Top Outliers
Benchmarks
Date Lang Batch Benchmark Mean Z-Score Error
2023-01-15 18:10 Python csv-read gzip, arrow_table, streaming, fanniemae_2016Q4 14.146 s -1.265
2023-01-15 18:14 Python dataframe-to-table type_floats 0.010 s 0.217
2023-01-15 18:14 Python dataframe-to-table type_nested 2.947 s 1.104
2023-01-15 18:11 Python csv-read uncompressed, arrow_table, file, nyctaxi_2010-01 1.114 s 0.280
2023-01-15 18:09 Python csv-read gzip, arrow_table, file, fanniemae_2016Q4 5.787 s -0.206
2023-01-15 18:11 Python csv-read gzip, arrow_table, file, nyctaxi_2010-01 8.442 s -0.467
2023-01-15 18:12 Python csv-read gzip, arrow_table, streaming, nyctaxi_2010-01 10.497 s 1.109
2023-01-15 18:09 Python csv-read uncompressed, arrow_table, file, fanniemae_2016Q4 1.241 s 0.285
2023-01-15 18:10 Python csv-read uncompressed, arrow_table, streaming, fanniemae_2016Q4 14.055 s -1.102
2023-01-15 18:12 Python csv-read uncompressed, arrow_table, streaming, nyctaxi_2010-01 10.476 s 1.007
2023-01-15 18:14 Python dataframe-to-table type_integers 0.010 s 0.390
2023-01-15 18:14 Python dataframe-to-table chi_traffic_2020_Q1 21.004 s 0.668
2023-01-15 18:14 Python dataframe-to-table type_dict 0.011 s 0.395
2023-01-15 18:14 Python dataset-filter nyctaxi_2010-01 1.029 s -0.263
2023-01-15 18:19 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 84.402 s -0.402
2023-01-15 18:14 Python dataframe-to-table type_strings 0.428 s 0.019
2023-01-15 18:23 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.719 s 0.986
2023-01-15 18:34 Python dataset-read async=True, nyctaxi_multi_ipc_s3 223.434 s -0.189
2023-01-15 18:35 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.056 s 0.186
2023-01-15 18:35 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.211 s 0.210
2023-01-15 18:34 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.193 s 0.097
2023-01-15 18:35 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.302 s 0.215
2023-01-15 18:35 Python dataset-selectivity 100%, chi_traffic_2020_Q1 1.141 s -0.264
2023-01-15 18:34 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.212 s 0.752
2023-01-15 18:35 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.047 s 0.225
2023-01-15 18:35 Python dataset-selectivity 1%, chi_traffic_2020_Q1 1.182 s -0.972
2023-01-15 18:35 Python dataset-serialize feather, 1pc, nyctaxi_multi_parquet_s3 0.023 s 0.784
2023-01-15 18:36 Python dataset-serialize parquet, 10pc, nyctaxi_multi_parquet_s3 2.930 s -1.721
2023-01-15 18:35 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.652 s 0.256
2023-01-15 18:35 Python dataset-selectivity 10%, chi_traffic_2020_Q1 1.210 s -0.275
2023-01-15 18:36 Python dataset-serialize feather, 10pc, nyctaxi_multi_parquet_s3 0.199 s 0.837
2023-01-15 18:35 Python dataset-serialize arrow, 1pc, nyctaxi_multi_parquet_s3 0.023 s 0.272
2023-01-15 18:35 Python dataset-serialize csv, 1pc, nyctaxi_multi_parquet_s3 0.757 s 1.008
2023-01-15 18:36 Python dataset-serialize arrow, 10pc, nyctaxi_multi_parquet_s3 0.199 s 1.254
2023-01-15 18:35 Python dataset-serialize parquet, 1pc, nyctaxi_multi_parquet_s3 0.306 s -1.465
2023-01-15 18:37 Python dataset-serialize csv, 10pc, nyctaxi_multi_parquet_s3 7.529 s 1.054
2023-01-15 18:40 Python dataset-serialize parquet, 100pc, nyctaxi_multi_parquet_s3 30.311 s -1.974
2023-01-15 18:40 Python dataset-serialize arrow, 100pc, nyctaxi_multi_parquet_s3 2.152 s -0.946
2023-01-15 18:40 Python dataset-serialize feather, 100pc, nyctaxi_multi_parquet_s3 2.156 s -2.140
2023-01-15 18:48 Python dataset-serialize csv, 1pc, nyctaxi_multi_ipc_s3 0.865 s 1.505
2023-01-15 19:03 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 5.613 s 0.382
2023-01-15 19:08 Python file-write uncompressed, feather, table, fanniemae_2016Q4 6.471 s -0.271
2023-01-15 19:09 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 15.345 s -0.148
2023-01-15 19:23 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.315 s 0.253
2023-01-15 19:02 Python dataset-serialize csv, 100pc, nyctaxi_multi_ipc_s3 86.197 s 1.457
2023-01-15 19:04 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 4.204 s 0.582
2023-01-15 19:05 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 10.784 s -1.998
2023-01-15 19:09 Python file-write lz4, feather, table, fanniemae_2016Q4 1.896 s -0.335
2023-01-15 19:11 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.761 s -0.778
2023-01-15 19:12 Python wide-dataframe use_legacy_dataset=true 0.437 s -5.085
2023-01-15 19:22 R dataframe-to-table type_nested, R 0.576 s -1.076
2023-01-15 19:22 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.575 s -0.170
2023-01-15 19:24 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 0.913 s -0.066
2023-01-15 19:24 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.893 s 0.308
