Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 22:47 Python csv-read gzip, streaming, nyctaxi_2010-01 10.486 s 0.979582
2021-10-08 23:12 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.004 s 0.278108
2021-10-08 23:21 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.814 s 0.447252
2021-10-08 23:22 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.741 s 0.107896
2021-10-08 23:22 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.196 s 1.130016
2021-10-08 23:23 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.773 s 0.082233
2021-10-08 23:26 Python file-read lz4, feather, table, nyctaxi_2010-01 0.680 s -1.828102
2021-10-08 23:26 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.955 s -0.573331
2021-10-08 23:28 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.445 s 0.532636
2021-10-08 23:29 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.673 s 0.171208
2021-10-08 23:30 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.265 s -0.309477
2021-10-08 23:30 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.860 s -0.414241
2021-10-08 23:32 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.350 s -0.000387
2021-10-08 23:33 Python file-write lz4, feather, table, nyctaxi_2010-01 1.863 s -2.941178
2021-10-08 23:33 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.826 s -0.606342
2021-10-08 23:33 Python wide-dataframe use_legacy_dataset=true 0.394 s 0.383409
2021-10-09 00:15 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.523 s -0.531441
2021-10-09 00:16 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s -0.055319
2021-10-09 00:25 JavaScript Parse readBatches, tracks 0.000 s 0.111495
2021-10-09 00:25 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.640 s 0.961318
2021-10-09 00:25 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.767829
2021-10-09 00:25 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.875 s 0.663160
2021-10-09 00:25 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.582640
2021-10-09 00:25 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.884 s -0.141512
2021-10-09 00:25 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.204708
2021-10-09 00:25 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.088646
2021-10-09 00:25 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.045 s 2.182941
2021-10-09 00:25 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 0.842368
2021-10-09 00:25 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.519 s -0.082329
2021-10-08 22:47 Python csv-read uncompressed, file, nyctaxi_2010-01 0.996 s 1.580357
2021-10-08 22:45 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.990 s -0.410753
2021-10-08 22:46 Python csv-read gzip, streaming, fanniemae_2016Q4 14.927 s -0.429226
2021-10-08 22:48 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -1.023112
2021-10-08 22:49 Python dataframe-to-table chi_traffic_2020_Q1 19.489 s 0.383800
2021-10-08 22:50 Python dataframe-to-table type_simple_features 0.914 s 0.056297
2021-10-08 23:23 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.854 s -0.168049
2021-10-08 22:50 Python dataframe-to-table type_integers 0.011 s 0.901601
2021-10-08 23:23 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.134 s 0.666250
2021-10-08 23:24 Python file-read lz4, feather, table, fanniemae_2016Q4 0.611 s -1.283655
2021-10-08 22:50 Python dataframe-to-table type_dict 0.012 s -1.063570
2021-10-08 22:45 Python csv-read uncompressed, file, fanniemae_2016Q4 1.190 s -0.853277
2021-10-08 22:47 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.503 s 0.911056
2021-10-08 23:24 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.246 s -0.544326
2021-10-08 23:25 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.360 s -0.884358
2021-10-08 23:24 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.938 s -0.522796
2021-10-08 23:55 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.931 s -0.498177
2021-10-08 22:50 Python dataframe-to-table type_floats 0.012 s -0.700365
2021-10-08 22:50 Python dataset-filter nyctaxi_2010-01 4.334 s 1.416138
2021-10-08 22:57 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.882 s 0.285213
2021-10-08 23:22 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.958 s 0.972270
2021-10-08 23:24 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.027 s 0.845987
2021-10-08 23:25 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.008 s 1.860077
2021-10-08 22:50 Python dataframe-to-table type_strings 0.369 s 0.428630
2021-10-08 23:26 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.462 s -0.608570
2021-10-08 23:23 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.787 s 0.952947
2021-10-08 23:30 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.839 s -0.809115
2021-10-08 23:32 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.911 s -0.737894
2021-10-08 23:30 Python file-write lz4, feather, table, fanniemae_2016Q4 1.158 s 0.274326
2021-10-08 22:46 Python csv-read gzip, file, fanniemae_2016Q4 6.035 s -0.773686
2021-10-08 23:07 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.453 s 0.074167
2021-10-08 23:12 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.040 s -0.877478
2021-10-08 23:22 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.952 s 0.384796
2021-10-08 23:33 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.338 s 0.062647
2021-10-08 23:33 Python wide-dataframe use_legacy_dataset=false 0.613 s 2.613854
2021-10-08 23:27 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.088 s 0.530462
2021-10-08 23:28 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.370 s -0.204484
2021-10-08 23:29 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.452 s -0.890529
2021-10-08 23:31 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.920 s -0.432989
2021-10-08 23:07 Python dataset-read async=True, nyctaxi_multi_ipc_s3 190.117 s -0.482898
2021-10-08 23:12 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.030 s 0.095970
2021-10-08 23:31 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.863 s -0.525348
2021-10-08 22:50 Python dataframe-to-table type_nested 2.879 s 0.281867
2021-10-08 22:53 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 57.832 s 1.120489
