Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 12:17 Python dataframe-to-table chi_traffic_2020_Q1 19.438 s 0.387547
2021-10-11 12:17 Python dataframe-to-table type_strings 0.366 s 0.564512
2021-10-11 12:17 Python dataframe-to-table type_dict 0.011 s 1.104989
2021-10-11 12:17 Python dataframe-to-table type_integers 0.011 s -0.401966
2021-10-11 12:17 Python dataframe-to-table type_floats 0.011 s 0.644520
2021-10-11 12:18 Python dataframe-to-table type_simple_features 0.934 s -0.977809
2021-10-11 12:18 Python dataset-filter nyctaxi_2010-01 4.326 s 1.105688
2021-10-11 12:35 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.646 s -0.526655
2021-10-11 12:35 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.232 s 0.216739
2021-10-11 12:39 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.067 s -1.339455
2021-10-11 12:39 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.063 s -0.516727
2021-10-11 12:53 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.288 s -0.123211
2021-10-11 12:57 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.086 s 0.577742
2021-10-11 13:16 R dataframe-to-table type_strings, R 0.494 s 0.231141
2021-10-11 13:16 R dataframe-to-table type_dict, R 0.051 s 0.218281
2021-10-11 13:24 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.075 s -2.252528
2021-10-11 13:25 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.411 s -1.343259
2021-10-11 13:25 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.036 s 2.630881
2021-10-11 13:25 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.217 s 1.083433
2021-10-11 13:26 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.218 s -2.066781
2021-10-11 13:40 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.275 s 1.506258
2021-10-11 13:44 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -0.138941
2021-10-11 13:54 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.888 s -0.266400
2021-10-11 13:54 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.338060
2021-10-11 13:54 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.614849
2021-10-11 13:54 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -1.570286
2021-10-11 12:12 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.893 s 0.247265
2021-10-11 13:54 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.779662
2021-10-11 12:13 Python csv-read gzip, streaming, fanniemae_2016Q4 14.830 s 0.213179
2021-10-11 12:14 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.721 s -0.675024
2021-10-11 12:18 Python dataframe-to-table type_nested 2.872 s 0.346368
2021-10-11 12:13 Python csv-read uncompressed, file, fanniemae_2016Q4 1.147 s 1.543932
2021-10-11 12:15 Python csv-read gzip, streaming, nyctaxi_2010-01 10.718 s -1.079542
2021-10-11 12:14 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -0.683968
2021-10-11 12:14 Python csv-read uncompressed, file, nyctaxi_2010-01 1.011 s -0.000063
2021-10-11 12:52 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.734 s 0.124168
2021-10-11 12:54 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.284 s 0.831069
2021-10-11 12:54 Python file-read lz4, feather, table, fanniemae_2016Q4 0.613 s -1.520627
2021-10-11 12:15 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 1.081230
2021-10-11 12:39 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.081 s -0.431489
2021-10-11 12:21 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.717 s -0.717013
2021-10-11 12:53 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.790 s 0.422304
2021-10-11 12:26 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.713 s -1.016105
2021-10-11 12:52 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.957 s 0.565228
2021-10-11 12:52 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.921 s 0.547608
2021-10-11 12:52 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.871 s 0.067733
2021-10-11 12:53 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.525 s 2.360646
2021-10-11 12:53 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.610 s 2.343156
2021-10-11 12:53 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.125 s 0.642220
2021-10-11 12:52 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.197 s 0.724548
2021-10-11 13:00 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.474 s -1.600700
2021-10-11 12:54 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.681 s 2.385322
2021-10-11 12:54 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.017 s 2.162509
2021-10-11 12:54 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.029 s 0.654606
2021-10-11 12:55 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.116 s 2.196192
2021-10-11 12:56 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.274 s 1.897700
2021-10-11 12:55 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.090 s 2.434415
2021-10-11 12:56 Python file-read lz4, feather, table, nyctaxi_2010-01 0.681 s -1.706580
2021-10-11 12:55 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.992 s 2.536511
2021-10-11 12:55 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.167 s 1.664697
2021-10-11 12:58 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.493 s -0.557647
2021-10-11 12:59 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.752 s 3.277545
2021-10-11 13:00 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.954 s -0.899717
2021-10-11 12:56 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.812 s 1.527266
2021-10-11 13:23 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.457 s 1.111364
2021-10-11 13:54 JavaScript Parse readBatches, tracks 0.000 s -1.682178
2021-10-11 13:54 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.822605
2021-10-11 13:54 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.635 s -0.422146
2021-10-11 12:58 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.444 s 0.566749
2021-10-11 12:59 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.858 s -0.548666
2021-10-11 13:00 Python file-write lz4, feather, table, fanniemae_2016Q4 1.149 s 0.740132
2021-10-11 13:01 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.875 s -0.200968
2021-10-11 13:54 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.685150
2021-10-11 13:54 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.535349
