Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-01 22:04 Python dataframe-to-table type_strings 0.369 s 0.293462
2021-10-01 22:02 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s 0.023537
2021-10-01 22:04 Python dataframe-to-table type_dict 0.012 s 0.305227
2021-10-01 22:01 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.689 s -0.227370
2021-10-01 22:11 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.546 s 1.231315
2021-10-01 22:00 Python csv-read gzip, streaming, fanniemae_2016Q4 14.746 s -0.160817
2021-10-01 22:04 Python dataframe-to-table type_floats 0.011 s 1.715945
2021-10-01 22:04 Python dataframe-to-table type_simple_features 0.909 s 0.328658
2021-10-01 22:04 Python dataset-filter nyctaxi_2010-01 4.345 s 0.668375
2021-10-01 22:00 Python csv-read gzip, file, fanniemae_2016Q4 6.032 s -0.221258
2021-10-01 22:01 Python csv-read uncompressed, file, nyctaxi_2010-01 1.014 s 0.070587
2021-10-01 21:59 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.796 s -0.125082
2021-10-01 22:04 Python dataframe-to-table type_integers 0.011 s 1.272964
2021-10-01 22:04 Python dataframe-to-table type_nested 2.883 s 1.155226
2021-10-01 21:59 Python csv-read uncompressed, file, fanniemae_2016Q4 1.140 s 0.619763
2021-10-01 22:03 Python dataframe-to-table chi_traffic_2020_Q1 19.778 s -0.196830
2021-10-01 22:07 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 56.363 s 0.835695
2021-10-01 22:01 Python csv-read gzip, streaming, nyctaxi_2010-01 10.670 s -0.256393
2021-10-01 22:21 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.287 s 0.082222
2021-10-01 22:21 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.036 s 0.056690
2021-10-01 23:26 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.853 s 0.696293
2021-10-01 23:59 JavaScript Parse readBatches, tracks 0.000 s 0.035545
2021-10-01 23:59 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.581 s -0.137556
2021-10-01 23:59 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.852 s 0.703181
2021-10-01 23:59 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.887027
2021-10-01 22:26 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.989 s 0.486660
2021-10-01 22:26 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.015 s 0.293296
2021-10-01 22:26 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.013 s 0.334669
2021-10-01 23:48 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.515 s 0.084168
2021-10-01 23:59 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.960869
2021-10-01 22:38 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.949 s -2.066376
2021-10-01 22:41 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 8.004 s -1.703405
2021-10-01 22:45 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.289 s -0.640621
2021-10-01 22:46 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.957 s -0.383649
2021-10-01 22:47 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.978 s -0.300708
2021-10-01 23:01 R dataframe-to-table chi_traffic_2020_Q1, R 5.321 s 1.518483
2021-10-01 23:48 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -1.014010
2021-10-01 23:59 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.638 s 0.781117
2021-10-01 22:36 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 2.431 s -3.601226
2021-10-01 22:38 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.512443
2021-10-01 22:46 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.027 s -1.601638
2021-10-01 23:35 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.608 s -1.035797
2021-10-01 23:47 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.170 s 1.580164
2021-10-01 23:59 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.094352
2021-10-01 22:37 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.179 s 1.532061
2021-10-01 22:39 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.030 s 0.443336
2021-10-01 22:44 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.211743
2021-10-01 23:25 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.194 s 0.630252
2021-10-01 23:27 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.916 s 0.272672
2021-10-01 23:28 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.365 s 1.004358
2021-10-01 23:32 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.166 s -1.114369
2021-10-01 23:48 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.320291
2021-10-01 23:59 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.483 s 0.324470
2021-10-01 22:39 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.085 s -1.490583
2021-10-01 23:02 R dataframe-to-table type_strings, R 0.492 s -0.424480
2021-10-01 22:40 Python file-read lz4, feather, table, nyctaxi_2010-01 0.681 s -2.703860
2021-10-01 22:41 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.391 s -1.138252
2021-10-01 22:48 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.369987
2021-10-01 23:25 R dataframe-to-table type_simple_features, R 274.505 s 0.786816
2021-10-01 23:44 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.751 s -0.455191
2021-10-01 23:59 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.401097
2021-10-01 23:02 R dataframe-to-table type_dict, R 0.050 s -0.027858
2021-10-01 23:41 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.943 s -0.920403
2021-10-01 23:46 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.489 s 0.193506
2021-10-01 23:48 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.578 s 1.160991
2021-10-01 23:59 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.742259
2021-10-01 22:37 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.938 s 1.464618
2021-10-01 22:37 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.766 s 1.450544
2021-10-01 22:43 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.955 s -0.895120
2021-10-01 23:28 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.562 s 0.185022
2021-10-01 23:49 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.919 s 1.032342
2021-10-01 22:38 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.119 s 0.982906
2021-10-01 22:47 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.349 s 0.053535
2021-10-01 23:26 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.201 s 0.550439
