Outliers: 4


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 03:41 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.825 s 1.077599
2021-10-10 03:42 Python csv-read gzip, streaming, fanniemae_2016Q4 14.769 s 0.921714
2021-10-10 03:43 Python csv-read uncompressed, file, nyctaxi_2010-01 1.008 s 0.338486
2021-10-10 03:44 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s -0.012244
2021-10-10 03:46 Python dataframe-to-table type_strings 0.368 s 0.358318
2021-10-10 03:46 Python dataframe-to-table type_dict 0.012 s 0.995995
2021-10-10 03:46 Python dataframe-to-table type_integers 0.011 s 0.638995
2021-10-10 03:46 Python dataframe-to-table type_floats 0.011 s 1.309951
2021-10-10 03:46 Python dataframe-to-table type_nested 2.884 s -0.129702
2021-10-10 03:46 Python dataframe-to-table type_simple_features 0.906 s 0.576680
2021-10-10 03:47 Python dataset-filter nyctaxi_2010-01 4.313 s 2.016829
2021-10-10 04:54 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.515 s 0.277107
2021-10-10 04:57 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.282 s 0.705669
2021-10-10 05:00 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.539 s 1.029849
2021-10-10 05:00 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.385 s 2.676877
2021-10-10 05:01 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.169 s 1.803796
2021-10-10 05:02 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.892 s -0.600028
2021-10-10 05:03 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.859 s -0.724056
2021-10-10 05:04 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.534 s -0.659996
2021-10-10 05:06 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.707 s -0.457771
2021-10-10 05:07 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.231 s 1.430283
2021-10-10 05:08 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.465 s 4.199749
2021-10-10 05:09 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.159 s 1.412830
2021-10-10 05:09 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s -0.087485
2021-10-10 05:09 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.567 s 1.165042
2021-10-10 05:09 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.499759
2021-10-10 05:10 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.186 s -0.768283
2021-10-10 05:10 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.590 s 1.826230
2021-10-10 05:10 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.521 s -0.279277
2021-10-10 05:11 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.574 s 0.242668
2021-10-10 05:11 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.130044
2021-10-10 05:12 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.358 s -0.079081
2021-10-10 05:12 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -0.831921
2021-10-10 05:13 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.155 s 1.167826
2021-10-10 05:20 JavaScript Parse Table.from, tracks 0.000 s 0.203384
2021-10-10 05:20 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.204918
2021-10-10 05:20 JavaScript Parse readBatches, tracks 0.000 s 0.421046
2021-10-10 05:20 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.659 s -0.482747
2021-10-10 05:20 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.519 s -0.241501
2021-10-10 05:20 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.232826
2021-10-10 05:20 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.198084
2021-10-10 05:20 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.707 s -0.501144
2021-10-10 05:20 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.688 s 0.325836
2021-10-10 05:20 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.759765
2021-10-10 05:20 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.714260
2021-10-10 05:20 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.867 s 0.267774
2021-10-10 05:20 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.559221
2021-10-10 05:20 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.296909
2021-10-10 05:20 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.307943
2021-10-10 05:20 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.314608
2021-10-10 05:20 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.494 s 0.383539
2021-10-10 04:03 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.191 s 0.245890
2021-10-10 04:07 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.030 s -0.153460
2021-10-10 04:07 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.594 s -10.791900
2021-10-10 04:07 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.011 s 0.164639
2021-10-10 04:18 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.666 s 0.833764
2021-10-10 04:19 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.127 s 0.886549
2021-10-10 04:19 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.762 s 0.889148
2021-10-10 04:20 Python file-read lz4, feather, table, fanniemae_2016Q4 0.594 s 1.620502
2021-10-10 04:20 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.228 s 0.192363
2021-10-10 04:21 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.167 s 1.951054
2021-10-10 04:23 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.096 s 0.492179
2021-10-10 04:24 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.440 s 0.588690
2021-10-10 04:25 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.651 s 0.502527
2021-10-10 04:25 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.519 s -1.433256
2021-10-10 04:26 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.220 s 0.816081
2021-10-10 04:26 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.856 s -0.247630
2021-10-10 04:28 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.882 s -0.171709
2021-10-10 04:28 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.338 s 0.886731
2021-10-10 04:28 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.393 s -1.078132
2021-10-10 04:28 Python file-write lz4, feather, table, nyctaxi_2010-01 1.786 s 1.358007
2021-10-10 04:29 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.806 s 0.357270
2021-10-10 04:29 Python wide-dataframe use_legacy_dataset=true 0.392 s 1.273514
2021-10-10 04:29 Python wide-dataframe use_legacy_dataset=false 0.621 s 0.297004
