Top Outliers
Benchmarks
Date Lang Batch Benchmark Mean Z-Score Error
2023-01-15 02:03 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 1.587 s 0.322
2023-01-15 01:33 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.502 s -1.246
2023-01-15 01:34 Python dataset-selectivity 1%, chi_traffic_2020_Q1 1.177 s -0.883
2023-01-15 01:08 Python csv-read uncompressed, arrow_table, file, fanniemae_2016Q4 1.252 s 0.156
2023-01-15 01:08 Python csv-read gzip, arrow_table, file, fanniemae_2016Q4 5.806 s -1.897
2023-01-15 01:09 Python csv-read gzip, arrow_table, streaming, fanniemae_2016Q4 14.167 s -1.455
2023-01-15 01:10 Python csv-read uncompressed, arrow_table, file, nyctaxi_2010-01 1.108 s 0.339
2023-01-15 01:10 Python csv-read uncompressed, arrow_table, streaming, fanniemae_2016Q4 14.043 s -1.189
2023-01-15 01:11 Python csv-read gzip, arrow_table, streaming, nyctaxi_2010-01 10.465 s 1.307
2023-01-15 01:10 Python csv-read gzip, arrow_table, file, nyctaxi_2010-01 8.435 s 0.406
2023-01-15 01:11 Python csv-read uncompressed, arrow_table, streaming, nyctaxi_2010-01 10.421 s 1.255
2023-01-15 01:13 Python dataframe-to-table type_dict 0.011 s 1.119
2023-01-15 01:13 Python dataframe-to-table type_integers 0.010 s -0.925
2023-01-15 01:13 Python dataframe-to-table chi_traffic_2020_Q1 20.962 s 0.982
2023-01-15 01:13 Python dataframe-to-table type_strings 0.427 s 0.425
2023-01-15 01:13 Python dataframe-to-table type_floats 0.010 s 0.208
2023-01-15 01:13 Python dataframe-to-table type_nested 2.951 s 0.997
2023-01-15 01:14 Python dataset-filter nyctaxi_2010-01 1.024 s -0.202
2023-01-15 01:33 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.219 s 0.108
2023-01-15 01:47 Python dataset-serialize arrow, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.988
2023-01-15 02:01 Python dataset-serialize csv, 100pc, nyctaxi_multi_ipc_s3 86.202 s 0.980
2023-01-15 02:02 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.502 s 0.014
2023-01-15 02:02 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 1.495 s 0.116
2023-01-15 02:03 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 0.976 s 0.071
2023-01-15 02:03 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.944 s -0.092
2023-01-15 01:33 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.193 s 0.048
2023-01-15 01:34 Python dataset-serialize feather, 1pc, nyctaxi_multi_parquet_s3 0.022 s 2.098
2023-01-15 01:47 Python dataset-serialize csv, 1pc, nyctaxi_multi_ipc_s3 0.865 s 1.121
2023-01-15 02:02 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.326 s 0.327
2023-01-15 02:03 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 0.924 s 0.171
2023-01-15 01:17 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 72.643 s 0.936
2023-01-15 01:33 Python dataset-read async=True, nyctaxi_multi_ipc_s3 222.999 s -0.120
2023-01-15 01:33 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.045 s 0.214
2023-01-15 01:34 Python dataset-selectivity 100%, chi_traffic_2020_Q1 1.134 s -0.064
2023-01-15 02:03 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 1.319 s 0.443
2023-01-15 02:08 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 10.902 s 0.285
2023-01-15 02:10 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.797 s -1.192
2023-01-15 01:22 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.804 s -0.178
2023-01-15 01:33 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.311 s 0.120
2023-01-15 01:34 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.649 s 0.242
2023-01-15 01:34 Python dataset-serialize arrow, 10pc, nyctaxi_multi_parquet_s3 0.199 s 0.024
2023-01-15 01:47 Python dataset-serialize parquet, 10pc, nyctaxi_multi_ipc_s3 3.047 s -2.194
2023-01-15 01:47 Python dataset-serialize feather, 10pc, nyctaxi_multi_ipc_s3 0.225 s 0.583
2023-01-15 01:52 Python dataset-serialize feather, 100pc, nyctaxi_multi_ipc_s3 2.406 s 1.156
2023-01-15 02:01 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.917 s -3.549
2023-01-15 02:02 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 4.210 s 0.553
2023-01-15 02:08 Python file-write lz4, feather, table, fanniemae_2016Q4 1.848 s 0.099
2023-01-15 01:33 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.034 s 0.258
2023-01-15 01:34 Python dataset-selectivity 10%, chi_traffic_2020_Q1 1.216 s -0.635
2023-01-15 01:34 Python dataset-serialize feather, 10pc, nyctaxi_multi_parquet_s3 0.199 s 1.193
2023-01-15 02:05 Python file-write snappy, parquet, table, fanniemae_2016Q4 10.911 s -1.978
2023-01-15 02:06 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 20.388 s -1.226
2023-01-15 02:11 Python wide-dataframe use_legacy_dataset=true 0.379 s 0.030
2023-01-15 02:19 R dataframe-to-table chi_traffic_2020_Q1, R 4.244 s 0.305
2023-01-15 02:22 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.853 s 0.203