2021-10-01 23:31 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.002 s -1.725236
2021-10-01 23:40 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.193 s 1.102408
2021-10-01 23:59 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.448975
2021-10-01 23:59 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.912357
2021-10-01 22:36 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.986 s 0.159064
2021-10-01 22:38 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.777 s -1.301036
2021-10-01 22:42 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.585 s -0.873963
2021-10-01 22:48 Python wide-dataframe use_legacy_dataset=true 0.396 s -1.392857
2021-10-01 23:29 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.111 s 1.444959
2021-10-01 23:30 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.291039
2021-10-01 23:38 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.542 s 1.350854
2021-10-01 23:43 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.585 s -1.001113
2021-10-01 23:47 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.858 s 1.328569
2021-10-01 22:40 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.310 s -1.355458
2021-10-01 22:44 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.848 s -0.894792
2021-10-01 22:48 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.812 s 0.260572
2021-10-01 23:02 R dataframe-to-table type_floats, R 0.107 s 0.915978
2021-10-01 23:31 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.678 s 0.083327
2021-10-01 23:32 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.525 s -0.115340
2021-10-01 23:59 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.533029
2021-10-01 23:59 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.664453
2021-10-01 22:38 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.841 s -1.287448
2021-10-01 22:39 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.233 s -1.652539
2021-10-01 22:40 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.490 s -1.638740
2021-10-01 22:47 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.390 s -0.186740
2021-10-01 23:38 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.397 s 0.715217
2021-10-01 23:59 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.839634
2021-10-01 23:59 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.488150
2021-10-01 23:59 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.574204
2021-10-01 23:59 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.412974
2021-10-01 22:36 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.865 s 0.240406
2021-10-01 22:40 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.166 s 2.138010
2021-10-01 23:02 R dataframe-to-table type_nested, R 0.542 s -2.012545
2021-10-01 23:45 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.231 s 2.238087
2021-10-01 23:59 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.281911
2021-10-01 22:43 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.746 s -1.119128
2021-10-01 23:02 R dataframe-to-table type_integers, R 0.084 s 0.397306
2021-10-01 23:42 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.885 s -0.307718
2021-10-01 23:51 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.201 s -2.149714
2021-10-01 22:39 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.353 s -1.564951
2021-10-01 23:37 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.827 s 0.986174
2021-10-01 23:44 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.179483
2021-10-01 23:28 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.061 s -0.893983
2021-10-01 23:29 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.181 s -0.375633
2021-10-01 23:36 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.041 s -0.763677
2021-10-01 23:49 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.659 s -0.986642
2021-10-01 23:59 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.843270
2021-10-01 23:59 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.969454
2021-10-01 22:37 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.258 s 1.238429
2021-10-01 22:44 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.456 s -0.900078
2021-10-01 22:45 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.930 s -0.979460
2021-10-01 22:47 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.366083
2021-10-01 23:30 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.260 s -0.893576
2021-10-01 23:49 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.619 s -1.899491
2021-10-01 23:59 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.729 s 0.103000
2021-10-01 22:38 Python file-read lz4, feather, table, fanniemae_2016Q4 0.599 s 0.600228
2021-10-01 23:48 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.602 s 1.005723
2021-10-01 23:59 JavaScript Parse Table.from, tracks 0.000 s 0.187415
2021-10-01 23:59 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.575 s -0.198718
2021-10-01 23:59 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.590851
2021-10-01 23:59 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.211890
2021-10-01 23:34 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.588 s -0.721016
2021-10-01 23:50 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.106 s -4.799290
2021-10-01 23:50 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.483 s -2.714615
2021-10-01 23:51 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.179 s 0.962625
2021-10-01 23:59 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.174807
2021-10-01 23:59 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.454224
2021-10-01 23:27 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.878 s 0.480576
2021-10-01 23:27 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.291 s -2.004633
2021-10-01 23:47 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.580 s 1.168787
2021-10-01 23:50 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.358 s 0.618547
2021-10-01 23:51 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.522 s 0.041204
2021-10-01 23:59 JavaScript Parse serialize, tracks 0.004 s 0.633668
2021-10-01 23:59 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.940 s -0.761119
2021-10-01 23:59 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.887027