Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 05:19 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.914 s 0.019112
2021-10-11 05:20 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.314 s 2.123984
2021-10-11 04:31 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.852 s 0.707767
2021-10-11 04:31 Python csv-read uncompressed, file, fanniemae_2016Q4 1.152 s 1.282253
2021-10-11 04:32 Python csv-read gzip, streaming, fanniemae_2016Q4 14.806 s 0.466040
2021-10-11 04:32 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.374718
2021-10-11 04:32 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.626 s -0.030566
2021-10-11 04:33 Python csv-read gzip, streaming, nyctaxi_2010-01 10.602 s -0.081252
2021-10-11 04:35 Python dataframe-to-table chi_traffic_2020_Q1 19.383 s 0.545395
2021-10-11 04:35 Python dataframe-to-table type_strings 0.369 s 0.267386
2021-10-11 04:35 Python dataframe-to-table type_dict 0.012 s 0.474678
2021-10-11 04:35 Python dataframe-to-table type_integers 0.011 s -1.671814
2021-10-11 04:36 Python dataframe-to-table type_floats 0.011 s -0.354363
2021-10-11 04:36 Python dataframe-to-table type_nested 2.869 s 0.516924
2021-10-11 04:36 Python dataframe-to-table type_simple_features 0.928 s -0.610314
2021-10-11 04:36 Python dataset-filter nyctaxi_2010-01 4.318 s 1.522193
2021-10-11 04:58 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.072 s -0.325828
2021-10-11 04:58 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.002 s 0.341425
2021-10-11 05:09 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.790 s 0.557997
2021-10-11 05:09 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.909 s 0.613209
2021-10-11 05:10 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.783 s -0.347472
2021-10-11 05:10 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.204 s 0.535754
2021-10-11 05:11 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.783 s 0.647000
2021-10-11 05:11 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.273 s 0.749025
2021-10-11 05:11 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.629 s 2.415122
2021-10-11 05:11 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.125 s 0.724253
2021-10-11 05:11 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.542 s 2.466401
2021-10-11 05:11 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.292 s -0.347207
2021-10-11 05:12 Python file-read lz4, feather, table, fanniemae_2016Q4 0.612 s -1.481726
2021-10-11 05:12 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.015 s 2.459254
2021-10-11 05:12 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.076 s -1.253335
2021-10-11 05:12 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.145 s 1.531492
2021-10-11 05:13 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.150 s 1.537969
2021-10-11 05:13 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.276240
2021-10-11 05:14 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.289 s 1.593783
2021-10-11 05:14 Python file-read lz4, feather, table, nyctaxi_2010-01 0.675 s -0.821527
2021-10-11 05:14 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.782 s 1.693380
2021-10-11 05:16 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.449 s 0.526092
2021-10-11 05:16 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.885 s -0.790252
2021-10-11 05:17 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.679 s 5.131926
2021-10-11 05:17 Python file-write lz4, feather, table, fanniemae_2016Q4 1.136 s 1.668866
2021-10-11 05:18 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.489 s -1.910176
2021-10-11 05:18 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.843 s 0.146287
2021-10-11 06:11 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.310434
2021-10-11 06:11 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.892 s -0.371581
2021-10-11 06:11 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.876 s 0.605739
2021-10-11 06:11 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.569442
2021-10-11 06:11 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.543209
2021-10-11 06:11 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.613656
2021-10-11 06:11 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.209103
2021-10-11 06:11 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.209038
2021-10-11 06:12 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.196951
2021-10-11 05:20 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.305 s 1.350950
2021-10-11 05:21 Python wide-dataframe use_legacy_dataset=false 0.615 s 1.312210
2021-10-11 05:34 R dataframe-to-table chi_traffic_2020_Q1, R 3.384 s 0.271744
2021-10-11 05:34 R dataframe-to-table type_strings, R 0.491 s 0.233116
2021-10-11 05:34 R dataframe-to-table type_integers, R 0.011 s 1.189697
2021-10-11 05:34 R dataframe-to-table type_floats, R 0.013 s 1.205037
2021-10-11 05:35 R dataframe-to-table type_nested, R 0.534 s 0.235502
2021-10-11 05:41 R dataframe-to-table type_simple_features, R 3.358 s 1.005033
2021-10-11 05:41 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.923 s 1.022418
2021-10-11 05:41 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.447 s 1.146622
2021-10-11 05:41 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.320 s -2.279547
2021-10-11 05:42 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.133395
2021-10-11 05:43 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.368 s 1.161156
2021-10-11 05:43 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.108 s 1.040276
2021-10-11 05:43 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -2.049360
2021-10-11 05:44 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.992 s 0.063515
2021-10-11 05:44 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.691 s -0.014459
2021-10-11 05:45 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.529 s 0.106571
2021-10-11 05:47 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.256 s 0.627982
2021-10-11 05:48 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.301 s 0.556349
2021-10-11 05:50 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.738 s 0.464230
2021-10-11 05:52 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.384 s 1.899818
