Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 19:26 Python dataframe-to-table type_strings 0.367 s 0.435613
2021-10-11 20:07 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.983 s -1.058515
2021-10-11 20:09 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.931 s -0.222494
2021-10-11 20:47 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.540 s -0.578625
2021-10-11 20:54 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.489 s -1.064175
2021-10-11 21:03 JavaScript Parse readBatches, tracks 0.000 s 1.026992
2021-10-11 21:03 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.089365
2021-10-11 21:03 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.246212
2021-10-11 21:03 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.864428
2021-10-11 21:03 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.361204
2021-10-11 21:03 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.511223
2021-10-11 21:03 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.639532
2021-10-11 19:23 Python csv-read uncompressed, file, nyctaxi_2010-01 0.985 s 2.515514
2021-10-11 19:23 Python csv-read gzip, streaming, nyctaxi_2010-01 10.612 s -0.262548
2021-10-11 19:22 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.437982
2021-10-11 19:22 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.624 s -0.093242
2021-10-11 19:29 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.837 s 0.423430
2021-10-11 20:00 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.904 s -2.934710
2021-10-11 20:02 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.990 s 2.221232
2021-10-11 20:06 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.933 s -1.035961
2021-10-11 19:22 Python csv-read gzip, streaming, fanniemae_2016Q4 14.752 s 1.077814
2021-10-11 19:33 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.046 s 0.281820
2021-10-11 19:59 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.794 s 0.527888
2021-10-11 20:00 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.283 s 0.126881
2021-10-11 20:02 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.007 s 2.044607
2021-10-11 20:05 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.579 s -1.112812
2021-10-11 20:05 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.471 s 0.339417
2021-10-11 20:10 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.364 s -0.336809
2021-10-11 19:26 Python dataset-filter nyctaxi_2010-01 4.364 s -1.199932
2021-10-11 19:59 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.699 s 0.464450
2021-10-11 20:00 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.037 s -1.839933
2021-10-11 19:21 Python csv-read uncompressed, file, fanniemae_2016Q4 1.153 s 1.142903
2021-10-11 20:00 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.314 s -2.737121
2021-10-11 20:01 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.688 s 2.077739
2021-10-11 20:08 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.894 s -0.436691
2021-10-11 20:10 Python file-write lz4, feather, table, nyctaxi_2010-01 1.788 s 0.986907
2021-10-11 20:01 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.656933
2021-10-11 20:04 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.103 s 0.430984
2021-10-11 20:09 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.916 s -0.401154
2021-10-11 20:03 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.121 s 2.226061
2021-10-11 20:03 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.211506
2021-10-11 20:10 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.387 s -1.719110
2021-10-11 19:21 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.815 s 1.145793
2021-10-11 19:24 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.902498
2021-10-11 20:03 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.281 s 2.067242
2021-10-11 20:03 Python file-read lz4, feather, table, nyctaxi_2010-01 0.674 s -0.535595
2021-10-11 19:26 Python dataframe-to-table type_dict 0.011 s 1.377727
2021-10-11 19:48 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.014 s 0.175990
2021-10-11 20:07 Python file-write lz4, feather, table, fanniemae_2016Q4 1.147 s 0.819551
2021-10-11 19:26 Python dataframe-to-table type_floats 0.011 s -0.472038
2021-10-11 19:26 Python dataframe-to-table type_simple_features 0.926 s -0.533440
2021-10-11 19:47 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.065 s -1.161753
2021-10-11 19:26 Python dataframe-to-table type_integers 0.011 s -1.700611
2021-10-11 20:06 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.298 s 0.096534
2021-10-11 19:43 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.419 s 0.098044
2021-10-11 19:25 Python dataframe-to-table chi_traffic_2020_Q1 19.628 s -0.114676
2021-10-11 20:01 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.609 s 2.103397
2021-10-11 20:08 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.868 s -0.207892
2021-10-11 19:26 Python dataframe-to-table type_nested 2.885 s -0.352186
2021-10-11 20:07 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.526 s -1.945590
2021-10-11 19:48 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.084 s -0.451201
2021-10-11 20:01 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.131 s 0.355236
2021-10-11 20:04 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.780 s 2.110183
2021-10-11 20:01 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.540 s 1.972147
2021-10-11 20:10 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.815 s 0.226748
2021-10-11 20:02 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.067 s -1.829839
2021-10-11 20:11 Python wide-dataframe use_legacy_dataset=true 0.389 s 1.860334
2021-10-11 19:43 Python dataset-read async=True, nyctaxi_multi_ipc_s3 196.775 s -1.665537
2021-10-11 19:59 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.973 s 0.275005
2021-10-11 20:01 Python file-read lz4, feather, table, fanniemae_2016Q4 0.598 s 0.817714
2021-10-11 20:02 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.116 s 2.415368
2021-10-11 20:11 Python wide-dataframe use_legacy_dataset=false 0.614 s 1.289146
2021-10-11 20:49 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.239 s 0.338321
2021-10-11 20:25 R dataframe-to-table chi_traffic_2020_Q1, R 3.357 s 0.269119
