Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 21:46 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.605 s 1.242906
2021-10-13 21:47 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.677 s 0.937558
2021-10-13 21:47 Python file-read lz4, feather, table, fanniemae_2016Q4 0.592 s 1.089222
2021-10-13 21:47 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.182 s 0.732696
2021-10-13 21:48 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.140 s 1.216413
2021-10-13 21:49 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.178488
2021-10-13 21:49 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.856 s 0.494935
2021-10-13 21:54 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.954 s -0.979414
2021-10-13 21:55 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.326 s 1.134270
2021-10-13 22:10 R dataframe-to-table chi_traffic_2020_Q1, R 3.492 s 0.259267
2021-10-13 22:12 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.079 s -1.311863
2021-10-13 22:14 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.695 s -0.013500
2021-10-13 22:17 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.765 s -3.626716
2021-10-13 22:30 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.579 s -0.290056
2021-10-13 22:30 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s -0.102334
2021-10-13 22:32 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.791 s -2.361803
2021-10-13 22:32 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.923 s -0.724439
2021-10-13 22:32 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.537 s 0.936444
2021-10-13 22:33 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.499 s -2.190352
2021-10-13 22:34 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.499 s 0.184127
2021-10-13 22:41 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.071147
2021-10-13 22:41 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.154467
2021-10-13 22:41 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.462400
2021-10-13 22:41 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.601621
2021-10-13 22:41 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.132130
2021-10-13 22:41 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.493 s 0.497831
2021-10-13 21:06 Python csv-read uncompressed, file, fanniemae_2016Q4 1.177 s -0.159844
2021-10-13 21:06 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.684 s 2.411232
2021-10-13 21:07 Python csv-read gzip, file, fanniemae_2016Q4 6.025 s 0.991142
2021-10-13 21:07 Python csv-read gzip, streaming, fanniemae_2016Q4 14.562 s 2.853521
2021-10-13 21:08 Python csv-read uncompressed, file, nyctaxi_2010-01 1.011 s -0.228072
2021-10-13 21:56 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.772 s 1.193809
2021-10-13 22:14 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.001 s 0.004561
2021-10-13 22:21 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.390 s 0.618580
2021-10-13 22:29 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.472 s 0.817199
2021-10-13 22:41 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.856 s 0.633053
2021-10-13 22:41 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.762593
2021-10-13 22:41 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.169642
2021-10-13 21:11 Python dataframe-to-table chi_traffic_2020_Q1 19.023 s 1.436789
2021-10-13 21:08 Python csv-read gzip, streaming, nyctaxi_2010-01 10.460 s 1.024615
2021-10-13 21:29 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.220 s 0.231669
2021-10-13 21:50 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.333 s -2.922816
2021-10-13 22:31 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.597 s 0.121084
2021-10-13 22:41 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.162528
2021-10-13 21:08 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.491 s 0.861329
2021-10-13 21:11 Python dataframe-to-table type_integers 0.011 s -0.272006
2021-10-13 21:45 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.785 s -0.479656
2021-10-13 21:46 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.280 s 0.157632
2021-10-13 21:47 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.021 s 0.470125
2021-10-13 21:52 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.802 s 0.487167
2021-10-13 21:53 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.861 s -0.475075
2021-10-13 22:12 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.562 s -0.074820
2021-10-13 22:41 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.711 s 0.189038
2021-10-13 21:47 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.067 s -0.421106
2021-10-13 21:34 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.108 s -0.628146
2021-10-13 21:49 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.307 s 0.851890
2021-10-13 22:33 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s 0.860637
2021-10-13 22:41 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.077313
2021-10-13 22:41 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.703438
2021-10-13 21:09 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.429548
2021-10-13 21:11 Python dataframe-to-table type_nested 2.847 s 1.550978
2021-10-13 21:46 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.847 s -0.252939
2021-10-13 21:11 Python dataset-filter nyctaxi_2010-01 4.370 s -0.709821
2021-10-13 21:29 Python dataset-read async=True, nyctaxi_multi_ipc_s3 199.020 s -1.805170
2021-10-13 21:46 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.515 s 1.360027
2021-10-13 21:52 Python file-write lz4, feather, table, fanniemae_2016Q4 1.138 s 1.414749
2021-10-13 21:55 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.275 s 2.184553
2021-10-13 21:45 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.998 s 0.153967
2021-10-13 22:41 JavaScript Parse serialize, tracks 0.005 s -0.889367
2021-10-13 22:41 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.789333
2021-10-13 22:41 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.124798
2021-10-13 21:11 Python dataframe-to-table type_dict 0.011 s 1.043482
2021-10-13 21:19 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.588 s 0.733608
