Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 14:13 Python csv-read uncompressed, file, fanniemae_2016Q4 1.168 s 0.341314
2021-10-10 14:14 Python csv-read gzip, streaming, fanniemae_2016Q4 14.748 s 1.040176
2021-10-10 14:14 Python csv-read gzip, file, fanniemae_2016Q4 6.032 s -0.389295
2021-10-10 14:15 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.753 s -0.828348
2021-10-10 14:15 Python csv-read uncompressed, file, nyctaxi_2010-01 1.017 s -0.500844
2021-10-10 14:15 Python csv-read gzip, streaming, nyctaxi_2010-01 10.746 s -1.044262
2021-10-10 14:17 Python dataframe-to-table chi_traffic_2020_Q1 19.594 s -0.063187
2021-10-10 14:18 Python dataframe-to-table type_strings 0.369 s 0.312419
2021-10-10 14:18 Python dataframe-to-table type_dict 0.011 s 1.157879
2021-10-10 14:18 Python dataframe-to-table type_nested 2.867 s 0.517240
2021-10-10 14:18 Python dataframe-to-table type_simple_features 0.927 s -0.544561
2021-10-10 14:18 Python dataset-filter nyctaxi_2010-01 4.312 s 1.875150
2021-10-10 14:21 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.116 s 0.453521
2021-10-10 14:26 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.320 s -0.300501
2021-10-10 14:35 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.519 s 0.041454
2021-10-10 14:39 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.055 s -1.177271
2021-10-10 14:50 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.803 s 0.488179
2021-10-10 14:50 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.922 s 0.560105
2021-10-10 14:51 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.714 s 0.330654
2021-10-10 14:51 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.214 s 0.425458
2021-10-10 14:52 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.311 s -0.951086
2021-10-10 14:52 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.631 s 3.922560
2021-10-10 14:52 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.567 s 3.652403
2021-10-10 14:53 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.688 s 4.624286
2021-10-10 14:53 Python file-read lz4, feather, table, fanniemae_2016Q4 0.595 s 1.265756
2021-10-10 14:53 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.996 s 4.740496
2021-10-10 14:54 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.043 s -0.453351
2021-10-10 14:54 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.120 s 1.395586
2021-10-10 14:54 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.161 s 2.976606
2021-10-10 14:56 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.096 s 0.493024
2021-10-10 14:56 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.521 s -0.993265
2021-10-10 14:57 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.873 s -0.901783
2021-10-10 14:58 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.288 s 0.398352
2021-10-10 14:58 Python file-write lz4, feather, table, fanniemae_2016Q4 1.146 s 1.134853
2021-10-10 14:59 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.569 s -4.414998
2021-10-10 14:59 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.854 s -0.125342
2021-10-10 15:00 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.983 s -1.109614
2021-10-10 15:01 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.920 s -0.622644
2021-10-10 15:01 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.346 s 0.361621
2021-10-10 15:01 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.353 s 0.068328
2021-10-10 15:15 R dataframe-to-table chi_traffic_2020_Q1, R 3.343 s 0.274738
2021-10-10 15:16 R dataframe-to-table type_strings, R 0.488 s 0.234065
2021-10-10 15:16 R dataframe-to-table type_integers, R 0.010 s 1.465115
2021-10-10 15:16 R dataframe-to-table type_floats, R 0.013 s 1.466122
2021-10-10 15:23 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.447 s 1.369601
2021-10-10 15:23 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.055 s -2.792498
2021-10-10 15:24 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.412 s -1.607982
2021-10-10 15:24 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.162 s 1.385285
2021-10-10 15:24 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.104 s 1.569310
2021-10-10 15:25 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.214 s 1.366536
2021-10-10 15:25 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.218 s -3.312704
2021-10-10 15:26 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.018 s -0.263839
2021-10-10 15:26 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.505 s 0.356839
2021-10-10 15:27 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.842 s 0.610122
2021-10-10 15:29 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.296 s 0.597816
2021-10-10 15:33 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.562 s 0.079019
2021-10-10 15:33 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.375 s 3.756664
2021-10-10 15:34 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.177 s 1.248780
2021-10-10 15:35 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.886 s -0.375865
2021-10-10 15:36 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.866 s -0.777352
2021-10-10 15:37 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.542 s -0.744314
2021-10-10 15:40 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.243 s 0.196045
2021-10-10 15:42 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.162 s 0.936784
2021-10-10 15:42 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.567 s 1.037274
2021-10-10 15:16 R dataframe-to-table type_dict, R 0.050 s 0.076212
2021-10-10 15:42 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.087 s 1.087556
2021-10-10 15:53 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.268282
2021-10-10 15:53 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.681 s -2.801165
2021-10-10 15:42 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.192 s -1.342641
2021-10-10 15:43 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 1.133671
2021-10-10 15:43 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.521 s -0.089044
2021-10-10 15:44 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.541 s 0.804499
2021-10-10 15:44 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -0.966310
2021-10-10 15:44 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s -0.497110
