Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 15:59 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.949 s -0.325436
2021-10-10 16:00 Python csv-read uncompressed, file, fanniemae_2016Q4 1.153 s 1.259472
2021-10-10 16:00 Python csv-read gzip, streaming, fanniemae_2016Q4 14.901 s -0.500957
2021-10-10 16:01 Python csv-read gzip, file, fanniemae_2016Q4 6.035 s -0.952396
2021-10-10 16:04 Python dataframe-to-table chi_traffic_2020_Q1 20.042 s -1.308048
2021-10-10 16:04 Python dataframe-to-table type_strings 0.367 s 0.446428
2021-10-10 16:04 Python dataframe-to-table type_dict 0.011 s 1.200112
2021-10-10 16:05 Python dataframe-to-table type_floats 0.011 s -0.442515
2021-10-10 16:05 Python dataframe-to-table type_nested 2.889 s -0.609913
2021-10-10 16:05 Python dataframe-to-table type_simple_features 0.930 s -0.723024
2021-10-10 16:05 Python dataset-filter nyctaxi_2010-01 4.322 s 1.367380
2021-10-10 16:08 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.734 s -0.664574
2021-10-10 16:26 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.086 s -2.629669
2021-10-10 16:26 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.092 s -0.630911
2021-10-10 16:26 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.012 s 0.169987
2021-10-10 16:36 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.783 s 0.621350
2021-10-10 16:36 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.916 s 0.587990
2021-10-10 16:36 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.647 s 0.968658
2021-10-10 16:37 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.934 s 1.290262
2021-10-10 16:37 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.802 s 0.249572
2021-10-10 16:37 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.274 s 0.667906
2021-10-10 16:38 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.533 s 3.938109
2021-10-10 16:39 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.010 s 3.977741
2021-10-10 16:40 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.136 s 1.285811
2021-10-10 16:40 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.280 s 1.289253
2021-10-10 16:41 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.788 s 1.212929
2021-10-10 16:41 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.096 s 0.494956
2021-10-10 16:43 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.886 s -0.979962
2021-10-10 16:44 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.085 s -2.788605
2021-10-10 16:44 Python file-write lz4, feather, table, fanniemae_2016Q4 1.142 s 1.340588
2021-10-10 16:44 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.531 s -3.438592
2021-10-10 16:45 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.896 s -0.614276
2021-10-10 16:46 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.970 s -0.903428
2021-10-10 16:47 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.340 s 0.746983
2021-10-10 16:47 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.358 s -0.057289
2021-10-10 16:47 Python file-write lz4, feather, table, nyctaxi_2010-01 1.790 s 1.124871
2021-10-10 16:47 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.819 s 0.107038
2021-10-10 16:47 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.604499
2021-10-10 16:48 Python wide-dataframe use_legacy_dataset=false 0.613 s 2.119945
2021-10-10 17:01 R dataframe-to-table type_strings, R 0.489 s 0.233633
2021-10-10 17:01 R dataframe-to-table type_dict, R 0.053 s -0.327175
2021-10-10 17:01 R dataframe-to-table type_integers, R 0.010 s 1.430462
2021-10-10 17:01 R dataframe-to-table type_floats, R 0.012 s 1.431754
2021-10-10 17:08 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.215 s 0.719171
2021-10-10 17:08 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.321 s -3.308551
2021-10-10 17:09 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.039 s -2.304596
2021-10-10 17:09 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.570 s -1.466815
2021-10-10 17:09 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.426 s -2.488491
2021-10-10 17:09 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.052 s 0.581002
2021-10-10 17:10 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.168 s 1.352118
2021-10-10 17:10 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.252 s 1.328839
2021-10-10 17:10 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -2.844570
2021-10-10 17:11 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.693 s 0.025497
2021-10-10 17:12 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.527 s 0.139701
2021-10-10 17:12 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.838 s 0.637518
2021-10-10 17:15 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.282 s 0.700222
2021-10-10 17:17 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.818 s 1.521679
2021-10-10 17:18 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.539 s 0.885813
2021-10-10 17:19 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.185 s 0.867188
2021-10-10 17:20 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.896 s -0.571514
2021-10-10 17:21 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.860 s -0.652399
2021-10-10 17:24 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.716 s -0.542993
2021-10-10 17:24 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.274 s 2.623036
2021-10-10 17:01 R dataframe-to-table chi_traffic_2020_Q1, R 3.363 s 0.274157
2021-10-10 17:26 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.468 s 2.530182
2021-10-10 17:27 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.158 s 1.245624
2021-10-10 17:27 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.589 s -0.321355
2021-10-10 17:27 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.854 s 0.651090
2021-10-10 17:27 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.572 s 0.415947
2021-10-10 17:27 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.340292
2021-10-10 17:28 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.085708
2021-10-10 17:28 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 1.266641
2021-10-10 17:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.618 s -0.756066
2021-10-10 17:29 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.900 s 0.526488
