Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-30 17:04 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.282 s -1.204975
2021-09-30 16:29 Python dataframe-to-table type_strings 0.368 s 0.376256
2021-09-30 17:03 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.817 s 0.449616
2021-09-30 17:28 R dataframe-to-table type_strings, R 0.491 s -0.129756
2021-09-30 16:51 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.006 s 0.220141
2021-09-30 16:25 Python csv-read gzip, streaming, fanniemae_2016Q4 14.749 s -0.524250
2021-09-30 16:26 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.814 s -1.008641
2021-09-30 16:28 Python dataframe-to-table chi_traffic_2020_Q1 19.505 s 1.480537
2021-09-30 17:05 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.892882
2021-09-30 17:12 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.801 s 1.365111
2021-09-30 16:24 Python csv-read uncompressed, file, fanniemae_2016Q4 1.194 s -0.383682
2021-09-30 16:29 Python dataframe-to-table type_floats 0.011 s 0.039508
2021-09-30 17:12 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.808 s 0.622680
2021-09-30 17:28 R dataframe-to-table type_dict, R 0.054 s -0.505755
2021-09-30 16:25 Python csv-read gzip, file, fanniemae_2016Q4 6.030 s -0.015526
2021-09-30 16:46 Python dataset-read async=True, nyctaxi_multi_ipc_s3 184.944 s 0.395513
2021-09-30 16:51 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.028 s 0.085717
2021-09-30 17:05 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.861 s -2.806188
2021-09-30 17:05 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.799 s -2.616352
2021-09-30 17:11 Python file-write lz4, feather, table, fanniemae_2016Q4 1.176 s -1.570943
2021-09-30 17:04 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.858 s -1.033264
2021-09-30 17:14 Python file-write lz4, feather, table, nyctaxi_2010-01 1.811 s 0.025007
2021-09-30 17:03 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.957 s 0.372403
2021-09-30 17:04 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.043 s -1.380541
2021-09-30 17:06 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.213 s -2.941345
2021-09-30 17:08 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.631 s 0.181865
2021-09-30 16:29 Python dataframe-to-table type_dict 0.012 s 1.088268
2021-09-30 17:10 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.468 s 0.920537
2021-09-30 16:29 Python dataframe-to-table type_nested 2.853 s 3.118796
2021-09-30 17:05 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.167 s -1.383969
2021-09-30 16:24 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.809 s -0.512583
2021-09-30 16:37 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.674 s 1.926170
2021-09-30 17:09 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.264 s 0.562827
2021-09-30 17:15 Python wide-dataframe use_legacy_dataset=false 0.623 s -1.026272
2021-09-30 17:06 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.001 s 0.183827
2021-09-30 17:11 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.294 s -0.711364
2021-09-30 17:14 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.817 s 0.187198
2021-09-30 17:29 R dataframe-to-table type_nested, R 0.538 s -0.238955
2021-09-30 16:29 Python dataframe-to-table type_integers 0.012 s -3.402594
2021-09-30 17:03 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.699 s 0.425662
2021-09-30 17:06 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.063 s -0.753022
2021-09-30 17:14 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.349 s 0.115335
2021-09-30 16:26 Python csv-read uncompressed, file, nyctaxi_2010-01 1.023 s -0.087465
2021-09-30 16:29 Python dataset-filter nyctaxi_2010-01 4.394 s -0.880073
2021-09-30 16:27 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.527492
2021-09-30 16:51 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.035 s -0.024388
2021-09-30 17:04 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.412 s -6.904432
2021-09-30 17:06 Python file-read lz4, feather, table, fanniemae_2016Q4 0.595 s 1.158308
2021-09-30 17:06 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.032 s 0.279086
2021-09-30 17:28 R dataframe-to-table chi_traffic_2020_Q1, R 5.376 s 0.530851
2021-09-30 16:32 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.039 s 0.139266
2021-09-30 17:07 Python file-read lz4, feather, table, nyctaxi_2010-01 0.665 s 0.904484
2021-09-30 17:10 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.602 s 0.673151
2021-09-30 17:11 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.707 s 0.035676
2021-09-30 17:13 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.863 s 1.403841
2021-09-30 16:29 Python dataframe-to-table type_simple_features 0.933 s -2.265062
2021-09-30 16:46 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.862 s -3.111973
2021-09-30 17:07 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.175 s 0.235021
2021-09-30 16:26 Python csv-read gzip, streaming, nyctaxi_2010-01 10.807 s -1.032334
2021-09-30 17:07 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.155 s 0.052234
2021-09-30 17:14 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.271036
2021-09-30 17:11 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.329 s 0.116207
2021-09-30 17:08 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.090 s 1.110932
2021-09-30 17:28 R dataframe-to-table type_integers, R 0.085 s 0.072631
2021-09-30 17:52 R dataframe-to-table type_simple_features, R 275.636 s -1.566542
2021-09-30 17:05 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.914 s -3.455065
2021-09-30 17:07 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.000 s 0.155040
2021-09-30 17:14 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.362 s -0.002584
2021-09-30 17:52 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.242 s 0.114082
2021-09-30 17:53 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.858669
2021-09-30 17:53 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.920 s 0.016669
2021-09-30 17:53 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.282 s -0.345657
2021-09-30 17:54 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.932 s -0.721666
2021-09-30 17:53 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.935 s -0.144463
