Outliers: 7


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-28 13:01 R dataframe-to-table type_integers, R 0.085 s -0.606334
2021-09-28 13:01 R dataframe-to-table type_floats, R 0.109 s 0.131699
2021-09-28 13:01 R dataframe-to-table type_strings, R 0.488 s 0.832168
2021-09-28 13:01 R dataframe-to-table type_dict, R 0.054 s -0.281748
2021-09-28 12:00 Python csv-read gzip, streaming, nyctaxi_2010-01 10.627 s -0.392082
2021-09-28 12:01 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.365957
2021-09-28 12:36 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.984 s 0.211962
2021-09-28 12:42 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.439 s 1.552052
2021-09-28 12:00 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.693 s -0.609172
2021-09-28 12:03 Python dataframe-to-table type_floats 0.011 s 1.183259
2021-09-28 12:03 Python dataframe-to-table type_simple_features 0.904 s 0.806682
2021-09-28 11:59 Python csv-read gzip, file, fanniemae_2016Q4 6.036 s -1.826892
2021-09-28 12:00 Python csv-read uncompressed, file, nyctaxi_2010-01 1.019 s 0.007079
2021-09-28 12:03 Python dataset-filter nyctaxi_2010-01 4.398 s -1.481685
2021-09-28 12:20 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.273 s 0.125041
2021-09-28 12:24 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.028 s 0.099121
2021-09-28 12:40 Python file-read lz4, feather, table, nyctaxi_2010-01 0.666 s 0.827897
2021-09-28 12:20 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.222 s -0.057507
2021-09-28 12:36 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.826 s 0.431327
2021-09-28 12:40 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.094624
2021-09-28 12:37 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.856 s -1.460231
2021-09-28 12:47 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.182848
2021-09-28 11:58 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.045 s -0.913748
2021-09-28 12:38 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.105 s 1.003045
2021-09-28 12:44 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.566 s 1.265921
2021-09-28 12:46 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.817 s 1.017867
2021-09-28 12:47 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.336 s 0.275481
2021-09-28 12:37 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.322 s -3.434969
2021-09-28 12:44 Python file-write lz4, feather, table, fanniemae_2016Q4 1.154 s 0.646707
2021-09-28 12:03 Python dataframe-to-table type_nested 2.948 s 0.378121
2021-09-28 12:37 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.284 s -1.773101
2021-09-28 12:45 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.745 s 1.260243
2021-09-28 12:06 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.375 s -0.758278
2021-09-28 12:44 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.790 s 2.540874
2021-09-28 12:36 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.687 s 0.498624
2021-09-28 11:58 Python csv-read uncompressed, file, fanniemae_2016Q4 1.192 s -0.340492
2021-09-28 12:38 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.041 s 0.028966
2021-09-28 12:39 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.812 s 1.082576
2021-09-28 12:39 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.830 s 0.952053
2021-09-28 12:02 Python dataframe-to-table chi_traffic_2020_Q1 19.679 s 0.668540
2021-09-28 12:43 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.407 s -0.623408
2021-09-28 12:47 Python file-write lz4, feather, table, nyctaxi_2010-01 1.803 s 0.362305
2021-09-28 12:03 Python dataframe-to-table type_strings 0.367 s 0.618523
2021-09-28 12:40 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.473 s 0.986341
2021-09-28 12:47 Python wide-dataframe use_legacy_dataset=false 0.620 s -0.505700
2021-09-28 12:03 Python dataframe-to-table type_dict 0.012 s 1.217624
2021-09-28 12:41 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.080 s 1.600779
2021-09-28 12:45 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.856 s 2.689901
2021-09-28 13:00 R dataframe-to-table chi_traffic_2020_Q1, R 5.356 s 0.864003
2021-09-28 12:03 Python dataframe-to-table type_integers 0.011 s 0.294390
2021-09-28 12:24 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.005 s 0.217699
2021-09-28 12:38 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.765 s -4.408808
2021-09-28 12:47 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.771 s 0.600543
2021-09-28 12:38 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.284 s 1.004626
2021-09-28 12:38 Python file-read lz4, feather, table, fanniemae_2016Q4 0.597 s 0.739777
2021-09-28 12:40 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.948 s 1.069537
2021-09-28 12:37 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.826 s -5.947997
2021-09-28 12:38 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.810 s -0.326102
2021-09-28 12:39 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.045 s -0.463982
2021-09-28 12:42 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.083 s 1.671471
2021-09-28 12:43 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.534 s 1.243029
2021-09-28 12:44 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.083 s 1.266604
2021-09-28 13:01 R dataframe-to-table type_nested, R 0.536 s 0.168606
2021-09-28 11:59 Python csv-read gzip, streaming, fanniemae_2016Q4 14.971 s -0.910782
2021-09-28 12:11 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.869 s 7.796225
2021-09-28 12:24 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.012 s 0.334236
2021-09-28 12:36 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.054 s -2.204688
2021-09-28 12:37 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.172 s -2.669717
2021-09-28 12:46 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.348 s 0.105852
2021-09-28 13:57 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.610515
2021-09-28 13:57 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-28 13:29 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -0.742525
2021-09-28 13:27 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.063 s -1.238557
2021-09-28 13:35 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.761 s 1.451346
