Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 10:49 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.498 s -2.450533
2021-10-13 10:58 JavaScript Parse readBatches, tracks 0.000 s 0.621416
2021-10-13 10:58 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.802022
2021-10-13 09:50 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.091 s -1.603598
2021-10-13 10:02 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.795 s 0.503209
2021-10-13 10:03 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.923 s -2.123039
2021-10-13 10:03 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.275 s 0.656049
2021-10-13 10:04 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.291 s 0.086648
2021-10-13 10:05 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.180 s 0.034872
2021-10-13 10:26 R dataframe-to-table type_strings, R 0.496 s 0.229189
2021-10-13 10:27 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.544 s -3.309019
2021-10-13 10:27 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.318 s -1.170349
2021-10-13 10:28 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.570 s -1.404597
2021-10-13 10:31 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.520 s 0.194218
2021-10-13 10:32 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.858 s 0.244412
2021-10-13 10:33 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.375 s -0.571891
2021-10-13 10:34 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.310 s 0.241284
2021-10-13 10:36 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.838 s -0.630664
2021-10-13 10:38 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.385 s 1.246726
2021-10-13 09:26 Python csv-read uncompressed, file, nyctaxi_2010-01 1.010 s -0.000663
2021-10-13 09:27 Python csv-read gzip, streaming, nyctaxi_2010-01 10.656 s -0.375105
2021-10-13 10:05 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.003 s 1.014470
2021-10-13 10:13 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.376 s -0.915174
2021-10-13 10:26 R dataframe-to-table type_integers, R 0.009 s 0.788030
2021-10-13 10:27 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.771 s 0.658241
2021-10-13 09:24 Python csv-read uncompressed, file, fanniemae_2016Q4 1.165 s 0.252771
2021-10-13 10:58 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.430265
2021-10-13 09:26 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.667 s -0.313921
2021-10-13 10:06 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 0.304901
2021-10-13 10:10 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.350 s 0.002408
2021-10-13 10:13 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.769 s 1.470096
2021-10-13 10:26 R dataframe-to-table type_floats, R 0.013 s 0.778627
2021-10-13 10:04 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.066 s -0.419952
2021-10-13 10:09 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.726 s 0.412865
2021-10-13 10:11 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.920 s -0.230811
2021-10-13 10:29 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.047 s 0.913948
2021-10-13 10:29 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.110 s 0.614966
2021-10-13 09:29 Python dataframe-to-table type_strings 0.365 s 0.523772
2021-10-13 09:51 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.071 s -0.189506
2021-10-13 10:02 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.933 s 0.477748
2021-10-13 10:03 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.611 s 1.394906
2021-10-13 10:05 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.219 s 0.431538
2021-10-13 10:07 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.116 s 0.081523
2021-10-13 10:13 Python file-write lz4, feather, table, nyctaxi_2010-01 1.793 s 0.600913
2021-10-13 10:29 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.221 s 0.731709
2021-10-13 09:51 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.031 s 0.023416
2021-10-13 10:10 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.799 s 0.463475
2021-10-13 10:26 R dataframe-to-table chi_traffic_2020_Q1, R 3.453 s 0.261773
2021-10-13 10:03 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.128 s 0.681645
2021-10-13 10:10 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.853 s -0.185646
2021-10-13 10:30 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.032 s -0.380074
2021-10-13 09:30 Python dataset-filter nyctaxi_2010-01 4.429 s -3.597873
2021-10-13 10:02 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.646 s 0.838766
2021-10-13 10:04 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.528 s 1.461181
2021-10-13 10:27 R dataframe-to-table type_nested, R 0.542 s 0.230723
2021-10-13 10:02 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.097 s -2.155019
2021-10-13 10:05 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.162 s 1.127667
2021-10-13 10:06 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.287 s 1.179800
2021-10-13 10:13 Python wide-dataframe use_legacy_dataset=false 0.618 s 0.204825
2021-10-13 09:25 Python csv-read gzip, streaming, fanniemae_2016Q4 14.838 s 0.142798
2021-10-13 09:27 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 0.893264
2021-10-13 10:27 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.235 s -0.238175
2021-10-13 10:06 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.807 s 1.203004
2021-10-13 10:08 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.462 s 0.198793
2021-10-13 10:27 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.475 s 0.727412
2021-10-13 09:29 Python dataframe-to-table chi_traffic_2020_Q1 19.428 s 0.382501
2021-10-13 09:29 Python dataframe-to-table type_floats 0.011 s 0.310000
2021-10-13 09:47 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.441 s 0.086878
2021-10-13 10:08 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.371 s 0.325472
2021-10-13 10:12 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.339 s 0.542877
2021-10-13 10:13 Python wide-dataframe use_legacy_dataset=true 0.391 s 0.497441
2021-10-13 10:26 R dataframe-to-table type_dict, R 0.055 s -0.837189
2021-10-13 09:24 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.900 s 0.202476
