Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-01 20:35 Python file-read lz4, feather, table, nyctaxi_2010-01 0.666 s 0.687645
2021-10-01 20:35 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.993 s -1.724220
2021-10-01 19:58 Python dataframe-to-table type_dict 0.012 s 0.038115
2021-10-01 20:32 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.261 s 1.140184
2021-10-01 20:33 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.926 s -1.886193
2021-10-01 20:34 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.028 s 0.608859
2021-10-01 19:54 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s -0.156055
2021-10-01 20:06 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.310 s 1.309803
2021-10-01 19:56 Python csv-read gzip, streaming, nyctaxi_2010-01 10.673 s -0.286617
2021-10-01 19:58 Python dataframe-to-table type_simple_features 0.912 s 0.103779
2021-10-01 20:31 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.730 s 0.254047
2021-10-01 20:33 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.283 s 1.142460
2021-10-01 19:58 Python dataframe-to-table type_nested 2.880 s 1.351682
2021-10-01 20:31 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.896 s 0.801069
2021-10-01 20:33 Python file-read lz4, feather, table, fanniemae_2016Q4 0.604 s -0.411839
2021-10-01 20:32 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.116 s 1.154762
2021-10-01 20:33 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.241 s -2.058659
2021-10-01 19:53 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.801 s -0.200859
2021-10-01 19:58 Python dataframe-to-table chi_traffic_2020_Q1 19.470 s 1.438266
2021-10-01 20:19 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.004 s 0.447674
2021-10-01 20:30 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.789 s 0.604963
2021-10-01 20:31 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.926 s 1.872246
2021-10-01 19:55 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.683 s -0.214578
2021-10-01 20:33 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.028 s 0.622791
2021-10-01 20:32 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.761 s -1.105361
2021-10-01 20:34 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.311 s -1.391577
2021-10-01 19:56 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -0.737394
2021-10-01 19:59 Python dataset-filter nyctaxi_2010-01 4.347 s 0.621099
2021-10-01 20:02 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 66.195 s -1.265991
2021-10-01 19:58 Python dataframe-to-table type_strings 0.368 s 0.345682
2021-10-01 20:16 Python dataset-read async=True, nyctaxi_multi_ipc_s3 183.263 s 0.568535
2021-10-01 20:16 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.370 s -0.291636
2021-10-01 20:19 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.023 s 0.179125
2021-10-01 20:31 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.166 s 2.011937
2021-10-01 19:55 Python csv-read uncompressed, file, nyctaxi_2010-01 0.997 s 0.374956
2021-10-01 19:58 Python dataframe-to-table type_integers 0.011 s 1.709052
2021-10-01 20:32 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.842 s -1.389120
2021-10-01 20:34 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.109894
2021-10-01 19:53 Python csv-read uncompressed, file, fanniemae_2016Q4 1.179 s -0.067471
2021-10-01 19:58 Python dataframe-to-table type_floats 0.012 s -0.376727
2021-10-01 20:32 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.761 s 1.698392
2021-10-01 19:54 Python csv-read gzip, streaming, fanniemae_2016Q4 14.744 s -0.221910
2021-10-01 20:34 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.317 s -1.418484
2021-10-01 20:37 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.607 s -1.008510
2021-10-01 20:36 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.392 s -1.212296
2021-10-01 20:38 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.458 s -0.954556
2021-10-01 21:53 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.340057
2021-10-01 20:37 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.749 s -1.205113
2021-10-01 20:38 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.960 s -0.951290
2021-10-01 20:39 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.926 s -1.497808
2021-10-01 20:39 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.268 s -0.474705
2021-10-01 20:39 Python file-write lz4, feather, table, fanniemae_2016Q4 1.161 s 0.110678
2021-10-01 20:19 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.008 s 0.209815
2021-10-01 21:20 R dataframe-to-table type_simple_features, R 275.271 s -0.774066
2021-10-01 20:35 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.472 s -1.629799
2021-10-01 20:39 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.955 s -1.494706
2021-10-01 20:42 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.844 s -0.003564
2021-10-01 21:24 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.663281
2021-10-01 21:25 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.687 s -0.032966
2021-10-01 21:29 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.620 s -1.180448
2021-10-01 21:35 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.940 s -0.882304
2021-10-01 21:41 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.166 s 2.019924
2021-10-01 20:56 R dataframe-to-table type_integers, R 0.084 s 0.367767
2021-10-01 21:53 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.142464
2021-10-01 20:41 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.963 s -0.212289
2021-10-01 20:42 Python wide-dataframe use_legacy_dataset=false 0.625 s -1.327356
2021-10-01 20:56 R dataframe-to-table type_strings, R 0.494 s -1.292823
2021-10-01 20:56 R dataframe-to-table type_nested, R 0.542 s -2.046851
2021-10-01 21:24 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.234 s 0.461286
2021-10-01 21:20 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.869 s 0.556386
2021-10-01 21:32 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.558 s 1.049064
2021-10-01 21:39 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.292617
2021-10-01 21:40 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.483 s 1.240573
2021-10-01 21:42 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -1.109099
