Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-06 18:48 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.780 s 0.780542
2021-10-06 18:49 Python csv-read gzip, file, fanniemae_2016Q4 6.035 s -0.845829
2021-10-06 18:50 Python csv-read gzip, streaming, nyctaxi_2010-01 10.676 s -0.391834
2021-10-06 18:53 Python dataframe-to-table type_integers 0.011 s 0.791153
2021-10-06 19:10 Python dataset-read async=True, nyctaxi_multi_ipc_s3 178.972 s 1.146750
2021-10-06 18:50 Python csv-read uncompressed, file, nyctaxi_2010-01 1.016 s -0.285593
2021-10-06 18:48 Python csv-read uncompressed, file, fanniemae_2016Q4 1.181 s -0.432644
2021-10-06 18:51 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.452461
2021-10-06 18:53 Python dataframe-to-table type_dict 0.012 s 0.792634
2021-10-06 18:54 Python dataset-filter nyctaxi_2010-01 4.358 s 0.425166
2021-10-06 18:57 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.080 s -0.058206
2021-10-06 19:10 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.542 s -1.684833
2021-10-06 18:53 Python dataframe-to-table type_strings 0.375 s -0.661111
2021-10-06 18:53 Python dataframe-to-table type_floats 0.012 s -0.506010
2021-10-06 18:53 Python dataframe-to-table type_simple_features 0.910 s 0.354434
2021-10-06 18:50 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.711 s -0.425898
2021-10-06 18:53 Python dataframe-to-table chi_traffic_2020_Q1 19.461 s 0.906384
2021-10-06 18:53 Python dataframe-to-table type_nested 2.857 s 1.208100
2021-10-06 18:49 Python csv-read gzip, streaming, fanniemae_2016Q4 14.702 s 0.845111
2021-10-06 19:01 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.962 s 0.686559
2021-10-06 19:15 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.041 s -0.068708
2021-10-06 19:15 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.018 s 0.226305
2021-10-06 19:15 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.021 s 0.009944
2021-10-06 19:53 R dataframe-to-table type_dict, R 0.052 s -0.189970
2021-10-06 19:28 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.094 s -2.436078
2021-10-06 19:34 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.448 s 0.712201
2021-10-06 20:07 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.137 s -0.776980
2021-10-06 20:25 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.564 s 0.828070
2021-10-06 19:33 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.294 s 0.279531
2021-10-06 20:03 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.243 s 0.264816
2021-10-06 20:08 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.976 s 0.410180
2021-10-06 20:18 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.825 s 1.053326
2021-10-06 20:36 JavaScript Parse Table.from, tracks 0.000 s 0.209748
2021-10-06 20:36 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.815683
2021-10-06 19:28 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.889 s -1.625553
2021-10-06 19:38 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.342 s 0.119221
2021-10-06 19:39 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.840 s -0.182561
2021-10-06 20:04 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.893 s 0.323086
2021-10-06 20:14 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.825 s 1.344224
2021-10-06 20:19 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.788 s 1.166702
2021-10-06 20:36 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.529 s -0.104687
2021-10-06 20:36 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.728 s 0.104795
2021-10-06 19:29 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.805 s -1.046021
2021-10-06 19:35 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.221339
2021-10-06 19:39 Python wide-dataframe use_legacy_dataset=false 0.622 s -0.080584
2021-10-06 19:31 Python file-read lz4, feather, table, nyctaxi_2010-01 0.662 s 1.561131
2021-10-06 20:06 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.058 s -0.203277
2021-10-06 20:27 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.896 s 0.728381
2021-10-06 20:28 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.196 s 0.678991
2021-10-06 20:36 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.288790
2021-10-06 20:36 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.210621
2021-10-06 20:03 R dataframe-to-table type_simple_features, R 3.313 s 9.898189
2021-10-06 20:04 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.432798
2021-10-06 20:14 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.736 s 0.753556
2021-10-06 20:36 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.798982
2021-10-06 20:22 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.747531
2021-10-06 20:26 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -1.108640
2021-10-06 20:36 JavaScript Parse serialize, tracks 0.005 s -0.563567
2021-10-06 19:27 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.974 s 0.243107
2021-10-06 19:30 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.328 s -1.200077
2021-10-06 19:32 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.956 s -1.056523
2021-10-06 19:52 R dataframe-to-table chi_traffic_2020_Q1, R 5.492 s -1.735269
2021-10-06 20:03 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.908 s 0.301042
2021-10-06 20:05 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.925 s -0.342770
2021-10-06 20:36 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.258453
2021-10-06 19:27 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.783 s -0.359008
2021-10-06 19:29 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.292 s -0.182321
2021-10-06 19:35 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.368 s -0.235877
2021-10-06 19:53 R dataframe-to-table type_strings, R 0.491 s 0.104844
2021-10-06 20:06 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.379 s 0.386004
2021-10-06 20:09 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.537 s -0.300838
2021-10-06 20:25 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.873 s 0.735918
2021-10-06 19:53 R dataframe-to-table type_integers, R 0.085 s -0.990479
2021-10-06 20:20 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.453 s 1.236355