2021-10-13 09:47 Python dataset-read async=True, nyctaxi_multi_ipc_s3 190.648 s -0.564454
2021-10-13 10:11 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.813 s 0.508398
2021-10-13 10:29 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.383 s 0.484284
2021-10-13 10:29 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.162 s 0.737079
2021-10-13 10:04 Python file-read lz4, feather, table, fanniemae_2016Q4 0.605 s -0.123652
2021-10-13 10:09 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.254 s 0.237386
2021-10-13 10:10 Python file-write lz4, feather, table, fanniemae_2016Q4 1.148 s 0.636908
2021-10-13 10:28 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.031 s -0.767123
2021-10-13 09:29 Python dataframe-to-table type_dict 0.011 s 1.205222
2021-10-13 09:29 Python dataframe-to-table type_nested 2.858 s 1.062779
2021-10-13 09:26 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.296580
2021-10-13 09:29 Python dataframe-to-table type_integers 0.011 s 0.112567
2021-10-13 10:04 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.005 s 1.381401
2021-10-13 10:12 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.857 s 0.384629
2021-10-13 09:33 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 67.442 s -1.811484
2021-10-13 09:37 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.170 s 1.057594
2021-10-13 10:30 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.692 s 0.012637
2021-10-13 10:36 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.809 s 1.628587
2021-10-13 10:37 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.649 s -2.947171
2021-10-13 10:48 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.950 s -1.607008
2021-10-13 10:58 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.841649
2021-10-13 10:58 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.646 s -0.395179
2021-10-13 10:49 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.578 s 0.225842
2021-10-13 10:49 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s 0.798102
2021-10-13 10:40 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.889 s -0.454359
2021-10-13 10:45 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.471 s 1.106145
2021-10-13 10:47 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.595 s 0.513498
2021-10-13 10:58 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.410260
2021-10-13 10:58 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.618762
2021-10-13 10:58 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.476 s 0.790317
2021-10-13 10:46 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.197 s -2.826254
2021-10-13 10:41 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.897 s -1.389771
2021-10-13 10:58 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.618 s 1.314079
2021-10-13 10:58 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.852 s 0.768354
2021-10-13 10:39 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.311 s -4.208932
2021-10-13 10:45 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.260 s -1.839953
2021-10-13 10:58 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.589 s -0.214929
2021-10-13 10:46 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.515 s 4.879108
2021-10-13 10:58 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.718 s 0.166495
2021-10-13 10:58 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.868 s 0.794145
2021-10-13 10:58 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.064850
2021-10-13 10:58 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.799880
2021-10-13 10:44 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 1.231841
2021-10-13 10:48 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.825 s -4.260493
2021-10-13 10:50 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.493 s 0.951968
2021-10-13 10:58 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.026323
2021-10-13 10:58 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.587000
2021-10-13 10:58 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.447689
2021-10-13 10:58 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.021 s 1.513710
2021-10-13 10:58 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.553445
2021-10-13 10:47 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.572 s 0.537976
2021-10-13 10:50 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.213 s -2.173214
2021-10-13 10:58 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.821405
2021-10-13 10:47 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.195 s -1.674440
2021-10-13 10:49 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.115 s -1.924445
2021-10-13 10:58 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.426806
2021-10-13 10:42 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.523 s -0.455638
2021-10-13 10:47 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.817 s 1.677535
2021-10-13 10:58 JavaScript Parse Table.from, tracks 0.000 s 0.084862
2021-10-13 10:58 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 1.381732
2021-10-13 10:58 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.108434
2021-10-13 10:58 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.125048
2021-10-13 10:47 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 0.749754
2021-10-13 10:50 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.113 s 3.291177
2021-10-13 10:58 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.579631
2021-10-13 10:58 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.708972
2021-10-13 10:58 JavaScript Parse serialize, tracks 0.005 s -0.726937
2021-10-13 10:58 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.050543
2021-10-13 10:43 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.749 s -1.220288
2021-10-13 10:48 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.530 s -0.863993
2021-10-13 10:58 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.782924
2021-10-13 10:03 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.345 s -2.324376
2021-10-13 10:04 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.684 s 1.013663
2021-10-13 10:29 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -1.297960