Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 21:09 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.807 s 1.252773
2021-10-11 21:12 Python csv-read gzip, streaming, nyctaxi_2010-01 10.615 s -0.291616
2021-10-11 21:14 Python dataframe-to-table type_strings 0.370 s 0.212828
2021-10-11 21:14 Python dataframe-to-table type_simple_features 0.927 s -0.577867
2021-10-11 21:48 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.578548
2021-10-11 21:49 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.126 s 2.181835
2021-10-11 21:51 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.782 s 2.016546
2021-10-11 21:56 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.881 s -0.277647
2021-10-11 21:57 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.325 s 0.749474
2021-10-11 22:21 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.215 s -1.541351
2021-10-11 22:28 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.678 s 0.837607
2021-10-11 22:30 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.391 s 0.865557
2021-10-11 22:33 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.867 s -0.637787
2021-10-11 22:40 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s 0.102351
2021-10-11 22:50 JavaScript Parse readBatches, tracks 0.000 s -0.014146
2021-10-11 22:50 JavaScript Parse serialize, tracks 0.004 s 0.534261
2021-10-11 22:50 JavaScript Parse Table.from, tracks 0.000 s -0.636104
2021-10-11 22:50 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.182932
2021-10-11 22:50 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.587 s -0.343004
2021-10-11 22:50 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -0.248002
2021-10-11 22:50 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -0.209532
2021-10-11 22:50 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.707 s 0.200705
2021-10-11 22:50 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.862 s 0.945700
2021-10-11 21:11 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.622 s -0.084295
2021-10-11 21:11 Python csv-read uncompressed, file, nyctaxi_2010-01 1.004 s 0.637682
2021-10-11 21:47 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.308 s -2.436289
2021-10-11 21:54 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.284 s 0.169628
2021-10-11 21:18 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 69.194 s -2.408435
2021-10-11 21:46 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.930 s 0.492878
2021-10-11 21:47 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.754 s -0.066695
2021-10-11 21:47 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.058 s -2.400740
2021-10-11 21:48 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.123 s 0.769880
2021-10-11 21:54 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.021 s -1.339450
2021-10-11 21:09 Python csv-read uncompressed, file, fanniemae_2016Q4 1.149 s 1.397137
2021-10-11 21:36 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.087 s -2.004244
2021-10-11 21:46 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.809 s 0.427485
2021-10-11 21:54 Python file-write lz4, feather, table, fanniemae_2016Q4 1.146 s 0.906183
2021-10-11 21:36 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.066 s -0.209671
2021-10-11 21:55 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.864 s -0.166715
2021-10-11 21:14 Python dataframe-to-table type_dict 0.011 s 1.200808
2021-10-11 21:49 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.051 s -0.245930
2021-10-11 21:50 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.131 s 2.015403
2021-10-11 21:55 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.470 s -1.353835
2021-10-11 21:10 Python csv-read gzip, streaming, fanniemae_2016Q4 14.731 s 1.323040
2021-10-11 21:15 Python dataset-filter nyctaxi_2010-01 4.367 s -1.395033
2021-10-11 21:22 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.819 s -0.315380
2021-10-11 21:48 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.524 s 2.073146
2021-10-11 21:49 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.684 s 2.053178
2021-10-11 21:51 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.510951
2021-10-11 21:14 Python dataframe-to-table type_integers 0.011 s -1.840700
2021-10-11 21:14 Python dataframe-to-table chi_traffic_2020_Q1 19.656 s -0.206716
2021-10-11 21:48 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.289 s -0.206017
2021-10-11 21:52 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.071 s 0.660450
2021-10-11 21:53 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.441 s 0.567829
2021-10-11 21:53 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.894 s -0.766300
2021-10-11 21:12 Python csv-read gzip, file, nyctaxi_2010-01 9.049 s -1.700841
2021-10-11 21:14 Python dataframe-to-table type_nested 2.880 s -0.039920
2021-10-11 21:10 Python csv-read gzip, file, fanniemae_2016Q4 6.024 s 1.336891
2021-10-11 21:48 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.883 s -2.190700
2021-10-11 21:52 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.564 s -1.010924
2021-10-11 21:14 Python dataframe-to-table type_floats 0.011 s -0.484312
2021-10-11 21:50 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.045 s -0.556792
2021-10-11 21:51 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.268 s 2.160673
2021-10-11 21:58 Python wide-dataframe use_legacy_dataset=false 0.613 s 1.436282
2021-10-11 21:32 Python dataset-read async=True, nyctaxi_multi_ipc_s3 195.332 s -1.394916
2021-10-11 21:32 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.154 s 0.268573
2021-10-11 21:50 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.162 s 2.544488
2021-10-11 21:36 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.061 s -0.481094
2021-10-11 21:57 Python file-write lz4, feather, table, nyctaxi_2010-01 1.781 s 1.373192
2021-10-11 21:58 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.804 s 0.574980
2021-10-11 21:48 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.607 s 2.056131
2021-10-11 21:49 Python file-read lz4, feather, table, fanniemae_2016Q4 0.600 s 0.495147
2021-10-11 21:49 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.999 s 2.065920
2021-10-11 21:56 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.926 s -0.155383
