Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-02 00:10 Python dataframe-to-table type_floats 0.012 s -0.151204
2021-10-02 00:45 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.180 s -0.846273
2021-10-02 00:52 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.020 s -1.457729
2021-10-02 01:07 R dataframe-to-table type_floats, R 0.107 s 0.802065
2021-10-02 01:52 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.495 s -0.849032
2021-10-02 02:04 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574204
2021-10-02 00:46 Python file-read lz4, feather, table, nyctaxi_2010-01 0.675 s -1.229881
2021-10-02 00:06 Python csv-read gzip, streaming, fanniemae_2016Q4 14.752 s -0.142166
2021-10-02 00:07 Python csv-read uncompressed, file, nyctaxi_2010-01 1.014 s 0.069177
2021-10-02 00:10 Python dataframe-to-table type_strings 0.372 s -0.092219
2021-10-02 00:10 Python dataframe-to-table type_nested 2.855 s 1.805704
2021-10-02 00:51 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.934 s -0.223059
2021-10-02 01:44 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.648379
2021-10-02 01:48 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.582 s -0.954360
2021-10-02 01:56 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.485 s -3.142991
2021-10-02 00:05 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.805 s -0.108563
2021-10-02 00:08 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s 0.123295
2021-10-02 00:10 Python dataframe-to-table type_integers 0.011 s 1.495967
2021-10-02 00:45 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.306 s -1.359332
2021-10-02 01:36 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.677 s 0.096116
2021-10-02 01:46 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.946 s -1.004811
2021-10-02 01:55 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.106 s -3.848041
2021-10-02 01:56 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.170 s 0.979941
2021-10-02 02:04 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.279067
2021-10-02 00:42 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.872 s 0.179570
2021-10-02 00:43 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.264 s 0.986306
2021-10-02 00:44 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.972 s -2.406032
2021-10-02 01:35 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.232 s 0.641542
2021-10-02 00:06 Python csv-read gzip, file, fanniemae_2016Q4 6.026 s 0.919085
2021-10-02 00:07 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.714 s -0.337295
2021-10-02 00:07 Python csv-read gzip, streaming, nyctaxi_2010-01 10.686 s -0.323284
2021-10-02 00:47 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.418 s -1.317254
2021-10-02 00:50 Python file-write lz4, feather, table, fanniemae_2016Q4 1.161 s 0.064443
2021-10-02 02:04 JavaScript Parse Table.from, tracks 0.000 s 1.105338
2021-10-02 02:04 JavaScript Parse serialize, tracks 0.005 s -0.512682
2021-10-02 00:10 Python dataframe-to-table type_dict 0.013 s -3.405355
2021-10-02 00:13 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 55.250 s 1.060989
2021-10-02 00:53 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.349 s 0.152458
2021-10-02 01:32 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.865 s 0.607561
2021-10-02 01:32 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.327549
2021-10-02 00:10 Python dataframe-to-table chi_traffic_2020_Q1 19.643 s 0.465327
2021-10-02 00:44 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.029 s 0.622314
2021-10-02 00:48 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.743 s -1.083972
2021-10-02 00:49 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.455 s -0.887499
2021-10-02 01:53 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.170 s 1.543071
2021-10-02 02:04 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.861 s 0.841797
2021-10-02 00:43 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.759 s 1.614449
2021-10-02 00:45 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.051 s -0.891207
2021-10-02 01:35 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.164 s 0.697051
2021-10-02 01:53 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.577 s 1.182908
2021-10-02 01:53 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.099918
2021-10-02 02:04 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -2.738737
2021-10-02 02:04 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.665 s 0.468624
2021-10-02 02:04 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.828716
2021-10-02 02:04 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.670750
2021-10-02 02:04 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.045 s 1.747405
2021-10-02 00:05 Python csv-read uncompressed, file, fanniemae_2016Q4 1.155 s 0.371243
2021-10-02 00:27 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.148 s 0.804189
2021-10-02 00:42 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.977 s 0.220758
2021-10-02 00:43 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.820 s -0.897925
2021-10-02 00:42 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.175 s 1.619933
2021-10-02 00:43 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.116 s 1.127462
2021-10-02 01:07 R dataframe-to-table type_integers, R 0.084 s 0.530893
2021-10-02 01:32 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.205 s 0.509554
2021-10-02 01:40 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.618 s -1.090272
2021-10-02 01:50 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 0.947303
2021-10-02 01:53 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 1.193011
2021-10-02 02:04 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.268 s 2.954213
2021-10-02 00:42 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.750 s 0.100393
2021-10-02 00:46 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.482 s -1.595326
2021-10-02 00:50 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.803 s -0.569113
2021-10-02 00:50 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.353 s -1.196868
2021-10-02 00:53 Python wide-dataframe use_legacy_dataset=false 0.628 s -1.864753
2021-10-02 01:49 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.760 s -0.676702
