Benchmarks
Date Lang Batch Benchmark Mean Z-Score Error
2023-01-16 08:22 Python csv-read uncompressed, arrow_table, streaming, nyctaxi_2010-01 10.505 s 0.950
2023-01-16 08:24 Python dataframe-to-table type_integers 0.010 s 0.165
2023-01-16 08:22 Python csv-read gzip, arrow_table, streaming, nyctaxi_2010-01 10.543 s 1.014
2023-01-16 08:21 Python csv-read uncompressed, arrow_table, file, nyctaxi_2010-01 1.113 s 0.279
2023-01-16 08:24 Python dataframe-to-table type_nested 2.965 s 0.262
2023-01-16 08:21 Python csv-read uncompressed, arrow_table, streaming, fanniemae_2016Q4 13.997 s -0.957
2023-01-16 08:29 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 80.602 s 0.072
2023-01-16 08:19 Python csv-read uncompressed, arrow_table, file, fanniemae_2016Q4 1.241 s 0.288
2023-01-16 08:33 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 84.017 s -0.415
2023-01-16 08:19 Python csv-read gzip, arrow_table, file, fanniemae_2016Q4 5.800 s -1.044
2023-01-16 08:20 Python csv-read gzip, arrow_table, streaming, fanniemae_2016Q4 14.073 s -1.104
2023-01-16 08:24 Python dataframe-to-table type_strings 0.427 s 0.090
2023-01-16 08:44 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.567 s -1.784
2023-01-16 08:45 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.316 s 0.165
2023-01-16 08:21 Python csv-read gzip, arrow_table, file, nyctaxi_2010-01 8.436 s 0.287
2023-01-16 08:24 Python dataframe-to-table chi_traffic_2020_Q1 20.974 s 0.818
2023-01-16 08:24 Python dataframe-to-table type_dict 0.011 s 0.260
2023-01-16 08:24 Python dataframe-to-table type_floats 0.010 s 0.243
2023-01-16 08:25 Python dataset-filter nyctaxi_2010-01 1.028 s -0.250
2023-01-16 08:44 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.194 s 0.127
2023-01-16 08:45 Python dataset-selectivity 100%, chi_traffic_2020_Q1 1.138 s -0.079
2023-01-16 08:45 Python dataset-serialize feather, 1pc, nyctaxi_multi_parquet_s3 0.023 s -2.189
2023-01-16 08:59 Python dataset-serialize feather, 10pc, nyctaxi_multi_ipc_s3 0.226 s -1.428
2023-01-16 09:00 Python dataset-serialize csv, 10pc, nyctaxi_multi_ipc_s3 8.692 s 1.055
2023-01-16 08:45 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.221 s 0.172
2023-01-16 08:45 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.674 s 0.256
2023-01-16 08:45 Python dataset-selectivity 10%, chi_traffic_2020_Q1 1.204 s -0.033
2023-01-16 08:50 Python dataset-serialize parquet, 100pc, nyctaxi_multi_parquet_s3 30.297 s -1.590
2023-01-16 08:45 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.036 s 0.285
2023-01-16 08:46 Python dataset-serialize arrow, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.975
2023-01-16 08:47 Python dataset-serialize csv, 10pc, nyctaxi_multi_parquet_s3 7.534 s 0.845
2023-01-16 08:50 Python dataset-serialize arrow, 100pc, nyctaxi_multi_parquet_s3 2.151 s -0.556
2023-01-16 08:44 Python dataset-read async=True, nyctaxi_multi_ipc_s3 225.172 s -0.510
2023-01-16 08:45 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.033 s 0.308
2023-01-16 08:46 Python dataset-serialize parquet, 10pc, nyctaxi_multi_parquet_s3 2.931 s -1.533
2023-01-16 08:45 Python dataset-serialize arrow, 1pc, nyctaxi_multi_parquet_s3 0.023 s 0.212
2023-01-16 08:45 Python dataset-selectivity 1%, chi_traffic_2020_Q1 1.194 s -1.437
2023-01-16 08:45 Python dataset-serialize parquet, 1pc, nyctaxi_multi_parquet_s3 0.306 s -1.002
2023-01-16 08:46 Python dataset-serialize csv, 1pc, nyctaxi_multi_parquet_s3 0.758 s 0.858
2023-01-16 08:58 Python dataset-serialize arrow, 1pc, nyctaxi_multi_ipc_s3 0.026 s -1.537
2023-01-16 08:59 Python dataset-serialize arrow, 10pc, nyctaxi_multi_ipc_s3 0.224 s 1.451
2023-01-16 08:46 Python dataset-serialize feather, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.034
2023-01-16 08:58 Python dataset-serialize csv, 100pc, nyctaxi_multi_parquet_s3 76.047 s 0.911
2023-01-16 08:58 Python dataset-serialize feather, 1pc, nyctaxi_multi_ipc_s3 0.025 s 1.251
2023-01-16 08:58 Python dataset-serialize csv, 1pc, nyctaxi_multi_ipc_s3 0.865 s 1.406
2023-01-16 08:50 Python dataset-serialize feather, 100pc, nyctaxi_multi_parquet_s3 2.153 s -0.823
2023-01-16 09:03 Python dataset-serialize parquet, 100pc, nyctaxi_multi_ipc_s3 30.886 s -1.603
2023-01-16 09:04 Python dataset-serialize feather, 100pc, nyctaxi_multi_ipc_s3 2.412 s -0.762
2023-01-16 08:58 Python dataset-serialize parquet, 1pc, nyctaxi_multi_ipc_s3 0.287 s -1.339
2023-01-16 08:59 Python dataset-serialize parquet, 10pc, nyctaxi_multi_ipc_s3 3.047 s -1.600
2023-01-16 09:03 Python dataset-serialize arrow, 100pc, nyctaxi_multi_ipc_s3 2.407 s 0.058
2023-01-16 09:13 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.636 s 0.321
2023-01-16 09:12 Python dataset-serialize csv, 100pc, nyctaxi_multi_ipc_s3 86.285 s 1.036
