Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 07:25 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.037 s -1.433756
2021-09-29 07:28 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.818 s 0.928891
2021-09-29 07:29 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.477 s 0.887304
2021-09-29 07:32 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.565 s 1.147945
2021-09-29 07:35 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.769 s 1.225095
2021-09-29 07:35 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.269 s 0.753488
2021-09-29 07:49 R dataframe-to-table type_floats, R 0.109 s 0.008550
2021-09-29 06:52 Python csv-read gzip, file, nyctaxi_2010-01 9.050 s -1.243945
2021-09-29 07:26 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.866 s -1.498189
2021-09-29 06:52 Python csv-read gzip, streaming, nyctaxi_2010-01 10.612 s -0.309927
2021-09-29 07:15 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.046 s -0.167266
2021-09-29 07:15 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.005 s 0.221144
2021-09-29 07:25 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.985 s 0.187498
2021-09-29 07:32 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.439 s 1.494065
2021-09-29 07:49 R dataframe-to-table chi_traffic_2020_Q1, R 5.407 s -0.028019
2021-09-29 06:54 Python dataframe-to-table type_floats 0.012 s -1.219319
2021-09-29 07:27 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.116 s -0.234792
2021-09-29 08:14 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.917 s 0.010318
2021-09-29 07:12 Python dataset-read async=True, nyctaxi_multi_ipc_s3 183.375 s 0.507431
2021-09-29 07:12 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.170 s 0.663408
2021-09-29 07:27 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.795 s 1.238559
2021-09-29 06:55 Python dataframe-to-table type_simple_features 0.911 s -0.477679
2021-09-29 07:25 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.289 s -1.646220
2021-09-29 07:29 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.952 s 0.975238
2021-09-29 07:32 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.324 s 0.127686
2021-09-29 06:54 Python dataframe-to-table type_dict 0.012 s 0.342266
2021-09-29 07:24 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.828 s 0.428526
2021-09-29 07:26 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.171 s -2.003332
2021-09-29 07:36 Python wide-dataframe use_legacy_dataset=true 0.397 s -0.714610
2021-09-29 08:18 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.286236
2021-09-29 08:19 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.511 s 0.169694
2021-09-29 07:25 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.747 s 0.235878
2021-09-29 07:27 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.283 s 1.203188
2021-09-29 07:30 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.114 s 1.108653
2021-09-29 06:50 Python csv-read uncompressed, file, fanniemae_2016Q4 1.188 s -0.263338
2021-09-29 06:55 Python dataframe-to-table type_nested 2.947 s 0.373252
2021-09-29 06:51 Python csv-read uncompressed, file, nyctaxi_2010-01 1.010 s 0.163511
2021-09-29 07:26 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.313 s -1.938146
2021-09-29 07:27 Python file-read lz4, feather, table, fanniemae_2016Q4 0.602 s -0.038819
2021-09-29 07:49 R dataframe-to-table type_dict, R 0.051 s 0.097530
2021-09-29 06:55 Python dataset-filter nyctaxi_2010-01 4.400 s -1.318415
2021-09-29 07:35 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.301549
2021-09-29 07:49 R dataframe-to-table type_integers, R 0.085 s -0.190016
2021-09-29 07:28 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.819 s 0.951332
2021-09-29 07:34 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.757 s 1.046691
2021-09-29 08:14 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.918 s 0.024498
2021-09-29 06:54 Python dataframe-to-table type_integers 0.011 s -2.213628
2021-09-29 06:58 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.278 s -0.477811
2021-09-29 07:02 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.116 s 2.947841
2021-09-29 07:26 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.755 s -1.118660
2021-09-29 07:26 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.693 s -1.132359
2021-09-29 07:29 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s -0.224501
2021-09-29 07:31 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.434 s 1.387961
2021-09-29 07:33 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.162 s 0.458122
2021-09-29 07:36 Python wide-dataframe use_legacy_dataset=false 0.619 s -0.246010
2021-09-29 07:50 R dataframe-to-table type_nested, R 0.539 s -0.659997
2021-09-29 08:14 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.254 s -0.025666
2021-09-29 08:16 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.388 s -0.615294
2021-09-29 07:27 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.088 s -1.639671
2021-09-29 07:28 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.016 s 1.305280
2021-09-29 07:28 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s 0.111876
2021-09-29 07:30 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.138 s 1.226860
2021-09-29 06:54 Python dataframe-to-table type_strings 0.367 s 0.554675
2021-09-29 07:33 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.801 s 1.741510
2021-09-29 07:36 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.750 s 0.729582
2021-09-29 08:18 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.956 s 0.509492
2021-09-29 06:49 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.059 s -0.853519
2021-09-29 06:51 Python csv-read gzip, file, fanniemae_2016Q4 6.036 s -1.621692
2021-09-29 06:51 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.619 s -0.291593
2021-09-29 06:54 Python dataframe-to-table chi_traffic_2020_Q1 19.969 s -1.062803
2021-09-29 07:32 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.119480
2021-09-29 07:34 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.847 s 2.205840
2021-09-29 07:49 R dataframe-to-table type_strings, R 0.492 s -0.653923
2021-09-29 08:15 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.939 s -1.125445