2023-01-15 02:24 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 9.866 s -1.937
2023-01-15 02:41 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.561 s 0.007
2023-01-15 02:41 R partitioned-dataset-filter dims, dataset-taxi-parquet, R 0.605 s -0.534
2023-01-15 02:42 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=0.1, R 0.281 s 0.186
2023-01-15 01:38 Python dataset-serialize parquet, 100pc, nyctaxi_multi_parquet_s3 30.313 s -2.343
2023-01-15 02:04 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 10.693 s -1.607
2023-01-15 02:20 R dataframe-to-table type_strings, R 0.534 s 0.257
2023-01-15 02:26 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 16.813 s -1.717
2023-01-15 02:33 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 7.455 s 0.886
2023-01-15 02:37 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.737 s -1.099
2023-01-15 02:40 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.472 s -0.602
2023-01-15 02:42 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=1, R 0.275 s -0.028
2023-01-15 02:46 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=0.01, R 0.198 s 0.295
2023-01-15 01:34 Python dataset-serialize parquet, 1pc, nyctaxi_multi_parquet_s3 0.306 s -1.981
2023-01-15 01:34 Python dataset-serialize parquet, 10pc, nyctaxi_multi_parquet_s3 2.932 s -2.153
2023-01-15 01:39 Python dataset-serialize arrow, 100pc, nyctaxi_multi_parquet_s3 2.150 s -0.237
2023-01-15 01:48 Python dataset-serialize csv, 10pc, nyctaxi_multi_ipc_s3 8.683 s 1.021
2023-01-15 02:10 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 9.264 s -0.963
2023-01-15 02:20 R dataframe-to-table type_floats, R 0.013 s 0.712
2023-01-15 02:42 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=0.1, R 0.201 s 0.154
2023-01-15 02:42 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=10, R 1.000 s 0.461
2023-01-15 02:43 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=0.1, R 0.290 s 0.067
2023-01-15 02:45 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=1, R 0.301 s 0.108
2023-01-15 02:47 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=10, R 0.924 s -0.380
2023-01-15 02:48 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=0.1, R 0.266 s 0.188
2023-01-15 02:49 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=0.1, R 0.160 s 0.278
2023-01-15 02:50 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=1, R 0.507 s 0.167
2023-01-15 02:51 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=0.01, R 0.292 s 0.230
2023-01-15 02:53 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=0.1, R 0.339 s 0.222
2023-01-15 01:34 Python dataset-serialize arrow, 1pc, nyctaxi_multi_parquet_s3 0.023 s 0.446
2023-01-15 01:34 Python dataset-serialize csv, 1pc, nyctaxi_multi_parquet_s3 0.757 s 0.872
2023-01-15 01:47 Python dataset-serialize feather, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.376
2023-01-15 01:47 Python dataset-serialize arrow, 10pc, nyctaxi_multi_ipc_s3 0.225 s 0.268
2023-01-15 01:51 Python dataset-serialize parquet, 100pc, nyctaxi_multi_ipc_s3 30.907 s -2.297
2023-01-15 01:52 Python dataset-serialize arrow, 100pc, nyctaxi_multi_ipc_s3 2.401 s 1.892
2023-01-15 02:03 Python file-read uncompressed, feather, table, nyctaxi_2010-01 0.938 s 0.313
2023-01-15 02:05 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 20.248 s -1.258
2023-01-15 02:07 Python file-write uncompressed, feather, table, fanniemae_2016Q4 6.425 s -0.197
2023-01-15 02:09 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.887 s -0.761
2023-01-15 02:11 Python wide-dataframe use_legacy_dataset=false 0.510 s 0.726
2023-01-15 02:21 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.331 s -0.478
2023-01-15 02:22 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.593 s 0.308
2023-01-15 02:22 R file-read snappy, parquet, table, nyctaxi_2010-01, R 0.567 s -0.244
2023-01-15 02:23 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.580 s 0.290
2023-01-15 01:35 Python dataset-serialize csv, 10pc, nyctaxi_multi_parquet_s3 7.526 s 0.734
2023-01-15 01:39 Python dataset-serialize feather, 100pc, nyctaxi_multi_parquet_s3 2.147 s 1.700
2023-01-15 01:47 Python dataset-serialize parquet, 1pc, nyctaxi_multi_ipc_s3 0.288 s -2.258
2023-01-15 02:02 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 5.630 s 0.356
2023-01-15 02:02 Python file-read lz4, feather, table, fanniemae_2016Q4 0.820 s 0.173
2023-01-15 02:03 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.982 s -0.035
2023-01-15 02:08 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 15.236 s 0.072
2023-01-15 02:20 R dataframe-to-table type_integers, R 0.010 s 0.601
2023-01-15 02:20 R dataframe-to-table type_nested, R 0.573 s 0.149
2023-01-15 02:21 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.315 s 0.150