2023-01-16 09:13 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 1.624 s 0.228
2023-01-16 09:13 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 5.648 s 0.286
2023-01-16 09:13 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.346 s 0.283
2023-01-16 09:13 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.507 s -0.040
2023-01-16 09:13 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 1.491 s 0.351
2023-01-16 09:14 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.949 s -0.102
2023-01-16 09:14 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 1.585 s 0.355
2023-01-16 09:13 Python file-read lz4, feather, table, fanniemae_2016Q4 0.828 s 0.003
2023-01-16 09:14 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.994 s -0.096
2023-01-16 09:14 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 0.952 s -0.101
2023-01-16 09:14 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 0.960 s 0.321
2023-01-16 09:14 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 4.220 s 0.201
2023-01-16 09:14 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 1.372 s -0.085
2023-01-16 09:14 Python file-read uncompressed, feather, table, nyctaxi_2010-01 0.936 s 0.344
2023-01-16 09:14 Python file-read lz4, feather, table, nyctaxi_2010-01 0.683 s 0.234
2023-01-16 09:15 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 10.461 s 0.179
2023-01-16 09:16 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 20.031 s -0.085
2023-01-16 09:17 Python file-write snappy, parquet, table, fanniemae_2016Q4 10.852 s -1.184
2023-01-16 09:18 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.532 s 3.079
2023-01-16 09:18 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 20.389 s -1.100
2023-01-16 09:19 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 13.522 s 3.127
2023-01-16 09:19 Python file-write lz4, feather, table, fanniemae_2016Q4 1.543 s 2.874
2023-01-16 09:20 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.582 s 1.516
2023-01-16 09:19 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 10.598 s 2.612
2023-01-16 09:20 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 7.072 s 1.541
2023-01-16 09:20 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.498 s 1.472
2023-01-16 09:21 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 8.990 s 1.457
2023-01-16 09:21 Python file-write uncompressed, feather, table, nyctaxi_2010-01 1.949 s 3.254
2023-01-16 09:21 Python file-write lz4, feather, table, nyctaxi_2010-01 1.371 s 3.345
2023-01-16 09:21 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 3.360 s 3.391
2023-01-16 09:22 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 2.762 s 3.504
2023-01-16 09:22 Python wide-dataframe use_legacy_dataset=false 0.508 s 0.904
2023-01-16 09:22 Python wide-dataframe use_legacy_dataset=true 0.374 s 0.468
2023-01-16 09:44 R dataframe-to-table chi_traffic_2020_Q1, R 4.400 s -1.774
2023-01-16 09:45 R dataframe-to-table type_dict, R 0.048 s 0.909
2023-01-16 09:45 R dataframe-to-table type_floats, R 0.013 s 0.571
2023-01-16 09:44 R dataframe-to-table type_strings, R 0.534 s 0.111
2023-01-16 09:45 R dataframe-to-table type_integers, R 0.010 s 0.592
2023-01-16 09:45 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.319 s -0.021
2023-01-16 09:45 R dataframe-to-table type_nested, R 0.577 s -1.506
2023-01-16 09:46 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.575 s -0.142
2023-01-16 09:46 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.331 s -0.414
2023-01-16 09:46 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 0.572 s 0.264
2023-01-16 09:46 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.603 s 0.264
2023-01-16 09:46 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.315 s 0.254
2023-01-16 09:46 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.585 s -0.474
2023-01-16 09:47 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.862 s 0.206
2023-01-16 09:47 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 0.570 s -0.193
2023-01-16 09:47 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 0.915 s -0.121
2023-01-16 09:47 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 0.918 s -0.218
2023-01-16 09:48 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.821 s 0.312
2023-01-16 09:47 R file-read snappy, parquet, table, nyctaxi_2010-01, R 0.573 s -0.329
2023-01-16 09:48 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.590 s 0.274
2023-01-16 09:47 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s 0.315
2023-01-16 09:48 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.907 s 0.273
2023-01-16 09:49 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 9.863 s -1.483
2023-01-16 09:51 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 16.798 s -1.266
