Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-06 05:56 Python csv-read gzip, streaming, fanniemae_2016Q4 14.877 s -0.253146
2021-10-06 05:55 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.952 s -0.311074
2021-10-06 05:55 Python csv-read uncompressed, file, fanniemae_2016Q4 1.153 s 1.179053
2021-10-06 05:57 Python csv-read uncompressed, file, nyctaxi_2010-01 1.016 s -0.265190
2021-10-06 06:00 Python dataframe-to-table type_dict 0.011 s 1.385046
2021-10-06 06:18 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.246 s 0.301911
2021-10-06 06:32 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.742 s 0.036489
2021-10-06 06:37 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.087 s 0.830824
2021-10-06 07:20 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.250 s 0.205988
2021-10-06 07:25 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.224 s 0.971957
2021-10-06 05:56 Python csv-read gzip, file, fanniemae_2016Q4 6.026 s 1.062757
2021-10-06 05:57 Python csv-read gzip, streaming, nyctaxi_2010-01 10.574 s 0.538618
2021-10-06 06:00 Python dataframe-to-table type_integers 0.011 s 0.772357
2021-10-06 06:22 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.031 s 0.078327
2021-10-06 06:42 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.336 s 0.271817
2021-10-06 07:22 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.932 s -0.739587
2021-10-06 07:29 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.262 s 0.923655
2021-10-06 06:08 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.624 s 0.753759
2021-10-06 06:39 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.644 s 0.404957
2021-10-06 06:56 R dataframe-to-table type_dict, R 0.050 s 0.033212
2021-10-06 07:36 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.795 s 1.164588
2021-10-06 06:34 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.247 s -1.077428
2021-10-06 06:42 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.351 s -0.095634
2021-10-06 07:21 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.258 s -0.073622
2021-10-06 07:21 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.971166
2021-10-06 06:00 Python dataset-filter nyctaxi_2010-01 4.361 s 0.259198
2021-10-06 06:31 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.963 s 0.338190
2021-10-06 06:33 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.834 s -0.228421
2021-10-06 06:33 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.290 s -0.056626
2021-10-06 06:33 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.153 s -0.561835
2021-10-06 06:39 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.272 s 0.455200
2021-10-06 06:43 Python wide-dataframe use_legacy_dataset=true 0.392 s 1.138770
2021-10-06 07:23 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.057 s -0.056206
2021-10-06 06:41 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.777 s 0.717012
2021-10-06 07:21 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.931 s -0.091882
2021-10-06 07:33 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.581 s 0.124372
2021-10-06 07:34 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.205 s 0.547996
2021-10-06 06:42 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.819 s 0.685216
2021-10-06 06:56 R dataframe-to-table type_integers, R 0.084 s 0.586240
2021-10-06 06:57 R dataframe-to-table type_nested, R 0.542 s -1.826504
2021-10-06 07:24 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.116 s 0.874044
2021-10-06 05:58 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s 0.065731
2021-10-06 06:18 Python dataset-read async=True, nyctaxi_multi_ipc_s3 196.035 s -0.865678
2021-10-06 06:36 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.970 s -1.245808
2021-10-06 06:00 Python dataframe-to-table type_simple_features 0.914 s -0.112100
2021-10-06 06:31 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.479 s -3.594422
2021-10-06 06:35 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.050 s -0.796423
2021-10-06 06:35 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.723890
2021-10-06 05:57 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.560 s 0.771316
2021-10-06 06:36 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.508 s -1.404912
2021-10-06 06:40 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.408 s -1.596649
2021-10-06 07:24 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.168 s 0.398424
2021-10-06 06:00 Python dataframe-to-table type_floats 0.011 s 1.558191
2021-10-06 06:35 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.333 s -1.310251
2021-10-06 06:43 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.820 s 0.150831
2021-10-06 07:25 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.803182
2021-10-06 06:00 Python dataframe-to-table type_strings 0.369 s 0.276033
2021-10-06 06:40 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.779 s 1.203764
2021-10-06 07:37 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.454 s 1.364013
2021-10-06 06:00 Python dataframe-to-table type_nested 2.879 s 0.793497
2021-10-06 06:32 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.990 s 0.163368
2021-10-06 06:34 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.046 s 0.086244
2021-10-06 06:39 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.757 s -0.175764
2021-10-06 06:56 R dataframe-to-table type_strings, R 0.491 s 0.197192
2021-10-06 06:22 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.021 s 0.178578
2021-10-06 06:33 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.810 s -1.226203
2021-10-06 06:34 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.942 s -1.074011
2021-10-06 07:20 R dataframe-to-table type_simple_features, R 275.502 s -0.914936
2021-10-06 07:23 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.423 s -2.256395
2021-10-06 07:29 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.300 s 0.833757
2021-10-06 06:23 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.997 s 0.380020
2021-10-06 06:41 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.846 s 1.119329
2021-10-06 06:56 R dataframe-to-table chi_traffic_2020_Q1, R 5.345 s 0.980589
2021-10-06 07:26 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.541 s -0.585111
