
2021-10-05 22:16 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.826 s -0.099890
2021-10-05 22:19 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.964537
2021-10-05 22:27 Python wide-dataframe use_legacy_dataset=false 0.624 s -0.887762
2021-10-05 22:03 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.016 s 0.093005
2021-10-05 22:23 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.823 s -0.601714
2021-10-05 22:27 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.020758
2021-10-05 22:20 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s -0.051327
2021-10-05 22:26 Python file-write lz4, feather, table, nyctaxi_2010-01 1.803 s 0.454190
2021-10-05 21:58 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.268 s 0.134409