2023-01-16 09:52 R file-write snappy, parquet, table, fanniemae_2016Q4, R 10.271 s -1.439
2023-01-16 09:58 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 7.454 s 0.933
2023-01-16 10:00 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.799 s -1.026
2023-01-16 09:56 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 8.987 s 1.193
2023-01-16 10:02 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.761 s -1.183
2023-01-16 09:56 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.492 s 0.340
2023-01-16 09:59 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.981 s -1.124
2023-01-16 10:03 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.315 s -1.653
2023-01-16 10:06 R partitioned-dataset-filter dims, dataset-taxi-parquet, R 0.607 s -0.411
2023-01-16 10:04 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.172 s -0.218
2023-01-16 10:09 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=0.1, R 0.303 s 0.262
2023-01-16 09:54 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 17.267 s -1.351
2023-01-16 09:54 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.961 s -0.172
2023-01-16 10:07 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=1, R 0.586 s 0.024
2023-01-16 10:08 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=0.01, R 0.281 s -0.171
2023-01-16 10:09 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=10, R 0.668 s -1.292
2023-01-16 10:10 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=1, R 0.301 s 0.167
2023-01-16 10:06 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=0.1, R 0.201 s 0.234
2023-01-16 10:08 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=1, R 0.336 s 0.144
2023-01-16 10:09 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=0.1, R 0.232 s 0.236
2023-01-16 10:01 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.719 s -1.189
2023-01-16 10:06 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.616 s -1.352
2023-01-16 10:07 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=1, R 0.274 s 0.105
2023-01-16 10:08 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=1, R 0.431 s 0.234
2023-01-16 10:09 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=10, R 1.074 s -0.965
2023-01-16 10:09 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=0.01, R 0.269 s 0.223
2023-01-16 10:04 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.471 s -0.029
2023-01-16 10:05 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.080 s -0.567
2023-01-16 10:11 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=0.1, R 0.201 s 0.272
2023-01-16 10:12 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=0.01, R 0.261 s 0.150
2023-01-16 10:06 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.252 s 0.052
2023-01-16 10:11 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=0.01, R 0.233 s 0.312
2023-01-16 10:11 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=0.1, R 0.242 s 0.297
2023-01-16 10:06 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.559 s 0.163
2023-01-16 10:06 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=0.01, R 0.200 s 0.246
2023-01-16 10:08 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=10, R 3.711 s 0.475
2023-01-16 10:08 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=0.1, R 0.349 s 0.144
2023-01-16 10:07 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=0.1, R 0.282 s 0.201
2023-01-16 10:08 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=0.01, R 0.333 s 0.185
2023-01-16 10:10 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=1, R 0.587 s 0.124
2023-01-16 10:11 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=10, R 3.527 s -0.052
2023-01-16 10:11 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=1, R 0.327 s 0.392
2023-01-16 10:06 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=0.01, R 0.239 s 0.280
2023-01-16 10:07 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=10, R 1.001 s 0.447
2023-01-16 10:08 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=0.1, R 0.296 s -0.312
2023-01-16 10:09 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=0.01, R 0.228 s 0.206
2023-01-16 10:10 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=10, R 0.904 s -0.249
2023-01-16 10:11 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=0.01, R 0.197 s 0.360
2023-01-16 10:11 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=1, R 0.273 s 0.088
2023-01-16 10:12 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=10, R 0.913 s 0.249
2023-01-16 10:12 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=10, R 1.200 s -0.099
2023-01-16 10:12 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=0.01, R 0.313 s 0.272
2023-01-16 10:13 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=1, R 0.325 s -0.069