2021-10-13 21:45 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.266 s -0.295171
2021-10-13 21:46 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.114 s 1.530615
2021-10-13 21:48 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.168 s 0.181537
2021-10-13 21:51 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.693 s -2.929547
2021-10-13 21:53 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.380 s -0.240567
2021-10-13 22:10 R dataframe-to-table type_strings, R 0.493 s 0.230113
2021-10-13 22:10 R dataframe-to-table type_integers, R 0.009 s 0.671848
2021-10-13 22:11 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.729 s 0.561516
2021-10-13 21:46 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.264 s 1.193991
2021-10-13 21:50 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.602 s -1.424555
2021-10-13 21:54 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.849 s -0.163442
2021-10-13 22:10 R dataframe-to-table type_floats, R 0.013 s 0.663086
2021-10-13 22:11 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.321 s -1.193585
2021-10-13 22:41 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.971177
2021-10-13 21:11 Python dataframe-to-table type_floats 0.011 s 0.621697
2021-10-13 21:33 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.072 s -0.803293
2021-10-13 21:56 Python wide-dataframe use_legacy_dataset=false 0.623 s -0.676082
2021-10-13 22:20 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.811 s 1.181847
2021-10-13 22:27 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.275 s 0.728699
2021-10-13 22:41 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.029158
2021-10-13 21:15 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 65.556 s -1.132930
2021-10-13 21:44 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.836 s 0.260446
2021-10-13 21:55 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.898 s -0.536922
2021-10-13 21:56 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.253657
2021-10-13 22:11 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.250 s -0.921833
2021-10-13 22:13 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -1.033783
2021-10-13 22:15 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.530 s 0.080273
2021-10-13 22:19 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.144 s -3.203937
2021-10-13 22:27 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.772 s -1.689169
2021-10-13 21:11 Python dataframe-to-table type_strings 0.366 s 0.402198
2021-10-13 21:47 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.991 s 1.312146
2021-10-13 21:52 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.695 s -2.390663
2021-10-13 22:10 R dataframe-to-table type_dict, R 0.061 s -1.784444
2021-10-13 22:30 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.193 s -1.803479
2021-10-13 22:31 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.198 s -1.893506
2021-10-13 22:33 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.213 s -1.579572
2021-10-13 22:41 JavaScript Parse Table.from, tracks 0.000 s 1.136643
2021-10-13 22:41 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.040504
2021-10-13 21:34 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.054 s -0.269110
2021-10-13 21:52 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.980 s -1.491270
2021-10-13 22:13 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.214 s 0.619807
2021-10-13 22:25 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.525 s -0.631229
2021-10-13 22:31 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.529 s -0.683252
2021-10-13 22:41 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.762030
2021-10-13 21:44 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.952 s 0.393331
2021-10-13 22:18 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.559 s -2.875751
2021-10-13 22:34 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.127 s 1.803395
2021-10-13 22:41 JavaScript Parse readBatches, tracks 0.000 s 1.296586
2021-10-13 22:41 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.995301
2021-10-13 21:55 Python file-write lz4, feather, table, nyctaxi_2010-01 1.772 s 1.805668
2021-10-13 22:30 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.505 s 3.497558
2021-10-13 22:30 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.823 s 1.294483
2021-10-13 22:10 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.554 s -2.495839
2021-10-13 22:41 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.813 s 1.763649
2021-10-13 22:41 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-13 22:41 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.033621
2021-10-13 22:12 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.388 s 0.287106
2021-10-13 22:13 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.105 s 0.941929
2021-10-13 22:21 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.676 s -2.791446
2021-10-13 22:24 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.911 s -1.706629
2021-10-13 22:28 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.269 s -2.251955
2021-10-13 22:33 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.116 s -2.327618
2021-10-13 22:41 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.699 s -0.489326
2021-10-13 22:41 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.690 s -0.113936
2021-10-13 22:10 R dataframe-to-table type_nested, R 0.540 s 0.231141
2021-10-13 22:11 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.452 s 0.622103
2021-10-13 22:41 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.408981
2021-10-13 22:41 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.736191
2021-10-13 22:13 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.169 s 0.621309
2021-10-13 22:15 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.108 s -2.907597
2021-10-13 22:22 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.307 s -2.654637
2021-10-13 22:41 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.635 s -0.368088
2021-10-13 22:12 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -1.063402
2021-10-13 22:23 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.935 s -1.825697