Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 04:48 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.277073
2021-09-29 04:45 Python csv-read uncompressed, file, fanniemae_2016Q4 1.197 s -0.408598
2021-09-29 04:46 Python csv-read uncompressed, file, nyctaxi_2010-01 1.025 s -0.100545
2021-09-29 04:50 Python dataframe-to-table type_floats 0.012 s -0.861931
2021-09-29 04:46 Python csv-read gzip, file, fanniemae_2016Q4 6.037 s -1.901370
2021-09-29 04:50 Python dataframe-to-table type_nested 2.936 s 1.041253
2021-09-29 04:49 Python dataframe-to-table chi_traffic_2020_Q1 19.760 s 0.193244
2021-09-29 04:45 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.094 s -0.901553
2021-09-29 04:47 Python csv-read gzip, streaming, nyctaxi_2010-01 10.620 s -0.344743
2021-09-29 04:45 Python csv-read gzip, streaming, fanniemae_2016Q4 15.017 s -0.892559
2021-09-29 04:49 Python dataframe-to-table type_dict 0.012 s 0.376631
2021-09-29 04:46 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.631 s -0.341607
2021-09-29 04:50 Python dataframe-to-table type_integers 0.011 s -1.386938
2021-09-29 04:49 Python dataframe-to-table type_strings 0.371 s 0.101501
2021-09-29 04:50 Python dataframe-to-table type_simple_features 0.904 s 0.774898
2021-09-29 04:50 Python dataset-filter nyctaxi_2010-01 4.434 s -2.514563
2021-09-29 04:53 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 56.932 s 0.467319
2021-09-29 05:27 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.979 s 0.869701
2021-09-29 05:27 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.483 s 0.869289
2021-09-29 05:29 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.427 s 1.466769
2021-09-29 06:17 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.517 s -0.130809
2021-09-29 06:32 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.584 s 2.349519
2021-09-29 05:32 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.858 s 2.034222
2021-09-29 05:47 R dataframe-to-table type_floats, R 0.108 s 0.465700
2021-09-29 06:11 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.927 s -0.081311
2021-09-29 06:29 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.673 s 2.247685
2021-09-29 05:22 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.945 s 0.460705
2021-09-29 05:23 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.042 s -1.618290
2021-09-29 05:25 Python file-read lz4, feather, table, fanniemae_2016Q4 0.607 s -1.018988
2021-09-29 05:32 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.744 s 1.148239
2021-09-29 05:47 R dataframe-to-table type_nested, R 0.539 s -0.830062
2021-09-29 06:15 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.393974
2021-09-29 06:35 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.506 s 1.534766
2021-09-29 05:11 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.016 s 0.070996
2021-09-29 05:33 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.784 s 1.137060
2021-09-29 05:34 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.205752
2021-09-29 06:11 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.239 s 0.140632
2021-09-29 06:23 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.605 s 0.379788
2021-09-29 05:27 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s -0.141206
2021-09-29 05:34 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.758 s 0.672420
2021-09-29 06:33 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.240179
2021-09-29 05:07 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.218 s 0.404859
2021-09-29 05:33 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.315 s 0.393947
2021-09-29 05:47 R dataframe-to-table type_dict, R 0.052 s 0.015480
2021-09-29 05:07 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.922 s -0.165640
2021-09-29 05:24 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.311 s -1.844711
2021-09-29 05:28 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.124 s 1.315069
2021-09-29 06:11 R dataframe-to-table type_simple_features, R 275.711 s -1.890403
2021-09-29 05:24 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.169 s -1.942641
2021-09-29 06:16 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.673 s 0.812482
2021-09-29 06:30 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.277388
2021-09-29 05:33 Python file-write lz4, feather, table, nyctaxi_2010-01 1.806 s 0.234319
2021-09-29 06:12 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.287 s 1.302507
2021-09-29 06:18 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.846 s 1.487993
2021-09-29 06:22 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.743 s 1.363527
2021-09-29 05:24 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.796 s -3.240783
2021-09-29 06:26 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.832 s 2.414135
2021-09-29 06:27 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.810 s 2.348227
2021-09-29 05:27 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.077 s 1.434928
2021-09-29 05:29 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.464 s 1.404715
2021-09-29 05:33 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.350 s 0.007218
2021-09-29 06:13 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.564 s -0.327809
2021-09-29 06:31 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.264 s 0.251219
2021-09-29 05:24 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.677 s -0.548537
2021-09-29 05:23 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.872 s -1.730728
2021-09-29 05:30 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.366 s -0.230091
2021-09-29 05:47 R dataframe-to-table type_strings, R 0.496 s -2.379744
2021-09-29 06:14 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.388 s -0.575469
2021-09-29 06:12 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.249 s 0.020867
2021-09-29 05:26 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.179 s -0.557524
2021-09-29 05:31 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.798 s 1.850590
2021-09-29 05:34 Python wide-dataframe use_legacy_dataset=false 0.623 s -1.162957
2021-09-29 06:32 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.196 s -0.146455
2021-09-29 05:11 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.038 s -0.025459
