Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 12:44 Python csv-read gzip, file, fanniemae_2016Q4 6.026 s 0.772029
2021-10-13 13:09 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.096 s -1.709838
2021-10-13 13:59 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.882 s -1.062337
2021-10-13 12:44 Python csv-read gzip, streaming, fanniemae_2016Q4 14.850 s -0.051060
2021-10-13 12:47 Python dataframe-to-table type_strings 0.365 s 0.531287
2021-10-13 12:48 Python dataframe-to-table type_nested 2.859 s 0.985185
2021-10-13 13:21 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.935 s -2.243810
2021-10-13 14:05 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.191 s -1.920031
2021-10-13 12:43 Python csv-read uncompressed, file, fanniemae_2016Q4 1.169 s 0.090447
2021-10-13 13:46 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.224 s 0.310797
2021-10-13 13:54 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.836 s -0.682343
2021-10-13 12:48 Python dataset-filter nyctaxi_2010-01 4.427 s -3.215163
2021-10-13 13:10 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.104 s -0.600852
2021-10-13 12:46 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.560667
2021-10-13 12:56 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.189 s -0.546679
2021-10-13 13:21 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.281 s 0.439507
2021-10-13 13:27 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.234 s 0.339773
2021-10-13 13:29 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.809 s 0.549674
2021-10-13 13:29 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.916 s -0.207479
2021-10-13 13:46 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.481 s 0.692648
2021-10-13 13:25 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.104 s 0.157729
2021-10-13 13:30 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.344 s 0.271094
2021-10-13 13:46 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.319 s -1.197531
2021-10-13 14:03 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.266 s -2.306102
2021-10-13 13:21 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.143 s -0.097652
2021-10-13 13:24 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.827 s 0.917285
2021-10-13 13:48 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -1.200686
2021-10-13 13:57 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.296 s -3.096828
2021-10-13 12:47 Python dataframe-to-table chi_traffic_2020_Q1 19.422 s 0.370571
2021-10-13 13:31 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.751 s 1.956175
2021-10-13 13:44 R dataframe-to-table chi_traffic_2020_Q1, R 3.504 s 0.260483
2021-10-13 13:45 R dataframe-to-table type_nested, R 0.540 s 0.231031
2021-10-13 13:46 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.043 s -0.914216
2021-10-13 13:47 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.404 s -0.683329
2021-10-13 13:49 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.539 s 0.000997
2021-10-13 13:52 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.379 s -0.671814
2021-10-13 13:20 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.952 s 0.381831
2021-10-13 13:23 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.227 s 0.305058
2021-10-13 13:47 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.107 s 0.816644
2021-10-13 13:58 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.893 s -0.602595
2021-10-13 14:16 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.341649
2021-10-13 12:45 Python csv-read uncompressed, file, nyctaxi_2010-01 0.992 s 1.589935
2021-10-13 13:05 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.547 s -0.395750
2021-10-13 13:21 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.610 s 1.342644
2021-10-13 13:22 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.119721
2021-10-13 13:31 Python wide-dataframe use_legacy_dataset=true 0.390 s 0.801588
2021-10-13 13:23 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s 0.070104
2021-10-13 13:45 R dataframe-to-table type_floats, R 0.013 s 0.745325
2021-10-13 13:56 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.661 s -3.020291
2021-10-13 14:04 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.470 s 1.109457
2021-10-13 12:44 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.673 s -0.389560
2021-10-13 13:22 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.691 s 0.937980
2021-10-13 13:22 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.021 s 1.180767
2021-10-13 13:31 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.319 s 0.695041
2021-10-13 13:31 Python wide-dataframe use_legacy_dataset=false 0.614 s 0.942994
2021-10-13 13:47 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.154 s 0.705139
2021-10-13 14:01 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.741 s -1.042713
2021-10-13 12:43 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.896 s 0.208186
2021-10-13 12:48 Python dataframe-to-table type_integers 0.011 s -0.230209
2021-10-13 13:10 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.054 s -0.289735
2021-10-13 13:26 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.364 s 0.351901
2021-10-13 13:50 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.857 s 0.206339
2021-10-13 12:45 Python csv-read gzip, streaming, nyctaxi_2010-01 10.669 s -0.513574
2021-10-13 12:48 Python dataframe-to-table type_floats 0.011 s 0.408989
2021-10-13 13:21 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.349 s -2.296935
2021-10-13 13:28 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.408 s -0.520434
2021-10-13 13:47 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.043 s 1.363177
2021-10-13 12:47 Python dataframe-to-table type_dict 0.011 s 1.117412
2021-10-13 13:05 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.472 s 0.067368
2021-10-13 13:22 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s 0.069253
2021-10-13 13:23 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.160 s 1.111828
2021-10-13 13:45 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.526 s -2.756789
2021-10-13 13:49 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.693 s 0.008288