2023-01-15 18:48 Python dataset-serialize csv, 100pc, nyctaxi_multi_parquet_s3 76.002 s 1.099
2023-01-15 19:04 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 0.409
2023-01-15 19:21 R dataframe-to-table type_strings, R 0.535 s -0.245
2023-01-15 19:24 R file-read snappy, parquet, table, nyctaxi_2010-01, R 0.570 s -0.254
2023-01-15 19:25 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 9.846 s -1.521
2023-01-15 19:27 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 16.801 s -1.400
2023-01-15 18:49 Python dataset-serialize feather, 10pc, nyctaxi_multi_ipc_s3 0.225 s -0.189
2023-01-15 18:54 Python dataset-serialize feather, 100pc, nyctaxi_multi_ipc_s3 2.412 s -0.783
2023-01-15 19:03 Python file-read lz4, feather, table, fanniemae_2016Q4 0.812 s 0.352
2023-01-15 19:04 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 1.587 s 0.326
2023-01-15 19:21 R dataframe-to-table type_integers, R 0.010 s 0.626
2023-01-15 19:32 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 8.991 s 1.075
2023-01-15 19:34 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 7.448 s 1.061
2023-01-15 18:50 Python dataset-serialize csv, 10pc, nyctaxi_multi_ipc_s3 8.686 s 1.390
2023-01-15 18:53 Python dataset-serialize arrow, 100pc, nyctaxi_multi_ipc_s3 2.409 s -0.675
2023-01-15 19:04 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.999 s -0.219
2023-01-15 19:07 Python file-write snappy, parquet, table, fanniemae_2016Q4 10.934 s -1.844
2023-01-15 18:48 Python dataset-serialize arrow, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.729
2023-01-15 18:49 Python dataset-serialize arrow, 10pc, nyctaxi_multi_ipc_s3 0.225 s 0.390
2023-01-15 19:03 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 1.724 s -1.131
2023-01-15 19:03 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 1.491 s 0.391
2023-01-15 19:04 Python file-read uncompressed, feather, table, nyctaxi_2010-01 0.950 s 0.253
2023-01-15 18:48 Python dataset-serialize feather, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.976
2023-01-15 18:49 Python dataset-serialize parquet, 10pc, nyctaxi_multi_ipc_s3 3.048 s -1.848
2023-01-15 19:04 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.915 s 0.315
2023-01-15 19:12 Python wide-dataframe use_legacy_dataset=false 0.561 s -4.615
2023-01-15 19:23 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.590 s 0.347
2023-01-15 19:24 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.219 s 0.306
2023-01-15 19:31 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.954 s 0.862
2023-01-15 19:37 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.710 s -1.193
2023-01-15 19:02 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.626 s 0.444
2023-01-15 19:04 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 0.980 s 0.052
2023-01-15 19:11 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.750 s -0.065
2023-01-15 19:12 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 4.188 s -0.224
2023-01-15 19:21 R dataframe-to-table chi_traffic_2020_Q1, R 4.389 s -1.784
2023-01-15 19:23 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 0.570 s 0.269
2023-01-15 19:23 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.846 s 0.279
2023-01-15 19:42 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.640 s -4.378
2023-01-15 18:48 Python dataset-serialize parquet, 1pc, nyctaxi_multi_ipc_s3 0.287 s -1.666
2023-01-15 18:53 Python dataset-serialize parquet, 100pc, nyctaxi_multi_ipc_s3 30.905 s -1.896
2023-01-15 19:03 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.496 s 0.290
2023-01-15 19:03 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.316 s 0.350
2023-01-15 19:04 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 0.931 s 0.125
2023-01-15 19:04 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 1.330 s 0.342
2023-01-15 19:06 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 20.258 s -1.239
2023-01-15 19:12 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 3.300 s -0.798
2023-01-15 19:21 R dataframe-to-table type_dict, R 0.046 s 1.249
2023-01-15 19:23 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.585 s -0.431
2023-01-15 19:23 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 0.914 s -0.100
2023-01-15 19:24 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.815 s 0.324
2023-01-15 19:28 R file-write snappy, parquet, table, fanniemae_2016Q4, R 10.261 s -1.525
2023-01-15 19:30 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 17.238 s -1.276
2023-01-15 19:36 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.797 s -1.147
2023-01-15 19:08 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 20.414 s -1.264
2023-01-15 19:10 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 7.397 s -1.014
2023-01-15 19:22 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.321 s -0.050