2021-09-28 13:26 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.508927
2021-09-28 13:34 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.303 s 1.654573
2021-09-28 13:30 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.995 s -1.513112
2021-09-28 13:47 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.191 s -1.904961
2021-09-28 13:57 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.013620
2021-09-28 13:29 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.244 s -0.214340
2021-09-28 13:42 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.466 s 3.029625
2021-09-28 13:57 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.705 s -0.554938
2021-09-28 13:57 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s 0.071482
2021-09-28 13:41 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.803 s 3.569287
2021-09-28 13:50 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.162 s 56.660193
2021-09-28 13:57 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.573214
2021-09-28 13:57 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.626726
2021-09-28 13:31 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.851 s 1.678773
2021-09-28 13:39 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.839 s 2.918913
2021-09-28 13:46 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.879 s 3.021034
2021-09-28 13:50 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 1.178772
2021-09-28 13:57 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.034 s -4.929018
2021-09-28 13:57 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.384995
2021-09-28 13:25 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.237 s 0.161279
2021-09-28 13:37 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.410 s -1.328122
2021-09-28 13:57 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.840582
2021-09-28 13:27 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.566 s -0.716697
2021-09-28 13:31 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.508 s 0.254748
2021-09-28 13:49 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.472 s 0.377724
2021-09-28 13:57 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.362 s 4.253714
2021-09-28 13:36 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.840 s -1.995232
2021-09-28 13:47 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.600 s 21.784786
2021-09-28 13:48 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.480527
2021-09-28 13:57 JavaScript Parse readBatches, tracks 0.000 s 1.005858
2021-09-28 13:57 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.330 s 4.220939
2021-09-28 13:57 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.477876
2021-09-28 13:27 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.384 s -0.408199
2021-09-28 13:33 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.284 s 1.607475
2021-09-28 13:44 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.269 s -0.215612
2021-09-28 13:47 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.518 s -0.339113
2021-09-28 13:57 JavaScript Parse Table.from, tracks 0.000 s 1.154918
2021-09-28 13:57 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.700046
2021-09-28 13:26 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.897 s 0.764977
2021-09-28 13:28 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.178 s -0.544300
2021-09-28 13:43 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -1.039581
2021-09-28 13:45 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.489 s 0.167900
2021-09-28 13:57 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.177237
2021-09-28 13:25 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.234 s 0.197366
2021-09-28 13:25 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.912 s 0.067273
2021-09-28 13:49 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.076525
2021-09-28 13:57 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -4.120476
2021-09-28 13:57 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.102138
2021-09-28 13:57 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 1.317176
2021-09-28 13:39 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.224 s 0.781225
2021-09-28 13:43 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.663 s 3.350696
2021-09-28 13:46 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.564 s 5.377505
2021-09-28 13:57 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.722 s 0.130667
2021-09-28 13:57 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.939 s -0.852469
2021-09-28 13:57 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.548 s -0.595589
2021-09-28 13:49 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.347 s 14.681258
2021-09-28 13:57 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.850268
2021-09-28 13:57 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.011409
2021-09-28 13:30 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.439413
2021-09-28 13:50 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.471 s 0.202301
2021-09-28 13:57 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.963 s -1.992280
2021-09-28 13:24 R dataframe-to-table type_simple_features, R 273.575 s 2.535869
2021-09-28 13:28 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.140 s -0.712733
2021-09-28 13:46 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.192 s 0.116708
2021-09-28 13:57 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-28 13:26 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.928 s -0.074676
2021-09-28 13:47 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.361921
2021-09-28 13:57 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.483908
2021-09-28 13:37 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.584 s 0.880558
2021-09-28 13:46 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.581 s 3.305725
2021-09-28 13:57 JavaScript Parse serialize, tracks 0.004 s 1.588600
2021-09-28 13:57 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.586090
2021-09-28 13:57 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.494149
2021-09-28 13:48 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.947 s 57.234240
2021-09-28 13:48 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.650 s -0.721618