Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-12 10:12 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.497 s 0.484501
2021-10-12 10:20 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.710 s -0.544016
2021-10-12 10:20 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.773060
2021-10-12 10:20 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.638 s 0.964005
2021-10-12 10:20 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.988858
2021-10-12 10:20 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.107823
2021-10-12 10:20 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.744405
2021-10-12 08:42 Python csv-read gzip, file, fanniemae_2016Q4 6.026 s 0.872386
2021-10-12 08:43 Python csv-read gzip, streaming, nyctaxi_2010-01 10.667 s -0.731239
2021-10-12 08:46 Python dataset-filter nyctaxi_2010-01 4.369 s -1.426877
2021-10-12 09:19 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.286 s 0.018882
2021-10-12 10:02 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.893 s -0.410437
2021-10-12 10:09 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.523 s -0.167310
2021-10-12 10:11 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s -0.541493
2021-10-12 10:12 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s 0.249081
2021-10-12 10:20 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.872543
2021-10-12 09:04 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.601 s -0.019886
2021-10-12 09:08 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.071 s -1.317391
2021-10-12 09:08 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.065 s -0.180199
2021-10-12 09:20 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.675 s 1.971830
2021-10-12 08:43 Python csv-read uncompressed, file, nyctaxi_2010-01 1.009 s 0.109519
2021-10-12 09:19 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.534 s 1.815909
2021-10-12 09:20 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.037 s 0.277924
2021-10-12 09:17 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.845 s 0.233349
2021-10-12 08:46 Python dataframe-to-table type_nested 2.850 s 1.659883
2021-10-12 09:08 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.027 s 0.008388
2021-10-12 08:44 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.739899
2021-10-12 08:50 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.068 s -0.214605
2021-10-12 09:21 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.421520
2021-10-12 08:43 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.703 s -0.618016
2021-10-12 09:18 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.304 s -2.117145
2021-10-12 09:26 Python file-write lz4, feather, table, fanniemae_2016Q4 1.156 s 0.222601
2021-10-12 09:19 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.602 s 1.938470
2021-10-12 08:41 Python csv-read uncompressed, file, fanniemae_2016Q4 1.157 s 0.903643
2021-10-12 08:54 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.586 s -0.126951
2021-10-12 09:18 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.063 s -2.337389
2021-10-12 08:46 Python dataframe-to-table type_strings 0.366 s 0.517993
2021-10-12 08:46 Python dataframe-to-table type_simple_features 0.933 s -0.888186
2021-10-12 08:45 Python dataframe-to-table chi_traffic_2020_Q1 19.419 s 0.457352
2021-10-12 08:46 Python dataframe-to-table type_dict 0.012 s 0.188502
2021-10-12 09:17 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.970 s 0.293607
2021-10-12 09:19 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.279 s 1.420363
2021-10-12 08:42 Python csv-read gzip, streaming, fanniemae_2016Q4 14.819 s 0.371803
2021-10-12 09:22 Python file-read lz4, feather, table, nyctaxi_2010-01 0.685 s -2.346546
2021-10-12 08:41 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.867 s 0.580394
2021-10-12 09:18 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.728 s 0.181707
2021-10-12 08:46 Python dataframe-to-table type_integers 0.011 s -0.112892
2021-10-12 08:46 Python dataframe-to-table type_floats 0.011 s 0.567563
2021-10-12 09:19 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.885 s -2.026719
2021-10-12 09:19 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.134 s 0.193119
2021-10-12 09:20 Python file-read lz4, feather, table, fanniemae_2016Q4 0.600 s 0.468701
2021-10-12 09:03 Python dataset-read async=True, nyctaxi_multi_ipc_s3 192.602 s -0.916324
2021-10-12 09:20 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.998 s 1.896917
2021-10-12 09:20 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.176 s 1.345441
2021-10-12 09:21 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.010 s 1.444453
2021-10-12 09:21 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.137 s 1.806755
2021-10-12 09:25 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.909 s -0.417884
2021-10-12 09:22 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.283 s 1.817407
2021-10-12 09:22 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.800 s 1.640295
2021-10-12 09:23 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.068 s 0.656852
2021-10-12 09:23 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.519 s -0.698689
2021-10-12 09:24 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.444 s 0.500089
2021-10-12 09:27 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.917 s -0.045799
2021-10-12 09:25 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.951 s 1.983095
2021-10-12 09:28 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.890 s -0.038751
2021-10-12 09:26 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.845 s 0.097029
2021-10-12 09:25 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.911 s -0.910408
2021-10-12 09:27 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.833 s 0.333875
2021-10-12 09:28 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.308 s 2.069815
2021-10-12 09:29 Python file-write lz4, feather, table, nyctaxi_2010-01 1.821 s -0.868784
2021-10-12 09:26 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.473 s -1.306543
2021-10-12 09:51 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.163 s 0.954622
2021-10-12 09:52 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.985 s 0.155226