2021-09-29 05:23 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.666 s 0.576902
2021-09-29 05:25 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.787 s 1.087932
2021-09-29 06:12 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.915 s 0.056420
2021-09-29 06:28 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.470 s 2.286567
2021-09-29 06:33 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.175 s 0.216415
2021-09-29 05:22 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.860 s 0.288233
2021-09-29 06:14 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.777094
2021-09-29 06:15 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.233 s 0.369209
2021-09-29 06:35 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -1.042480
2021-09-29 05:23 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.290 s -1.730262
2021-09-29 05:25 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.808 s -0.091513
2021-09-29 05:25 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.083 s -1.482614
2021-09-29 05:26 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.804 s 0.997952
2021-09-29 06:22 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.836 s -1.129043
2021-09-29 06:24 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.395 s 1.098227
2021-09-29 06:34 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.504 s 1.580350
2021-09-29 06:34 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.952 s 2.454454
2021-09-29 04:57 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 80.609 s 3.159738
2021-09-29 05:11 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.031 s 0.052568
2021-09-29 05:24 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.293 s -0.290150
2021-09-29 05:25 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.102 s 1.252483
2021-09-29 05:26 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.036 s 0.098387
2021-09-29 05:30 Python file-write lz4, feather, table, fanniemae_2016Q4 1.158 s 0.294330
2021-09-29 05:31 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.084 s 1.139674
2021-09-29 05:47 R dataframe-to-table type_integers, R 0.083 s 1.321773
2021-09-29 06:13 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.918 s -0.159427
2021-09-29 06:19 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.271 s 1.469399
2021-09-29 06:33 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.600 s 2.447524
2021-09-29 06:25 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.214 s 0.970587
2021-09-29 06:14 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.195 s -1.602172
2021-09-29 06:31 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.486 s 0.778051
2021-09-29 06:33 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.572 s 3.095130
2021-09-29 06:33 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.019 s 2.053215
2021-09-29 06:34 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.246996
2021-09-29 06:35 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.347 s 2.466678
2021-09-29 06:35 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.470 s 0.745763
2021-09-29 06:36 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.199 s -0.944784
2021-09-29 06:36 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.151 s 2.518591
2021-09-29 06:36 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.491 s 0.152427
2021-09-29 06:44 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.114306
2021-09-29 06:44 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.045602
2021-09-29 06:44 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.874 s 0.224712
2021-09-29 06:44 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.211855
2021-09-29 06:44 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.420607
2021-09-29 06:44 JavaScript Parse Table.from, tracks 0.000 s 0.172476
2021-09-29 06:44 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.699 s 0.271364
2021-09-29 06:44 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.862 s 0.772089
2021-09-29 06:44 JavaScript Parse serialize, tracks 0.004 s 1.958338
2021-09-29 06:44 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.632257
2021-09-29 06:44 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.102371
2021-09-29 06:44 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.397801
2021-09-29 06:44 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.569891
2021-09-29 06:44 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.489 s 0.360490
2021-09-29 06:44 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.083045
2021-09-29 06:44 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.540690
2021-09-29 06:44 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.631 s -0.233530
2021-09-29 06:44 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.510310
2021-09-29 06:44 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.493655
2021-09-29 06:44 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.676430
2021-09-29 06:44 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.013480
2021-09-29 06:44 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.539456
2021-09-29 06:44 JavaScript Parse readBatches, tracks 0.000 s -0.010763
2021-09-29 06:44 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.671 s -0.275572
2021-09-29 06:44 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.313615
2021-09-29 06:44 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.494149
2021-09-29 06:44 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.671 s 0.117538
2021-09-29 06:44 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.196616
2021-09-29 06:44 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.703572
2021-09-29 06:44 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.041513
2021-09-29 06:44 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -2.047642
2021-09-29 05:30 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.593 s 0.937852
2021-09-29 05:47 R dataframe-to-table chi_traffic_2020_Q1, R 5.386 s 0.351612
2021-09-29 06:15 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.126 s 0.278624
2021-09-29 06:16 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.981 s -0.722128
2021-09-29 06:20 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.307 s 1.391155