Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-28 16:17 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.367 s -0.952434
2021-09-28 16:14 Python dataset-filter nyctaxi_2010-01 4.396 s -1.329965
2021-09-28 16:10 Python csv-read uncompressed, file, nyctaxi_2010-01 1.002 s 0.290228
2021-09-28 16:13 Python dataframe-to-table type_dict 0.012 s 0.466769
2021-09-28 16:13 Python dataframe-to-table chi_traffic_2020_Q1 19.786 s 0.043124
2021-09-28 16:08 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.092 s -0.942355
2021-09-28 16:13 Python dataframe-to-table type_floats 0.011 s 1.132658
2021-09-28 16:14 Python dataframe-to-table type_simple_features 0.908 s 0.144766
2021-09-28 16:08 Python csv-read uncompressed, file, fanniemae_2016Q4 1.165 s 0.078930
2021-09-28 16:10 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.688 s -0.579453
2021-09-28 16:11 Python csv-read gzip, file, nyctaxi_2010-01 9.049 s -0.952166
2021-09-28 16:13 Python dataframe-to-table type_integers 0.011 s 0.701519
2021-09-28 16:10 Python csv-read gzip, file, fanniemae_2016Q4 6.032 s -0.785238
2021-09-28 16:13 Python dataframe-to-table type_nested 2.955 s -0.015959
2021-09-28 16:09 Python csv-read gzip, streaming, fanniemae_2016Q4 15.029 s -0.949534
2021-09-28 16:11 Python csv-read gzip, streaming, nyctaxi_2010-01 10.679 s -0.587691
2021-09-28 16:21 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.116 s 4.697649
2021-09-28 16:13 Python dataframe-to-table type_strings 0.363 s 0.964932
2021-09-28 16:31 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.991 s -0.145317
2021-09-28 16:31 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.238 s 0.302343
2021-09-28 17:37 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.252 s 0.004702
2021-09-28 18:01 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.657 s -0.791407
2021-09-28 16:51 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.102 s 1.322430
2021-09-28 16:59 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.343015
2021-09-28 16:50 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.231449
2021-09-28 16:50 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s -0.322215
2021-09-28 16:51 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.032 s 0.355032
2021-09-28 16:53 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.073 s 1.591623
2021-09-28 17:39 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.462105
2021-09-28 18:10 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.943 s -0.915198
2021-09-28 16:35 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.030 s 0.062679
2021-09-28 17:13 R dataframe-to-table type_floats, R 0.108 s 0.427828
2021-09-28 17:38 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.199704
2021-09-28 17:43 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.539 s -1.173651
2021-09-28 17:53 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.804 s 3.099501
2021-09-28 17:58 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.494 s -0.808545
2021-09-28 17:59 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.578 s 2.991146
2021-09-28 18:01 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.194731
2021-09-28 18:02 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.162 s 4.213086
2021-09-28 18:10 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.097237
2021-09-28 18:10 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.538 s -0.445030
2021-09-28 16:48 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.030 s -1.318068
2021-09-28 16:49 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.869 s -1.787000
2021-09-28 16:50 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.168 s -2.156263
2021-09-28 16:52 Python file-read lz4, feather, table, nyctaxi_2010-01 0.666 s 0.804135
2021-09-28 16:58 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.857 s 2.387997
2021-09-28 17:51 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.220 s 0.874277
2021-09-28 18:10 JavaScript Parse serialize, tracks 0.005 s -0.587414
2021-09-28 18:10 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.002602
2021-09-28 16:48 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.749 s 0.239872
2021-09-28 17:38 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.918 s 0.007295
2021-09-28 17:41 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.182 s -0.849788
2021-09-28 17:45 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.290 s 1.493996
2021-09-28 18:01 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.347 s 4.121753
2021-09-28 16:35 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.495 s -9.306197
2021-09-28 16:49 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.288 s -1.819569
2021-09-28 16:52 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.018 s 0.737196
2021-09-28 16:56 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.502 s 1.699275
2021-09-28 16:57 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.763 s 1.094640
2021-09-28 16:59 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.289 s 0.623162
2021-09-28 16:59 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.763 s 0.653631
2021-09-28 17:37 R dataframe-to-table type_simple_features, R 274.297 s 0.980923
2021-09-28 17:40 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.399 s -1.175744
2021-09-28 17:59 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.415827
2021-09-28 18:10 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.847 s 0.909046
2021-09-28 18:10 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.377371
2021-09-28 16:50 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.742 s -0.688026
2021-09-28 16:50 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.687 s -0.967226
2021-09-28 16:53 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.498 s 0.854472
2021-09-28 17:13 R dataframe-to-table type_integers, R 0.083 s 1.529432
2021-09-28 17:38 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.917 s 0.040294
2021-09-28 17:48 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.728 s 1.590372
2021-09-28 17:50 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.410 s -1.271514
2021-09-28 17:59 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.874 s 2.738701