2023-01-15 19:23 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 0.569 s -0.174
2023-01-15 19:47 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=10, R 1.059 s -0.584
2023-01-15 19:47 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=0.01, R 0.270 s 0.136
2023-01-15 19:49 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=0.01, R 0.199 s 0.273
2023-01-15 19:49 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=0.1, R 0.201 s 0.250
2023-01-15 19:50 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=10, R 0.919 s -0.089
2023-01-15 19:52 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=0.1, R 0.161 s 0.218
2023-01-15 19:55 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=0.01, R 0.335 s 0.205
2023-01-15 19:57 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=0.01, R 0.272 s 0.266
2023-01-15 19:59 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=0.1, R 0.336 s 0.257
2023-01-15 20:00 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=0.01, R 0.211 s 0.130
2023-01-15 20:01 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=10, R 3.282 s -1.009
2023-01-15 20:07 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=10, R 3.476 s -3.904
2023-01-15 20:09 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=0.01, R 0.209 s 0.288
2023-01-15 20:10 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=10, R 0.625 s -0.435
2023-01-15 20:11 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=1, R 0.326 s 0.156
2023-01-15 20:13 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=10, R 0.664 s 0.225
2023-01-15 19:10 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 10.919 s 0.080
2023-01-15 19:10 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.919 s -0.902
2023-01-15 19:11 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 9.284 s -1.062
2023-01-15 19:12 Python file-write lz4, feather, table, nyctaxi_2010-01 1.897 s -0.879
2023-01-15 19:21 R dataframe-to-table type_floats, R 0.013 s 0.606
2023-01-15 19:22 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.328 s -0.299
2023-01-15 19:24 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.583 s 0.280
2023-01-15 19:35 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.991 s -1.371
2023-01-15 19:40 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.174 s -0.273
2023-01-15 19:43 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.641 s -2.149
2023-01-15 19:46 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=1, R 0.432 s 0.173
2023-01-15 19:47 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=0.1, R 0.304 s 0.158
2023-01-15 19:50 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=0.1, R 0.271 s -0.081
2023-01-15 19:56 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=10, R 0.615 s 0.075
2023-01-15 19:57 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=1, R 0.442 s 0.095
2023-01-15 19:33 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.491 s 0.423
2023-01-15 19:43 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.279 s -4.394
2023-01-15 19:45 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=1, R 0.716 s -8.011
2023-01-15 19:46 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=0.01, R 0.334 s 0.089
2023-01-15 19:46 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=0.1, R 0.349 s 0.169
2023-01-15 19:51 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=1, R 0.322 s 0.091
2023-01-15 19:52 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=0.01, R 0.162 s 0.138
2023-01-15 20:02 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=0.01, R 0.257 s 0.087
2023-01-15 20:07 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=0.1, R 0.243 s 0.136
2023-01-15 20:08 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=10, R 1.567 s -0.646
2023-01-15 20:09 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=0.1, R 0.211 s 0.224
2023-01-15 20:19 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=10, R 5.673 s -2.431
2023-01-15 20:20 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=0.01, R 0.321 s 0.124
2023-01-15 20:21 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=10, R 6.064 s -0.146
2023-01-15 20:22 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=0.1, R 0.799 s -0.165
2023-01-15 19:39 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.743 s -1.049
2023-01-15 19:44 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=0.01, R 0.401 s -5.239
2023-01-15 19:44 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=0.1, R 0.432 s -4.571
2023-01-15 19:48 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=1, R 0.590 s -0.015
2023-01-15 19:51 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=10, R 0.636 s -0.153
2023-01-15 19:52 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=1, R 0.179 s 0.275
