Outliers: 5


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 10:42 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.865 s 0.598660
2021-10-10 10:43 Python csv-read gzip, streaming, fanniemae_2016Q4 14.805 s 0.489814
2021-10-10 10:44 Python csv-read uncompressed, file, nyctaxi_2010-01 1.008 s 0.351849
2021-10-10 10:44 Python csv-read gzip, streaming, nyctaxi_2010-01 10.618 s -0.116748
2021-10-10 10:46 Python dataframe-to-table chi_traffic_2020_Q1 19.318 s 0.708739
2021-10-10 11:21 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.010 s 6.055872
2021-10-10 11:22 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.012 s 1.331557
2021-10-10 11:22 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.110 s 1.351527
2021-10-10 11:22 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.023 s 0.818593
2021-10-10 11:23 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.296 s 1.082195
2021-10-10 11:23 Python file-read lz4, feather, table, nyctaxi_2010-01 0.676 s -1.067955
2021-10-10 11:24 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.792 s 1.109933
2021-10-10 11:24 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.105 s 0.433751
2021-10-10 11:26 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.891 s -1.072693
2021-10-10 11:27 Python file-write lz4, feather, table, fanniemae_2016Q4 1.149 s 0.896221
2021-10-10 11:28 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.888 s -0.622141
2021-10-10 11:28 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.942 s -1.305182
2021-10-10 11:29 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.026 s -1.779926
2021-10-10 11:30 Python file-write lz4, feather, table, nyctaxi_2010-01 1.792 s 1.048268
2021-10-10 11:30 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.819 s 0.085558
2021-10-10 11:30 Python wide-dataframe use_legacy_dataset=false 0.616 s 1.523326
2021-10-10 11:44 R dataframe-to-table type_strings, R 0.491 s 0.234468
2021-10-10 11:44 R dataframe-to-table type_dict, R 0.040 s 1.251129
2021-10-10 11:44 R dataframe-to-table type_nested, R 0.532 s 0.237392
2021-10-10 11:50 R dataframe-to-table type_simple_features, R 3.305 s 1.234702
2021-10-10 11:50 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.203 s 0.555497
2021-10-10 11:50 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.233 s -0.134260
2021-10-10 11:51 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.452 s 1.422841
2021-10-10 11:51 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.314 s -2.939638
2021-10-10 11:51 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.560 s 0.430883
2021-10-10 11:52 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.057 s -0.213302
2021-10-10 11:52 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.105 s 1.624591
2021-10-10 11:53 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.218 s -3.905945
2021-10-10 11:53 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.972 s 0.288650
2021-10-10 11:54 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.535 s 0.068419
2021-10-10 11:55 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.855 s 0.516167
2021-10-10 11:57 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.318 s 0.444764
2021-10-10 12:02 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.176 s 1.360041
2021-10-10 12:03 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.893 s -0.541744
2021-10-10 12:06 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.727 s -0.809465
2021-10-10 12:07 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.272 s 3.669533
2021-10-10 12:09 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.463 s 3.557809
2021-10-10 12:10 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.587 s -0.168506
2021-10-10 12:10 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.994 s -3.013750
2021-10-10 12:10 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.594 s -2.996347
2021-10-10 12:10 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.228 s -8.812633
2021-10-10 12:11 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.590 s 1.806488
2021-10-10 12:11 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.617 s -0.536086
2021-10-10 12:12 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.902 s 0.484631
2021-10-10 12:12 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -0.997067
2021-10-10 12:12 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.359 s -0.149240
2021-10-10 12:13 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.491 s -1.790613
2021-10-10 12:13 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.207 s -0.551617
2021-10-10 12:13 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.160 s 0.955540
2021-10-10 12:21 JavaScript Parse Table.from, tracks 0.000 s -0.962776
2021-10-10 12:04 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.856 s -0.598664
2021-10-10 12:21 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.856780
2021-10-10 12:21 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.826809
2021-10-10 12:21 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.667 s -0.508787
2021-10-10 12:21 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.021 s 2.808732
2021-10-10 12:21 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.627 s 1.120740
2021-10-10 12:21 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.660 s 0.504900
2021-10-10 12:21 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.914508
2021-10-10 12:21 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.432323
2021-10-10 12:21 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.864 s 0.309899
2021-10-10 12:21 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.878 s 0.524555
2021-10-10 12:21 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.519115
2021-10-10 12:21 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.565084
2021-10-10 12:21 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.471977
2021-10-10 12:21 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.211731
2021-10-10 12:21 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.362945
2021-10-10 12:21 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.816975
2021-10-10 12:21 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.556378
2021-10-10 10:47 Python dataframe-to-table type_strings 0.365 s 0.592022
