Top Outliers
Benchmarks
Date Lang Batch Benchmark Mean Z-Score Error
2023-01-16 02:39 Python csv-read uncompressed, arrow_table, file, fanniemae_2016Q4 1.255 s 0.194
2023-01-16 02:39 Python csv-read gzip, arrow_table, file, fanniemae_2016Q4 5.820 s -2.584
2023-01-16 02:40 Python csv-read gzip, arrow_table, streaming, fanniemae_2016Q4 14.030 s -0.962
2023-01-16 02:40 Python csv-read uncompressed, arrow_table, streaming, fanniemae_2016Q4 14.070 s -1.178
2023-01-16 02:41 Python csv-read gzip, arrow_table, file, nyctaxi_2010-01 8.438 s 0.083
2023-01-16 02:41 Python csv-read uncompressed, arrow_table, file, nyctaxi_2010-01 1.109 s 0.323
2023-01-16 02:42 Python csv-read gzip, arrow_table, streaming, nyctaxi_2010-01 10.575 s 0.930
2023-01-16 02:42 Python csv-read uncompressed, arrow_table, streaming, nyctaxi_2010-01 10.484 s 0.983
2023-01-16 02:44 Python dataframe-to-table chi_traffic_2020_Q1 21.181 s -0.398
2023-01-16 02:44 Python dataframe-to-table type_strings 0.427 s 0.211
2023-01-16 02:44 Python dataframe-to-table type_integers 0.010 s 1.117
2023-01-16 02:44 Python dataframe-to-table type_floats 0.010 s 0.313
2023-01-16 02:44 Python dataframe-to-table type_nested 2.970 s 0.084
2023-01-16 02:44 Python dataframe-to-table type_dict 0.011 s 0.313
2023-01-16 02:45 Python dataset-filter nyctaxi_2010-01 1.029 s -0.260
2023-01-16 02:50 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 102.874 s -2.506
2023-01-16 02:54 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.363 s 0.279
2023-01-16 03:07 Python dataset-selectivity 1%, chi_traffic_2020_Q1 1.222 s -2.939
2023-01-16 03:07 Python dataset-selectivity 10%, chi_traffic_2020_Q1 1.269 s -2.809
2023-01-16 03:08 Python dataset-serialize parquet, 10pc, nyctaxi_multi_parquet_s3 2.931 s -1.639
2023-01-16 03:08 Python dataset-serialize arrow, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.830
2023-01-16 03:08 Python dataset-serialize csv, 10pc, nyctaxi_multi_parquet_s3 7.524 s 1.502
2023-01-16 03:06 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 3.740 s -3.820
2023-01-16 03:07 Python dataset-serialize parquet, 1pc, nyctaxi_multi_parquet_s3 0.306 s -1.207
2023-01-16 03:07 Python dataset-serialize csv, 1pc, nyctaxi_multi_parquet_s3 0.757 s 1.072
2023-01-16 03:06 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.177 s 1.012
2023-01-16 03:06 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.207 s 0.049
2023-01-16 03:06 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.224 s 0.146
2023-01-16 03:07 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 3.724 s -3.812
2023-01-16 03:20 Python dataset-serialize parquet, 1pc, nyctaxi_multi_ipc_s3 0.287 s -1.378
2023-01-16 03:06 Python dataset-read async=True, nyctaxi_multi_ipc_s3 228.759 s -1.220
2023-01-16 03:07 Python dataset-serialize arrow, 1pc, nyctaxi_multi_parquet_s3 0.023 s -0.506
2023-01-16 03:06 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.523 s -1.083
2023-01-16 03:06 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 3.723 s -4.127
2023-01-16 03:07 Python dataset-selectivity 100%, chi_traffic_2020_Q1 1.239 s -3.975
2023-01-16 03:20 Python dataset-serialize parquet, 10pc, nyctaxi_multi_ipc_s3 3.049 s -1.760
2023-01-16 03:07 Python dataset-serialize feather, 1pc, nyctaxi_multi_parquet_s3 0.023 s -0.084
2023-01-16 03:08 Python dataset-serialize feather, 10pc, nyctaxi_multi_parquet_s3 0.199 s 0.720
2023-01-16 03:20 Python dataset-serialize csv, 1pc, nyctaxi_multi_ipc_s3 0.865 s 1.473
2023-01-16 03:12 Python dataset-serialize arrow, 100pc, nyctaxi_multi_parquet_s3 2.150 s -0.315
2023-01-16 03:20 Python dataset-serialize feather, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.022
2023-01-16 03:35 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.321 s 0.340
2023-01-16 03:36 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.195 s -3.384
2023-01-16 03:21 Python dataset-serialize arrow, 10pc, nyctaxi_multi_ipc_s3 0.224 s 1.532
2023-01-16 03:34 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 1.637 s 0.058
2023-01-16 03:25 Python dataset-serialize parquet, 100pc, nyctaxi_multi_ipc_s3 30.903 s -1.747
2023-01-16 03:34 Python dataset-serialize csv, 100pc, nyctaxi_multi_ipc_s3 86.196 s 1.546