2023-01-15 19:53 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=10, R 0.354 s 0.151
2023-01-15 19:54 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=0.1, R 0.292 s 0.340
2023-01-15 19:55 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=10, R 0.788 s -0.032
2023-01-15 19:56 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=1, R 0.370 s 0.177
2023-01-15 19:57 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=0.1, R 0.296 s 0.254
2023-01-15 20:05 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=0.01, R 0.384 s -6.357
2023-01-15 20:07 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=0.01, R 0.217 s 0.091
2023-01-15 20:08 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=1, R 0.444 s -0.091
2023-01-15 20:11 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=0.01, R 0.196 s 0.258
2023-01-15 19:39 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.311 s -0.386
2023-01-15 19:41 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.469 s 0.655
2023-01-15 19:49 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=0.01, R 0.234 s 0.259
2023-01-15 19:50 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=10, R 1.197 s -0.043
2023-01-15 19:51 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=1, R 0.642 s 0.020
2023-01-15 19:52 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=0.01, R 0.202 s 0.217
2023-01-15 19:54 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=0.1, R 0.374 s 0.275
2023-01-15 19:54 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=1, R 0.661 s 0.164
2023-01-15 19:56 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=1, R 0.725 s -0.100
2023-01-15 19:59 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=1, R 0.650 s 0.197
2023-01-15 20:03 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=10, R 2.724 s -4.112
2023-01-15 20:06 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=1, R 0.649 s -3.770
2023-01-15 20:06 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=1, R 0.982 s -4.073
2023-01-15 20:08 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=1, R 0.940 s -0.016
2023-01-15 20:17 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=0.01, R 0.445 s -4.029
2023-01-15 20:19 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=10, R 9.478 s -2.360
2023-01-15 20:19 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=0.01, R 0.257 s 0.112
2023-01-15 19:42 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.126 s -2.854
2023-01-15 19:45 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=1, R 0.337 s -7.649
2023-01-15 19:46 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=0.01, R 0.280 s -0.042
2023-01-15 19:47 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=10, R 0.669 s -1.757
2023-01-15 19:47 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=1, R 0.304 s -0.121
2023-01-15 19:48 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=10, R 3.564 s -0.449
2023-01-15 19:53 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=10, R 3.260 s -0.099
2023-01-15 19:58 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=1, R 0.766 s 0.329
2023-01-15 19:58 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=10, R 0.968 s -0.583
2023-01-15 19:59 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=1, R 0.323 s 0.054
2023-01-15 20:01 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=10, R 0.871 s -0.282
2023-01-15 20:04 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=0.01, R 0.269 s -5.098
2023-01-15 20:09 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=0.01, R 0.254 s 0.244
2023-01-15 20:09 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=0.1, R 0.285 s 0.231
2023-01-15 20:10 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=10, R 3.611 s -0.528
2023-01-15 20:11 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=0.01, R 0.237 s 0.285
2023-01-15 20:11 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=0.1, R 0.284 s 0.276
2023-01-15 19:45 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=10, R 1.004 s 0.119
2023-01-15 19:47 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=0.1, R 0.233 s 0.137
2023-01-15 19:49 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=1, R 0.329 s 0.323
2023-01-15 19:50 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=0.01, R 0.314 s 0.252
2023-01-15 19:52 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=0.1, R 0.233 s 0.345
2023-01-15 19:53 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=0.01, R 0.339 s 0.356
2023-01-15 19:57 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=10, R 3.798 s -0.432
2023-01-15 19:57 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=0.01, R 0.323 s 0.318
2023-01-15 19:57 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=0.1, R 0.370 s 0.313