2021-09-28 16:50 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.822 s -1.451105
2021-09-28 16:52 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.831 s 0.928301
2021-09-28 16:55 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.465 s 1.507541
2021-09-28 16:48 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.860 s 0.299318
2021-09-28 17:13 R dataframe-to-table type_strings, R 0.491 s -0.337643
2021-09-28 17:14 R dataframe-to-table type_nested, R 0.537 s -0.230132
2021-09-28 17:39 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.917 s -0.131283
2021-09-28 17:52 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.833 s 2.848154
2021-09-28 17:56 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.286 s -1.491976
2021-09-28 17:59 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.154975
2021-09-28 16:48 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.982 s 0.216983
2021-09-28 16:55 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.583 s -2.105356
2021-09-28 16:59 Python wide-dataframe use_legacy_dataset=false 0.620 s -0.587284
2021-09-28 17:13 R dataframe-to-table type_dict, R 0.054 s -0.288457
2021-09-28 17:41 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.110 s 1.510615
2021-09-28 18:10 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.113509
2021-09-28 16:54 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.434 s 1.524204
2021-09-28 17:55 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.674 s 2.637197
2021-09-28 18:00 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.609 s 4.083216
2021-09-28 16:35 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.531 s -9.204586
2021-09-28 16:51 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.796 s 1.117127
2021-09-28 18:03 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.489 s 0.166156
2021-09-28 18:10 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-28 18:10 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.289725
2021-09-28 16:56 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.067 s 1.356953
2021-09-28 17:41 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.248 s -0.454134
2021-09-28 18:02 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 1.120020
2021-09-28 18:10 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.139559
2021-09-28 18:10 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.557 s 0.009362
2021-09-28 18:10 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.752 s -0.042644
2021-09-28 16:49 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.314 s -2.438070
2021-09-28 17:13 R dataframe-to-table chi_traffic_2020_Q1, R 5.386 s 0.334709
2021-09-28 17:42 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.977 s -0.518762
2021-09-28 17:44 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.851 s 1.594158
2021-09-28 18:10 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.622277
2021-09-28 18:10 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.497442
2021-09-28 18:10 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.981172
2021-09-28 18:10 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.390782
2021-09-28 18:10 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.070796
2021-09-28 18:10 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.080708
2021-09-28 16:54 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.196 s 1.080524
2021-09-28 17:54 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.469 s 2.692811
2021-09-28 17:59 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.186 s 0.584901
2021-09-28 16:52 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.774856
2021-09-28 16:56 Python file-write lz4, feather, table, fanniemae_2016Q4 1.163 s -0.254760
2021-09-28 16:57 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.794 s 2.196743
2021-09-28 17:42 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.274342
2021-09-28 18:10 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.469944
2021-09-28 18:10 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.143725
2021-09-28 16:58 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.795 s 1.134391
2021-09-28 18:00 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.518 s -0.227313
2021-09-28 18:10 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.696 s -0.354071
2021-09-28 18:10 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.408569
2021-09-28 16:51 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.051 s -0.834832
2021-09-28 16:58 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.350 s -0.048307
2021-09-28 16:59 Python file-write lz4, feather, table, nyctaxi_2010-01 1.803 s 0.377626
2021-09-28 17:38 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.237 s 0.166697
2021-09-28 17:46 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.300 s 1.595852
2021-09-28 17:48 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.831 s -0.147739
2021-09-28 18:00 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.373324
2021-09-28 18:02 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.474 s -0.426512
2021-09-28 17:40 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.062 s -1.153564
2021-09-28 17:50 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.573 s 1.066445
2021-09-28 17:59 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.570 s 4.117976
2021-09-28 18:10 JavaScript Parse Table.from, tracks 0.000 s 0.156533
2021-09-28 18:10 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.124804
2021-09-28 18:10 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.055349
2021-09-28 17:42 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.677 s 0.034818
2021-09-28 17:57 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.277 s -0.765676
2021-09-28 18:10 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.483111
2021-09-28 18:10 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.175845
2021-09-28 18:01 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.951 s 4.248743
2021-09-28 18:10 JavaScript Parse readBatches, tracks 0.000 s 0.322334
2021-09-28 18:10 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.769 s -1.750648
2021-09-28 18:10 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.955323