2023-01-15 02:22 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 0.914 s -0.169
2023-01-15 02:40 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.062 s 0.286
2023-01-15 02:41 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.259 s -1.150
2023-01-15 02:44 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=0.1, R 0.232 s 0.203
2023-01-15 02:46 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=1, R 0.272 s 0.097
2023-01-15 02:53 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=0.01, R 0.393 s -2.560
2023-01-15 02:53 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=0.01, R 0.435 s -0.926
2023-01-15 01:47 Python dataset-serialize csv, 100pc, nyctaxi_multi_parquet_s3 75.970 s 0.800
2023-01-15 02:01 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 1.859 s -3.336
2023-01-15 02:10 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 4.135 s 0.030
2023-01-15 02:20 R dataframe-to-table type_dict, R 0.047 s 1.209
2023-01-15 02:23 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s 0.336
2023-01-15 02:23 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.898 s 0.286
2023-01-15 02:43 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=0.1, R 0.349 s 0.144
2023-01-15 02:48 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=0.1, R 0.351 s 0.189
2023-01-15 02:50 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=10, R 0.354 s 0.149
2023-01-15 02:51 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=0.01, R 0.338 s 0.336
2023-01-15 02:51 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=0.1, R 0.372 s 0.286
2023-01-15 02:51 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=1, R 0.714 s -1.282
2023-01-15 02:56 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=10, R 4.155 s -0.405
2023-01-15 02:57 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=0.1, R 0.335 s 0.250
2023-01-15 02:57 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=1, R 0.321 s 0.147
2023-01-15 02:58 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=10, R 3.752 s -0.318
2023-01-15 02:03 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.370
2023-01-15 02:09 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 7.427 s -1.342
2023-01-15 02:21 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 0.568 s 0.258
2023-01-15 02:31 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.492 s 0.203
2023-01-15 02:34 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.986 s -1.481
2023-01-15 02:39 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.157 s 0.353
2023-01-15 02:42 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=1, R 0.592 s -0.482
2023-01-15 02:43 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=10, R 3.717 s 0.391
2023-01-15 02:49 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=0.01, R 0.201 s 0.235
2023-01-15 02:52 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=10, R 4.145 s -4.298
2023-01-15 02:58 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=0.01, R 0.261 s 0.225
2023-01-15 03:04 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=0.01, R 0.267 s 0.220
2023-01-15 03:04 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=0.1, R 0.336 s 0.158
2023-01-15 02:10 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.748 s -0.063
2023-01-15 02:11 Python file-write lz4, feather, table, nyctaxi_2010-01 1.776 s 0.151
2023-01-15 02:11 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 3.199 s 0.102
2023-01-15 02:21 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.316 s 0.225
2023-01-15 02:27 R file-write snappy, parquet, table, fanniemae_2016Q4, R 10.273 s -1.860
2023-01-15 02:31 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 8.988 s 1.094
2023-01-15 02:35 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.768 s -0.958
2023-01-15 02:41 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.593 s -0.765
2023-01-15 02:41 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=0.01, R 0.239 s 0.236
2023-01-15 02:45 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=10, R 0.905 s -0.273
2023-01-15 02:47 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=0.01, R 0.313 s 0.265
2023-01-15 02:48 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=1, R 0.318 s 0.324
2023-01-15 02:49 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=0.1, R 0.233 s 0.337
2023-01-15 02:55 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=0.01, R 0.272 s 0.279
2023-01-15 02:21 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.571 s -0.087
2023-01-15 02:21 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.579 s -0.362
2023-01-15 02:22 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 0.565 s -0.163
2023-01-15 02:41 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=0.01, R 0.200 s 0.223
