Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-03 04:32 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.101 s -1.402457
2021-10-03 04:33 Python csv-read gzip, streaming, fanniemae_2016Q4 15.041 s -1.403789
2021-10-03 04:33 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.910394
2021-10-03 04:37 Python dataframe-to-table type_strings 0.374 s -0.284279
2021-10-03 04:37 Python dataframe-to-table type_integers 0.011 s 1.098129
2021-10-03 04:37 Python dataframe-to-table type_simple_features 0.914 s -0.109926
2021-10-03 04:37 Python dataset-filter nyctaxi_2010-01 4.353 s 0.490198
2021-10-03 04:40 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.519 s -0.492045
2021-10-03 04:45 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.186 s 1.069208
2021-10-03 04:59 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.016 s 0.264931
2021-10-03 05:11 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.809 s 0.493875
2021-10-03 05:11 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.936 s 1.436486
2021-10-03 04:32 Python csv-read uncompressed, file, fanniemae_2016Q4 1.183 s -0.583378
2021-10-03 04:34 Python csv-read gzip, streaming, nyctaxi_2010-01 10.833 s -1.580707
2021-10-03 04:35 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.295821
2021-10-03 04:36 Python dataframe-to-table chi_traffic_2020_Q1 19.634 s 0.486508
2021-10-03 04:37 Python dataframe-to-table type_dict 0.012 s 0.175791
2021-10-03 04:54 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.383 s -0.555410
2021-10-03 04:59 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.036 s 0.013563
2021-10-03 04:33 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.844 s -1.314156
2021-10-03 04:37 Python dataframe-to-table type_floats 0.011 s 1.630947
2021-10-03 05:12 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.755 s 1.651502
2021-10-03 04:34 Python csv-read uncompressed, file, nyctaxi_2010-01 1.015 s -0.179401
2021-10-03 04:37 Python dataframe-to-table type_nested 2.938 s -0.224609
2021-10-03 04:54 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.508 s -0.329053
2021-10-03 05:11 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.944 s 0.459262
2021-10-03 05:11 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 2.405 s -3.440427
2021-10-03 04:59 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.012 s 0.156808
2021-10-03 05:12 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.173 s 1.601695
2021-10-03 05:13 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.782 s -1.257057
2021-10-03 05:12 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.271 s 0.646213
2021-10-03 05:12 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.117 s 1.070742
2021-10-03 05:13 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.156680
2021-10-03 05:12 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.824 s -0.807947
2021-10-03 05:13 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.240 s -1.495319
2021-10-03 05:13 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.054 s -0.286990
2021-10-03 05:13 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.292 s -0.190204
2021-10-03 05:13 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.924 s -1.283774
2021-10-03 05:14 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.012 s 1.629780
2021-10-03 05:14 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.343 s -1.566621
2021-10-03 05:14 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.292 s -1.304502
2021-10-03 05:14 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.182 s -1.245399
2021-10-03 05:16 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.410 s -1.184737
2021-10-03 05:15 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.989 s -1.575113
2021-10-03 05:15 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 1.218221
2021-10-03 05:17 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.616 s -0.950372
2021-10-03 05:36 R dataframe-to-table type_floats, R 0.107 s 1.108989
2021-10-03 06:00 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.854 s 0.701675
2021-10-03 06:06 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.168 s -1.047953
2021-10-03 06:21 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.294654
2021-10-03 06:32 JavaScript Parse serialize, tracks 0.005 s 0.414613
2021-10-03 05:15 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.496 s -1.623225
2021-10-03 05:17 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.735 s -0.974838
2021-10-03 05:18 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 14.011 s -1.077888
2021-10-03 05:19 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.293 s -0.643938
2021-10-03 05:22 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.315508
2021-10-03 06:02 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.624308
2021-10-03 06:11 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.832 s -0.048061
2021-10-03 05:19 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.268939
2021-10-03 05:36 R dataframe-to-table type_nested, R 0.541 s -1.512059
2021-10-03 05:59 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.273 s 0.036844
2021-10-03 06:15 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.893 s -0.556848
2021-10-03 05:18 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.350 s 0.110583
2021-10-03 05:21 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.968 s -0.211888
2021-10-03 05:22 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.422 s -0.415282
2021-10-03 05:22 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.801 s 0.367286
2021-10-03 06:33 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.337453
2021-10-03 06:04 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.217 s 1.470626
2021-10-03 06:08 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.576 s -0.626794
2021-10-03 06:09 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.624 s -1.070433
2021-10-03 06:33 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.687 s -0.078720
2021-10-03 06:00 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.229 s 0.239955
2021-10-03 06:03 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.180 s -0.317376