2023-01-15 19:59 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=0.01, R 0.255 s 0.207
2023-01-15 19:59 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=0.1, R 0.259 s 0.219
2023-01-15 20:00 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=10, R 3.757 s -0.254
2023-01-15 20:02 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=1, R 0.540 s -6.521
2023-01-15 20:09 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=10, R 5.998 s -0.611
2023-01-15 20:15 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=1, R 0.712 s -5.889
2023-01-15 20:17 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=0.1, R 0.458 s -1.051
2023-01-15 20:20 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=0.1, R 0.258 s 0.043
2023-01-15 20:21 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=0.01, R 0.396 s -0.226
2023-01-15 19:43 R partitioned-dataset-filter dims, dataset-taxi-parquet, R 0.654 s -3.881
2023-01-15 19:43 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=0.01, R 0.297 s -5.534
2023-01-15 19:44 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=0.1, R 0.294 s -4.919
2023-01-15 19:46 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=0.1, R 0.291 s 0.048
2023-01-15 19:46 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=1, R 0.336 s 0.139
2023-01-15 19:48 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=10, R 0.905 s -0.305
2023-01-15 19:49 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=0.1, R 0.243 s 0.265
2023-01-15 19:55 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=0.01, R 0.396 s 0.232
2023-01-15 20:00 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=0.01, R 0.262 s 0.214
2023-01-15 20:01 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=1, R 0.329 s 0.183
2023-01-15 20:02 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=0.01, R 0.209 s 0.132
2023-01-15 20:02 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=0.1, R 0.305 s -0.304
2023-01-15 20:05 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=0.1, R 0.387 s -5.159
2023-01-15 20:07 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=10, R 5.537 s -0.718
2023-01-15 20:07 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=0.01, R 0.268 s 0.205
2023-01-15 20:12 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=0.1, R 0.319 s 0.320
2023-01-15 20:13 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=1, R 0.987 s -0.254
2023-01-15 20:14 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=10, R 7.779 s -2.851
2023-01-15 19:46 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=10, R 4.023 s -1.645
2023-01-15 19:49 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=1, R 0.272 s 0.103
2023-01-15 19:50 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=0.01, R 0.261 s 0.157
2023-01-15 19:53 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=0.01, R 0.292 s 0.289
2023-01-15 19:54 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=1, R 0.351 s 0.246
2023-01-15 19:55 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=0.1, R 0.344 s 0.013
2023-01-15 19:58 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=10, R 4.156 s -0.292
2023-01-15 20:00 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=10, R 0.876 s 0.133
2023-01-15 20:01 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=0.1, R 0.232 s 0.145
2023-01-15 20:01 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=0.1, R 0.303 s 0.264
2023-01-15 20:02 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=0.1, R 0.369 s -0.089
2023-01-15 20:04 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=10, R 8.143 s -6.447
2023-01-15 20:05 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=0.1, R 0.514 s -4.782
2023-01-15 20:10 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=1, R 0.243 s 0.170
2023-01-15 20:11 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=0.1, R 0.221 s 0.260
2023-01-15 20:12 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=1, R 0.258 s 0.258
2023-01-15 20:16 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=10, R 3.534 s -3.149
2023-01-15 20:17 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=0.01, R 0.323 s -3.052
2023-01-15 20:27 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=0.01, R 0.199 s 0.077
2023-01-15 19:47 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=0.01, R 0.229 s 0.104
2023-01-15 19:51 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=0.1, R 0.352 s 0.200
2023-01-15 19:52 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=10, R 3.674 s 0.032
2023-01-15 19:53 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=1, R 0.507 s 0.194
2023-01-15 19:55 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=10, R 3.661 s -0.386
2023-01-15 19:55 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=0.1, R 0.430 s 0.142