2023-01-15 02:43 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=0.01, R 0.334 s 0.104
2023-01-15 02:44 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=10, R 0.668 s -1.792
2023-01-15 02:44 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=0.01, R 0.229 s 0.118
2023-01-15 02:46 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=10, R 3.556 s -0.562
2023-01-15 02:49 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=0.01, R 0.160 s 0.205
2023-01-15 02:51 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=1, R 0.352 s 0.157
2023-01-15 02:54 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=10, R 3.786 s -0.452
2023-01-15 02:55 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=0.1, R 0.295 s 0.312
2023-01-15 02:57 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=0.1, R 0.259 s 0.209
2023-01-15 02:58 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=10, R 0.878 s 0.146
2023-01-15 02:58 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=0.1, R 0.231 s 0.162
2023-01-15 03:00 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=0.1, R 0.403 s -2.012
2023-01-15 02:22 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 0.908 s -0.061
2023-01-15 02:23 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.812 s 0.331
2023-01-15 02:29 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 17.245 s -1.507
2023-01-15 02:29 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.989 s -4.646
2023-01-15 02:43 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=0.01, R 0.280 s -0.076
2023-01-15 02:43 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=1, R 0.336 s 0.069
2023-01-15 02:45 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=0.1, R 0.302 s 0.231
2023-01-15 02:46 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=0.01, R 0.232 s 0.289
2023-01-15 02:50 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=1, R 0.178 s 0.322
2023-01-15 02:54 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=1, R 0.715 s 0.151
2023-01-15 02:55 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=0.1, R 0.371 s 0.248
2023-01-15 03:05 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=10, R 6.994 s -5.557
2023-01-15 03:32 JavaScript Table tracks, 1,000,000 0.095 s 0.027
2023-01-15 02:36 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.712 s -1.379
2023-01-15 02:38 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.311 s -0.307
2023-01-15 02:44 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=1, R 0.427 s 0.377
2023-01-15 02:44 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=10, R 1.065 s -0.921
2023-01-15 02:45 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=1, R 0.590 s -0.120
2023-01-15 02:46 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=0.1, R 0.243 s 0.217
2023-01-15 02:46 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=1, R 0.330 s 0.260
2023-01-15 02:47 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=10, R 1.198 s -0.153
2023-01-15 02:47 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=0.01, R 0.260 s 0.204
2023-01-15 02:48 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=1, R 0.640 s 0.025
2023-01-15 02:52 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=10, R 0.827 s -2.116
2023-01-15 02:55 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=1, R 0.768 s 0.244
2023-01-15 02:59 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=0.01, R 0.256 s 0.106
2023-01-15 03:02 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=0.1, R 0.429 s -0.271
2023-01-15 03:03 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=10, R 3.440 s -6.124
2023-01-15 03:06 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=0.1, R 0.412 s -4.732
2023-01-15 03:08 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=1, R 0.529 s 0.113
2023-01-15 03:09 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=10, R 3.090 s -0.104
2023-01-15 03:15 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=1, R 0.289 s 0.036
2023-01-15 02:44 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=0.01, R 0.268 s 0.252
2023-01-15 02:49 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=10, R 3.758 s -1.255
2023-01-15 02:53 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=0.1, R 0.427 s 0.221
2023-01-15 02:57 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=1, R 0.655 s -0.036
2023-01-15 02:58 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=0.1, R 0.304 s 0.172
2023-01-15 03:03 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=10, R 5.510 s -0.703
2023-01-15 03:03 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=0.01, R 0.216 s 0.109
2023-01-15 03:06 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=0.01, R 0.285 s -3.839
