Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-04 11:54 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.922 s -0.236164
2021-10-04 11:56 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.535 s 1.335493
2021-10-04 11:59 Python dataframe-to-table type_integers 0.011 s -0.566058
2021-10-04 11:54 Python csv-read uncompressed, file, fanniemae_2016Q4 1.190 s -0.996498
2021-10-04 11:59 Python dataframe-to-table type_strings 0.373 s -0.131003
2021-10-04 12:17 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.206 s 0.567183
2021-10-04 12:00 Python dataframe-to-table type_simple_features 0.913 s 0.021582
2021-10-04 12:17 Python dataset-read async=True, nyctaxi_multi_ipc_s3 184.777 s 0.425649
2021-10-04 11:55 Python csv-read gzip, streaming, fanniemae_2016Q4 14.864 s -0.281530
2021-10-04 11:59 Python dataframe-to-table type_dict 0.012 s 1.154281
2021-10-04 11:56 Python csv-read uncompressed, file, nyctaxi_2010-01 1.013 s -0.014830
2021-10-04 11:59 Python dataframe-to-table chi_traffic_2020_Q1 19.408 s 1.496803
2021-10-04 12:00 Python dataframe-to-table type_nested 2.876 s 1.113769
2021-10-04 12:03 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.336 s -0.675769
2021-10-04 12:08 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.355 s 0.998502
2021-10-04 11:56 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.328307
2021-10-04 11:57 Python csv-read gzip, streaming, nyctaxi_2010-01 10.513 s 1.479181
2021-10-04 11:57 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.874568
2021-10-04 11:59 Python dataframe-to-table type_floats 0.011 s 1.005833
2021-10-04 12:00 Python dataset-filter nyctaxi_2010-01 4.351 s 0.557389
2021-10-04 12:21 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.030 s 0.106463
2021-10-04 12:21 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.011 s 0.338728
2021-10-04 12:21 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.994 s 0.412158
2021-10-04 12:31 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.887 s 0.100702
2021-10-04 12:35 Python file-read lz4, feather, table, nyctaxi_2010-01 0.661 s 1.793360
2021-10-04 13:25 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.972 s 0.268088
2021-10-04 13:34 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.187 s 1.140482
2021-10-04 13:42 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.431864
2021-10-04 13:44 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.930 s 0.907257
2021-10-04 12:39 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.769 s 5.043091
2021-10-04 12:41 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.925 s -0.135994
2021-10-04 13:21 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.205 s 0.509665
2021-10-04 13:25 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.679 s 0.070227
2021-10-04 13:36 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.891 s -0.523772
2021-10-04 13:43 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.613 s -0.775476
2021-10-04 12:34 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.060 s -0.544720
2021-10-04 12:56 R dataframe-to-table type_floats, R 0.107 s 1.115328
2021-10-04 13:22 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.928 s -0.413930
2021-10-04 13:45 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.485 s -2.406163
2021-10-04 13:53 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.167529
2021-10-04 13:53 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.905 s -0.013241
2021-10-04 12:31 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.966 s 0.310974
2021-10-04 12:34 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.242 s -1.389267
2021-10-04 13:22 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.573 s -2.178408
2021-10-04 13:44 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.517 s 1.280355
2021-10-04 13:53 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.358022
2021-10-04 13:53 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.566125
2021-10-04 13:53 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.044886
2021-10-04 13:53 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.168318
2021-10-04 12:39 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.762 s -0.204557
2021-10-04 13:21 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.863 s 0.627363
2021-10-04 13:26 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.557 s -1.549390
2021-10-04 13:31 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.825 s 1.228060
2021-10-04 13:53 JavaScript Parse Table.from, tracks 0.000 s 0.210349
2021-10-04 13:53 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.923464
2021-10-04 12:42 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.350 s 0.082800
2021-10-04 13:23 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.054 s 0.358114
2021-10-04 13:29 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.622 s -1.008484
2021-10-04 13:31 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.048 s -0.730179
2021-10-04 13:32 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.556 s 0.879355
2021-10-04 13:41 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.169 s 1.285819
2021-10-04 13:45 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.201 s -1.488297
2021-10-04 13:46 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.498 s 0.096770
2021-10-04 13:53 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.646 s -0.309107
2021-10-04 12:40 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.934 s -0.914477
2021-10-04 13:24 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.057483
2021-10-04 13:38 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.748 s -0.478945
2021-10-04 12:32 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.268 s 0.726465
2021-10-04 12:35 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s -0.038244
2021-10-04 13:24 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.250 s -0.404794
2021-10-04 13:43 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.603 s 0.885591
2021-10-04 13:45 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.183 s 0.840097
2021-10-04 13:53 JavaScript Parse serialize, tracks 0.005 s 0.434913
2021-10-04 12:43 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.814 s 0.259890