2021-10-11 13:00 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.856 s -0.033375
2021-10-11 13:02 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.344 s 0.406433
2021-10-11 13:01 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.925 s -0.127465
2021-10-11 13:02 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.905 s -0.255343
2021-10-11 13:23 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.215 s 0.566770
2021-10-11 13:23 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.456 s 1.083936
2021-10-11 13:43 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.593 s -0.716359
2021-10-11 13:44 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.598 s 0.125424
2021-10-11 13:46 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.164 s 0.788883
2021-10-11 13:23 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.226 s 0.318148
2021-10-11 13:24 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.316 s -1.717691
2021-10-11 13:28 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.836 s 0.637357
2021-10-11 13:54 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.686638
2021-10-11 13:03 Python wide-dataframe use_legacy_dataset=false 0.616 s 1.023615
2021-10-11 13:41 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.270 s -2.935111
2021-10-11 13:03 Python file-write lz4, feather, table, nyctaxi_2010-01 1.795 s 0.663841
2021-10-11 13:45 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.187581
2021-10-11 13:46 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s -0.020346
2021-10-11 13:47 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.517 s -1.631781
2021-10-11 13:17 R dataframe-to-table type_integers, R 0.011 s 1.126240
2021-10-11 13:30 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.321 s 0.168727
2021-10-11 13:42 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.474 s 1.348869
2021-10-11 13:44 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.523 s -0.239980
2021-10-11 13:54 JavaScript Parse serialize, tracks 0.002 s 4.888835
2021-10-11 13:54 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.819332
2021-10-11 13:32 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.811 s 1.970286
2021-10-11 13:42 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 0.444983
2021-10-11 13:43 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.850 s 0.671541
2021-10-11 13:43 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.576 s -0.328291
2021-10-11 13:43 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.014919
2021-10-11 13:43 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.175544
2021-10-11 13:54 JavaScript Parse Table.from, tracks 0.000 s -2.067912
2021-10-11 13:32 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.725 s 0.548373
2021-10-11 13:45 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.886 s 0.919993
2021-10-11 13:45 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.582 s 0.206956
2021-10-11 13:45 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s -0.825893
2021-10-11 13:46 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.493 s -1.839575
2021-10-11 13:54 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.538 s -0.263407
2021-10-11 13:03 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.329 s 0.627985
2021-10-11 13:34 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.386 s 1.566102
2021-10-11 13:03 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.796 s 0.851214
2021-10-11 13:03 Python wide-dataframe use_legacy_dataset=true 0.391 s 1.289096
2021-10-11 13:25 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.157 s 1.095305
2021-10-11 13:25 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.106 s 1.143465
2021-10-11 13:27 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.519 s 0.203308
2021-10-11 13:54 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.037876
2021-10-11 13:54 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.595620
2021-10-11 13:17 R dataframe-to-table type_nested, R 0.532 s 0.234782
2021-10-11 13:26 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.692 s -0.018278
2021-10-11 13:39 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.735 s -0.801010
2021-10-11 13:54 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.697176
2021-10-11 13:54 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.591460
2021-10-11 13:54 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.793644
2021-10-11 13:23 R dataframe-to-table type_simple_features, R 3.356 s 0.953969
2021-10-11 13:34 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.567 s -0.164985
2021-10-11 13:54 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.565084
2021-10-11 13:54 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.167987
2021-10-11 13:54 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.773840
2021-10-11 13:26 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.999 s -0.019879
2021-10-11 13:36 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.901 s -0.546961
2021-10-11 13:54 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.681 s 0.044031
2021-10-11 13:17 R dataframe-to-table type_floats, R 0.013 s 1.137543
2021-10-11 13:54 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.909 s -0.056925
2021-10-11 13:54 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.257671
2021-10-11 13:54 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.706458
2021-10-11 13:54 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.472 s 0.834189
2021-10-11 13:16 R dataframe-to-table chi_traffic_2020_Q1, R 3.374 s 0.269805
2021-10-11 13:24 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.253490
2021-10-11 13:30 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.300 s 0.552597
2021-10-11 13:35 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.207 s -0.361967
2021-10-11 13:37 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.872 s -0.744380
2021-10-11 13:38 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.541 s -0.599496
2021-10-11 13:54 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.694 s 0.302595
2021-10-11 13:54 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.462872
2021-10-11 13:54 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.574060