2023-01-15 03:08 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=0.1, R 0.222 s 0.213
2023-01-15 03:10 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=1, R 0.990 s -0.482
2023-01-15 03:11 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=0.01, R 0.241 s 0.272
2023-01-15 03:15 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=0.01, R 0.320 s 0.168
2023-01-15 03:18 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=1, R 2.907 s -2.320
2023-01-15 03:22 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=0.01, R 0.244 s 0.154
2023-01-15 03:32 JavaScript Iterate Vector float64Array 0.002 s 0.841
2023-01-15 02:46 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=0.1, R 0.201 s 0.228
2023-01-15 02:49 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=10, R 0.634 s -0.047
2023-01-15 02:50 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=10, R 3.252 s -0.090
2023-01-15 02:51 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=0.1, R 0.296 s 0.128
2023-01-15 02:55 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=1, R 0.442 s 0.012
2023-01-15 02:58 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=0.01, R 0.210 s 0.174
2023-01-15 02:59 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=1, R 0.334 s -0.346
2023-01-15 02:59 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=10, R 0.873 s -0.538
2023-01-15 03:02 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=0.1, R 0.332 s -0.101
2023-01-15 03:02 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=1, R 0.609 s -1.343
2023-01-15 03:07 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=10, R 0.617 s -0.052
2023-01-15 03:11 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=0.01, R 0.194 s 0.253
2023-01-15 03:11 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=0.1, R 0.266 s 0.128
2023-01-15 03:11 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=1, R 0.541 s -0.209
2023-01-15 03:12 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=10, R 3.494 s -1.768
2023-01-15 03:13 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=0.1, R 0.443 s -0.201
2023-01-15 03:13 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=0.1, R 0.444 s 0.040
2023-01-15 03:13 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=1, R 0.892 s -0.877
2023-01-15 03:15 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=0.1, R 0.258 s 0.068
2023-01-15 03:16 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=10, R 0.606 s -0.097
2023-01-15 02:53 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=1, R 0.370 s 0.181
2023-01-15 02:55 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=0.01, R 0.323 s 0.321
2023-01-15 02:56 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=10, R 0.963 s -0.335
2023-01-15 02:56 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=0.01, R 0.255 s 0.228
2023-01-15 03:02 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=10, R 6.555 s -1.170
2023-01-15 03:02 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=0.01, R 0.184 s 0.130
2023-01-15 03:02 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=0.01, R 0.239 s -0.061
2023-01-15 03:11 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=10, R 6.978 s -0.766
2023-01-15 03:17 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=0.01, R 0.452 s -0.007
2023-01-15 03:32 JavaScript vectorFromArray dictionary 0.017 s 0.383
2023-01-15 03:32 JavaScript Iterate Vector uint64Array 0.004 s 0.169
2023-01-15 03:32 JavaScript Spread Vector int16Array 0.007 s -3.544
2023-01-15 02:54 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=10, R 0.614 s 0.112
2023-01-15 02:56 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=0.01, R 0.297 s 0.333
2023-01-15 03:00 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=0.1, R 0.305 s -0.366
2023-01-15 03:00 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=1, R 1.136 s -5.463
2023-01-15 03:04 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=1, R 0.942 s -0.132
2023-01-15 03:05 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=10, R 1.568 s -0.784
2023-01-15 03:06 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=0.01, R 0.372 s -3.577
2023-01-15 03:07 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=1, R 0.309 s -4.643
2023-01-15 03:07 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=1, R 0.630 s -1.136
2023-01-15 03:09 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=1, R 0.258 s 0.201
2023-01-15 03:16 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=1, R 0.975 s -0.147
2023-01-15 03:17 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=0.1, R 0.800 s -0.253
2023-01-15 03:20 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=10, R 30.201 s -4.375
2023-01-15 03:23 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=1, R 0.227 s 0.209
