Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 17:50 Python dataset-filter nyctaxi_2010-01 4.322 s 1.458289
2021-10-10 17:58 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.360 s 0.074535
2021-10-10 18:07 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.294 s 0.184940
2021-10-10 18:11 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.092 s -2.757769
2021-10-10 18:11 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.050 s -0.550980
2021-10-10 18:23 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.858 s 0.125920
2021-10-10 18:23 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.942 s 0.460945
2021-10-10 18:23 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.704 s 0.409255
2021-10-10 18:24 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.931 s 1.329705
2021-10-10 18:24 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.197 s 0.834081
2021-10-10 18:24 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.787 s 0.624673
2021-10-10 18:25 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.125 s 0.794670
2021-10-10 18:25 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.523 s 3.776706
2021-10-10 18:25 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.281 s 1.212644
2021-10-10 18:25 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.684 s 3.894451
2021-10-10 18:25 Python file-read lz4, feather, table, fanniemae_2016Q4 0.604 s -0.138755
2021-10-10 18:26 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.005 s 3.737651
2021-10-10 18:26 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.065 s -0.850428
2021-10-10 18:26 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.144 s 1.166108
2021-10-10 18:27 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.155 s 1.147484
2021-10-10 18:27 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.168 s 1.440755
2021-10-10 18:27 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.282 s 1.302956
2021-10-10 18:27 Python file-read lz4, feather, table, nyctaxi_2010-01 0.674 s -0.644559
2021-10-10 18:28 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.815 s 1.010104
2021-10-10 18:28 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.090 s 0.546780
2021-10-10 18:29 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.543 s -1.096569
2021-10-10 18:31 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.399 s -0.454775
2021-10-10 18:31 Python file-write lz4, feather, table, fanniemae_2016Q4 1.151 s 0.751810
2021-10-10 18:31 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.511 s -2.939987
2021-10-10 18:32 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.876 s -0.411154
2021-10-10 18:32 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.870 s -0.260999
2021-10-10 18:33 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.971 s -0.899130
2021-10-10 18:34 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.948 s -0.987627
2021-10-10 18:34 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.333 s 1.129695
2021-10-10 18:34 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.370 s -0.394751
2021-10-10 18:34 Python file-write lz4, feather, table, nyctaxi_2010-01 1.794 s 0.864093
2021-10-10 18:34 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.906 s -2.828335
2021-10-10 18:35 Python wide-dataframe use_legacy_dataset=true 0.389 s 2.731540
2021-10-10 18:49 R dataframe-to-table chi_traffic_2020_Q1, R 3.448 s 0.272661
2021-10-10 18:49 R dataframe-to-table type_strings, R 0.487 s 0.234269
2021-10-10 18:49 R dataframe-to-table type_dict, R 0.049 s 0.244682
2021-10-10 18:49 R dataframe-to-table type_integers, R 0.010 s 1.394929
2021-10-10 18:49 R dataframe-to-table type_nested, R 0.532 s 0.235974
2021-10-10 18:56 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.451 s 1.334842
2021-10-10 18:56 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.209 s 0.968613
2021-10-10 18:56 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.447 s 1.308344
2021-10-10 18:58 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.360 s 1.830035
2021-10-10 18:58 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.157 s 1.322961
2021-10-10 18:58 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.102 s 1.611199
2021-10-10 18:58 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -2.837522
2021-10-10 18:59 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.987 s 0.115079
2021-10-10 19:00 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.521 s 0.198468
2021-10-10 19:01 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.830 s 0.699130
2021-10-10 19:02 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.275 s 0.496546
2021-10-10 19:03 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.298 s 0.579903
2021-10-10 19:05 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.814 s 2.067458
2021-10-10 19:06 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.563 s 0.007662
2021-10-10 19:07 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.383 s 2.308821
2021-10-10 19:09 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.903 s -0.683597
2021-10-10 19:10 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.875 s -0.943833
2021-10-10 19:11 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.527 s -0.428600
2021-10-10 19:13 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.272 s 2.960945
2021-10-10 19:13 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.249 s -0.394705
2021-10-10 19:15 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.190 s -1.316921
2021-10-10 19:27 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.946605
2021-10-10 18:56 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.319 s -2.826138
2021-10-10 19:15 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.582 s 0.437826
2021-10-10 17:45 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.866 s 0.567625
2021-10-10 17:45 Python csv-read uncompressed, file, fanniemae_2016Q4 1.140 s 2.055785
2021-10-10 17:46 Python csv-read gzip, streaming, fanniemae_2016Q4 14.801 s 0.518475
2021-10-10 17:46 Python csv-read gzip, file, fanniemae_2016Q4 6.032 s -0.268037
2021-10-10 17:47 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.646 s -0.154853
2021-10-10 17:47 Python csv-read uncompressed, file, nyctaxi_2010-01 0.996 s 1.505941