2021-10-08 23:23 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.283 s 0.443684
2021-10-08 23:25 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.118176
2021-10-08 23:57 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.516268
2021-10-08 23:25 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.336 s -0.718700
2021-10-08 23:24 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.293 s -0.433517
2021-10-09 00:12 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.242 s 0.612215
2021-10-09 00:14 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.299164
2021-10-08 23:48 R dataframe-to-table type_integers, R 0.010 s 2.435325
2021-10-08 23:47 R dataframe-to-table type_strings, R 0.489 s 0.230184
2021-10-08 23:48 R dataframe-to-table type_nested, R 0.537 s 0.231229
2021-10-08 23:48 R dataframe-to-table type_floats, R 0.013 s 2.438466
2021-10-08 23:47 R dataframe-to-table chi_traffic_2020_Q1, R 3.404 s 0.275197
2021-10-09 00:16 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.900 s 0.294797
2021-10-09 00:15 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.595 s 0.336748
2021-10-09 00:18 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.504 s -0.555025
2021-10-09 00:25 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.479885
2021-10-08 23:54 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.217 s 0.484656
2021-10-08 23:55 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -1.414904
2021-10-08 23:59 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.839 s 0.630005
2021-10-09 00:25 JavaScript Parse serialize, tracks 0.005 s -0.691691
2021-10-09 00:25 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.892906
2021-10-09 00:25 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.164213
2021-10-08 23:54 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.443 s 2.262165
2021-10-09 00:09 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.530 s -0.670017
2021-10-09 00:25 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.902341
2021-10-09 00:25 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.195250
2021-10-09 00:25 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.252154
2021-10-09 00:25 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.045 s 1.875945
2021-10-09 00:25 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.670 s -0.578626
2021-10-09 00:25 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.761571
2021-10-09 00:11 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.362137
2021-10-09 00:17 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.209 s -1.277955
2021-10-09 00:25 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.750 s -0.691920
2021-10-09 00:25 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.659 s 0.524997
2021-10-09 00:25 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.579457
2021-10-08 23:55 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.440 s 2.187923
2021-10-08 23:55 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.564 s -0.118362
2021-10-08 23:54 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.233 s 0.213530
2021-10-09 00:17 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.488 s -1.594882
2021-10-09 00:25 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.539236
2021-10-09 00:25 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.476660
2021-10-09 00:25 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.171562
2021-10-09 00:25 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.378745
2021-10-09 00:25 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.541990
2021-10-09 00:15 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.613 s -0.183801
2021-10-09 00:16 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.604 s -0.171096
2021-10-09 00:16 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -1.341100
2021-10-09 00:17 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.171 s 0.312259
2021-10-09 00:25 JavaScript Parse Table.from, tracks 0.000 s 0.236390
2021-10-09 00:25 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.750455
2021-10-08 23:48 R dataframe-to-table type_dict, R 0.050 s 0.071001
2021-10-09 00:01 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.251 s 0.746838
2021-10-09 00:05 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.708362
2021-10-09 00:07 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.898 s -0.783348
2021-10-09 00:14 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.169 s 0.832034
2021-10-08 23:56 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.170 s 2.238318
2021-10-08 23:57 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.247 s 2.169339
2021-10-08 23:58 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.542 s -0.000900
2021-10-09 00:14 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.860 s 0.375821
2021-10-09 00:14 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.587 s 0.184633
2021-10-08 23:58 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.683 s 0.087510
2021-10-09 00:13 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.482 s 1.532806
2021-10-09 00:14 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.214956
2021-10-08 23:56 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.414 s -1.889519
2021-10-08 23:56 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.061 s -0.712024
2021-10-08 23:57 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.975 s 0.234718
2021-10-09 00:05 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.531 s 1.499821
2021-10-09 00:08 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.868 s -0.952918
2021-10-09 00:10 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.719 s -0.735817
2021-10-08 23:54 R dataframe-to-table type_simple_features, R 3.315 s 1.796651
2021-10-08 23:56 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.133 s -0.475821
2021-10-09 00:01 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.287 s 0.645994
2021-10-09 00:03 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.701 s 0.799442
2021-10-09 00:03 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.416375
2021-10-09 00:06 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.198 s 0.573774
2021-10-09 00:14 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.572 s 0.323560