Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-02 04:19 Python csv-read uncompressed, file, nyctaxi_2010-01 1.023 s -0.079345
2021-10-02 04:57 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.170 s 1.734617
2021-10-02 05:00 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.185 s -1.973164
2021-10-02 05:07 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.359 s -0.433448
2021-10-02 05:56 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.057 s -0.842014
2021-10-02 06:04 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.501324
2021-10-02 06:18 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.552 s -0.094029
2021-10-02 04:22 Python dataframe-to-table type_floats 0.011 s 0.830332
2021-10-02 04:22 Python dataset-filter nyctaxi_2010-01 4.346 s 0.670746
2021-10-02 04:30 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.031 s 1.155062
2021-10-02 05:01 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.398127
2021-10-02 05:03 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.738 s -1.030915
2021-10-02 05:21 R dataframe-to-table chi_traffic_2020_Q1, R 5.397 s 0.081346
2021-10-02 05:21 R dataframe-to-table type_dict, R 0.050 s -0.063751
2021-10-02 06:09 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.105 s -3.064064
2021-10-02 04:17 Python csv-read uncompressed, file, fanniemae_2016Q4 1.183 s -0.100085
2021-10-02 05:00 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.033 s 0.286116
2021-10-02 05:54 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.587 s -0.705433
2021-10-02 06:18 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.107446
2021-10-02 06:18 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.542945
2021-10-02 06:18 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.991342
2021-10-02 04:39 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.251 s 0.252952
2021-10-02 05:04 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.818 s -0.655180
2021-10-02 04:44 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.014 s 0.316240
2021-10-02 04:57 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.755 s 0.067039
2021-10-02 04:58 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.256 s 1.297994
2021-10-02 05:21 R dataframe-to-table type_floats, R 0.107 s 0.911407
2021-10-02 05:46 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.198 s 0.584205
2021-10-02 05:59 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.231 s 0.088540
2021-10-02 04:58 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.765 s -1.028846
2021-10-02 05:02 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.595 s -0.883163
2021-10-02 05:22 R dataframe-to-table type_nested, R 0.541 s -1.392502
2021-10-02 05:45 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.220 s 0.435133
2021-10-02 06:18 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.099179
2021-10-02 06:18 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.694211
2021-10-02 04:56 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.988 s 0.101581
2021-10-02 05:00 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.333 s -1.492594
2021-10-02 05:06 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.999 s -1.084574
2021-10-02 05:45 R dataframe-to-table type_simple_features, R 274.857 s 0.136705
2021-10-02 05:54 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.613 s -1.035203
2021-10-02 06:18 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.557557
2021-10-02 04:19 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.663 s 0.021193
2021-10-02 04:20 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.217734
2021-10-02 05:08 Python file-write lz4, feather, table, nyctaxi_2010-01 1.802 s 0.523717
2021-10-02 05:21 R dataframe-to-table type_integers, R 0.085 s -1.022867
2021-10-02 05:46 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.874 s 0.512560
2021-10-02 05:49 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.155 s 1.290603
2021-10-02 06:07 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.224626
2021-10-02 06:18 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.642 s 0.720606
2021-10-02 04:17 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.792 s 0.003928
2021-10-02 04:58 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.821 s -0.879687
2021-10-02 05:01 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.486 s -1.585169
2021-10-02 05:01 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.983 s -1.584707
2021-10-02 05:56 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.825 s 1.282782
2021-10-02 06:07 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.999 s 0.880949
2021-10-02 06:18 JavaScript Parse readBatches, tracks 0.000 s 0.625897
2021-10-02 06:18 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.138554
2021-10-02 04:18 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.371807
2021-10-02 04:21 Python dataframe-to-table chi_traffic_2020_Q1 19.507 s 1.110152
2021-10-02 04:59 Python file-read lz4, feather, table, fanniemae_2016Q4 0.604 s -0.423199
2021-10-02 05:04 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.949 s -0.843620
2021-10-02 05:08 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.831 s 0.107715
2021-10-02 05:47 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s 0.000101
2021-10-02 06:02 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.576 s -0.819223
2021-10-02 06:18 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.694 s 0.308063
2021-10-02 04:44 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.016 s 0.087634
2021-10-02 04:58 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.119 s 0.996695
2021-10-02 04:58 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.296 s -0.838021
2021-10-02 04:59 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.035 s 0.400349
2021-10-02 05:07 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 12.014 s -0.529817
2021-10-02 05:46 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -1.289576
2021-10-02 05:50 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.683 s 0.019383
2021-10-02 06:01 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.888 s -0.406548
2021-10-02 06:06 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.491 s -0.273923