2023-01-16 03:35 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 1.507 s -0.264
2023-01-16 03:36 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.590 s -3.021
2023-01-16 03:12 Python dataset-serialize feather, 100pc, nyctaxi_multi_parquet_s3 2.154 s -1.354
2023-01-16 03:20 Python dataset-serialize csv, 100pc, nyctaxi_multi_parquet_s3 75.946 s 1.549
2023-01-16 03:25 Python dataset-serialize feather, 100pc, nyctaxi_multi_ipc_s3 2.410 s -0.125
2023-01-16 03:35 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 5.630 s 0.334
2023-01-16 03:35 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 4.213 s 0.355
2023-01-16 03:40 Python file-write uncompressed, feather, table, fanniemae_2016Q4 6.769 s -0.796
2023-01-16 03:12 Python dataset-serialize parquet, 100pc, nyctaxi_multi_parquet_s3 30.300 s -1.699
2023-01-16 03:34 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.643 s 0.235
2023-01-16 03:20 Python dataset-serialize arrow, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.189
2023-01-16 03:21 Python dataset-serialize feather, 10pc, nyctaxi_multi_ipc_s3 0.224 s 1.087
2023-01-16 03:21 Python dataset-serialize csv, 10pc, nyctaxi_multi_ipc_s3 8.682 s 1.673
2023-01-16 03:25 Python dataset-serialize arrow, 100pc, nyctaxi_multi_ipc_s3 2.411 s -1.396
2023-01-16 03:34 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.502 s 0.111
2023-01-16 03:36 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 2.246 s -2.835
2023-01-16 03:40 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 20.430 s -1.321
2023-01-16 03:43 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 9.337 s -1.452
2023-01-16 03:44 Python wide-dataframe use_legacy_dataset=true 0.375 s 0.378
2023-01-16 03:53 R dataframe-to-table type_dict, R 0.045 s 1.283
2023-01-16 03:56 R file-read snappy, parquet, table, nyctaxi_2010-01, R 0.570 s -0.235
2023-01-16 03:57 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.812 s 0.334
2023-01-16 03:35 Python file-read lz4, feather, table, fanniemae_2016Q4 0.818 s 0.219
2023-01-16 03:36 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.258 s -4.014
2023-01-16 03:39 Python file-write snappy, parquet, table, fanniemae_2016Q4 10.909 s -1.570
2023-01-16 03:41 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 15.516 s -0.475
2023-01-16 03:44 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 4.383 s -1.111
2023-01-16 03:44 Python file-write lz4, feather, table, nyctaxi_2010-01 1.900 s -0.874
2023-01-16 03:54 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.393 s -2.880
2023-01-16 03:55 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.767 s -1.625
2023-01-16 03:36 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 1.256 s -3.455
2023-01-16 03:38 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 20.236 s -1.096
2023-01-16 03:53 R dataframe-to-table type_strings, R 0.535 s -0.276
2023-01-16 03:41 Python file-write lz4, feather, table, fanniemae_2016Q4 1.914 s -0.485
2023-01-16 03:36 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 1.204 s -3.359
2023-01-16 03:36 Python file-read lz4, feather, table, nyctaxi_2010-01 0.974 s -2.863
2023-01-16 03:42 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 11.051 s -1.067
2023-01-16 03:42 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 7.402 s -0.992
2023-01-16 03:43 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.810 s -1.121
2023-01-16 03:43 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.984 s -1.083
2023-01-16 03:44 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 3.290 s -0.677
2023-01-16 03:36 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 1.622 s -2.876
2023-01-16 03:37 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 10.770 s -1.714
2023-01-16 03:53 R dataframe-to-table chi_traffic_2020_Q1, R 4.376 s -1.522
2023-01-16 03:53 R dataframe-to-table type_integers, R 0.010 s 0.594
2023-01-16 03:42 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 6.045 s -1.735
2023-01-16 03:54 R dataframe-to-table type_nested, R 0.575 s -0.599
2023-01-16 03:55 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.379 s -2.197
2023-01-16 03:56 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.601 s 0.274
