Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-01 11:48 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.011 s 0.362708
2021-10-01 12:04 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.347 s -1.477441
2021-10-01 12:12 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.176797
2021-10-01 12:25 R dataframe-to-table chi_traffic_2020_Q1, R 5.368 s 0.685801
2021-10-01 12:12 Python wide-dataframe use_legacy_dataset=false 0.626 s -1.414854
2021-10-01 11:26 Python dataframe-to-table chi_traffic_2020_Q1 19.688 s 0.268734
2021-10-01 11:27 Python dataframe-to-table type_dict 0.012 s -0.463770
2021-10-01 12:04 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.020 s 1.115172
2021-10-01 12:26 R dataframe-to-table type_nested, R 0.537 s -0.125863
2021-10-01 11:23 Python csv-read gzip, streaming, fanniemae_2016Q4 14.866 s -0.423644
2021-10-01 11:24 Python csv-read uncompressed, file, nyctaxi_2010-01 1.011 s 0.133167
2021-10-01 11:22 Python csv-read uncompressed, file, fanniemae_2016Q4 1.173 s 0.028553
2021-10-01 12:00 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.983 s 0.196564
2021-10-01 12:02 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.843 s -1.388734
2021-10-01 12:03 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.316 s -1.356788
2021-10-01 12:05 Python file-read lz4, feather, table, nyctaxi_2010-01 0.676 s -1.529918
2021-10-01 12:09 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.291 s -0.677347
2021-10-01 12:12 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.828 s 0.116220
2021-10-01 12:26 R dataframe-to-table type_floats, R 0.107 s 0.840107
2021-10-01 12:02 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.114 s 1.241855
2021-10-01 11:22 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.916 s -0.395336
2021-10-01 11:27 Python dataframe-to-table type_floats 0.011 s 1.694004
2021-10-01 11:30 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.842 s -0.130043
2021-10-01 12:03 Python file-read lz4, feather, table, fanniemae_2016Q4 0.600 s 0.323963
2021-10-01 12:08 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.437 s -0.752700
2021-10-01 12:08 Python file-write lz4, feather, table, fanniemae_2016Q4 1.196 s -2.741704
2021-10-01 12:11 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.975 s -0.286879
2021-10-01 12:05 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 8.007 s -1.701478
2021-10-01 12:06 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.388 s -1.143562
2021-10-01 12:09 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.930 s -0.993191
2021-10-01 11:24 Python csv-read gzip, streaming, nyctaxi_2010-01 10.481 s 0.620709
2021-10-01 11:44 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.667 s -1.883763
2021-10-01 12:03 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.065 s -0.736817
2021-10-01 11:44 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.014 s 0.049177
2021-10-01 11:48 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.030 s 0.089969
2021-10-01 12:04 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.175 s 0.361565
2021-10-01 12:05 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.486 s -1.610841
2021-10-01 12:10 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.948 s -0.332091
2021-10-01 12:11 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.404 s -0.313177
2021-10-01 12:25 R dataframe-to-table type_strings, R 0.496 s -2.054064
2021-10-01 11:23 Python csv-read gzip, file, fanniemae_2016Q4 6.034 s -0.814529
2021-10-01 11:27 Python dataframe-to-table type_strings 0.375 s -0.484733
2021-10-01 11:27 Python dataframe-to-table type_simple_features 0.913 s -0.075556
2021-10-01 12:00 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.843 s 0.347432
2021-10-01 12:02 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.268 s 0.807377
2021-10-01 12:02 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.295 s -0.671757
2021-10-01 12:07 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.746 s -1.145555
2021-10-01 12:03 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.241 s -1.935676
2021-10-01 11:27 Python dataframe-to-table type_nested 2.853 s 1.986211
2021-10-01 12:01 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.733 s 0.241802
2021-10-01 12:01 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.766 s 1.475749
2021-10-01 12:10 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.988 s -0.973735
2021-10-01 12:11 Python file-write lz4, feather, table, nyctaxi_2010-01 1.799 s 0.626960
2021-10-01 11:27 Python dataframe-to-table type_integers 0.011 s 1.439413
2021-10-01 11:35 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.255 s 1.282574
2021-10-01 11:48 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.006 s 0.236914
2021-10-01 12:01 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.948 s 1.259080
2021-10-01 12:02 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.762 s -1.104314
2021-10-01 12:03 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.935 s -1.913307
2021-10-01 12:08 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.929 s -0.794510
2021-10-01 12:11 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.349 s 0.080739
2021-10-01 11:23 Python csv-read uncompressed, streaming, nyctaxi_2010-01 11.262 s -3.140079
2021-10-01 11:27 Python dataset-filter nyctaxi_2010-01 4.348 s 0.570868
2021-10-01 11:25 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s 0.258041
2021-10-01 12:06 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.615 s -1.019936
2021-10-01 12:25 R dataframe-to-table type_dict, R 0.049 s 0.039494
2021-10-01 12:26 R dataframe-to-table type_integers, R 0.084 s 0.223175
2021-10-01 13:22 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.532 s -0.367162
2021-10-01 12:50 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.865 s 0.602157
2021-10-01 12:50 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.861 s 0.649844
2021-10-01 12:54 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.161243
2021-10-01 13:04 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.942 s -0.874663
2021-10-01 12:51 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.568 s -1.136247
2021-10-01 12:52 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.048 s 1.504575