2021-10-11 21:57 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.342 s 0.444652
2021-10-11 22:39 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.531 s -1.289511
2021-10-11 22:50 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.887257
2021-10-11 22:50 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.440708
2021-10-11 22:12 R dataframe-to-table chi_traffic_2020_Q1, R 3.353 s 0.268861
2021-10-11 22:13 R dataframe-to-table type_nested, R 0.531 s 0.234857
2021-10-11 22:12 R dataframe-to-table type_integers, R 0.010 s 1.063498
2021-10-11 22:12 R dataframe-to-table type_floats, R 0.013 s 1.045687
2021-10-11 22:12 R dataframe-to-table type_strings, R 0.487 s 0.232942
2021-10-11 22:12 R dataframe-to-table type_dict, R 0.064 s -2.420806
2021-10-11 22:19 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.231 s 0.271145
2021-10-11 22:19 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.447 s 0.999652
2021-10-11 22:26 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.287 s 0.603074
2021-10-11 22:31 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.174 s 1.323292
2021-10-11 22:34 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.538 s -0.533387
2021-10-11 22:19 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.469 s 1.018360
2021-10-11 22:32 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.900 s -0.526345
2021-10-11 22:20 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.109 s -2.500655
2021-10-11 22:41 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.731 s -2.181540
2021-10-11 22:41 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.109 s 0.168786
2021-10-11 22:20 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.553 s 1.772981
2021-10-11 22:21 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.226 s 0.995318
2021-10-11 22:23 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.511 s 0.280671
2021-10-11 22:29 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.521 s 1.374866
2021-10-11 22:19 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.318 s -1.701175
2021-10-11 22:22 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.999 s -0.005671
2021-10-11 22:21 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.401 s -0.675651
2021-10-11 22:21 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.043 s 1.642110
2021-10-11 22:22 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.691 s 0.001639
2021-10-11 22:38 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.168 s 0.318953
2021-10-11 22:19 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.208 s 0.946401
2021-10-11 22:21 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.157 s 1.006785
2021-10-11 22:21 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.121 s 0.106632
2021-10-11 22:28 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.808 s 2.152053
2021-10-11 22:36 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.272 s 1.957646
2021-10-11 22:37 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.246 s -0.438986
2021-10-11 22:39 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.108343
2021-10-11 22:41 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s -0.474054
2021-10-11 22:50 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.176648
2021-10-11 22:50 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.885 s -0.184807
2021-10-11 22:50 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.070528
2021-10-11 22:50 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.373338
2021-10-11 22:50 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.503477
2021-10-11 22:50 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.485 s 0.638308
2021-10-11 22:19 R dataframe-to-table type_simple_features, R 3.354 s 0.877148
2021-10-11 22:42 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.165 s 0.737824
2021-10-11 22:50 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.224236
2021-10-11 22:38 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.599 s -1.287009
2021-10-11 22:39 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.578 s -0.433629
2021-10-11 22:39 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.403926
2021-10-11 22:39 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.594 s 0.758163
2021-10-11 22:50 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.565084
2021-10-11 22:50 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-11 22:50 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.142559
2021-10-11 22:39 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.881 s -0.098995
2021-10-11 22:42 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.510 s -0.888195
2021-10-11 22:50 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.100485
2021-10-11 22:24 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.838 s 0.585454
2021-10-11 22:25 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.258 s 0.563890
2021-10-11 22:35 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.730 s -0.704819
2021-10-11 22:37 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.467 s 1.882328
2021-10-11 22:40 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.875 s 1.391063
2021-10-11 22:50 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.682 s -0.001799
2021-10-11 22:50 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.521760
2021-10-11 22:42 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.204 s 0.348646
2021-10-11 22:50 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.625 s -0.383650
2021-10-11 22:50 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.591460
2021-10-11 22:50 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.078315
2021-10-11 22:50 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.591435
2021-10-11 22:50 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.222202
2021-10-11 22:41 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.492 s -1.589102
2021-10-11 22:50 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.612998
2021-10-11 22:50 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.232125
2021-10-11 21:57 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.912 s -0.346601
2021-10-11 21:58 Python wide-dataframe use_legacy_dataset=true 0.391 s 1.126946