2021-10-10 17:49 Python dataframe-to-table chi_traffic_2020_Q1 19.546 s 0.082147
2021-10-10 17:50 Python dataframe-to-table type_dict 0.012 s 0.455892
2021-10-10 17:50 Python dataframe-to-table type_nested 2.871 s 0.369691
2021-10-10 19:15 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.850 s 0.737839
2021-10-10 19:16 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.580 s -0.799309
2021-10-10 19:16 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.924969
2021-10-10 19:16 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.186 s -0.334606
2021-10-10 19:16 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.589 s 1.855011
2021-10-10 19:16 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.536 s -2.228247
2021-10-10 19:17 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s 0.074395
2021-10-10 19:17 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.885 s 1.091621
2021-10-10 19:17 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.584 s 0.090146
2021-10-10 19:18 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.276046
2021-10-10 19:18 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s -0.636901
2021-10-10 19:18 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.492 s -1.767812
2021-10-10 19:19 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s -0.062827
2021-10-10 19:19 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.169 s 0.456798
2021-10-10 19:19 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.492 s 0.798464
2021-10-10 19:27 JavaScript Parse Table.from, tracks 0.000 s -0.238333
2021-10-10 19:27 JavaScript Parse readBatches, tracks 0.000 s 0.702451
2021-10-10 19:27 JavaScript Parse serialize, tracks 0.004 s 0.729508
2021-10-10 19:27 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.850500
2021-10-10 19:27 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.575 s -0.283061
2021-10-10 19:27 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.914463
2021-10-10 19:27 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.444867
2021-10-10 19:27 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.309482
2021-10-10 19:27 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.866 s 0.271877
2021-10-10 19:27 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.551428
2021-10-10 19:27 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.109233
2021-10-10 19:27 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.607615
2021-10-10 19:27 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.145301
2021-10-10 19:27 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.217612
2021-10-10 19:27 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.381799
2021-10-10 19:27 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.176208
2021-10-10 19:27 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.186937
2021-10-10 19:27 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.305800
2021-10-10 19:27 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.312239
2021-10-10 17:50 Python dataframe-to-table type_strings 0.367 s 0.422726
2021-10-10 17:50 Python dataframe-to-table type_simple_features 0.928 s -0.609418
2021-10-10 18:07 Python dataset-read async=True, nyctaxi_multi_ipc_s3 179.399 s 1.077681
2021-10-10 18:35 Python wide-dataframe use_legacy_dataset=false 0.610 s 2.580805
2021-10-10 17:48 Python csv-read gzip, file, nyctaxi_2010-01 9.049 s -1.528992
2021-10-10 17:50 Python dataframe-to-table type_integers 0.011 s -1.598191
2021-10-10 18:29 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.458 s 0.453862
2021-10-10 18:30 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.919 s -1.195445
2021-10-10 18:31 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.101 s -2.804809
2021-10-10 18:55 R dataframe-to-table type_simple_features, R 3.352 s 1.141368
2021-10-10 18:57 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.554 s 1.608670
2021-10-10 18:59 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.696 s 0.005031
2021-10-10 17:47 Python csv-read gzip, streaming, nyctaxi_2010-01 10.617 s -0.125661
2021-10-10 17:50 Python dataframe-to-table type_floats 0.011 s -0.255271
2021-10-10 18:11 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.061 s -0.229823
2021-10-10 18:24 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.273 s 0.731605
2021-10-10 18:25 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.595 s 3.844626
2021-10-10 18:57 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.021 s -1.839965
2021-10-10 18:58 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.220 s 1.304544
2021-10-10 19:14 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.473 s 1.801335
2021-10-10 19:27 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.641 s 0.797343
2021-10-10 19:27 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.684 s 0.376274
2021-10-10 18:49 R dataframe-to-table type_floats, R 0.013 s 1.388735
2021-10-10 18:56 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.218 s 0.383618
2021-10-10 19:27 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.848608
2021-10-10 19:27 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.539 s -0.253449
2021-10-10 17:54 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.075 s 0.447141
2021-10-10 18:26 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.994 s 2.720485
2021-10-10 18:58 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.055 s 0.028926
2021-10-10 19:04 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.756 s 0.347824
2021-10-10 19:08 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.207 s -0.253538
2021-10-10 19:12 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.735 s -0.918371
2021-10-10 19:27 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.922440
2021-10-10 19:27 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.899 s 0.106881
2021-10-10 19:27 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.596024
2021-10-10 19:27 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.031271
2021-10-10 19:27 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.210312
2021-10-10 19:27 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 0.941471
2021-10-10 19:27 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.494 s 0.395573