2023-01-16 03:57 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.579 s 0.313
2023-01-16 03:44 Python wide-dataframe use_legacy_dataset=false 0.507 s 0.987
2023-01-16 03:53 R dataframe-to-table type_floats, R 0.013 s 0.611
2023-01-16 03:56 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.856 s 0.240
2023-01-16 03:56 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 0.565 s -0.092
2023-01-16 03:57 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.896 s 0.313
2023-01-16 03:57 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.215 s 0.329
2023-01-16 03:56 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 0.909 s -0.007
2023-01-16 04:00 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 16.800 s -1.322
2023-01-16 03:54 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.661 s -3.300
2023-01-16 03:55 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.635 s -2.309
2023-01-16 03:55 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 0.568 s 0.280
2023-01-16 03:58 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 9.861 s -1.528
2023-01-16 03:56 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 0.914 s -0.089
2023-01-16 04:01 R file-write snappy, parquet, table, fanniemae_2016Q4, R 10.292 s -1.625
2023-01-16 04:03 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 17.257 s -1.333
2023-01-16 04:03 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.959 s 0.085
2023-01-16 04:05 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 8.988 s 1.178
2023-01-16 04:05 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.493 s 0.082
2023-01-16 04:07 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 7.446 s 1.112
2023-01-16 04:08 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 5.019 s -1.662
2023-01-16 04:09 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.866 s -1.883
2023-01-16 04:10 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.734 s -1.432
2023-01-16 04:11 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.776 s -1.419
2023-01-16 04:12 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.312 s -0.505
2023-01-16 04:13 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.177 s -0.420
2023-01-16 04:14 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.471 s -0.233
2023-01-16 04:15 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.075 s -0.365
2023-01-16 04:15 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.598 s -1.840
2023-01-16 04:15 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=0.01, R 0.200 s 0.238
2023-01-16 04:15 R partitioned-dataset-filter dims, dataset-taxi-parquet, R 0.604 s -0.222
2023-01-16 04:16 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=0.1, R 0.200 s 0.239
2023-01-16 04:15 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.616 s -1.365
2023-01-16 04:16 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=0.01, R 0.240 s 0.254
2023-01-16 04:15 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.251 s 0.246
2023-01-16 04:16 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=1, R 0.275 s 0.033
2023-01-16 04:16 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=0.1, R 0.281 s 0.215
2023-01-16 04:16 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=1, R 0.587 s -0.039
2023-01-16 04:17 R tpch arrow, native, memory_map=False, TPCH-01, scale_factor=10, R 1.006 s -0.210
2023-01-16 04:17 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=0.01, R 0.281 s -0.129
2023-01-16 04:17 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=0.01, R 0.334 s 0.103
2023-01-16 04:18 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=1, R 0.334 s 0.320
2023-01-16 04:17 R tpch arrow, parquet, memory_map=False, TPCH-01, scale_factor=10, R 3.712 s 0.481
2023-01-16 04:18 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=0.1, R 0.348 s 0.191
2023-01-16 04:17 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=0.1, R 0.291 s 0.041
2023-01-16 04:21 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=0.01, R 0.258 s -2.425
2023-01-16 04:23 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=0.1, R 0.350 s 0.268
2023-01-16 04:24 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=1, R 0.640 s 0.086
2023-01-16 04:26 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=0.01, R 0.293 s 0.283