2021-10-11 20:34 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.050 s 0.711743
2021-10-11 20:43 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.384 s 1.728217
2021-10-11 20:34 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.406 s -0.943531
2021-10-11 20:34 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.228 s 1.015839
2021-10-11 20:50 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.477 s 0.829285
2021-10-11 20:51 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.601 s -1.532718
2021-10-11 20:52 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.525 s -0.483416
2021-10-11 20:54 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.150686
2021-10-11 20:33 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.087 s -2.233435
2021-10-11 20:34 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.171 s 1.025791
2021-10-11 20:42 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.527 s 1.205100
2021-10-11 20:52 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.590 s -2.464057
2021-10-11 20:55 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -0.611220
2021-10-11 20:32 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.446 s 1.046951
2021-10-11 20:32 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.213 s 0.671193
2021-10-11 20:32 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.317 s -1.650335
2021-10-11 20:52 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.187 s -0.350148
2021-10-11 20:34 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.126 s -0.227884
2021-10-11 20:51 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.166 s 0.565083
2021-10-11 20:33 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.560 s 0.343036
2021-10-11 20:34 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.215 s -1.600371
2021-10-11 20:52 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.599 s -0.022884
2021-10-11 20:55 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.172 s 0.257702
2021-10-11 20:32 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.465 s 1.014970
2021-10-11 20:48 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.727 s -0.623641
2021-10-11 20:54 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s -0.596523
2021-10-11 21:03 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.656 s -0.505882
2021-10-11 20:46 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.860 s -0.482704
2021-10-11 20:40 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.700 s 0.696914
2021-10-11 20:53 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.879 s 1.220871
2021-10-11 21:03 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.700 s -0.562602
2021-10-11 21:03 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.671518
2021-10-11 21:03 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.521760
2021-10-11 21:03 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.534 s -0.160822
2021-10-11 20:25 R dataframe-to-table type_dict, R 0.063 s -2.165574
2021-10-11 20:25 R dataframe-to-table type_nested, R 0.537 s 0.233448
2021-10-11 20:32 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.211 s 0.444923
2021-10-11 20:35 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.686 s 0.048102
2021-10-11 20:38 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.237 s 0.726400
2021-10-11 20:55 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.494 s 0.780913
2021-10-11 21:03 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.902 s -0.569104
2021-10-11 21:03 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.749070
2021-10-11 21:03 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.049190
2021-10-11 21:03 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 1.027275
2021-10-11 20:25 R dataframe-to-table type_floats, R 0.013 s 1.067077
2021-10-11 21:03 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.698 s 0.283322
2021-10-11 21:03 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.069504
2021-10-11 20:25 R dataframe-to-table type_strings, R 0.490 s 0.232174
2021-10-11 20:53 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s 0.262247
2021-10-11 21:03 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.201180
2021-10-11 21:03 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.347099
2021-10-11 21:03 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.676 s 0.175400
2021-10-11 21:03 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.747626
2021-10-11 21:03 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.251669
2021-10-11 20:31 R dataframe-to-table type_simple_features, R 3.353 s 0.895871
2021-10-11 20:36 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.493 s 0.455494
2021-10-11 20:39 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.294 s 0.566613
2021-10-11 20:52 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.406507
2021-10-11 20:53 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.724 s -2.143122
2021-10-11 21:03 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.197487
2021-10-11 21:03 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 0.992758
2021-10-11 20:25 R dataframe-to-table type_integers, R 0.009 s 1.087060
2021-10-11 20:37 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.843 s 0.563726
2021-10-11 20:41 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.813 s 1.575863
2021-10-11 20:49 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.272 s 2.027611
2021-10-11 21:03 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-11 21:03 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.266750
2021-10-11 20:44 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.178 s 1.151513
2021-10-11 21:03 JavaScript Parse Table.from, tracks 0.000 s 0.837173
2021-10-11 20:45 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.909 s -0.689141
2021-10-11 20:51 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.881 s -0.070539
2021-10-11 20:35 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.968 s 0.353382
2021-10-11 21:03 JavaScript Parse serialize, tracks 0.005 s -0.675713
2021-10-11 21:03 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.894 s 0.272165
2021-10-11 21:03 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.090735