Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-09 13:33 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.278 s 0.619390
2021-10-09 13:33 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.133 s 0.657002
2021-10-09 13:34 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.256 s -0.671022
2021-10-09 13:35 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.044 s -0.466241
2021-10-09 13:35 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.302 s -0.371440
2021-10-09 13:35 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.558818
2021-10-09 13:36 Python file-read lz4, feather, table, nyctaxi_2010-01 0.666 s 0.472059
2021-10-09 12:55 Python csv-read uncompressed, file, nyctaxi_2010-01 1.011 s 0.095393
2021-10-09 12:59 Python dataframe-to-table type_simple_features 0.908 s 0.440126
2021-10-09 13:32 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.991 s 0.184425
2021-10-09 13:34 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.940 s -0.419956
2021-10-09 12:58 Python dataframe-to-table type_floats 0.012 s -0.781136
2021-10-09 13:02 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.928 s -0.046713
2021-10-09 12:58 Python dataframe-to-table type_dict 0.012 s -0.513748
2021-10-09 13:16 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.162 s 0.265405
2021-10-09 13:34 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.041 s 0.232622
2021-10-09 13:36 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.483 s -0.572522
2021-10-09 13:33 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.864 s -0.207294
2021-10-09 12:58 Python dataframe-to-table type_strings 0.368 s 0.382873
2021-10-09 13:33 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.778 s 0.271988
2021-10-09 13:35 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.309 s -0.483382
2021-10-09 13:36 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.993 s -0.643640
2021-10-09 12:58 Python dataframe-to-table type_integers 0.011 s -0.948281
2021-10-09 13:34 Python file-read lz4, feather, table, fanniemae_2016Q4 0.610 s -1.173971
2021-10-09 12:58 Python dataframe-to-table chi_traffic_2020_Q1 19.716 s -0.348179
2021-10-09 13:32 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.739 s 0.134733
2021-10-09 13:32 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.960 s 0.772895
2021-10-09 12:54 Python csv-read uncompressed, file, fanniemae_2016Q4 1.188 s -0.853596
2021-10-09 12:55 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.486 s 0.934691
2021-10-09 12:53 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.984 s -0.497514
2021-10-09 12:54 Python csv-read gzip, streaming, fanniemae_2016Q4 14.894 s -0.265877
2021-10-09 12:56 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.174415
2021-10-09 12:59 Python dataframe-to-table type_nested 2.892 s -0.366626
2021-10-09 13:21 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.057 s -0.460912
2021-10-09 12:55 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s 0.113298
2021-10-09 13:21 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.037 s -0.688189
2021-10-09 13:21 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.003 s 0.306021
2021-10-09 13:07 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.338 s -0.334473
2021-10-09 12:59 Python dataset-filter nyctaxi_2010-01 4.338 s 1.071601
2021-10-09 13:32 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.857 s 0.139710
2021-10-09 13:33 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.778 s 0.993449
2021-10-09 12:56 Python csv-read gzip, streaming, nyctaxi_2010-01 10.462 s 1.061238
2021-10-09 13:32 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.204 s 0.773041
2021-10-09 13:16 Python dataset-read async=True, nyctaxi_multi_ipc_s3 187.953 s -0.167243
2021-10-10 00:12 Python dataframe-to-table type_simple_features 0.905 s 0.604725
2021-10-10 00:29 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.248 s 0.210867
2021-10-10 00:45 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.930 s -0.159399
2021-10-10 00:54 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.397 s -1.240481
2021-10-10 00:54 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.219811
2021-10-10 01:26 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.522 s 1.669120
2021-10-10 01:30 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.855 s -0.662599
2021-10-10 01:46 JavaScript Parse Table.from, tracks 0.000 s -0.545533
2021-10-09 13:34 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.290 s 0.044212
2021-10-10 01:46 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.667 s 0.474427
2021-10-10 01:47 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.504203
2021-10-10 01:47 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.200605
2021-10-10 01:47 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.530146
2021-10-10 01:47 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.764371
2021-10-10 00:48 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.942 s -0.332470
2021-10-10 01:17 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.395 s -0.536389
2021-10-10 01:17 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.070 s -7.919944
2021-10-10 01:17 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s -0.166068
2021-10-10 01:35 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.151 s 2.122152
2021-10-10 01:46 JavaScript Parse serialize, tracks 0.005 s -0.598819