2023-01-16 04:19 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=0.1, R 0.259 s -1.050
2023-01-16 04:19 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=1, R 0.370 s -5.299
2023-01-16 04:22 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=1, R 0.308 s -2.549
2023-01-16 04:18 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=0.01, R 0.269 s 0.245
2023-01-16 04:22 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=1, R 0.476 s -3.332
2023-01-16 04:23 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=0.01, R 0.261 s 0.177
2023-01-16 04:24 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=10, R 0.636 s -0.124
2023-01-16 04:18 R tpch arrow, native, memory_map=False, TPCH-02, scale_factor=10, R 0.668 s -1.335
2023-01-16 04:25 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=0.1, R 0.161 s 0.252
2023-01-16 04:27 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=10, R 0.787 s 0.078
2023-01-16 04:30 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=1, R 0.443 s 0.091
2023-01-16 04:19 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=1, R 0.720 s -4.396
2023-01-16 04:23 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=10, R 0.923 s -0.273
2023-01-16 04:24 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=1, R 0.325 s -0.082
2023-01-16 04:28 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=0.1, R 0.427 s 0.275
2023-01-16 04:20 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=10, R 0.948 s -2.700
2023-01-16 04:21 R tpch arrow, native, memory_map=False, TPCH-04, scale_factor=0.1, R 0.266 s -2.814
2023-01-16 04:25 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=1, R 0.178 s 0.315
2023-01-16 04:28 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=0.1, R 0.340 s 0.233
2023-01-16 04:19 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=0.1, R 0.419 s -3.486
2023-01-16 04:27 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=0.1, R 0.374 s 0.315
2023-01-16 04:28 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=0.01, R 0.395 s 0.308
2023-01-16 04:30 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=0.01, R 0.323 s 0.360
2023-01-16 04:31 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=10, R 0.973 s -0.895
2023-01-16 04:18 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=1, R 0.429 s 0.350
2023-01-16 04:18 R tpch arrow, parquet, memory_map=False, TPCH-02, scale_factor=10, R 1.062 s -0.528
2023-01-16 04:25 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=1, R 0.507 s 0.208
2023-01-16 04:18 R tpch arrow, native, memory_map=False, TPCH-03, scale_factor=0.01, R 0.228 s 0.191
2023-01-16 04:23 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=10, R 1.208 s -0.256
2023-01-16 04:26 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=10, R 0.354 s 0.200
2023-01-16 04:26 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=0.1, R 0.294 s 0.294
2023-01-16 04:27 R tpch arrow, native, memory_map=False, TPCH-07, scale_factor=1, R 0.351 s 0.248
2023-01-16 04:28 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=0.01, R 0.335 s 0.207
2023-01-16 04:21 R tpch arrow, parquet, memory_map=False, TPCH-03, scale_factor=10, R 3.886 s -3.241
2023-01-16 04:21 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=0.01, R 0.334 s -3.232
2023-01-16 04:22 R tpch arrow, parquet, memory_map=False, TPCH-04, scale_factor=0.1, R 0.370 s -3.633
2023-01-16 04:23 R tpch arrow, native, memory_map=False, TPCH-05, scale_factor=0.1, R 0.267 s 0.154
2023-01-16 04:25 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=0.01, R 0.202 s 0.262
2023-01-16 04:25 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=0.1, R 0.233 s 0.369
2023-01-16 04:26 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=0.01, R 0.339 s 0.366
2023-01-16 04:29 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=10, R 3.761 s -0.057
2023-01-16 04:31 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=0.01, R 0.256 s 0.180
2023-01-16 04:23 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=0.01, R 0.314 s 0.214
2023-01-16 04:25 R tpch arrow, native, memory_map=False, TPCH-06, scale_factor=0.01, R 0.162 s 0.176
2023-01-16 04:29 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=1, R 0.369 s 0.261