2021-10-10 15:45 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.207 s -0.663682
2021-10-10 15:46 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.501 s -0.115209
2021-10-10 15:53 JavaScript Parse Table.from, tracks 0.000 s 0.534210
2021-10-10 15:53 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.393047
2021-10-10 15:53 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.941 s 1.094944
2021-10-10 15:53 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.148755
2021-10-10 15:53 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.810 s -2.703147
2021-10-10 15:53 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.870 s -0.914263
2021-10-10 15:53 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.308960
2021-10-10 15:53 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.989 s -1.742476
2021-10-10 15:53 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.708972
2021-10-10 15:53 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.507810
2021-10-10 15:53 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.162860
2021-10-10 15:53 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.071774
2021-10-10 15:53 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.430982
2021-10-10 15:53 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.263827
2021-10-10 15:53 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.048 s -1.833116
2021-10-10 15:53 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -1.703871
2021-10-10 15:53 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.813302
2021-10-10 15:53 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.261535
2021-10-10 15:16 R dataframe-to-table type_nested, R 0.530 s 0.236649
2021-10-10 15:43 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.621 s -1.235134
2021-10-10 14:52 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.404824
2021-10-10 14:58 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.087 s -2.959245
2021-10-10 15:02 Python wide-dataframe use_legacy_dataset=false 0.616 s 1.369931
2021-10-10 15:22 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.204 s 0.535358
2021-10-10 15:29 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.253 s 0.653716
2021-10-10 15:39 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.275 s 2.261445
2021-10-10 14:16 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s -0.248045
2021-10-10 14:40 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.034 s -0.248516
2021-10-10 14:57 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.440 s 0.595228
2021-10-10 15:00 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.915 s -0.875687
2021-10-10 15:01 Python file-write lz4, feather, table, nyctaxi_2010-01 1.807 s 0.135064
2021-10-10 15:23 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.202 s 1.345894
2021-10-10 15:24 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.572 s -1.803585
2021-10-10 15:26 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.691 s 0.044141
2021-10-10 15:42 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 0.005510
2021-10-10 15:44 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.882 s 1.228255
2021-10-10 14:51 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.962 s 0.578090
2021-10-10 14:52 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.797 s 0.381134
2021-10-10 14:52 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.209 s -3.347579
2021-10-10 14:53 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.030 s 0.614184
2021-10-10 14:53 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.119 s 1.319077
2021-10-10 15:23 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.318 s -3.176533
2021-10-10 15:45 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.489 s -1.302265
2021-10-10 15:53 JavaScript Parse serialize, tracks 0.005 s -0.676901
2021-10-10 15:53 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -2.311152
2021-10-10 15:53 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.639 s 1.674699
2021-10-10 15:53 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.115788
2021-10-10 15:53 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.506573
2021-10-10 15:53 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.902 s -0.652194
2021-10-10 15:53 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.852786
2021-10-10 15:53 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.468718
2021-10-10 15:53 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.210011
2021-10-10 15:53 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.389404
2021-10-10 14:18 Python dataframe-to-table type_floats 0.011 s -0.498145
2021-10-10 14:55 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.266 s 1.374212
2021-10-10 15:31 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.822 s 0.976189
2021-10-10 14:13 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.803 s 1.227113
2021-10-10 14:18 Python dataframe-to-table type_integers 0.011 s -1.476543
2021-10-10 14:55 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.765 s 1.377566
2021-10-10 14:55 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.242067
2021-10-10 14:35 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.659 s -0.652804
2021-10-10 14:40 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.091 s -0.636802
2021-10-10 15:02 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.873 s -1.766300
2021-10-10 15:02 Python wide-dataframe use_legacy_dataset=true 0.392 s 1.395833
2021-10-10 15:22 R dataframe-to-table type_simple_features, R 3.318 s 1.192003
2021-10-10 15:22 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.456 s 1.396717
2021-10-10 15:24 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.049 s 0.973559
2021-10-10 15:31 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.682 s 0.869542
2021-10-10 15:38 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.726 s -0.765901
2021-10-10 15:41 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.472 s 2.112435
2021-10-10 15:42 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.849 s 0.795283
2021-10-10 15:45 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.161 s 0.882390
2021-10-10 15:53 JavaScript Parse readBatches, tracks 0.000 s 0.370245