Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-26 05:46 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.639 s -0.603205
2021-09-26 05:49 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.266867
2021-09-26 05:52 Python dataframe-to-table type_nested 2.949 s 0.386665
2021-09-26 05:48 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.581 s -0.248966
2021-09-26 05:48 Python csv-read uncompressed, file, nyctaxi_2010-01 1.005 s 0.230702
2021-09-26 05:52 Python dataset-filter nyctaxi_2010-01 4.364 s -0.337560
2021-09-26 05:47 Python csv-read gzip, streaming, fanniemae_2016Q4 14.563 s -0.603831
2021-09-26 05:47 Python csv-read uncompressed, file, fanniemae_2016Q4 1.189 s -0.299602
2021-09-26 05:48 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -1.000404
2021-09-26 05:55 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 66.246 s -1.628377
2021-09-26 05:49 Python csv-read gzip, streaming, nyctaxi_2010-01 10.542 s -0.144693
2021-09-26 05:51 Python dataframe-to-table type_strings 0.362 s 1.232265
2021-09-26 05:51 Python dataframe-to-table type_integers 0.011 s -1.422814
2021-09-26 05:51 Python dataframe-to-table type_dict 0.012 s 0.547795
2021-09-26 05:51 Python dataframe-to-table chi_traffic_2020_Q1 19.734 s 0.347958
2021-09-26 05:51 Python dataframe-to-table type_floats 0.012 s -0.581860
2021-09-26 05:52 Python dataframe-to-table type_simple_features 0.907 s 0.331582
2021-09-26 06:59 R dataframe-to-table type_floats, R 0.113 s -1.110154
2021-09-26 06:35 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.855 s -1.633667
2021-09-26 06:38 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.992 s 1.018227
2021-09-26 06:42 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.598 s 1.328877
2021-09-26 06:43 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.150 s 0.909278
2021-09-26 06:44 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.898 s 2.382362
2021-09-26 06:45 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.356 s -0.287015
2021-09-26 07:24 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.222 s 0.557499
2021-09-26 07:47 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.864 s 1.307173
2021-09-26 06:09 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 271.450 s 0.062337
2021-09-26 06:36 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.732 s -0.370310
2021-09-26 06:44 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.791 s 1.119273
2021-09-26 06:45 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.826 s 1.143780
2021-09-26 06:24 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.999 s 0.309896
2021-09-26 06:40 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.113 s 2.079810
2021-09-26 06:45 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.768 s 0.727259
2021-09-26 06:46 Python wide-dataframe use_legacy_dataset=false 0.615 s 0.202251
2021-09-26 06:59 R dataframe-to-table type_integers, R 0.086 s -0.933888
2021-09-26 07:23 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.872 s 0.937876
2021-09-26 07:37 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.205 s 1.156807
2021-09-26 07:42 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.417686
2021-09-26 07:47 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.515 s 1.305601
2021-09-26 07:56 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.150352
2021-09-26 06:41 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.533 s 1.757499
2021-09-26 06:43 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.834 s 2.250578
2021-09-26 07:32 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.288 s 2.174939
2021-09-26 07:46 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.972 s 0.691845
2021-09-26 07:56 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.699 s -0.360946
2021-09-26 07:56 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.000520
2021-09-26 06:34 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.961 s 0.381247
2021-09-26 07:39 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.846 s 1.843735
2021-09-26 07:43 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.501 s -2.095824
2021-09-26 07:56 JavaScript Parse Table.from, tracks 0.000 s 0.123031
2021-09-26 07:56 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.358695
2021-09-26 07:56 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.542402
2021-09-26 07:56 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.986284
2021-09-26 06:24 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.634 s -41.493131
2021-09-26 06:45 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.293 s 0.709325
2021-09-26 06:59 R dataframe-to-table type_nested, R 0.538 s -0.422698
2021-09-26 07:28 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.983 s -1.012496
2021-09-26 07:40 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.524 s 0.754620
2021-09-26 07:44 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.831 s -1.945210
2021-09-26 07:48 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.198 s 0.238599
2021-09-26 07:56 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.176054
2021-09-26 07:27 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -2.056831
2021-09-26 06:42 Python file-write lz4, feather, table, fanniemae_2016Q4 1.156 s 0.452501
2021-09-26 07:26 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.353 s 1.086834
2021-09-26 07:49 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.501 s 0.181229
2021-09-26 07:56 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.665468
2021-09-26 07:56 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.542402
2021-09-26 06:37 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.099648
2021-09-26 07:23 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.227 s 0.287547
2021-09-26 07:56 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.609 s -0.136612
2021-09-26 07:56 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.694 s 0.298984
2021-09-26 06:19 Python dataset-read async=True, nyctaxi_multi_ipc_s3 206.310 s -2.097568
2021-09-26 06:38 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.851 s 1.027864
2021-09-26 06:39 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.462 s 1.198981