2021-10-10 00:11 Python dataframe-to-table type_nested 2.896 s -0.535851
2021-10-10 00:12 Python dataset-filter nyctaxi_2010-01 4.306 s 2.421987
2021-10-10 00:45 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.281 s 1.356455
2021-10-10 00:45 Python file-read lz4, feather, table, fanniemae_2016Q4 0.596 s 1.257132
2021-10-10 01:18 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.045 s 1.781524
2021-10-10 01:46 JavaScript Parse readBatches, tracks 0.000 s -0.605603
2021-10-10 01:46 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.084760
2021-10-10 00:06 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.945 s -0.104402
2021-10-10 00:11 Python dataframe-to-table type_integers 0.011 s 0.953263
2021-10-10 00:48 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.100 s 0.455132
2021-10-10 00:50 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.607 s 0.764205
2021-10-10 00:47 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.277 s -0.220511
2021-10-10 00:47 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.165 s 2.460924
2021-10-10 01:46 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.517 s -0.255357
2021-10-10 00:44 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.785 s 0.832113
2021-10-10 00:46 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.228 s 0.089323
2021-10-10 01:09 R dataframe-to-table type_dict, R 0.049 s 0.264724
2021-10-10 01:09 R dataframe-to-table type_nested, R 0.539 s 0.233074
2021-10-10 00:47 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.411 s -0.135315
2021-10-10 00:47 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.320104
2021-10-10 01:47 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.427380
2021-10-10 00:46 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.264 s -0.220545
2021-10-10 00:54 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.815 s 0.006727
2021-10-10 01:18 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.155 s 1.713510
2021-10-10 01:35 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.563 s 1.804841
2021-10-10 01:47 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-10 00:09 Python csv-read gzip, streaming, nyctaxi_2010-01 10.458 s 1.090009
2021-10-10 00:11 Python dataframe-to-table chi_traffic_2020_Q1 19.453 s 0.373828
2021-10-10 00:15 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.952 s 0.550234
2021-10-10 00:45 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.760 s 0.818625
2021-10-10 00:49 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.221 s 0.891349
2021-10-10 00:51 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.787 s -0.126550
2021-10-10 01:16 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.460 s 1.723654
2021-10-10 01:22 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.233 s 0.837676
2021-10-10 01:24 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.681 s 0.917517
2021-10-10 01:35 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.587 s -0.170118
2021-10-10 00:08 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.473 s 1.022384
2021-10-10 00:43 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.663 s 0.870786
2021-10-10 00:49 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.435 s 0.625685
2021-10-10 00:07 Python csv-read gzip, streaming, fanniemae_2016Q4 14.888 s -0.211089
2021-10-10 00:09 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -1.365303
2021-10-10 00:11 Python dataframe-to-table type_strings 0.367 s 0.447957
2021-10-10 00:34 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.036 s -0.315697
2021-10-10 00:44 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.263 s 1.270211
2021-10-10 00:45 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.129 s 0.815826
2021-10-10 00:52 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.861 s -0.351261
2021-10-10 00:52 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.954 s -1.635554
2021-10-10 00:53 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.885 s -0.234923
2021-10-10 00:54 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.380 s -1.639330
2021-10-10 00:06 Python csv-read uncompressed, file, fanniemae_2016Q4 1.175 s -0.082421
2021-10-10 00:43 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.985 s 0.215882
2021-10-10 00:44 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.946 s 1.107935
2021-10-10 00:46 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.016 s 1.365630
2021-10-10 00:54 Python file-write lz4, feather, table, nyctaxi_2010-01 1.801 s 0.559460
2021-10-10 01:16 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.211 s 1.082926
2021-10-10 01:18 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.099 s 2.423602
2021-10-10 01:20 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.491 s 0.524310
2021-10-10 01:36 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.026916
2021-10-10 00:53 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.928 s -0.478700
2021-10-10 01:32 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.707 s -0.475803
2021-10-10 01:36 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.519 s 0.040504
2021-10-10 01:37 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.260562
2021-10-10 01:38 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -1.032177
2021-10-10 01:46 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.881700
2021-10-10 01:47 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.529419