2021-10-01 21:33 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.402 s -0.111382
2021-10-01 21:34 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.204 s 0.883750
2021-10-01 21:45 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.162 s 1.077111
2021-10-01 20:41 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.387 s -2.131550
2021-10-01 21:23 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.146 s 1.725891
2021-10-01 21:38 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.754 s -0.506962
2021-10-01 21:42 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.601 s 1.075983
2021-10-01 21:43 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.516 s -0.014259
2021-10-01 20:42 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.341 s 0.213456
2021-10-01 20:42 Python wide-dataframe use_legacy_dataset=true 0.397 s -1.854490
2021-10-01 21:22 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.364 s 1.021512
2021-10-01 21:25 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.982 s -0.545674
2021-10-01 21:31 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.459278
2021-10-01 21:44 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.482 s -2.634944
2021-10-01 21:53 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.988858
2021-10-01 21:26 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.545 s -1.077436
2021-10-01 21:41 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.578 s 1.278436
2021-10-01 21:43 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.939 s 1.088755
2021-10-01 21:44 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.106 s -6.047332
2021-10-01 21:53 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.108364
2021-10-01 21:53 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.599663
2021-10-01 21:22 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.560 s 0.651725
2021-10-01 21:31 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.074 s -0.992342
2021-10-01 21:42 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.687729
2021-10-01 21:44 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s 0.596902
2021-10-01 20:40 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.924 s -0.175192
2021-10-01 21:21 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.189003
2021-10-01 21:42 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.856 s 1.432810
2021-10-01 21:43 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -1.206082
2021-10-01 21:44 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.590 s 0.095216
2021-10-01 21:53 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.634 s -0.266074
2021-10-01 21:53 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.651 s 0.596749
2021-10-01 21:53 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.879 s 0.094468
2021-10-01 21:21 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.851 s 0.763121
2021-10-01 21:37 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.582 s -0.943889
2021-10-01 21:42 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.572 s 1.330704
2021-10-01 21:53 JavaScript Parse serialize, tracks 0.005 s -0.595551
2021-10-01 20:56 R dataframe-to-table chi_traffic_2020_Q1, R 5.367 s 0.668472
2021-10-01 20:56 R dataframe-to-table type_floats, R 0.107 s 0.966644
2021-10-01 21:53 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.183333
2021-10-01 21:53 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.046098
2021-10-01 21:53 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.463 s 0.639007
2021-10-01 21:22 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.922 s -0.075882
2021-10-01 21:27 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.171 s -1.207931
2021-10-01 21:53 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.863531
2021-10-01 21:20 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.208 s 0.498741
2021-10-01 21:36 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.899 s -0.586374
2021-10-01 21:45 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.202 s -3.000551
2021-10-01 21:23 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.050 s 1.036486
2021-10-01 21:53 JavaScript Parse readBatches, tracks 0.000 s 0.301665
2021-10-01 20:40 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.031 s -1.760845
2021-10-01 20:42 Python file-write lz4, feather, table, nyctaxi_2010-01 1.848 s -2.064063
2021-10-01 20:56 R dataframe-to-table type_dict, R 0.051 s -0.112727
2021-10-01 21:23 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.142 s -0.897698
2021-10-01 21:53 JavaScript Parse Table.from, tracks 0.000 s 0.148244
2021-10-01 21:53 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.692 s 0.315269
2021-10-01 21:28 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.589 s -0.753024
2021-10-01 21:53 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.114509
2021-10-01 21:53 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.796119
2021-10-01 21:53 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.599663
2021-10-01 21:53 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.371727
2021-10-01 21:53 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.118813
2021-10-01 21:53 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.080712
2021-10-01 21:53 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.158288
2021-10-01 21:53 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.792895
2021-10-01 21:53 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.017410
2021-10-01 21:40 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.229 s 2.556158
2021-10-01 21:45 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.494 s 0.114824
2021-10-01 21:53 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.101112
2021-10-01 21:53 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.988858
2021-10-01 21:53 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.912226
2021-10-01 21:53 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.031896
2021-10-01 21:53 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.668 s -0.422679
2021-10-01 21:53 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.870 s 0.643129
2021-10-01 21:20 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.198 s 0.597827