2023-01-16 04:29 R tpch arrow, native, memory_map=False, TPCH-08, scale_factor=10, R 0.615 s 0.133
2023-01-16 04:30 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=0.1, R 0.296 s 0.334
2023-01-16 04:25 R tpch arrow, parquet, memory_map=False, TPCH-05, scale_factor=10, R 3.756 s -0.855
2023-01-16 04:26 R tpch arrow, parquet, memory_map=False, TPCH-06, scale_factor=10, R 3.256 s 0.009
2023-01-16 04:27 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=1, R 0.663 s 0.137
2023-01-16 04:28 R tpch arrow, parquet, memory_map=False, TPCH-07, scale_factor=10, R 3.628 s -0.106
2023-01-16 04:29 R tpch arrow, parquet, memory_map=False, TPCH-08, scale_factor=1, R 0.717 s 0.179
2023-01-16 04:30 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=0.1, R 0.371 s 0.323
2023-01-16 04:32 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=1, R 0.328 s -0.339
2023-01-16 04:30 R tpch arrow, native, memory_map=False, TPCH-09, scale_factor=0.01, R 0.271 s 0.357
2023-01-16 04:32 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=0.1, R 0.336 s 0.257
2023-01-16 04:30 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=1, R 0.772 s 0.204
2023-01-16 04:32 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=0.01, R 0.298 s 0.322
2023-01-16 04:31 R tpch arrow, parquet, memory_map=False, TPCH-09, scale_factor=10, R 4.135 s -0.112
2023-01-16 04:32 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=0.1, R 0.263 s 0.045
2023-01-16 04:33 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=0.1, R 0.233 s 0.113
2023-01-16 04:32 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=1, R 0.648 s 0.295
2023-01-16 04:33 R tpch arrow, native, memory_map=False, TPCH-10, scale_factor=10, R 0.877 s 0.060
2023-01-16 04:33 R tpch arrow, parquet, memory_map=False, TPCH-10, scale_factor=10, R 3.752 s -0.182
2023-01-16 04:33 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=0.01, R 0.261 s 0.236
2023-01-16 04:34 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=10, R 0.875 s -0.541
2023-01-16 04:33 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=0.01, R 0.211 s 0.142
2023-01-16 04:34 R tpch arrow, native, memory_map=False, TPCH-11, scale_factor=1, R 0.332 s -0.082
2023-01-16 04:34 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=1, R 0.541 s -0.045
2023-01-16 04:34 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=0.1, R 0.303 s 0.264
2023-01-16 04:35 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=0.01, R 0.257 s 0.047
2023-01-16 04:35 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=1, R 0.497 s -1.611
2023-01-16 04:34 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=0.01, R 0.210 s 0.098
2023-01-16 04:35 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=1, R 0.915 s -0.453
2023-01-16 04:34 R tpch arrow, parquet, memory_map=False, TPCH-11, scale_factor=10, R 3.260 s -0.724
2023-01-16 04:35 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=0.1, R 0.369 s -0.069
2023-01-16 04:35 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=0.1, R 0.306 s -0.360
2023-01-16 04:36 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=0.01, R 0.184 s 0.156
2023-01-16 04:37 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=0.1, R 0.430 s -0.116
2023-01-16 04:36 R tpch arrow, native, memory_map=False, TPCH-12, scale_factor=10, R 2.694 s -2.796
2023-01-16 04:37 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=0.1, R 0.334 s -0.146
2023-01-16 04:36 R tpch arrow, parquet, memory_map=False, TPCH-12, scale_factor=10, R 6.677 s -1.132
2023-01-16 04:37 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=1, R 0.614 s -1.117
2023-01-16 04:37 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=0.01, R 0.240 s -0.014
2023-01-16 04:38 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=0.01, R 0.216 s 0.193
2023-01-16 04:38 R tpch arrow, native, memory_map=False, TPCH-13, scale_factor=10, R 3.445 s -2.596
2023-01-16 04:37 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=1, R 0.873 s -0.515
2023-01-16 04:38 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=0.01, R 0.269 s 0.212
2023-01-16 04:38 R tpch arrow, parquet, memory_map=False, TPCH-13, scale_factor=10, R 5.530 s -0.563