2023-01-15 03:32 JavaScript vectorFromArray booleans 0.018 s 0.172
2023-01-15 02:59 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=1, R 0.541 s -0.066
2023-01-15 02:59 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=10, R 3.264 s -1.143
2023-01-15 03:01 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=10, R 2.715 s -9.321
2023-01-15 03:07 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=10, R 3.591 s -0.415
2023-01-15 03:08 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=1, R 0.327 s 0.082
2023-01-15 03:09 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=0.01, R 0.216 s 0.238
2023-01-15 03:13 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=0.01, R 0.327 s 0.174
2023-01-15 03:15 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=10, R 9.494 s -3.263
2023-01-15 03:32 JavaScript Iterate Vector uint16Array 0.002 s 0.584
2023-01-15 03:32 JavaScript Iterate Vector int16Array 0.002 s 0.621
2023-01-15 03:32 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s -0.928
2023-01-15 02:59 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=0.01, R 0.209 s 0.075
2023-01-15 03:03 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=1, R 0.864 s -0.534
2023-01-15 03:04 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=0.1, R 0.242 s 0.141
2023-01-15 03:08 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=0.01, R 0.196 s 0.242
2023-01-15 03:09 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=0.01, R 0.267 s 0.214
2023-01-15 03:09 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=0.1, R 0.220 s 0.167
2023-01-15 03:10 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=10, R 0.667 s 0.004
2023-01-15 03:11 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=0.1, R 0.211 s 0.102
2023-01-15 03:11 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=1, R 0.408 s -0.235
2023-01-15 03:12 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=10, R 4.314 s -0.975
2023-01-15 03:13 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=0.01, R 0.272 s 0.102
2023-01-15 03:13 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=1, R 1.336 s -0.385
2023-01-15 03:15 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=0.1, R 0.371 s 0.213
2023-01-15 03:17 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=10, R 6.199 s -0.729
2023-01-15 03:18 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=1, R 3.211 s -4.584
2023-01-15 03:22 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=0.1, R 0.203 s 0.226
2023-01-15 03:32 JavaScript Iterate Vector dictionary 0.004 s -2.606
2023-01-15 03:00 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=1, R 0.523 s -6.658
2023-01-15 03:22 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=0.01, R 0.198 s 0.080
2023-01-15 03:32 JavaScript Iterate Vector uint32Array 0.002 s 0.763
2023-01-15 03:32 JavaScript Iterate Vector int8Array 0.002 s 0.282
2023-01-15 03:32 JavaScript toArray Vector uint16Array
2023-01-15 03:32 JavaScript toArray Vector uint64Array
2023-01-15 03:32 JavaScript toArray Vector int16Array
2023-01-15 03:04 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=1, R 0.443 s -0.153
2023-01-15 03:06 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=0.1, R 0.294 s -5.086
2023-01-15 03:08 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=0.01, R 0.237 s 0.287
2023-01-15 03:08 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=0.1, R 0.284 s 0.268
2023-01-15 03:08 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=10, R 0.895 s -0.498
2023-01-15 03:09 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=0.1, R 0.320 s 0.257
2023-01-15 03:14 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=10, R 5.667 s -2.847
2023-01-15 03:15 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=0.01, R 0.256 s 0.164
2023-01-15 03:22 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=0.1, R 0.262 s 0.173
2023-01-15 03:23 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=1, R 0.454 s 0.085
2023-01-15 03:23 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=10, R 0.310 s 0.011
2023-01-15 03:32 JavaScript Iterate Vector string 0.125 s 0.974
2023-01-15 03:32 JavaScript Spread Vector uint16Array 0.007 s -2.843
2023-01-15 03:32 JavaScript Spread Vector string 0.145 s 0.081
2023-01-15 03:32 JavaScript Spread vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.110 s -2.379
2023-01-15 03:17 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=0.01, R 0.396 s -0.189
2023-01-15 03:17 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=0.1, R 0.735 s -0.854
2023-01-15 03:22 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=10, R 33.253 s -3.250
2023-01-15 03:23 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=10, R 2.303 s 0.034
2023-01-15 03:32 JavaScript Spread Vector uint8Array 0.007 s -2.907