2023-01-15 20:08 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=0.1, R 0.334 s 0.233
2023-01-15 20:10 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=1, R 0.588 s 0.080
2023-01-15 20:12 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=10, R 3.110 s -0.195
2023-01-15 20:12 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=0.01, R 0.267 s 0.253
2023-01-15 20:14 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=0.01, R 0.385 s -3.899
2023-01-15 20:18 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=1, R 0.893 s -0.871
2023-01-15 20:22 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=0.1, R 0.736 s -0.833
2023-01-15 20:22 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=1, R 2.903 s -1.571
2023-01-15 20:27 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=10, R 32.732 s -0.710
2023-01-15 20:28 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=10, R 2.325 s -0.122
2023-01-15 20:36 JavaScript Iterate Vector numbers 0.002 s -1.141
2023-01-15 19:59 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=0.01, R 0.299 s 0.282
2023-01-15 20:01 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=1, R 0.542 s -0.022
2023-01-15 20:03 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=1, R 1.182 s -5.491
2023-01-15 20:12 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=0.01, R 0.217 s 0.215
2023-01-15 20:12 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=0.1, R 0.220 s 0.191
2023-01-15 20:15 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=0.1, R 0.273 s -3.039
2023-01-15 20:21 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=0.01, R 0.452 s 0.017
2023-01-15 20:22 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=1, R 3.188 s -2.085
2023-01-15 20:25 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=10, R 30.203 s -3.094
2023-01-15 20:27 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=0.1, R 0.205 s 0.176
2023-01-15 20:36 JavaScript Spread Vector dictionary 0.010 s 0.890
2023-01-15 20:36 JavaScript toArray Vector booleans 0.010 s 1.116
2023-01-15 20:36 JavaScript toArray Vector string 0.147 s -0.554
2023-01-15 20:36 JavaScript Spread vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.107 s 1.405
2023-01-15 20:11 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=1, R 0.528 s 0.161
2023-01-15 20:27 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=1, R 0.455 s 0.091
2023-01-15 20:36 JavaScript Spread Vector uint64Array 0.012 s 0.123
2023-01-15 20:36 JavaScript Spread Vector float64Array 0.008 s 0.184
2023-01-15 20:36 JavaScript toArray Vector int64Array
2023-01-15 20:36 JavaScript toArray Vector float32Array
2023-01-15 20:36 JavaScript toArray Vector numbers
2023-01-15 20:36 JavaScript toArray Vector dictionary 0.010 s 0.829
2023-01-15 20:11 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=10, R 0.891 s -0.268
2023-01-15 20:18 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=1, R 1.337 s -0.370
2023-01-15 20:20 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=0.1, R 0.370 s 0.210
2023-01-15 20:28 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=10, R 0.323 s -0.807
2023-01-15 20:36 JavaScript Iterate Vector dictionary 0.004 s -0.568
2023-01-15 20:36 JavaScript Spread Vector int8Array 0.007 s -0.284
2023-01-15 20:36 JavaScript Spread Vector int32Array 0.007 s -0.228
2023-01-15 20:36 JavaScript toArray Vector int8Array
2023-01-15 20:36 JavaScript toArray Vector int32Array
2023-01-15 20:14 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=0.01, R 0.242 s -2.767
2023-01-15 20:15 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=1, R 0.468 s -4.599
2023-01-15 20:18 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=0.1, R 0.444 s 0.110
2023-01-15 20:20 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=1, R 0.968 s 0.019
2023-01-15 20:27 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=1, R 0.228 s 0.156
2023-01-15 20:36 JavaScript vectorFromArray numbers 0.017 s -1.584
2023-01-15 20:36 JavaScript vectorFromArray dictionary 0.017 s 0.240
2023-01-15 20:36 JavaScript Iterate Vector uint16Array 0.002 s -0.084
2023-01-15 20:36 JavaScript Iterate Vector uint64Array 0.004 s 0.127
2023-01-15 20:36 JavaScript Iterate Vector string 0.127 s -0.502
2023-01-15 20:15 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=0.1, R 0.423 s -4.670
2023-01-15 20:17 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=10, R 4.661 s -4.549
2023-01-15 20:36 JavaScript Iterate Vector booleans 0.004 s 0.345
2023-01-15 20:36 JavaScript Spread Vector int64Array 0.012 s 0.177
2023-01-15 20:36 JavaScript toArray Vector uint16Array
2023-01-15 20:36 JavaScript get Vector numbers 0.002 s -0.594
2023-01-15 20:20 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=1, R 0.288 s 0.082