2023-01-16 04:38 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=0.1, R 0.242 s 0.202
2023-01-16 04:39 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=1, R 0.941 s 0.017
2023-01-16 04:38 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=0.1, R 0.333 s 0.258
2023-01-16 04:39 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=1, R 0.442 s 0.039
2023-01-16 04:39 R tpch arrow, native, memory_map=False, TPCH-14, scale_factor=10, R 1.664 s -2.565
2023-01-16 04:41 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=0.01, R 0.430 s -4.531
2023-01-16 04:40 R tpch arrow, parquet, memory_map=False, TPCH-14, scale_factor=10, R 6.979 s -3.789
2023-01-16 04:40 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=0.01, R 0.287 s -3.139
2023-01-16 04:41 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=0.1, R 0.338 s -5.888
2023-01-16 04:41 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=0.1, R 0.462 s -5.030
2023-01-16 04:42 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=1, R 0.358 s -6.482
2023-01-16 04:42 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=1, R 0.780 s -5.462
2023-01-16 04:43 R tpch arrow, parquet, memory_map=False, TPCH-15, scale_factor=10, R 3.974 s -4.532
2023-01-16 04:43 R tpch arrow, native, memory_map=False, TPCH-15, scale_factor=10, R 0.723 s -5.370
2023-01-16 04:44 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=0.01, R 0.263 s -2.522
2023-01-16 04:44 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=0.01, R 0.359 s -3.085
2023-01-16 04:46 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=10, R 0.950 s -3.370
2023-01-16 04:45 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=1, R 0.370 s -2.105
2023-01-16 04:44 R tpch arrow, native, memory_map=False, TPCH-16, scale_factor=0.1, R 0.300 s -3.317
2023-01-16 04:45 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=0.1, R 0.416 s -3.552
2023-01-16 04:45 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=1, R 0.684 s -3.844
2023-01-16 04:46 R tpch arrow, parquet, memory_map=False, TPCH-16, scale_factor=10, R 3.640 s -4.379
2023-01-16 04:47 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=0.01, R 0.313 s -3.482
2023-01-16 04:47 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=0.01, R 0.426 s -4.558
2023-01-16 04:47 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=0.1, R 0.317 s -3.784
2023-01-16 04:48 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=1, R 0.259 s 0.254
2023-01-16 04:48 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=1, R 0.994 s -0.289
2023-01-16 04:47 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=0.1, R 0.457 s -3.526
2023-01-16 04:49 R tpch arrow, parquet, memory_map=False, TPCH-17, scale_factor=10, R 6.827 s 0.034
2023-01-16 04:48 R tpch arrow, native, memory_map=False, TPCH-17, scale_factor=10, R 0.665 s 0.217
2023-01-16 04:49 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=0.1, R 0.208 s 0.293
2023-01-16 04:49 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=0.01, R 0.194 s 0.261
2023-01-16 04:49 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=1, R 0.408 s -0.088
2023-01-16 04:49 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=0.1, R 0.264 s 0.272
2023-01-16 04:49 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=0.01, R 0.240 s 0.330
2023-01-16 04:50 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=1, R 0.537 s 0.144
2023-01-16 04:50 R tpch arrow, native, memory_map=False, TPCH-18, scale_factor=10, R 3.491 s -1.461
2023-01-16 04:51 R tpch arrow, parquet, memory_map=False, TPCH-18, scale_factor=10, R 4.359 s -0.924
2023-01-16 04:51 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=0.01, R 0.274 s 0.114
2023-01-16 04:51 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=0.1, R 0.443 s -0.073
2023-01-16 04:51 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=0.01, R 0.326 s 0.282
2023-01-16 04:51 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=0.1, R 0.444 s 0.127
2023-01-16 04:52 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=1, R 1.338 s -0.407
2023-01-16 04:51 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=1, R 0.895 s -1.104
2023-01-16 04:54 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=1, R 0.979 s -0.193