2023-01-16 10:12 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=0.1, R 0.266 s 0.213
2023-01-16 10:13 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=0.1, R 0.350 s 0.241
2023-01-16 10:13 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=1, R 0.643 s -0.026
2023-01-16 10:14 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=10, R 3.770 s -0.983
2023-01-16 10:13 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=10, R 0.635 s -0.046
2023-01-16 10:14 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=0.01, R 0.161 s 0.214
2023-01-16 10:14 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=0.01, R 0.201 s 0.266
2023-01-16 10:15 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=1, R 0.507 s 0.221
2023-01-16 10:15 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=10, R 0.356 s 0.072
2023-01-16 10:14 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=0.1, R 0.160 s 0.268
2023-01-16 10:14 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=1, R 0.178 s 0.318
2023-01-16 10:14 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=0.1, R 0.233 s 0.354
2023-01-16 10:16 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=1, R 0.663 s 0.147
2023-01-16 10:16 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=0.01, R 0.339 s 0.381
2023-01-16 10:16 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=0.1, R 0.373 s 0.338
2023-01-16 10:15 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=10, R 3.244 s 0.160
2023-01-16 10:16 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=1, R 0.351 s 0.268
2023-01-16 10:15 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=0.01, R 0.291 s 0.334
2023-01-16 10:16 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=0.1, R 0.293 s 0.325
2023-01-16 10:17 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=10, R 3.656 s -0.272
2023-01-16 10:17 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=0.01, R 0.334 s 0.248
2023-01-16 10:17 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=0.01, R 0.395 s 0.299
2023-01-16 10:17 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=0.1, R 0.339 s 0.265
2023-01-16 10:17 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=10, R 0.790 s -0.076
2023-01-16 10:18 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=0.1, R 0.428 s 0.246
2023-01-16 10:18 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=1, R 0.370 s 0.236
2023-01-16 10:18 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=1, R 0.711 s 0.329
2023-01-16 10:18 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=10, R 0.616 s 0.076
2023-01-16 10:19 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=0.01, R 0.273 s 0.285
2023-01-16 10:19 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=0.1, R 0.295 s 0.342
2023-01-16 10:19 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=0.01, R 0.323 s 0.352
2023-01-16 10:19 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=10, R 3.773 s -0.119
2023-01-16 10:19 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=1, R 0.443 s 0.076
2023-01-16 10:20 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=1, R 0.771 s 0.199
2023-01-16 10:19 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=0.1, R 0.370 s 0.340
2023-01-16 10:20 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=10, R 0.972 s -0.804
2023-01-16 10:21 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=0.01, R 0.255 s 0.223
2023-01-16 10:21 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=10, R 4.152 s -0.224
2023-01-16 10:21 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=0.01, R 0.298 s 0.306
2023-01-16 10:21 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=0.1, R 0.260 s 0.207
2023-01-16 10:21 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=0.1, R 0.335 s 0.285
2023-01-16 10:21 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=1, R 0.324 s -0.040
2023-01-16 10:22 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=0.01, R 0.261 s 0.248
2023-01-16 10:21 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=1, R 0.654 s 0.095
2023-01-16 10:22 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=10, R 0.879 s -0.168
2023-01-16 10:23 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=1, R 0.328 s 0.277
2023-01-16 10:22 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=10, R 3.740 s -0.068
2023-01-16 10:22 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=0.01, R 0.211 s 0.148
2023-01-16 10:23 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=0.1, R 0.304 s 0.228
2023-01-16 10:23 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=0.1, R 0.230 s 0.230
2023-01-16 10:23 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=1, R 0.541 s -0.024
2023-01-16 10:24 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=0.1, R 0.368 s -0.028