2021-09-26 06:59 R dataframe-to-table chi_traffic_2020_Q1, R 5.500 s -1.412856
2021-09-26 07:24 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -1.300072
2021-09-26 07:31 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.266 s 2.191189
2021-09-26 07:56 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.744165
2021-09-26 07:56 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.512 s -0.048808
2021-09-26 06:36 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.675 s -0.786307
2021-09-26 06:38 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.171 s 1.060835
2021-09-26 06:38 Python file-read lz4, feather, table, nyctaxi_2010-01 0.675 s -1.210873
2021-09-26 07:30 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.842 s 2.131330
2021-09-26 07:44 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.170 s 1.589306
2021-09-26 07:56 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.114960
2021-09-26 07:56 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.130969
2021-09-26 06:24 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.016 s 0.260849
2021-09-26 06:41 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.443 s 2.238553
2021-09-26 07:28 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.686 s -1.754178
2021-09-26 07:29 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.560 s -2.476814
2021-09-26 07:34 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.724 s 2.153584
2021-09-26 07:38 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.887 s 0.784130
2021-09-26 07:45 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.437080
2021-09-26 07:48 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.473 s -0.305630
2021-09-26 07:56 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.131595
2021-09-26 07:56 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.220919
2021-09-26 07:56 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.760083
2021-09-26 07:24 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.868 s 1.251222
2021-09-26 07:43 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.251 s 1.037877
2021-09-26 07:48 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.395 s -0.620503
2021-09-26 07:56 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.590126
2021-09-26 06:35 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.018 s -1.161663
2021-09-26 06:37 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.862 s 1.048861
2021-09-26 06:46 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.083242
2021-09-26 07:25 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.719406
2021-09-26 07:27 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.118 s 0.862255
2021-09-26 07:45 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.770 s 0.530928
2021-09-26 07:56 JavaScript Parse serialize, tracks 0.005 s -0.768385
2021-09-26 07:56 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.622277
2021-09-26 07:56 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.249607
2021-09-26 06:36 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.137 s -0.305798
2021-09-26 06:36 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.294 s -0.371740
2021-09-26 06:39 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.106 s 2.041013
2021-09-26 06:45 Python file-write lz4, feather, table, nyctaxi_2010-01 1.812 s 0.042386
2021-09-26 07:56 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.922 s -0.447750
2021-09-26 06:34 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.822 s 0.459427
2021-09-26 06:37 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.049 s -0.747874
2021-09-26 07:25 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.933 s -0.812476
2021-09-26 07:35 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.562 s 1.372546
2021-09-26 07:56 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.728 s -0.936660
2021-09-26 07:56 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.884 s 0.020841
2021-09-26 07:56 JavaScript DataFrame Iterate 1,000,000, tracks 0.051 s 2.475296
2021-09-26 06:20 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.144 s 0.818396
2021-09-26 06:34 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.741 s 0.316124
2021-09-26 06:35 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.269 s -1.407863
2021-09-26 06:37 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.119 s -0.538585
2021-09-26 06:42 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.365 s -0.227204
2021-09-26 06:59 R dataframe-to-table type_strings, R 0.494 s -1.696462
2021-09-26 07:34 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.834 s -0.743067
2021-09-26 07:56 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-26 06:36 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.288 s -0.732038
2021-09-26 06:37 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.083 s -1.556634
2021-09-26 06:59 R dataframe-to-table type_dict, R 0.054 s -0.152623
2021-09-26 07:26 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.059 s -0.557069
2021-09-26 07:45 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.306365
2021-09-26 07:46 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.508 s 0.941687
2021-09-26 07:47 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.575783
2021-09-26 06:37 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.806 s -0.024469
2021-09-26 07:26 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.153 s 0.996732
2021-09-26 07:46 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.421433
2021-09-26 07:56 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.967514
2021-09-26 07:36 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.402 s 0.074575
2021-09-26 07:23 R dataframe-to-table type_simple_features, R 274.914 s -0.134424
2021-09-26 07:27 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.215 s 1.431222
2021-09-26 07:41 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.715 s 1.237342
2021-09-26 07:45 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.746 s 1.042812
2021-09-26 07:56 JavaScript Parse readBatches, tracks 0.000 s -0.399012
2021-09-26 07:49 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.813 s 0.431526
2021-09-26 07:56 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.724027