2023-01-16 04:52 R tpch arrow, native, memory_map=False, TPCH-19, scale_factor=10, R 5.685 s -2.637
2023-01-16 04:53 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=0.01, R 0.321 s 0.138
2023-01-16 04:53 R tpch arrow, parquet, memory_map=False, TPCH-19, scale_factor=10, R 9.473 s -2.067
2023-01-16 04:53 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=0.1, R 0.372 s 0.158
2023-01-16 04:54 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=1, R 0.290 s -0.029
2023-01-16 04:53 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=0.01, R 0.258 s 0.076
2023-01-16 04:53 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=0.1, R 0.259 s 0.010
2023-01-16 04:54 R tpch arrow, native, memory_map=False, TPCH-20, scale_factor=10, R 0.607 s -0.170
2023-01-16 04:56 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=1, R 3.175 s -1.506
2023-01-16 04:55 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=0.01, R 0.452 s -0.079
2023-01-16 05:01 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=1, R 0.452 s 0.178
2023-01-16 04:55 R tpch arrow, parquet, memory_map=False, TPCH-20, scale_factor=10, R 6.280 s -1.002
2023-01-16 04:55 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=0.1, R 0.734 s -0.653
2023-01-16 04:56 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=1, R 2.921 s -2.426
2023-01-16 04:55 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=0.1, R 0.803 s -0.387
2023-01-16 05:01 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=0.1, R 0.204 s 0.253
2023-01-16 04:55 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=0.01, R 0.397 s -0.343
2023-01-16 05:00 R tpch arrow, parquet, memory_map=False, TPCH-21, scale_factor=10, R 33.255 s -2.643
2023-01-16 05:00 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=0.01, R 0.242 s 0.255
2023-01-16 05:10 JavaScript vectorFromArray numbers 0.016 s 0.242
2023-01-16 05:10 JavaScript vectorFromArray dictionary 0.017 s -0.825
2023-01-16 04:58 R tpch arrow, native, memory_map=False, TPCH-21, scale_factor=10, R 30.168 s -2.468
2023-01-16 05:10 JavaScript toArray Vector int64Array
2023-01-16 05:01 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=10, R 2.351 s -0.364
2023-01-16 05:10 JavaScript Iterate Vector int16Array 0.002 s 0.845
2023-01-16 05:00 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=0.01, R 0.200 s -0.000
2023-01-16 05:01 R tpch arrow, parquet, memory_map=False, TPCH-22, scale_factor=0.1, R 0.260 s 0.258
2023-01-16 05:01 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=1, R 0.227 s 0.203
2023-01-16 05:01 R tpch arrow, native, memory_map=False, TPCH-22, scale_factor=10, R 0.309 s 0.171
2023-01-16 05:10 JavaScript Spread Vector float64Array 0.008 s 0.154
2023-01-16 05:10 JavaScript get Vector float32Array 0.002 s 0.862
2023-01-16 05:10 JavaScript Iterate Vector uint32Array 0.002 s 0.847
2023-01-16 05:10 JavaScript Spread Vector int32Array 0.006 s 0.672
2023-01-16 05:10 JavaScript Spread Vector dictionary 0.010 s 0.239
2023-01-16 05:10 JavaScript toArray Vector int16Array
2023-01-16 05:10 JavaScript Iterate Vector uint8Array 0.002 s 1.749
2023-01-16 05:10 JavaScript Iterate Vector numbers 0.002 s 0.531
2023-01-16 05:10 JavaScript Iterate Vector dictionary 0.004 s -0.520
2023-01-16 05:10 JavaScript get Vector uint8Array 0.003 s -0.121
2023-01-16 05:10 JavaScript get Vector uint32Array 0.003 s -0.104
2023-01-16 05:10 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.030 s 0.670
2023-01-16 05:10 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s -0.546
2023-01-16 05:10 JavaScript Iterate Vector float32Array 0.002 s 0.212
2023-01-16 05:10 JavaScript Spread Vector numbers 0.008 s 0.581
2023-01-16 05:10 JavaScript get Vector int16Array 0.003 s -0.173
2023-01-16 05:10 JavaScript Spread vectors lat, 1,000,000, Float32, tracks 0.185 s 1.228
2023-01-16 05:10 JavaScript Spread vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.208
2023-01-16 05:10 JavaScript Table tracks, 1,000,000 0.050 s 1.693
2023-01-16 05:10 JavaScript vectorFromArray booleans 0.018 s 0.331
2023-01-16 05:10 JavaScript toArray Vector uint16Array
2023-01-16 05:10 JavaScript toArray Vector float64Array
2023-01-16 05:10 JavaScript get Vector uint16Array 0.003 s -0.109