2023-01-16 10:23 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=10, R 0.873 s -0.390
2023-01-16 10:24 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=10, R 3.276 s -0.877
2023-01-16 10:24 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=0.01, R 0.209 s 0.117
2023-01-16 10:24 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=0.01, R 0.257 s 0.070
2023-01-16 10:24 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=0.1, R 0.307 s -0.422
2023-01-16 10:24 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=1, R 0.914 s -0.457
2023-01-16 10:24 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=1, R 0.497 s -1.555
2023-01-16 10:26 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=0.1, R 0.430 s -0.123
2023-01-16 10:26 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=10, R 6.613 s -0.913
2023-01-16 10:26 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=0.01, R 0.185 s 0.124
2023-01-16 10:25 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=10, R 2.691 s -2.497
2023-01-16 10:26 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=0.01, R 0.240 s -0.014
2023-01-16 10:26 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=1, R 0.614 s -1.094
2023-01-16 10:26 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=0.1, R 0.335 s -0.194
2023-01-16 10:26 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=1, R 0.871 s -0.424
2023-01-16 10:27 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=10, R 3.450 s -2.458
2023-01-16 10:28 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=0.1, R 0.334 s 0.241
2023-01-16 10:27 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=0.01, R 0.266 s 0.287
2023-01-16 10:28 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=1, R 0.442 s 0.029
2023-01-16 10:27 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=10, R 5.561 s -0.760
2023-01-16 10:27 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=0.01, R 0.216 s 0.182
2023-01-16 10:28 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=1, R 0.933 s 0.114
2023-01-16 10:28 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=0.1, R 0.242 s 0.217
2023-01-16 10:29 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=10, R 5.977 s -0.357
2023-01-16 10:29 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=0.01, R 0.252 s 0.353
2023-01-16 10:29 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=10, R 1.562 s -0.412
2023-01-16 10:29 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=0.1, R 0.211 s 0.261
2023-01-16 10:29 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=0.01, R 0.208 s 0.363
2023-01-16 10:31 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=10, R 3.598 s -0.233
2023-01-16 10:30 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=0.1, R 0.285 s 0.285
2023-01-16 10:30 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=1, R 0.242 s 0.256
2023-01-16 10:30 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=1, R 0.589 s 0.094
2023-01-16 10:31 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=0.01, R 0.197 s 0.259
2023-01-16 10:32 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=0.1, R 0.220 s 0.253
2023-01-16 10:30 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=10, R 0.607 s 0.539
2023-01-16 10:31 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=0.1, R 0.223 s 0.218
2023-01-16 10:31 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=0.01, R 0.237 s 0.308
2023-01-16 10:32 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=10, R 0.893 s -0.256
2023-01-16 10:31 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=0.1, R 0.284 s 0.311
2023-01-16 10:31 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=1, R 0.327 s 0.155
2023-01-16 10:32 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=10, R 3.099 s -0.018
2023-01-16 10:31 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=1, R 0.526 s 0.237
2023-01-16 10:32 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=0.01, R 0.216 s 0.304
2023-01-16 10:32 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=0.01, R 0.268 s 0.270
2023-01-16 10:32 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=0.1, R 0.321 s 0.304
2023-01-16 10:33 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=1, R 0.258 s 0.282
2023-01-16 10:33 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=1, R 0.988 s -0.190
2023-01-16 10:34 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=10, R 7.008 s -0.451
2023-01-16 10:33 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=10, R 0.667 s 0.116
2023-01-16 10:34 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=0.1, R 0.209 s 0.277
2023-01-16 10:35 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=1, R 0.543 s -0.024
2023-01-16 10:39 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=10, R 0.606 s -0.034