2021-10-02 06:07 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.572 s 1.151544
2021-10-02 06:10 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.203 s -3.190379
2021-10-02 06:18 JavaScript Parse Table.from, tracks 0.000 s 0.479757
2021-10-02 04:22 Python dataframe-to-table type_integers 0.011 s 1.548653
2021-10-02 04:39 Python dataset-read async=True, nyctaxi_multi_ipc_s3 187.146 s 0.158523
2021-10-02 05:48 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.365 s 1.052948
2021-10-02 06:07 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 1.692995
2021-10-02 06:18 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.327538
2021-10-02 06:18 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.214220
2021-10-02 04:26 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.237 s -0.633250
2021-10-02 05:07 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.434 s -0.536099
2021-10-02 06:18 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.557557
2021-10-02 06:18 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.317284
2021-10-02 05:02 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.400 s -1.160562
2021-10-02 05:08 Python wide-dataframe use_legacy_dataset=true 0.396 s -1.196661
2021-10-02 05:46 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.933 s 0.116344
2021-10-02 05:52 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.174 s -1.125853
2021-10-02 06:18 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.568236
2021-10-02 04:18 Python csv-read gzip, streaming, fanniemae_2016Q4 14.730 s -0.013521
2021-10-02 04:56 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.807 s 0.505674
2021-10-02 04:57 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.748 s 1.902784
2021-10-02 05:08 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.305110
2021-10-02 05:21 R dataframe-to-table type_strings, R 0.495 s -1.570384
2021-10-02 05:50 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.255 s -0.630749
2021-10-02 05:50 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.992 s -1.066064
2021-10-02 05:51 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.547 s -1.175810
2021-10-02 06:05 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.244 s 1.179906
2021-10-02 06:08 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.511 s 0.662950
2021-10-02 06:09 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.930 s 0.986533
2021-10-02 06:10 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.359 s 0.566920
2021-10-02 06:18 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.540888
2021-10-02 06:18 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.455036
2021-10-02 06:18 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.999069
2021-10-02 04:22 Python dataframe-to-table type_dict 0.012 s 0.312984
2021-10-02 04:22 Python dataframe-to-table type_simple_features 0.911 s 0.144967
2021-10-02 05:06 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.967 s -0.434262
2021-10-02 05:48 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.061 s -0.886849
2021-10-02 05:49 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.141 s -0.802815
2021-10-02 05:50 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.260493
2021-10-02 06:11 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.504 s 0.083110
2021-10-02 04:22 Python dataframe-to-table type_nested 2.861 s 1.598314
2021-10-02 04:59 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.258 s -1.985041
2021-10-02 04:59 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.289 s -1.277666
2021-10-02 05:04 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.341 s 0.091875
2021-10-02 06:07 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -1.014363
2021-10-02 06:08 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -1.231343
2021-10-02 06:18 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.036956
2021-10-02 04:19 Python csv-read gzip, streaming, nyctaxi_2010-01 10.648 s -0.027532
2021-10-02 04:22 Python dataframe-to-table type_strings 0.371 s 0.013790
2021-10-02 04:44 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.022 s 0.203447
2021-10-02 04:57 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.940 s 1.409093
2021-10-02 04:59 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.937 s -1.639790
2021-10-02 05:05 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.299 s -0.701748
2021-10-02 06:03 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.763 s -0.759972
2021-10-02 06:18 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.855 s 0.611390
2021-10-02 05:04 Python file-write lz4, feather, table, fanniemae_2016Q4 1.160 s 0.135651
2021-10-02 05:58 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.400 s 0.268562
2021-10-02 06:10 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.479 s -1.401726
2021-10-02 06:18 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.774044
2021-10-02 06:18 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.166359
2021-10-02 06:18 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.138610
2021-10-02 05:05 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.951 s -1.294380
2021-10-02 05:47 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.946 s -1.414967
2021-10-02 05:58 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.543 s 1.262182
2021-10-02 06:08 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.600 s 0.980925
2021-10-02 06:18 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.601885
2021-10-02 06:00 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.940 s -0.880983
2021-10-02 06:07 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.580 s 1.085544
2021-10-02 06:09 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.571 s 0.416867
2021-10-02 06:18 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.225084
2021-10-02 06:10 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.166 s 0.965382
2021-10-02 06:18 JavaScript Parse serialize, tracks 0.005 s 0.434745
2021-10-02 06:18 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.625 s -0.337909
2021-10-02 06:18 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.926 s -0.468351
2021-10-02 06:18 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.506 s -0.026971