2021-10-01 13:02 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.076327
2021-10-01 12:51 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.136451
2021-10-01 12:01 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.179 s 1.588524
2021-10-01 12:08 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.795 s -0.539846
2021-10-01 13:12 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.613 s 0.978946
2021-10-01 13:13 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.106 s -9.586273
2021-10-01 13:22 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.032434
2021-10-01 13:22 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.999942
2021-10-01 13:06 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.576 s -0.790821
2021-10-01 13:09 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.242 s 1.572591
2021-10-01 13:22 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.002 s -2.406043
2021-10-01 13:22 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.586588
2021-10-01 13:22 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.034062
2021-10-01 12:55 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.000 s -1.591452
2021-10-01 12:55 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.534 s -0.616503
2021-10-01 12:56 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.168 s -1.157948
2021-10-01 13:05 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.885 s -0.266192
2021-10-01 13:14 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.482 s -2.878785
2021-10-01 13:14 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.201 s -2.610578
2021-10-01 13:22 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.671 s -0.352497
2021-10-01 13:22 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.631 s 0.930629
2021-10-01 13:22 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.873 s 0.211825
2021-10-01 13:22 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.030237
2021-10-01 13:22 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.289480
2021-10-01 13:22 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.556154
2021-10-01 12:59 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.624 s -1.174904
2021-10-01 13:01 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.835 s -0.645220
2021-10-01 13:11 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.492333
2021-10-01 13:13 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.625 s -0.450940
2021-10-01 13:00 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.062 s -0.900241
2021-10-01 13:11 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.572 s 1.294985
2021-10-01 13:22 JavaScript Parse readBatches, tracks 0.000 s -0.875601
2021-10-01 13:15 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.167 s 1.031149
2021-10-01 13:15 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.488 s 0.127352
2021-10-01 12:55 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.104890
2021-10-01 13:10 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.501 s -2.130262
2021-10-01 13:22 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.029 s -4.411643
2021-10-01 13:12 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -1.202250
2021-10-01 13:22 JavaScript Parse serialize, tracks 0.005 s -0.681037
2021-10-01 12:54 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.212 s 1.614465
2021-10-01 12:58 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.571 s -0.631176
2021-10-01 13:08 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.448168
2021-10-01 13:12 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.519 s -0.482581
2021-10-01 13:22 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.874 s 0.592785
2021-10-01 12:53 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.134 s -0.308401
2021-10-01 12:53 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.172 s 0.155284
2021-10-01 13:02 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.538 s 1.512069
2021-10-01 13:13 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.618 s -1.807801
2021-10-01 13:22 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.059694
2021-10-01 13:22 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.666 s -0.418399
2021-10-01 13:22 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.728 s 0.105418
2021-10-01 13:11 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.210 s -1.484618
2021-10-01 13:07 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.760 s -0.602567
2021-10-01 12:49 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.198 s 0.598994
2021-10-01 13:03 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.178 s 1.538367
2021-10-01 13:14 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.358 s 0.638162
2021-10-01 12:49 R dataframe-to-table type_simple_features, R 274.811 s 0.134189
2021-10-01 12:52 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.384 s -0.033783
2021-10-01 12:50 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.208 s 0.473344
2021-10-01 12:51 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.887 s 1.760815
2021-10-01 13:11 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.858 s 1.384553
2021-10-01 13:22 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.200492
2021-10-01 13:22 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.603070
2021-10-01 13:22 JavaScript Parse Table.from, tracks 0.000 s -0.447738
2021-10-01 13:11 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.583 s 1.175993
2021-10-01 13:22 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.226129
2021-10-01 13:13 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.924 s 1.061289
2021-10-01 13:22 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -2.918888
2021-10-01 13:22 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.030237
2021-10-01 13:22 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.233549
2021-10-01 13:22 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.389404
2021-10-01 13:22 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -1.918596
2021-10-01 13:22 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.279463
2021-10-01 13:22 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.019 s -4.992820
2021-10-01 13:22 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.006651