2021-10-10 01:47 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.439 s 1.359674
2021-10-10 01:37 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.588 s 0.043120
2021-10-10 01:38 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.490 s -1.691621
2021-10-10 01:46 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.643 s 0.862114
2021-10-10 01:47 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.805 s 1.852347
2021-10-10 01:09 R dataframe-to-table chi_traffic_2020_Q1, R 3.384 s 0.275436
2021-10-10 01:37 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.878 s 1.564886
2021-10-10 01:39 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -1.004813
2021-10-10 01:46 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.102284
2021-10-10 01:47 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -2.267686
2021-10-10 01:47 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.857 s 1.018193
2021-10-10 00:08 Python csv-read gzip, file, fanniemae_2016Q4 6.034 s -0.576529
2021-10-10 00:08 Python csv-read uncompressed, file, nyctaxi_2010-01 0.990 s 2.051698
2021-10-10 00:19 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.111 s 0.232249
2021-10-10 00:44 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.197 s 0.948816
2021-10-10 00:46 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.045 s 0.080893
2021-10-10 00:50 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.471 s -1.084647
2021-10-10 01:23 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.292 s 0.634772
2021-10-10 01:34 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.473 s 3.149911
2021-10-10 01:35 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.852 s 0.732727
2021-10-10 01:47 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.216659
2021-10-10 01:47 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.765066
2021-10-10 00:29 Python dataset-read async=True, nyctaxi_multi_ipc_s3 195.887 s -1.233735
2021-10-10 00:34 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.076 s -3.024292
2021-10-10 00:45 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.849 s 0.210750
2021-10-10 00:55 Python wide-dataframe use_legacy_dataset=false 0.624 s -0.321604
2021-10-10 01:29 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.898 s -0.728732
2021-10-10 01:36 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.182 s -0.126578
2021-10-10 01:38 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s -0.584628
2021-10-10 00:51 Python file-write lz4, feather, table, fanniemae_2016Q4 1.143 s 1.368182
2021-10-10 00:51 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.244 s 0.274948
2021-10-10 01:18 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.212 s 1.681131
2021-10-10 01:19 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.960 s 0.421123
2021-10-10 00:11 Python dataframe-to-table type_dict 0.012 s 0.446435
2021-10-10 00:11 Python dataframe-to-table type_floats 0.011 s 1.205343
2021-10-10 00:34 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.074 s -0.781449
2021-10-10 00:43 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.873 s 0.039819
2021-10-10 01:16 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.319 s -27.083521
2021-10-10 01:21 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.854 s 0.529866
2021-10-10 01:28 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.170 s 1.826172
2021-10-10 01:47 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574060
2021-10-10 01:47 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.215138
2021-10-10 01:47 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.045630
2021-10-10 01:39 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.152 s 1.341936
2021-10-10 01:47 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.240128
2021-10-10 01:47 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.153122
2021-10-10 01:09 R dataframe-to-table type_integers, R 0.010 s 1.824621
2021-10-10 01:09 R dataframe-to-table type_floats, R 0.012 s 1.828173
2021-10-10 01:16 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.227 s 0.329265
2021-10-10 01:18 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -32.819393
2021-10-10 01:25 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.814 s 3.568732
2021-10-10 01:39 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.501 s -0.150439
2021-10-10 01:47 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.836326
2021-10-10 01:15 R dataframe-to-table type_simple_features, R 3.295 s 1.440689
2021-10-10 01:31 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.534 s -0.691961
2021-10-10 01:32 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.275 s 3.943971
2021-10-10 01:36 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.588 s 2.276027
2021-10-10 01:46 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.581 s -0.343934
2021-10-10 01:47 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.562513
2021-10-10 01:09 R dataframe-to-table type_strings, R 0.488 s 0.232630
2021-10-10 01:16 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.476 s 1.674675
2021-10-10 01:19 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.702 s -0.060987
2021-10-10 01:27 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.381 s 3.956783
2021-10-10 01:33 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.232 s 1.372510
2021-10-10 01:46 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.885220