2021-09-29 08:19 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.679 s -0.474665
2021-09-29 08:14 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.896198
2021-09-29 08:18 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.263 s -1.221577
2021-09-29 08:13 R dataframe-to-table type_simple_features, R 275.778 s -1.991417
2021-09-29 08:13 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.247 s 0.059301
2021-09-29 08:16 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.056 s -0.033885
2021-09-29 08:17 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.118 s 0.861858
2021-09-29 08:15 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.566 s -0.687038
2021-09-29 08:20 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.860 s 1.334776
2021-09-29 08:22 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.272 s 1.427457
2021-09-29 08:22 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.309 s 1.344201
2021-09-29 08:24 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.761 s 1.226019
2021-09-29 06:50 Python csv-read gzip, streaming, fanniemae_2016Q4 14.988 s -0.852169
2021-09-29 07:15 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.010 s 0.340713
2021-09-29 07:35 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.375 s -1.589490
2021-09-29 08:17 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.189 s -1.183510
2021-09-29 08:24 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.833 s -0.573471
2021-09-29 08:46 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.505037
2021-09-29 08:46 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.939179
2021-09-29 08:46 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.590064
2021-09-29 08:46 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.572026
2021-09-29 08:46 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.487 s 0.391354
2021-09-29 08:27 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.236 s 0.485485
2021-09-29 08:36 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.594 s 2.309376
2021-09-29 08:46 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.263 s 4.064194
2021-09-29 08:30 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.469 s 2.221537
2021-09-29 08:46 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.621604
2021-09-29 08:33 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.265 s 0.181924
2021-09-29 08:46 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.664 s 0.470080
2021-09-29 08:46 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.931 s -0.651721
2021-09-29 08:26 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.406 s -0.581413
2021-09-29 08:37 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.725 s -1.723404
2021-09-29 08:38 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.199 s -1.282161
2021-09-29 08:46 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.286 s 4.042884
2021-09-29 08:46 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.376114
2021-09-29 08:26 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.604 s 0.397430
2021-09-29 08:35 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.189 s 0.407132
2021-09-29 08:31 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.683 s 1.897974
2021-09-29 08:28 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.821 s 2.651086
2021-09-29 08:37 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.965 s 2.266004
2021-09-29 08:39 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.494 s 0.145242
2021-09-29 08:46 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-29 08:35 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.566 s 3.023180
2021-09-29 08:46 JavaScript Parse readBatches, tracks 0.000 s -1.732794
2021-09-29 08:46 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.670 s 0.146723
2021-09-29 08:29 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.811 s 2.243518
2021-09-29 08:32 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.238536
2021-09-29 08:35 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.302766
2021-09-29 08:46 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-29 08:46 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.123885
2021-09-29 08:46 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.764063
2021-09-29 08:46 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.018594
2021-09-29 08:37 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -1.053454
2021-09-29 08:38 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.470 s 0.737215
2021-09-29 08:46 JavaScript Parse serialize, tracks 0.003 s 2.008988
2021-09-29 08:38 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.162 s 2.279845
2021-09-29 08:46 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -3.603565
2021-09-29 08:46 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.679602
2021-09-29 08:35 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.264301
2021-09-29 08:36 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.641 s -8.241337
2021-09-29 08:35 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.881 s 2.354891
2021-09-29 08:46 JavaScript Parse Table.from, tracks 0.000 s -1.517081
2021-09-29 08:46 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -3.712485
2021-09-29 08:34 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.485 s 0.988907
2021-09-29 08:35 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.581 s 2.310327
2021-09-29 08:36 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.521 s -0.633951
2021-09-29 08:38 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.349 s 2.152155
2021-09-29 08:46 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.558699
2021-09-29 08:46 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.846 s 0.891327
2021-09-29 08:46 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.139853
2021-09-29 08:46 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.673149
2021-09-29 08:46 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.014497
2021-09-29 08:46 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.002602
2021-09-29 08:46 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.217147
2021-09-29 08:46 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.731708