2021-10-12 10:05 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.712 s -0.350353
2021-10-12 10:20 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.503 s 0.339700
2021-10-12 10:20 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.869828
2021-10-12 09:28 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.287 s 1.747455
2021-10-12 09:29 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.807 s 0.484055
2021-10-12 09:29 Python wide-dataframe use_legacy_dataset=true 0.397 s -1.308597
2021-10-12 09:29 Python wide-dataframe use_legacy_dataset=false 0.618 s 0.540802
2021-10-12 09:42 R dataframe-to-table chi_traffic_2020_Q1, R 3.357 s 0.266733
2021-10-12 09:51 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.218 s 0.946479
2021-10-12 10:07 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.468 s 1.689334
2021-10-12 09:49 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.215 s 0.412056
2021-10-12 09:56 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.281 s 0.619706
2021-10-12 09:59 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.546 s 0.478919
2021-10-12 10:08 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.168 s 0.315190
2021-10-12 10:09 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.169560
2021-10-12 10:09 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.186 s -0.201056
2021-10-12 10:08 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.874 s 0.088314
2021-10-12 10:20 JavaScript Parse Table.from, tracks 0.000 s -2.097344
2021-10-12 10:10 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.618 s -0.421525
2021-10-12 10:20 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.908616
2021-10-12 10:20 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.675626
2021-10-12 10:20 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.262577
2021-10-12 09:42 R dataframe-to-table type_floats, R 0.013 s 0.992476
2021-10-12 09:42 R dataframe-to-table type_nested, R 0.531 s 0.233642
2021-10-12 10:08 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.595 s -0.773490
2021-10-12 10:20 JavaScript Parse readBatches, tracks 0.000 s -1.048883
2021-10-12 10:20 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.406559
2021-10-12 09:55 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.239 s 0.665611
2021-10-12 10:04 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.534 s -0.523406
2021-10-12 10:12 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.161 s 1.007731
2021-10-12 10:20 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.072095
2021-10-12 10:20 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.460564
2021-10-12 09:42 R dataframe-to-table type_integers, R 0.010 s 1.009012
2021-10-12 09:57 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.716 s 0.529104
2021-10-12 10:09 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.577 s -0.357774
2021-10-12 10:09 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 0.497418
2021-10-12 10:20 JavaScript Parse serialize, tracks 0.005 s -0.742414
2021-10-12 10:20 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.756486
2021-10-12 09:54 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.834 s 0.583149
2021-10-12 10:00 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.389 s 0.991580
2021-10-12 10:10 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.877 s 1.214430
2021-10-12 10:11 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.109 s 0.223449
2021-10-12 10:11 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.492 s -1.573724
2021-10-12 10:20 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.077577
2021-10-12 10:06 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 1.721273
2021-10-12 09:51 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.407 s -0.932581
2021-10-12 09:58 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.814 s 1.382239
2021-10-12 10:07 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.248 s -0.724065
2021-10-12 10:11 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.515 s 1.334009
2021-10-12 10:20 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.545842
2021-10-12 09:49 R dataframe-to-table type_simple_features, R 3.346 s 0.833280
2021-10-12 09:49 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.456 s 0.970914
2021-10-12 09:49 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.320 s -1.744209
2021-10-12 09:52 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.691 s -0.001418
2021-10-12 10:20 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.932 s -0.470234
2021-10-12 10:20 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.604220
2021-10-12 09:42 R dataframe-to-table type_strings, R 0.492 s 0.230340
2021-10-12 09:49 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.448 s 0.949593
2021-10-12 09:51 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.051 s 0.493117
2021-10-12 10:01 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.183 s 0.784067
2021-10-12 10:03 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.868 s -0.683349
2021-10-12 10:20 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.070165
2021-10-12 10:20 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.574 s -0.269844
2021-10-12 10:20 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.820877
2021-10-12 10:20 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.719 s 0.165969
2021-10-12 09:50 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.082 s -1.895417
2021-10-12 09:50 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.562 s -0.099410
2021-10-12 09:51 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.214 s -1.378792
2021-10-12 09:53 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.511 s 0.275871
2021-10-12 09:51 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.104 s 1.251145
2021-10-12 10:20 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.822 s 1.561077
2021-10-12 10:20 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578421
2021-10-12 09:42 R dataframe-to-table type_dict, R 0.052 s 0.023919
2021-10-12 09:49 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.207 s 1.023321
2021-10-12 10:20 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.024 s 0.154738
2021-10-12 10:20 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.784013