2023-01-15 03:32 JavaScript Spread Vector numbers 0.008 s -2.854
2023-01-15 03:32 JavaScript Spread Vector dictionary 0.010 s -2.427
2023-01-15 03:32 JavaScript Table tracks, 1,000,000 0.050 s 0.477
2023-01-15 03:32 JavaScript vectorFromArray numbers 0.016 s 0.546
2023-01-15 03:32 JavaScript toArray Vector int32Array
2023-01-15 03:32 JavaScript Iterate Vector uint8Array 0.002 s 0.015
2023-01-15 03:32 JavaScript Iterate Vector int32Array 0.002 s 0.607
2023-01-15 03:32 JavaScript Iterate Vector float32Array 0.002 s 0.816
2023-01-15 03:32 JavaScript Iterate Vector numbers 0.002 s 0.778
2023-01-15 03:32 JavaScript Spread Vector int8Array 0.007 s -2.740
2023-01-15 03:32 JavaScript Table tracks, 1,000,000 0.260 s 0.513
2023-01-15 03:32 JavaScript Iterate Vector int64Array 0.004 s 0.191
2023-01-15 03:32 JavaScript Spread Vector float64Array 0.008 s -2.083
2023-01-15 03:32 JavaScript Spread Vector booleans 0.010 s -1.140
2023-01-15 03:32 JavaScript toArray Vector uint8Array
2023-01-15 03:32 JavaScript toArray Vector uint32Array
2023-01-15 03:32 JavaScript toArray Vector int8Array
2023-01-15 03:32 JavaScript Table 1,000,000, tracks 0.247 s 1.202
2023-01-15 03:32 JavaScript Iterate Vector booleans 0.004 s 0.873
2023-01-15 03:32 JavaScript Spread Vector uint64Array 0.012 s -0.184
2023-01-15 03:32 JavaScript toArray Vector numbers
2023-01-15 03:32 JavaScript toArray Vector dictionary 0.010 s -2.892
2023-01-15 03:32 JavaScript get Vector uint8Array 0.003 s 0.042
2023-01-15 03:32 JavaScript get Vector int8Array 0.003 s 0.083
2023-01-15 03:32 JavaScript Spread Vector uint32Array 0.007 s -3.147
2023-01-15 03:32 JavaScript get Vector float64Array 0.002 s 1.011
2023-01-15 03:32 JavaScript get Vector booleans 0.002 s 0.706
2023-01-15 03:32 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.759
2023-01-15 03:32 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s
2023-01-15 03:32 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-15 03:32 JavaScript Spread Vector int32Array 0.007 s -2.664
2023-01-15 03:32 JavaScript Spread Vector float32Array 0.008 s -2.410
2023-01-15 03:32 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.431
2023-01-15 03:32 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.109 s -2.220
2023-01-15 03:32 JavaScript Spread Vector int64Array 0.012 s -0.161
2023-01-15 03:32 JavaScript toArray Vector float32Array
2023-01-15 03:32 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.023 s -0.311
2023-01-15 03:32 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.352
2023-01-15 03:32 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.109 s -1.700
2023-01-15 03:32 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s
2023-01-15 03:32 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-15 03:32 JavaScript Spread vectors lng, 1,000,000, Float32, tracks 0.192 s -1.831
2023-01-15 03:32 JavaScript toArray Vector int64Array
2023-01-15 03:32 JavaScript Parse write recordBatches, tracks 0.002 s -0.053
2023-01-15 03:32 JavaScript toArray Vector float64Array
2023-01-15 03:32 JavaScript toArray Vector booleans 0.010 s -0.314
2023-01-15 03:32 JavaScript toArray Vector string 0.144 s 0.698
2023-01-15 03:32 JavaScript get Vector uint16Array 0.003 s 0.069
2023-01-15 03:32 JavaScript get Vector uint64Array 0.003 s -0.140
2023-01-15 03:32 JavaScript Spread vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.109 s -2.262
2023-01-15 03:32 JavaScript Table Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.031 s 0.715
2023-01-15 03:32 JavaScript get Vector uint32Array 0.003 s 0.029
2023-01-15 03:32 JavaScript get Vector float32Array 0.002 s 0.295
2023-01-15 03:32 JavaScript get Vector numbers 0.002 s 0.069
2023-01-15 03:32 JavaScript Parse read recordBatches, tracks 0.000 s -1.374
2023-01-15 03:32 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.030 s 0.371
2023-01-15 03:32 JavaScript get Vector int16Array 0.003 s 0.029
2023-01-15 03:32 JavaScript get Vector int64Array 0.003 s -0.027
2023-01-15 03:32 JavaScript get Vector string 0.123 s 0.942
2023-01-15 03:32 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.030 s 0.570
2023-01-15 03:32 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.023 s -0.208
2023-01-15 03:32 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s -0.226
2023-01-15 03:32 JavaScript Spread vectors lat, 1,000,000, Float32, tracks 0.190 s -0.711
2023-01-15 03:32 JavaScript get Vector int32Array 0.003 s 0.025
2023-01-15 03:32 JavaScript get Vector dictionary 0.002 s -0.560
2023-01-15 03:32 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s -0.729
2023-01-15 03:32 JavaScript Table Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.702
2023-01-15 03:32 JavaScript Table Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.032 s -0.387