2021-10-03 06:23 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.588 s 0.147604
2021-10-03 06:24 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.106 s -2.570973
2021-10-03 06:33 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.156660
2021-10-03 06:33 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.669633
2021-10-03 06:23 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -1.092044
2021-10-03 06:33 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.887027
2021-10-03 06:18 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -1.003350
2021-10-03 06:32 JavaScript Parse Table.from, tracks 0.000 s 0.450106
2021-10-03 06:32 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.105066
2021-10-03 06:33 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.774044
2021-10-03 06:33 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.217587
2021-10-03 06:33 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.970028
2021-10-03 06:02 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.371 s 0.726505
2021-10-03 06:04 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.275467
2021-10-03 06:06 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.532 s -0.369976
2021-10-03 06:12 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.533 s 1.428781
2021-10-03 06:19 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.239 s 1.510203
2021-10-03 06:20 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.491 s -0.239283
2021-10-03 06:24 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.486 s -3.080166
2021-10-03 06:33 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.855 s 0.602605
2021-10-03 06:33 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.135693
2021-10-03 05:20 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.991 s -0.586594
2021-10-03 06:05 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.113357
2021-10-03 06:14 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.939 s -0.877264
2021-10-03 06:21 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.579 s 1.012616
2021-10-03 06:25 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.179 s 0.891611
2021-10-03 06:01 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.910 s 0.666420
2021-10-03 06:03 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.128 s 0.186920
2021-10-03 06:18 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.749 s -0.482407
2021-10-03 06:21 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 1.560284
2021-10-03 06:22 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.518 s -0.278610
2021-10-03 06:13 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.178 s 1.442344
2021-10-03 06:33 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.507850
2021-10-03 05:22 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.083587
2021-10-03 05:35 R dataframe-to-table type_strings, R 0.494 s -0.886917
2021-10-03 06:01 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.330480
2021-10-03 06:01 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.396276
2021-10-03 06:16 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.579 s -0.925053
2021-10-03 06:25 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.499 s 0.096421
2021-10-03 06:32 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.108431
2021-10-03 06:33 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.557557
2021-10-03 06:33 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.191851
2021-10-03 05:35 R dataframe-to-table chi_traffic_2020_Q1, R 5.369 s 0.553142
2021-10-03 05:35 R dataframe-to-table type_dict, R 0.051 s -0.171973
2021-10-03 06:32 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.416526
2021-10-03 06:33 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.282372
2021-10-03 05:21 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.005 s -1.142985
2021-10-03 05:21 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.343 s 0.506565
2021-10-03 05:22 Python file-write lz4, feather, table, nyctaxi_2010-01 1.806 s 0.289337
2021-10-03 05:36 R dataframe-to-table type_integers, R 0.085 s -0.619536
2021-10-03 05:59 R dataframe-to-table type_simple_features, R 274.823 s 0.245124
2021-10-03 06:05 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.981 s -0.264397
2021-10-03 06:21 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.581 s 1.006487
2021-10-03 06:32 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.579474
2021-10-03 05:19 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.794 s -0.435611
2021-10-03 06:10 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.054 s -0.787976
2021-10-03 06:22 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.606 s 0.911307
2021-10-03 06:32 JavaScript Parse readBatches, tracks 0.000 s
2021-10-03 06:33 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.393857
2021-10-03 06:33 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.325741
2021-10-03 06:33 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.977603
2021-10-03 05:20 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.941 s -1.091132
2021-10-03 06:12 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.397 s 0.663280
2021-10-03 06:01 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.864 s 0.626560
2021-10-03 06:33 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.729 s 0.103238
2021-10-03 06:33 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.824 s 1.631866
2021-10-03 06:21 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.867 s 1.154461
2021-10-03 06:22 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.967575
2021-10-03 06:23 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.947 s 0.943331
2021-10-03 06:24 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.201 s -1.649633
2021-10-03 06:24 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.356 s 0.684451
2021-10-03 06:32 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.660 s -0.352348
2021-10-03 06:33 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.459579
2021-10-03 06:33 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.489 s 0.235076
2021-10-03 06:32 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.633 s -0.359266
2021-10-03 06:33 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.557557