2021-10-11 05:54 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.894 s -0.430845
2021-10-11 05:55 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.863 s -0.594416
2021-10-11 05:56 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.529 s -0.389751
2021-10-11 05:57 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.725 s -0.626005
2021-10-11 05:58 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.275 s 1.445946
2021-10-11 05:59 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.243 s 0.021344
2021-10-11 05:59 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.476 s 1.118291
2021-10-11 06:00 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.170 s 0.283844
2021-10-11 06:00 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 0.183418
2021-10-11 06:01 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 0.220961
2021-10-11 06:01 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.273670
2021-10-11 06:01 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.196 s -1.888945
2021-10-11 06:01 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.597 s 0.398762
2021-10-11 06:02 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.622 s -1.317678
2021-10-11 06:02 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.880 s 1.214235
2021-10-11 06:03 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.650 s -0.922470
2021-10-11 06:03 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s -0.873643
2021-10-11 06:03 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.491 s -1.529362
2021-10-11 06:04 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s -0.043940
2021-10-11 06:04 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.165 s 0.781414
2021-10-11 06:04 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.509 s -0.802058
2021-10-11 06:11 JavaScript Parse Table.from, tracks 0.000 s 0.130744
2021-10-11 06:11 JavaScript Parse serialize, tracks 0.005 s 0.164836
2021-10-11 06:11 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.281712
2021-10-11 06:11 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.482 s 2.054051
2021-10-11 06:11 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -0.141476
2021-10-11 06:11 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -0.125489
2021-10-11 06:11 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.675 s 0.147584
2021-10-11 06:11 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.682 s 0.369675
2021-10-11 06:11 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.482431
2021-10-11 06:12 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.322489
2021-10-11 06:12 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.377763
2021-10-11 06:12 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.574074
2021-10-11 06:12 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.461261
2021-10-11 06:12 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.334141
2021-10-11 06:12 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.341806
2021-10-11 06:12 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.495 s 0.420858
2021-10-11 04:34 Python csv-read gzip, file, nyctaxi_2010-01 9.050 s -1.903267
2021-10-11 05:15 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.580 s -1.220384
2021-10-11 05:41 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.217 s 0.511949
2021-10-11 05:50 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.812 s 1.997601
2021-10-11 05:53 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.217 s -0.844159
2021-10-11 06:01 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.530 s -1.256287
2021-10-11 06:11 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -2.222187
2021-10-11 06:11 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.314 s 2.301970
2021-10-11 04:58 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.051 s -0.711788
2021-10-11 05:10 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.955 s 0.627661
2021-10-11 05:19 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.868 s -0.132221
2021-10-11 05:20 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.920 s -0.494011
2021-10-11 05:21 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.806 s 0.552300
2021-10-11 05:43 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.051 s 0.538411
2021-10-11 05:51 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.576 s -0.464014
2021-10-11 04:53 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.264 s 0.198556
2021-10-11 05:17 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.934 s -0.765819
2021-10-11 05:34 R dataframe-to-table type_dict, R 0.043 s 1.822231
2021-10-11 04:53 Python dataset-read async=True, nyctaxi_multi_ipc_s3 194.800 s -1.400648
2021-10-11 05:43 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.164 s 1.155922
2021-10-11 05:43 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.219 s 1.143298
2021-10-11 06:03 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.295072
2021-10-11 04:33 Python csv-read uncompressed, file, nyctaxi_2010-01 1.005 s 0.553594
2021-10-11 05:15 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.097 s 0.493611
2021-10-11 05:42 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.038 s -1.814477
2021-10-11 05:46 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.839 s 0.621472
2021-10-11 06:01 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.861 s 0.439088
2021-10-11 06:11 JavaScript Parse readBatches, tracks 0.000 s 0.354925
2021-10-11 06:11 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.504203
2021-10-11 06:12 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.468691
2021-10-11 04:39 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.832 s -0.745057
2021-10-11 04:44 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.113 s 0.236034
2021-10-11 05:12 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.692 s 2.561410
2021-10-11 05:13 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.017 s 1.073526
2021-10-11 05:20 Python file-write lz4, feather, table, nyctaxi_2010-01 1.768 s 2.318204
2021-10-11 05:21 Python wide-dataframe use_legacy_dataset=true 0.388 s 2.674465
2021-10-11 05:41 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.713 s -4.660624
2021-10-11 06:12 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.734219