2021-09-30 17:54 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.553 s 2.108921
2021-09-30 17:56 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.121 s 0.660359
2021-09-30 17:55 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.404 s -1.242788
2021-09-30 17:55 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.635738
2021-09-30 17:56 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.186 s -0.815840
2021-09-30 17:57 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.276 s -1.858780
2021-09-30 17:58 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.528 s -0.429850
2021-09-30 18:16 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.683 s -1.220547
2021-09-30 18:25 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.592669
2021-09-30 18:06 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.240 s 0.276089
2021-09-30 18:16 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.349 s 1.357473
2021-09-30 18:25 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.024 s -0.015639
2021-09-30 17:13 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.842 s 0.652462
2021-09-30 17:28 R dataframe-to-table type_floats, R 0.108 s 0.474293
2021-09-30 17:57 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.773315
2021-09-30 18:11 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -1.150837
2021-09-30 18:15 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.529 s -1.881245
2021-09-30 18:17 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.478 s -1.563955
2021-09-30 18:25 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.972880
2021-09-30 17:58 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.673 s 0.152346
2021-09-30 18:05 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.407 s -0.934224
2021-09-30 18:16 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.520175
2021-09-30 18:25 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.635 s -0.192846
2021-09-30 18:25 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.492757
2021-09-30 18:15 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.605 s 1.391126
2021-09-30 18:25 JavaScript Parse Table.from, tracks 0.000 s 0.549465
2021-09-30 18:25 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.535653
2021-09-30 18:03 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.734 s 1.122201
2021-09-30 18:13 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.497 s -1.365304
2021-09-30 18:25 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.468035
2021-09-30 18:25 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.037431
2021-09-30 18:09 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.470 s 1.603549
2021-09-30 18:14 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.883 s 1.691182
2021-09-30 18:14 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 1.884243
2021-09-30 18:15 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.503506
2021-09-30 18:25 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.366760
2021-09-30 18:25 JavaScript Parse readBatches, tracks 0.000 s 1.039894
2021-09-30 18:25 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.679 s 0.088112
2021-09-30 18:25 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.859 s 0.616800
2021-09-30 18:25 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.168738
2021-09-30 18:25 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.792236
2021-09-30 18:25 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.989446
2021-09-30 17:57 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.983 s -0.777705
2021-09-30 18:07 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.830 s 1.623085
2021-09-30 18:17 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.155 s 1.427601
2021-09-30 18:25 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.926273
2021-09-30 18:14 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.201 s -0.514744
2021-09-30 18:18 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.504 s 0.106863
2021-09-30 18:25 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.150798
2021-09-30 18:25 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.006344
2021-09-30 17:59 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.852 s 1.106880
2021-09-30 18:01 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.272 s 1.155595
2021-09-30 18:14 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.176 s 0.036409
2021-09-30 18:16 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.970 s 1.414847
2021-09-30 18:10 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.671 s 1.608011
2021-09-30 18:25 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.512 s -0.008694
2021-09-30 18:03 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.834 s -0.614346
2021-09-30 18:25 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.403496
2021-09-30 18:25 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.884 s 0.353827
2021-09-30 18:25 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.666384
2021-09-30 18:12 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.270 s -0.198848
2021-09-30 18:25 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.029049
2021-09-30 18:01 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.319 s 0.981707
2021-09-30 18:05 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.591 s 0.531696
2021-09-30 18:14 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.394549
2021-09-30 18:25 JavaScript Parse serialize, tracks 0.005 s -0.148164
2021-09-30 18:25 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.623 s -0.249934
2021-09-30 18:25 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.672 s 0.431041
2021-09-30 18:25 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.480386
2021-09-30 18:08 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.805 s 1.682721
2021-09-30 18:14 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.577 s 1.749860
2021-09-30 18:17 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 0.982655
2021-09-30 18:25 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578334
2021-09-30 18:25 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.594461
2021-09-30 18:25 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.556415