2021-10-02 02:04 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.122587
2021-10-02 02:04 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.128152
2021-10-02 02:04 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.557557
2021-10-02 02:04 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.022579
2021-10-02 02:04 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.045 s 1.933239
2021-10-02 02:04 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.002991
2021-10-02 00:18 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 80.756 s 1.211442
2021-10-02 00:52 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.974 s -0.265604
2021-10-02 00:53 Python file-write lz4, feather, table, nyctaxi_2010-01 1.834 s -1.275628
2021-10-02 01:40 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.607 s -0.822818
2021-10-02 01:42 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.023 s -0.655279
2021-10-02 01:47 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.895 s -0.533721
2021-10-02 00:48 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.607 s -0.960688
2021-10-02 01:33 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.916 s 0.278078
2021-10-02 01:34 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.374 s 0.536530
2021-10-02 01:51 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.243 s 1.329532
2021-10-02 01:54 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.605 s 0.976066
2021-10-02 00:44 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.240 s -1.738229
2021-10-02 00:46 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.988 s -1.618650
2021-10-02 01:07 R dataframe-to-table type_strings, R 0.494 s -1.188998
2021-10-02 01:31 R dataframe-to-table type_simple_features, R 275.493 s -1.156592
2021-10-02 01:32 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.862 s 0.631578
2021-10-02 01:37 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.528 s -0.274269
2021-10-02 00:31 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.999 s 0.328675
2021-10-02 00:44 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.301 s -1.682511
2021-10-02 00:45 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.290 s -1.268141
2021-10-02 00:49 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.943 s -0.833521
2021-10-02 00:53 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.853 s -0.079664
2021-10-02 01:08 R dataframe-to-table type_nested, R 0.539 s -0.818849
2021-10-02 00:11 Python dataset-filter nyctaxi_2010-01 4.345 s 0.672412
2021-10-02 00:31 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.047 s -0.144663
2021-10-02 01:33 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.453408
2021-10-02 01:53 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.958915
2021-10-02 02:04 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.232 s 3.075365
2021-10-02 02:04 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.164990
2021-10-02 02:04 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.864937
2021-10-02 02:04 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.991222
2021-10-02 00:31 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.011 s 0.363079
2021-10-02 01:31 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.203 s 0.564876
2021-10-02 01:42 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.824 s 1.576407
2021-10-02 01:54 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.617 s -1.644276
2021-10-02 00:10 Python dataframe-to-table type_simple_features 0.915 s -0.206179
2021-10-02 00:44 Python file-read lz4, feather, table, fanniemae_2016Q4 0.598 s 0.721120
2021-10-02 00:53 Python wide-dataframe use_legacy_dataset=true 0.396 s -1.277663
2021-10-02 01:07 R dataframe-to-table type_dict, R 0.051 s -0.082704
2021-10-02 01:34 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.799304
2021-10-02 01:53 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.009 s 0.899258
2021-10-02 02:04 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.505 s 0.000233
2021-10-02 00:27 Python dataset-read async=True, nyctaxi_multi_ipc_s3 183.323 s 0.592352
2021-10-02 00:42 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.936 s 1.536695
2021-10-02 00:44 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.762 s -1.020751
2021-10-02 01:35 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.120 s 0.786740
2021-10-02 01:38 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.174 s -1.151859
2021-10-02 01:55 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.929 s 1.013964
2021-10-02 02:04 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.658631
2021-10-02 00:51 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.930 s -0.965939
2021-10-02 01:07 R dataframe-to-table chi_traffic_2020_Q1, R 5.351 s 0.951797
2021-10-02 01:44 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.575 s 0.570581
2021-10-02 01:45 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.186 s 1.269368
2021-10-02 01:54 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.525 s -1.324629
2021-10-02 01:56 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.201 s -2.201259
2021-10-02 00:52 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.365 s -0.831074
2021-10-02 01:55 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.672 s -1.168213
2021-10-02 01:55 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.355 s 0.759986
2021-10-02 01:57 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.500 s 0.096348
2021-10-02 02:04 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.095053
2021-10-02 01:36 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.681867
2021-10-02 01:36 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.986 s -0.741261
2021-10-02 02:04 JavaScript Parse readBatches, tracks 0.000 s 0.627112
2021-10-02 02:04 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.651 s 0.545837
2021-10-02 02:04 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.827 s 1.270647
2021-10-02 02:04 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.828716
2021-10-02 02:04 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.468950
2021-10-02 02:04 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.799801
2021-10-02 02:04 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.733371