2021-10-04 13:53 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.700 s -0.505182
2021-10-04 13:53 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.493 s 0.232759
2021-10-04 12:33 Python file-read lz4, feather, table, fanniemae_2016Q4 0.608 s -1.046405
2021-10-04 12:42 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.329 s 0.379346
2021-10-04 12:43 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.098464
2021-10-04 13:37 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.573 s -0.797003
2021-10-04 13:41 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.489 s 0.208457
2021-10-04 13:44 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.105 s -2.022354
2021-10-04 13:53 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.146317
2021-10-04 13:53 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.029861
2021-10-04 12:33 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.757 s -0.790492
2021-10-04 12:34 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.294 s -1.271879
2021-10-04 12:41 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.996 s -0.963866
2021-10-04 12:56 R dataframe-to-table type_nested, R 0.540 s -1.083227
2021-10-04 13:21 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.277850
2021-10-04 13:33 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.033121
2021-10-04 13:42 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.580 s 0.965740
2021-10-04 13:44 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.354 s 0.753715
2021-10-04 13:53 JavaScript Parse readBatches, tracks 0.000 s 0.390234
2021-10-04 12:31 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.743 s 0.158962
2021-10-04 12:38 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 14.000 s -0.996918
2021-10-04 13:20 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.211 s 0.503793
2021-10-04 13:28 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.584 s -0.658422
2021-10-04 13:53 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.737 s 0.063141
2021-10-04 12:33 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.119 s 0.916446
2021-10-04 12:36 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.987 s -1.499986
2021-10-04 12:37 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.634 s -0.988031
2021-10-04 12:42 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.961 s -0.154829
2021-10-04 13:20 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.869 s 0.587502
2021-10-04 13:39 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 1.153085
2021-10-04 13:42 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.002 s 0.732692
2021-10-04 13:53 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.578336
2021-10-04 13:53 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.598719
2021-10-04 12:40 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.296 s -0.646385
2021-10-04 12:32 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.950 s 1.066204
2021-10-04 12:33 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.925 s -1.200110
2021-10-04 12:36 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.383 s -0.958263
2021-10-04 12:56 R dataframe-to-table type_integers, R 0.084 s 0.617868
2021-10-04 13:35 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.934 s -0.782185
2021-10-04 13:53 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.127177
2021-10-04 13:53 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.682 s 0.040435
2021-10-04 13:53 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.907 s -0.638759
2021-10-04 13:53 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.549823
2021-10-04 13:53 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.136486
2021-10-04 13:53 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.163174
2021-10-04 12:33 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.833 s -0.897290
2021-10-04 12:39 Python file-write lz4, feather, table, fanniemae_2016Q4 1.148 s 1.049686
2021-10-04 13:24 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.107 s 1.639654
2021-10-04 13:27 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.165 s -0.984865
2021-10-04 12:32 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.762 s 1.423363
2021-10-04 12:38 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.744 s -0.991943
2021-10-04 12:42 Python file-write lz4, feather, table, nyctaxi_2010-01 1.802 s 0.532844
2021-10-04 12:43 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.164308
2021-10-04 13:53 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.123996
2021-10-04 12:35 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.457 s -1.376891
2021-10-04 13:20 R dataframe-to-table type_simple_features, R 275.360 s -0.718248
2021-10-04 13:40 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.230 s 2.040353
2021-10-04 13:42 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.798105
2021-10-04 13:53 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.753891
2021-10-04 12:32 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.198 s 0.970526
2021-10-04 12:33 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.499988
2021-10-04 12:34 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.043 s -0.358471
2021-10-04 12:56 R dataframe-to-table chi_traffic_2020_Q1, R 5.327 s 1.259854
2021-10-04 12:56 R dataframe-to-table type_dict, R 0.052 s -0.199688
2021-10-04 13:43 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.520 s -0.493538
2021-10-04 12:35 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.321 s -1.428493
2021-10-04 12:56 R dataframe-to-table type_strings, R 0.491 s 0.061053
2021-10-04 13:42 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.583 s 0.894685
2021-10-04 13:53 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.998523
2021-10-04 13:23 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.391 s -0.373231
2021-10-04 13:23 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.148 s 1.721793
2021-10-04 13:53 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.134795
2021-10-04 13:53 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.296987
2021-10-04 13:53 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.023262
2021-10-04 13:53 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.549823