2021-10-13 13:52 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.309 s 0.211924
2021-10-13 12:52 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 69.368 s -2.293041
2021-10-13 13:45 R dataframe-to-table type_strings, R 0.495 s 0.229492
2021-10-13 13:47 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.579 s -3.081837
2021-10-13 13:48 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.215 s 0.699982
2021-10-13 13:54 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.816 s 0.796897
2021-10-13 13:56 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.386 s 1.056214
2021-10-13 13:22 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.078 s -0.665120
2021-10-13 13:24 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s 0.075737
2021-10-13 13:26 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.455 s 0.223815
2021-10-13 14:02 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.275 s 0.912740
2021-10-13 13:20 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.860 s 0.133598
2021-10-13 13:20 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.074 s -1.552826
2021-10-13 13:28 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.871 s -0.534464
2021-10-13 13:45 R dataframe-to-table type_integers, R 0.009 s 0.753464
2021-10-13 13:48 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.989 s 0.144045
2021-10-13 13:20 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.656 s 0.826966
2021-10-13 13:23 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.049 s -0.316553
2021-10-13 13:27 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.770 s 0.046157
2021-10-13 13:28 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.840 s 0.134819
2021-10-13 13:45 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.703 s 0.648729
2021-10-13 13:24 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.284 s 1.167242
2021-10-13 13:28 Python file-write lz4, feather, table, fanniemae_2016Q4 1.148 s 0.654159
2021-10-13 13:31 Python file-write lz4, feather, table, nyctaxi_2010-01 1.797 s 0.369825
2021-10-13 14:05 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.810 s 1.813723
2021-10-13 14:05 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.592 s -2.069967
2021-10-13 14:05 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.193 s -1.183414
2021-10-13 14:05 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.521 s 3.591321
2021-10-13 14:06 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.784 s -2.921105
2021-10-13 14:05 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.098 s -5.816030
2021-10-13 14:06 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.597 s 0.165079
2021-10-13 14:07 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.539 s 0.871921
2021-10-13 14:06 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.523 s 0.216693
2021-10-13 14:07 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.918 s -0.503361
2021-10-13 14:07 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.359 s 0.913031
2021-10-13 14:07 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -0.917875
2021-10-13 13:22 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.538 s 1.326792
2021-10-13 14:08 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.115 s 2.817451
2021-10-13 14:16 JavaScript Parse serialize, tracks 0.005 s -0.745385
2021-10-13 14:16 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.660 s -0.428862
2021-10-13 14:16 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.466034
2021-10-13 14:08 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.499 s -2.640205
2021-10-13 14:08 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.212 s -1.456799
2021-10-13 14:16 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s 0.070256
2021-10-13 14:16 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.155263
2021-10-13 14:16 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.021 s 2.791107
2021-10-13 14:16 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.496 s 0.455337
2021-10-13 14:16 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.897 s 0.226108
2021-10-13 14:16 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.296370
2021-10-13 14:16 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.064613
2021-10-13 14:16 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.803483
2021-10-13 14:16 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.629 s 1.090968
2021-10-13 14:16 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.038942
2021-10-13 14:16 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.255242
2021-10-13 14:16 JavaScript Parse readBatches, tracks 0.000 s -0.062840
2021-10-13 14:16 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.881 s -0.012245
2021-10-13 14:09 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.501 s 0.055647
2021-10-13 14:16 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578421
2021-10-13 14:16 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.648 s 0.558052
2021-10-13 14:16 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.240265
2021-10-13 14:16 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.021 s 2.843273
2021-10-13 14:16 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.002942
2021-10-13 14:16 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.316949
2021-10-13 14:16 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.261343
2021-10-13 14:16 JavaScript Parse Table.from, tracks 0.000 s 0.099381
2021-10-13 14:16 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.569797
2021-10-13 14:16 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.365668
2021-10-13 14:16 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.799066
2021-10-13 14:16 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.208597
2021-10-13 14:16 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.598 s -0.228161
2021-10-13 14:16 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.080808
2021-10-13 13:30 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.865 s 0.212000
2021-10-13 13:45 R dataframe-to-table type_dict, R 0.060 s -1.834703
2021-10-13 14:00 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.520 s -0.416740