2023-01-16 05:10 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.029 s 0.891
2023-01-16 05:10 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s -0.725
2023-01-16 05:10 JavaScript Iterate Vector uint16Array 0.002 s 0.794
2023-01-16 05:10 JavaScript Spread Vector int64Array 0.012 s 0.035
2023-01-16 05:10 JavaScript toArray Vector uint32Array
2023-01-16 05:10 JavaScript toArray Vector int8Array
2023-01-16 05:10 JavaScript toArray Vector float32Array
2023-01-16 05:10 JavaScript toArray Vector numbers
2023-01-16 05:10 JavaScript Iterate Vector uint64Array 0.004 s 0.189
2023-01-16 05:10 JavaScript Iterate Vector int64Array 0.004 s 0.138
2023-01-16 05:10 JavaScript Spread Vector uint16Array 0.006 s 0.938
2023-01-16 05:10 JavaScript Spread Vector uint64Array 0.012 s -0.016
2023-01-16 05:10 JavaScript Spread Vector string 0.150 s -2.633
2023-01-16 05:10 JavaScript toArray Vector uint8Array
2023-01-16 05:10 JavaScript Iterate Vector int8Array 0.002 s 0.538
2023-01-16 05:10 JavaScript Iterate Vector int32Array 0.002 s 0.470
2023-01-16 05:10 JavaScript toArray Vector uint64Array
2023-01-16 05:10 JavaScript get Vector float64Array 0.002 s 0.541
2023-01-16 05:10 JavaScript get Vector booleans 0.002 s -5.500
2023-01-16 05:10 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.828
2023-01-16 05:10 JavaScript Iterate Vector float64Array 0.002 s 0.864
2023-01-16 05:10 JavaScript Iterate Vector booleans 0.004 s 0.111
2023-01-16 05:10 JavaScript Iterate Vector string 0.131 s -3.173
2023-01-16 05:10 JavaScript Spread Vector int16Array 0.006 s 1.034
2023-01-16 05:10 JavaScript Spread Vector booleans 0.010 s 0.460
2023-01-16 05:10 JavaScript toArray Vector int32Array
2023-01-16 05:10 JavaScript Spread Vector uint8Array 0.006 s 0.286
2023-01-16 05:10 JavaScript Spread Vector uint32Array 0.007 s 0.395
2023-01-16 05:10 JavaScript Spread Vector int8Array 0.006 s 0.835
2023-01-16 05:10 JavaScript Spread Vector float32Array 0.008 s -0.361
2023-01-16 05:10 JavaScript toArray Vector booleans 0.010 s 0.546
2023-01-16 05:10 JavaScript toArray Vector string 0.151 s -2.680
2023-01-16 05:10 JavaScript toArray Vector dictionary 0.010 s 0.239
2023-01-16 05:10 JavaScript get Vector int32Array 0.003 s -0.416
2023-01-16 05:10 JavaScript Spread vectors lng, 1,000,000, Float32, tracks 0.189 s -0.114
2023-01-16 05:10 JavaScript Spread vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s -0.318
2023-01-16 05:10 JavaScript get Vector uint64Array 0.003 s -0.307
2023-01-16 05:10 JavaScript get Vector string 0.128 s -2.662
2023-01-16 05:10 JavaScript Parse write recordBatches, tracks 0.002 s 0.309
2023-01-16 05:10 JavaScript get Vector int8Array 0.003 s 0.034
2023-01-16 05:10 JavaScript get Vector numbers 0.002 s -2.202
2023-01-16 05:10 JavaScript get Vector dictionary 0.002 s 1.718
2023-01-16 05:10 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.107 s 0.787
2023-01-16 05:10 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s
2023-01-16 05:10 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-16 05:10 JavaScript get Vector int64Array 0.003 s -0.216
2023-01-16 05:10 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.274
2023-01-16 05:10 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.023 s -0.709
2023-01-16 05:10 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.382
2023-01-16 05:10 JavaScript Table 1,000,000, tracks 0.262 s 0.603
2023-01-16 05:10 JavaScript Parse read recordBatches, tracks 0.000 s 0.379
2023-01-16 05:10 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.782
2023-01-16 05:10 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.023 s -0.774
2023-01-16 05:10 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.835
2023-01-16 05:10 JavaScript Table tracks, 1,000,000 0.261 s 0.537
2023-01-16 05:10 JavaScript Table tracks, 1,000,000 0.094 s 0.842
2023-01-16 05:10 JavaScript Table Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.412
2023-01-16 05:10 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s
2023-01-16 05:10 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-16 05:10 JavaScript Table Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.671
2023-01-16 05:10 JavaScript Table Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.032 s 0.448