2021-10-06 20:28 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.209 s -2.521898
2021-10-06 19:29 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.936 s -0.887516
2021-10-06 19:32 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.087 s 0.746998
2021-10-06 19:36 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.784 s 1.009932
2021-10-06 20:07 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.493524
2021-10-06 20:08 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.674 s 0.131221
2021-10-06 20:25 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.180 s 0.192832
2021-10-06 20:25 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 0.646017
2021-10-06 20:25 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.743492
2021-10-06 20:36 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.890473
2021-10-06 20:36 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.562212
2021-10-06 20:36 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.219768
2021-10-06 19:31 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.181 s -0.952993
2021-10-06 20:15 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.584 s -0.064326
2021-10-06 20:27 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -2.848366
2021-10-06 20:29 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.506 s 0.067912
2021-10-06 20:36 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.017494
2021-10-06 19:29 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.885 s -1.159735
2021-10-06 19:37 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.847 s 0.995380
2021-10-06 19:28 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.299 s -0.379460
2021-10-06 19:30 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.090 s -1.748644
2021-10-06 19:29 Python file-read lz4, feather, table, fanniemae_2016Q4 0.607 s -0.767911
2021-10-06 19:30 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.031 s 0.372219
2021-10-06 19:31 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.477 s -1.138000
2021-10-06 19:37 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.796 s 0.512462
2021-10-06 19:38 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.838 s 0.473901
2021-10-06 20:07 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.250 s -0.447585
2021-10-06 20:25 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.577115
2021-10-06 19:27 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.845 s 0.251630
2021-10-06 19:29 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.160 s -0.838150
2021-10-06 19:53 R dataframe-to-table type_floats, R 0.113 s -1.245299
2021-10-06 20:36 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.867 s 0.322232
2021-10-06 20:36 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.493828
2021-10-06 19:31 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.338 s -1.197180
2021-10-06 19:38 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.390 s -2.359132
2021-10-06 19:39 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.761365
2021-10-06 20:04 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.242 s 0.113392
2021-10-06 20:05 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.441858
2021-10-06 20:06 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.185 s -0.654833
2021-10-06 20:11 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.316 s 0.519850
2021-10-06 20:17 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.234 s -0.533646
2021-10-06 20:26 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 0.753865
2021-10-06 20:27 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.617 s -0.374353
2021-10-06 20:36 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.483990
2021-10-06 20:23 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.249 s 0.401731
2021-10-06 19:36 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.261 s -0.365804
2021-10-06 19:38 Python file-write lz4, feather, table, nyctaxi_2010-01 1.799 s 0.580822
2021-10-06 20:16 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.252273
2021-10-06 19:30 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.240 s -0.882979
2021-10-06 19:34 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.643 s 0.361155
2021-10-06 19:53 R dataframe-to-table type_nested, R 0.538 s 0.091278
2021-10-06 20:12 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.299 s 0.759181
2021-10-06 20:21 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.643 s 1.310753
2021-10-06 20:28 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.482 s -1.409478
2021-10-06 20:36 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.851793
2021-10-06 20:36 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.139610
2021-10-06 19:28 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.308 s -1.618833
2021-10-06 19:35 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.764 s -0.284203
2021-10-06 20:24 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.480 s 2.144673
2021-10-06 20:27 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s 0.327283
2021-10-06 20:36 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.896998
2021-10-06 20:36 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.976 s -1.543985
2021-10-06 20:10 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.854 s 0.711652
2021-10-06 20:36 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.493828
2021-10-06 20:36 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.832357
2021-10-06 20:36 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.116492
2021-10-06 20:36 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.044992
2021-10-06 20:26 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.516 s 0.087848
2021-10-06 20:36 JavaScript Parse readBatches, tracks 0.000 s 0.410624
2021-10-06 20:36 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.041630
2021-10-06 20:36 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.595 s -0.296491
2021-10-06 20:36 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.692 s -0.163167
2021-10-06 20:36 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.564 s -1.003837
2021-10-06 20:36 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.572427
2021-10-06 20:36 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 1.301417