2021-10-10 17:29 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.615 s -0.411056
2021-10-10 17:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -0.821334
2021-10-10 17:30 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s -0.549684
2021-10-10 17:30 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.490 s -1.486518
2021-10-10 17:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.162 s 0.831075
2021-10-10 17:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.494 s 0.555586
2021-10-10 17:38 JavaScript Parse readBatches, tracks 0.000 s 0.024903
2021-10-10 17:38 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.510 s -0.193606
2021-10-10 17:38 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.250819
2021-10-10 17:38 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.218308
2021-10-10 17:38 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.669 s 0.235958
2021-10-10 17:38 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.717 s 0.066309
2021-10-10 17:38 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.669726
2021-10-10 17:38 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -3.798719
2021-10-10 17:38 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.891 s -0.385373
2021-10-10 17:38 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.928 s -0.513413
2021-10-10 17:38 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.551428
2021-10-10 17:38 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.193482
2021-10-10 17:38 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.765039
2021-10-10 17:38 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.953678
2021-10-10 17:38 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.235088
2021-10-10 17:38 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.401017
2021-10-10 17:38 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.684377
2021-10-10 17:39 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.719456
2021-10-10 16:05 Python dataframe-to-table type_integers 0.011 s -1.648515
2021-10-10 16:39 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.153 s 1.050302
2021-10-10 17:38 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.017503
2021-10-10 17:38 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.239727
2021-10-10 16:02 Python csv-read gzip, streaming, nyctaxi_2010-01 10.538 s 0.475712
2021-10-10 16:38 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.125 s 0.837424
2021-10-10 16:39 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.021 s 0.954655
2021-10-10 16:39 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.055 s -1.179726
2021-10-10 16:46 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.987 s -1.616434
2021-10-10 17:16 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.694 s 0.776930
2021-10-10 17:22 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.529 s -0.470082
2021-10-10 17:38 JavaScript Parse serialize, tracks 0.005 s -0.149590
2021-10-10 17:38 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.573 s -0.278076
2021-10-10 17:38 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.596024
2021-10-10 17:38 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.423921
2021-10-10 17:39 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.478 s 0.662409
2021-10-10 16:01 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.552 s 0.488372
2021-10-10 16:02 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.862630
2021-10-10 16:13 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.655 s -0.038553
2021-10-10 16:38 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.607 s 3.991483
2021-10-10 16:38 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.286 s 0.499246
2021-10-10 16:38 Python file-read lz4, feather, table, fanniemae_2016Q4 0.606 s -0.483603
2021-10-10 16:22 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.203 s 0.243619
2021-10-10 16:38 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.704 s 3.862823
2021-10-10 17:07 R dataframe-to-table type_simple_features, R 3.333 s 1.166339
2021-10-10 17:11 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.991 s 0.063243
2021-10-10 17:14 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.264 s 0.576713
2021-10-10 17:38 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.848184
2021-10-10 17:38 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.389609
2021-10-10 16:01 Python csv-read uncompressed, file, nyctaxi_2010-01 1.014 s -0.241475
2021-10-10 17:25 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.230 s 1.291929
2021-10-10 17:38 JavaScript Parse Table.from, tracks 0.000 s 0.150185
2021-10-10 17:38 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.857241
2021-10-10 17:38 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.124614
2021-10-10 17:39 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.707525
2021-10-10 16:40 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.490346
2021-10-10 16:42 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.585 s -1.421188
2021-10-10 16:42 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.447 s 0.538798
2021-10-10 16:45 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.892 s -0.641635
2021-10-10 17:07 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.217 s 0.399955
2021-10-10 17:10 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.102 s 1.696879
2021-10-10 16:22 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.739 s 0.001595
2021-10-10 16:37 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.215 s 0.383815
2021-10-10 16:40 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.169 s 1.280980
2021-10-10 16:43 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.489 s -1.183000
2021-10-10 17:01 R dataframe-to-table type_nested, R 0.533 s 0.235817
2021-10-10 17:08 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.454 s 1.365235
2021-10-10 17:08 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.448 s 1.337996
2021-10-10 17:18 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.387 s 1.780922
2021-10-10 17:28 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.528 s -1.103681
2021-10-10 17:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -0.746972