2021-10-10 04:43 R dataframe-to-table type_integers, R 0.010 s 1.724081
2021-10-10 04:43 R dataframe-to-table type_floats, R 0.012 s 1.726545
2021-10-10 04:43 R dataframe-to-table type_nested, R 0.530 s 0.235436
2021-10-10 04:49 R dataframe-to-table type_simple_features, R 3.290 s 1.374741
2021-10-10 04:50 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.231 s 0.052049
2021-10-10 04:50 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.320 s -6.441609
2021-10-10 04:51 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.079 s -5.556716
2021-10-10 04:51 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.562 s 0.160827
2021-10-10 04:52 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.217 s 1.593554
2021-10-10 04:18 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.236 s -0.048443
2021-10-10 04:21 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.314 s -0.494248
2021-10-10 04:22 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.430 s -0.204243
2021-10-10 04:25 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.731 s 0.539596
2021-10-10 05:12 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.488 s -1.447670
2021-10-10 03:41 Python csv-read uncompressed, file, fanniemae_2016Q4 1.178 s -0.299292
2021-10-10 03:46 Python dataframe-to-table chi_traffic_2020_Q1 19.491 s 0.261279
2021-10-10 04:03 Python dataset-read async=True, nyctaxi_multi_ipc_s3 175.897 s 1.489933
2021-10-10 04:18 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.966 s 0.574745
2021-10-10 04:21 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.307 s -0.371495
2021-10-10 04:25 Python file-write lz4, feather, table, fanniemae_2016Q4 1.170 s -0.543077
2021-10-10 03:42 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.322075
2021-10-10 04:19 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.829 s 0.896353
2021-10-10 04:20 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.924 s 0.074953
2021-10-10 04:19 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.792 s 0.626297
2021-10-10 04:19 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.281 s 0.438976
2021-10-10 04:50 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.440 s 1.600431
2021-10-10 03:43 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.773 s -1.022517
2021-10-10 04:43 R dataframe-to-table type_strings, R 0.487 s 0.232897
2021-10-10 04:52 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.150 s 1.623667
2021-10-10 04:52 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -6.373013
2021-10-10 05:11 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.904 s 0.573706
2021-10-10 03:43 Python csv-read gzip, streaming, nyctaxi_2010-01 10.771 s -1.337295
2021-10-10 04:17 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.799 s 0.524798
2021-10-10 04:17 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.988 s 0.224851
2021-10-10 04:23 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.297 s 0.415832
2021-10-10 04:27 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.958 s -0.890511
2021-10-10 04:43 R dataframe-to-table chi_traffic_2020_Q1, R 3.371 s 0.275000
2021-10-10 04:43 R dataframe-to-table type_dict, R 0.048 s 0.270102
2021-10-10 04:51 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.404 s -1.128202
2021-10-10 04:51 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.042 s 2.463724
2021-10-10 05:20 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.217108
2021-10-10 05:20 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.529419
2021-10-10 05:20 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-10 05:20 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.536733
2021-10-10 05:20 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.088433
2021-10-10 05:20 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.449685
2021-10-10 04:27 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.889 s -0.713063
2021-10-10 04:49 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.239 s 0.196875
2021-10-10 04:58 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.695 s 0.805789
2021-10-10 05:11 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -0.409895
2021-10-10 04:52 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.098 s 2.307563
2021-10-10 04:53 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.969 s 0.311332
2021-10-10 04:53 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.695 s -0.005180
2021-10-10 05:06 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 4.473571
2021-10-10 05:09 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.855 s 0.644669
2021-10-10 05:20 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.889 s 0.350513
2021-10-10 05:20 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574060
2021-10-10 05:20 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.504203
2021-10-10 05:20 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.234464
2021-10-10 05:20 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.453711
2021-10-10 05:20 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.559827
2021-10-10 03:50 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.675 s -0.293051
2021-10-10 04:20 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.009 s 1.569344
2021-10-10 04:22 Python file-read lz4, feather, table, nyctaxi_2010-01 0.677 s -1.243217
2021-10-10 04:54 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.817 s 0.793973
2021-10-10 04:56 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.223 s 0.896093
2021-10-10 04:59 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.812 s 3.413988
2021-10-10 05:20 JavaScript Parse serialize, tracks 0.005 s -0.495824
2021-10-10 05:20 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.805623
2021-10-10 03:54 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.319 s 0.612029
2021-10-10 04:19 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.282 s 1.133379
2021-10-10 04:21 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.046 s -0.594883
2021-10-10 04:22 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.958 s -0.398276
2021-10-10 04:50 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.465 s 1.630766
2021-10-10 05:13 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.503 s -0.330578