2023-01-16 10:34 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=1, R 0.406 s 0.076
2023-01-16 10:37 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=1, R 1.339 s -0.414
2023-01-16 10:40 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=0.01, R 0.454 s -0.136
2023-01-16 10:45 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=0.01, R 0.200 s -0.019
2023-01-16 10:34 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=0.01, R 0.194 s 0.270
2023-01-16 10:37 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=10, R 5.666 s -1.759
2023-01-16 10:39 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=1, R 0.292 s -0.124
2023-01-16 10:40 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=10, R 6.256 s -0.877
2023-01-16 10:35 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=10, R 3.496 s -1.682
2023-01-16 10:36 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=0.01, R 0.274 s 0.095
2023-01-16 10:38 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=0.1, R 0.374 s 0.099
2023-01-16 10:43 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=10, R 30.160 s -2.237
2023-01-16 10:34 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=0.1, R 0.265 s 0.264
2023-01-16 10:36 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=0.1, R 0.441 s 0.038
2023-01-16 10:36 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=0.1, R 0.442 s 0.213
2023-01-16 10:38 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=0.01, R 0.257 s 0.145
2023-01-16 10:40 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=0.1, R 0.799 s -0.190
2023-01-16 10:46 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=1, R 0.453 s 0.149
2023-01-16 10:36 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=1, R 0.894 s -0.912
2023-01-16 10:38 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=10, R 9.451 s -1.757
2023-01-16 10:46 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=1, R 0.227 s 0.214
2023-01-16 10:34 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=0.01, R 0.243 s 0.250
2023-01-16 10:38 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=0.01, R 0.320 s 0.185
2023-01-16 10:41 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=1, R 3.196 s -2.072
2023-01-16 10:45 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=0.01, R 0.244 s 0.180
2023-01-16 10:46 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=10, R 0.310 s 0.061
2023-01-16 10:55 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.040 s -0.846
2023-01-16 10:38 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=0.1, R 0.257 s 0.121
2023-01-16 10:39 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=1, R 1.006 s -0.739
2023-01-16 10:45 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=0.1, R 0.206 s 0.111
2023-01-16 10:55 JavaScript Iterate Vector uint64Array 0.004 s 0.164
2023-01-16 10:55 JavaScript Iterate Vector int16Array 0.002 s 0.109
2023-01-16 10:55 JavaScript Iterate Vector int64Array 0.004 s 0.129
2023-01-16 10:36 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=10, R 4.370 s -0.997
2023-01-16 10:36 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=0.01, R 0.328 s 0.228
2023-01-16 10:55 JavaScript toArray Vector uint32Array
2023-01-16 10:55 JavaScript toArray Vector int8Array
2023-01-16 10:55 JavaScript toArray Vector int32Array
2023-01-16 10:55 JavaScript toArray Vector float32Array
2023-01-16 10:40 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=0.01, R 0.397 s -0.368
2023-01-16 10:41 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=1, R 2.920 s -2.229
2023-01-16 10:45 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=10, R 32.963 s -1.399
2023-01-16 10:46 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=0.1, R 0.261 s 0.219
2023-01-16 10:55 JavaScript Iterate Vector uint16Array 0.002 s 0.309
2023-01-16 10:55 JavaScript Spread Vector booleans 0.010 s -0.016
2023-01-16 10:55 JavaScript get Vector string 0.125 s -0.482
2023-01-16 10:55 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.023 s 0.320
2023-01-16 10:40 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=0.1, R 0.737 s -0.987
2023-01-16 10:55 JavaScript vectorFromArray numbers 0.016 s 0.382
2023-01-16 10:55 JavaScript vectorFromArray dictionary 0.018 s -1.704
2023-01-16 10:55 JavaScript Spread Vector string 0.147 s -0.656
2023-01-16 10:55 JavaScript toArray Vector uint16Array
2023-01-16 10:55 JavaScript toArray Vector uint64Array
2023-01-16 10:55 JavaScript Spread Vector float64Array 0.008 s 1.174
2023-01-16 10:55 JavaScript Parse write recordBatches, tracks 0.002 s 0.637
2023-01-16 10:55 JavaScript Spread vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.109 s -1.032
2023-01-16 10:55 JavaScript Table tracks, 1,000,000 0.263 s 0.427