2021-10-06 06:04 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.756 s -0.689296
2021-10-06 06:35 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.325 s -1.309861
2021-10-06 06:36 Python file-read lz4, feather, table, nyctaxi_2010-01 0.665 s 0.731819
2021-10-06 07:21 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.920 s 0.206180
2021-10-06 07:26 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.684 s -0.005493
2021-10-06 06:00 Python dataframe-to-table chi_traffic_2020_Q1 19.723 s -0.225690
2021-10-06 06:56 R dataframe-to-table type_floats, R 0.106 s 1.140679
2021-10-06 07:31 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.824 s 1.508959
2021-10-06 06:33 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.293 s -0.473855
2021-10-06 06:34 Python file-read lz4, feather, table, fanniemae_2016Q4 0.611 s -1.355508
2021-10-06 07:31 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.722 s 0.912478
2021-10-06 06:33 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.874 s -1.116579
2021-10-06 06:38 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.449 s 0.787290
2021-10-06 06:43 Python wide-dataframe use_legacy_dataset=false 0.631 s -2.510354
2021-10-06 07:27 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.859 s 0.756317
2021-10-06 07:22 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.557 s 1.285769
2021-10-06 06:32 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.246 s -0.100900
2021-10-06 06:38 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.296 s 0.332821
2021-10-06 06:40 Python file-write lz4, feather, table, fanniemae_2016Q4 1.163 s -0.056394
2021-10-06 06:43 Python file-write lz4, feather, table, nyctaxi_2010-01 1.803 s 0.451638
2021-10-06 07:33 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.404 s -0.350247
2021-10-06 07:38 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.639 s 1.555786
2021-10-06 07:39 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.409529
2021-10-06 07:40 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.243 s 0.906850
2021-10-06 07:41 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.491 s -0.222923
2021-10-06 07:42 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.867 s 0.831398
2021-10-06 07:42 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.173 s 0.753967
2021-10-06 07:42 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 0.787354
2021-10-06 07:42 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.246586
2021-10-06 07:42 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 0.667071
2021-10-06 07:53 JavaScript Parse readBatches, tracks 0.000 s 0.750605
2021-10-06 07:53 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.424236
2021-10-06 07:53 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.234431
2021-10-06 07:42 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.891103
2021-10-06 07:45 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.485 s -2.136541
2021-10-06 07:45 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s 0.240545
2021-10-06 07:45 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.207 s -3.126872
2021-10-06 07:53 JavaScript Parse Table.from, tracks 0.000 s 0.211303
2021-10-06 07:53 JavaScript Parse serialize, tracks 0.005 s -0.424021
2021-10-06 07:53 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.518053
2021-10-06 07:43 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.604 s 0.761021
2021-10-06 07:46 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.499 s 0.089442
2021-10-06 07:53 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.283 s 2.756272
2021-10-06 07:53 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.695 s 0.293925
2021-10-06 07:53 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.698 s -0.244285
2021-10-06 07:53 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.798854
2021-10-06 07:53 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.685557
2021-10-06 07:53 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.604220
2021-10-06 07:53 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.282463
2021-10-06 07:53 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.001875
2021-10-06 07:43 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.520 s -0.476943
2021-10-06 07:44 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.918 s 0.787311
2021-10-06 07:53 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.866 s 0.381637
2021-10-06 07:44 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -3.983547
2021-10-06 07:53 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -2.545715
2021-10-06 07:45 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.179 s 0.805880
2021-10-06 07:53 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.258 s 2.839787
2021-10-06 07:53 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.028 s -1.677140
2021-10-06 07:53 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.576631
2021-10-06 07:53 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.082044
2021-10-06 07:53 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.002108
2021-10-06 07:43 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -0.976132
2021-10-06 07:44 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.752 s -2.454409
2021-10-06 07:53 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.434849
2021-10-06 07:53 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.687604
2021-10-06 07:53 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.901 s 0.104286
2021-10-06 07:53 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.131339
2021-10-06 07:53 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -1.620465
2021-10-06 07:53 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.555 s -0.843493
2021-10-06 07:53 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.552588
2021-10-06 07:53 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.816317
2021-10-06 07:53 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.518053
2021-10-06 07:53 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.078828
2021-10-06 07:25 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.993 s -0.679455
2021-10-06 07:35 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.820 s 1.271263