2023-01-15 20:21 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=10, R 0.606 s -0.093
2023-01-15 20:27 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=0.1, R 0.261 s 0.230
2023-01-15 20:36 JavaScript vectorFromArray booleans 0.017 s 0.558
2023-01-15 20:36 JavaScript Iterate Vector uint8Array 0.002 s -0.795
2023-01-15 20:36 JavaScript Iterate Vector uint32Array 0.002 s -0.493
2023-01-15 20:36 JavaScript Iterate Vector int8Array 0.002 s -0.846
2023-01-15 20:36 JavaScript toArray Vector uint8Array
2023-01-15 20:27 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=0.01, R 0.242 s 0.229
2023-01-15 20:36 JavaScript Spread Vector int16Array 0.007 s 0.044
2023-01-15 20:36 JavaScript Spread Vector booleans 0.010 s 1.384
2023-01-15 20:36 JavaScript toArray Vector uint64Array
2023-01-15 20:36 JavaScript toArray Vector int16Array
2023-01-15 20:36 JavaScript get Vector dictionary 0.002 s 1.139
2023-01-15 20:36 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 1.016
2023-01-15 20:36 JavaScript Iterate Vector int16Array 0.002 s -0.327
2023-01-15 20:36 JavaScript Iterate Vector int64Array 0.004 s 0.131
2023-01-15 20:36 JavaScript Iterate Vector float64Array 0.002 s -1.184
2023-01-15 20:36 JavaScript Spread Vector uint8Array 0.007 s -0.074
2023-01-15 20:36 JavaScript Spread Vector uint32Array 0.007 s -0.206
2023-01-15 20:36 JavaScript Spread vectors lng, 1,000,000, Float32, tracks 0.185 s 1.543
2023-01-15 20:36 JavaScript Iterate Vector int32Array 0.002 s -0.796
2023-01-15 20:36 JavaScript Iterate Vector float32Array 0.002 s -0.229
2023-01-15 20:36 JavaScript Table 1,000,000, tracks 0.245 s 1.331
2023-01-15 20:36 JavaScript Table Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.033 s -3.766
2023-01-15 20:36 JavaScript Spread Vector uint16Array 0.006 s 0.176
2023-01-15 20:36 JavaScript Spread Vector string 0.146 s -0.099
2023-01-15 20:36 JavaScript get Vector uint8Array 0.003 s 0.049
2023-01-15 20:36 JavaScript get Vector uint32Array 0.003 s 0.083
2023-01-15 20:36 JavaScript get Vector int8Array 0.003 s 0.154
2023-01-15 20:36 JavaScript get Vector int32Array 0.003 s 0.018
2023-01-15 20:36 JavaScript Spread Vector float32Array 0.008 s -0.515
2023-01-15 20:36 JavaScript toArray Vector float64Array
2023-01-15 20:36 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.953
2023-01-15 20:36 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.107 s 0.918
2023-01-15 20:36 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-15 20:36 JavaScript Spread Vector numbers 0.008 s 0.611
2023-01-15 20:36 JavaScript toArray Vector uint32Array
2023-01-15 20:36 JavaScript get Vector int64Array 0.003 s 0.306
2023-01-15 20:36 JavaScript get Vector float64Array 0.002 s -0.536
2023-01-15 20:36 JavaScript get Vector booleans 0.002 s 0.633
2023-01-15 20:36 JavaScript get Vector string 0.125 s -0.511
2023-01-15 20:36 JavaScript Table tracks, 1,000,000 0.288 s -0.816
2023-01-15 20:36 JavaScript get Vector uint16Array 0.003 s 0.051
2023-01-15 20:36 JavaScript get Vector int16Array 0.003 s 0.074
2023-01-15 20:36 JavaScript Parse write recordBatches, tracks 0.002 s -0.171
2023-01-15 20:36 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.030 s 0.664
2023-01-15 20:36 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s
2023-01-15 20:36 JavaScript Table Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.447
2023-01-15 20:36 JavaScript get Vector uint64Array 0.003 s 0.306
2023-01-15 20:36 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.023 s 0.256
2023-01-15 20:36 JavaScript get Vector float32Array 0.002 s 0.586
2023-01-15 20:36 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.030 s 0.552
2023-01-15 20:36 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.652
2023-01-15 20:36 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.870
2023-01-15 20:36 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.107 s 1.459
2023-01-15 20:36 JavaScript Spread vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.107 s 0.847
2023-01-15 20:36 JavaScript Table tracks, 1,000,000 0.050 s 1.064
2023-01-15 20:36 JavaScript Table Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.369
2023-01-15 20:36 JavaScript Parse read recordBatches, tracks 0.000 s -1.323
2023-01-15 20:36 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.023 s 0.467
2023-01-15 20:36 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s -0.234
2023-01-15 20:36 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s
2023-01-15 20:36 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-15 20:36 JavaScript Spread vectors lat, 1,000,000, Float32, tracks 0.186 s 0.717
2023-01-15 20:36 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 1.256
2023-01-15 20:36 JavaScript Table tracks, 1,000,000 0.095 s 0.171