2023-01-16 10:46 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=10, R 2.339 s -0.241
2023-01-16 10:55 JavaScript vectorFromArray booleans 0.018 s 0.296
2023-01-16 10:55 JavaScript Iterate Vector uint8Array 0.002 s -0.509
2023-01-16 10:55 JavaScript Iterate Vector uint32Array 0.002 s -1.023
2023-01-16 10:55 JavaScript Iterate Vector int8Array 0.002 s -0.109
2023-01-16 10:55 JavaScript toArray Vector float64Array
2023-01-16 10:55 JavaScript Iterate Vector int32Array 0.002 s 0.302
2023-01-16 10:55 JavaScript Iterate Vector float32Array 0.002 s -1.190
2023-01-16 10:55 JavaScript Iterate Vector numbers 0.002 s -1.250
2023-01-16 10:55 JavaScript Iterate Vector dictionary 0.004 s 0.104
2023-01-16 10:55 JavaScript Spread Vector uint8Array 0.007 s 0.082
2023-01-16 10:55 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.110 s -1.966
2023-01-16 10:55 JavaScript Iterate Vector float64Array 0.002 s -1.234
2023-01-16 10:55 JavaScript Spread Vector uint64Array 0.012 s 0.070
2023-01-16 10:55 JavaScript Spread Vector int16Array 0.006 s 0.343
2023-01-16 10:55 JavaScript Spread Vector int64Array 0.012 s 0.141
2023-01-16 10:55 JavaScript toArray Vector int16Array
2023-01-16 10:55 JavaScript toArray Vector int64Array
2023-01-16 10:55 JavaScript Spread vectors lat, 1,000,000, Float32, tracks 0.184 s 1.300
2023-01-16 10:55 JavaScript Iterate Vector booleans 0.004 s 0.580
2023-01-16 10:55 JavaScript Iterate Vector string 0.127 s -0.162
2023-01-16 10:55 JavaScript Spread Vector uint16Array 0.006 s 0.258
2023-01-16 10:55 JavaScript get Vector float64Array 0.002 s -0.121
2023-01-16 10:55 JavaScript get Vector booleans 0.002 s 1.031
2023-01-16 10:55 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.030 s -0.767
2023-01-16 10:55 JavaScript Spread vectors lng, 1,000,000, Float32, tracks 0.189 s -0.215
2023-01-16 10:55 JavaScript Spread Vector uint32Array 0.006 s 0.964
2023-01-16 10:55 JavaScript Spread Vector int8Array 0.006 s 0.546
2023-01-16 10:55 JavaScript Spread Vector int32Array 0.006 s 0.676
2023-01-16 10:55 JavaScript Spread Vector numbers 0.008 s 1.378
2023-01-16 10:55 JavaScript toArray Vector uint8Array
2023-01-16 10:55 JavaScript get Vector uint32Array 0.003 s 0.277
2023-01-16 10:55 JavaScript Spread Vector float32Array 0.008 s 1.090
2023-01-16 10:55 JavaScript Spread Vector dictionary 0.010 s -0.555
2023-01-16 10:55 JavaScript get Vector numbers 0.002 s 1.347
2023-01-16 10:55 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.028
2023-01-16 10:55 JavaScript Table 1,000,000, tracks 0.309 s -1.371
2023-01-16 10:55 JavaScript toArray Vector numbers
2023-01-16 10:55 JavaScript toArray Vector dictionary 0.010 s -1.004
2023-01-16 10:55 JavaScript get Vector uint8Array 0.003 s 0.197
2023-01-16 10:55 JavaScript get Vector int32Array 0.003 s 0.253
2023-01-16 10:55 JavaScript Parse read recordBatches, tracks 0.000 s -0.463
2023-01-16 10:55 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-16 10:55 JavaScript toArray Vector booleans 0.010 s -0.782
2023-01-16 10:55 JavaScript toArray Vector string 0.145 s 0.348
2023-01-16 10:55 JavaScript get Vector uint16Array 0.003 s 0.271
2023-01-16 10:55 JavaScript get Vector uint64Array 0.003 s 0.361
2023-01-16 10:55 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.858
2023-01-16 10:55 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-16 10:55 JavaScript Table tracks, 1,000,000 0.095 s 0.512
2023-01-16 10:55 JavaScript get Vector int8Array 0.003 s 0.230
2023-01-16 10:55 JavaScript get Vector dictionary 0.002 s 1.166
2023-01-16 10:55 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.366
2023-01-16 10:55 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.023 s 0.324
2023-01-16 10:55 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.040 s -0.923
2023-01-16 10:55 JavaScript Table tracks, 1,000,000 0.050 s 0.706
2023-01-16 10:55 JavaScript Table Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.264
2023-01-16 10:55 JavaScript get Vector int16Array 0.003 s 0.226
2023-01-16 10:55 JavaScript get Vector int64Array 0.003 s 0.312
2023-01-16 10:55 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s -0.350
2023-01-16 10:55 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s
2023-01-16 10:55 JavaScript get Vector float32Array 0.002 s -0.119
2023-01-16 10:55 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.030 s 0.448
2023-01-16 10:55 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.109 s -1.728
2023-01-16 10:55 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s
2023-01-16 10:55 JavaScript Spread vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.109 s -2.359
2023-01-16 10:55 JavaScript Table Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.032 s 0.636
2023-01-16 10:55 JavaScript Table Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.408