2021-10-10 10:47 Python dataframe-to-table type_integers 0.011 s -1.783569
2021-10-10 10:47 Python dataframe-to-table type_nested 2.854 s 1.199062
2021-10-10 10:47 Python dataframe-to-table type_simple_features 0.926 s -0.495432
2021-10-10 10:47 Python dataset-filter nyctaxi_2010-01 4.316 s 1.732390
2021-10-10 11:05 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.161 s 0.268317
2021-10-10 11:09 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.068 s -1.865817
2021-10-10 11:09 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.050 s -0.102296
2021-10-10 11:09 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.036 s -0.284780
2021-10-10 11:19 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.819 s 0.386532
2021-10-10 11:19 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.683 s 0.641891
2021-10-10 11:19 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.961 s 0.624331
2021-10-10 11:20 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.203 s 0.716620
2021-10-10 11:20 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.772 s 1.064944
2021-10-10 11:20 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.594 s 6.086507
2021-10-10 11:21 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.284 s 0.871572
2021-10-10 11:21 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.682 s 6.617673
2021-10-10 12:21 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.185301
2021-10-10 12:21 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.289169
2021-10-10 12:21 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.285375
2021-10-10 10:45 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.928658
2021-10-10 11:50 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.449 s 1.456298
2021-10-10 11:52 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.391 s -0.237837
2021-10-10 11:53 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.216 s 1.421027
2021-10-10 11:54 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.692 s 0.032030
2021-10-10 12:21 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.343428
2021-10-10 10:44 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.634 s -0.054474
2021-10-10 11:19 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.933 s 0.513818
2021-10-10 11:29 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.371 s -1.120376
2021-10-10 10:42 Python csv-read uncompressed, file, fanniemae_2016Q4 1.157 s 1.057627
2021-10-10 11:21 Python file-read lz4, feather, table, fanniemae_2016Q4 0.606 s -0.383452
2021-10-10 11:25 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.450 s 0.519126
2021-10-10 11:43 R dataframe-to-table chi_traffic_2020_Q1, R 3.340 s 0.276613
2021-10-10 12:01 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.389 s 1.707310
2021-10-10 11:27 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.133 s -4.011549
2021-10-10 11:27 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.497 s -4.042043
2021-10-10 11:51 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.044 s -2.883287
2021-10-10 11:59 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.705 s 0.720480
2021-10-10 12:21 JavaScript Parse readBatches, tracks 0.000 s -0.644376
2021-10-10 12:21 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.612 s -0.351881
2021-10-10 12:21 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.021 s 2.894011
2021-10-10 12:21 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.243510
2021-10-10 11:23 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.132 s 1.247957
2021-10-10 11:29 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.963 s -1.305764
2021-10-10 11:30 Python wide-dataframe use_legacy_dataset=true 0.388 s 3.422643
2021-10-10 11:57 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.239 s 0.770166
2021-10-10 12:11 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.531 s -1.631390
2021-10-10 10:43 Python csv-read gzip, file, fanniemae_2016Q4 6.026 s 1.076757
2021-10-10 10:47 Python dataframe-to-table type_dict 0.011 s 1.475230
2021-10-10 10:47 Python dataframe-to-table type_floats 0.011 s -0.207468
2021-10-10 11:30 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.407 s -1.385235
2021-10-10 12:08 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.223 s 1.974733
2021-10-10 12:10 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 0.891238
2021-10-10 10:51 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 74.089 s -3.826646
2021-10-10 11:20 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.296 s -0.278168
2021-10-10 11:21 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.511 s 6.204552
2021-10-10 11:25 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.540 s -1.163056
2021-10-10 11:26 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.420 s -0.644433
2021-10-10 11:59 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.813 s 2.586698
2021-10-10 12:21 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.583990
2021-10-10 10:55 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.303 s -0.294144
2021-10-10 11:44 R dataframe-to-table type_integers, R 0.010 s 1.526755
2021-10-10 11:44 R dataframe-to-table type_floats, R 0.012 s 1.529688
2021-10-10 12:01 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.550 s 0.515600
2021-10-10 12:10 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.160 s 1.182817
2021-10-10 11:05 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.712 s -0.615035
2021-10-10 11:21 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.117 s 1.274639
2021-10-10 11:23 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.397023
2021-10-10 11:52 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.167 s 1.441319
2021-10-10 12:05 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.531 s -0.550699
2021-10-10 12:12 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.646 s -0.948618
2021-10-10 12:14 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.506 s -0.532059
2021-10-10 12:21 JavaScript Parse serialize, tracks 0.004 s 0.546061
2021-10-10 12:21 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.002905
2021-10-10 12:21 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.050916
2021-10-10 12:21 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.474 s 0.716253