Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-07 21:06 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.296 s 0.608762
2021-10-07 21:07 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.694 s 0.895756
2021-10-07 21:09 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.827 s 0.878179
2021-10-07 21:09 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.827 s 0.878179
2021-10-07 21:12 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.770604
2021-10-07 21:11 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.541 s 1.273672
2021-10-07 21:11 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.541 s 1.273672
2021-10-07 21:13 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.198 s 0.740114
2021-10-07 21:14 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.819 s 0.950669
2021-10-07 21:16 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.780 s 1.105367
2021-10-07 21:17 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.460 s 0.867355
2021-10-07 21:19 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.638 s 1.175054
2021-10-07 21:17 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.460 s 0.867355
2021-10-07 21:22 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.492 s -0.435097
2021-10-07 21:20 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.895782
2021-10-07 21:21 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.236 s 1.288207
2021-10-07 21:24 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.170 s 0.817440
2021-10-07 21:26 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.334647
2021-10-07 21:26 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.594285
2021-10-07 21:22 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.492 s -0.435097
2021-10-07 21:25 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.865 s 0.600675
2021-10-07 21:26 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.608 s 0.505994
2021-10-07 21:24 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.589 s 0.433993
2021-10-07 21:29 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.111 s -1.650627
2021-10-07 21:25 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.579 s 0.479326
2021-10-07 21:27 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.528 s -1.623227
2021-10-07 21:28 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.903 s 0.562235
2021-10-07 21:30 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.177 s 0.571887
2021-10-07 21:27 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.603988
2021-10-07 21:28 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.633 s -0.704726
2021-10-07 21:29 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.365 s -0.032530
2021-10-07 21:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -1.781404
2021-10-07 21:29 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.488 s -2.107439
2021-10-07 21:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.496 s -0.043597
2021-10-07 21:39 JavaScript Parse readBatches, tracks 0.000 s 0.017847
2021-10-07 21:38 JavaScript Parse Table.from, tracks 0.000 s -0.220788
2021-10-07 21:40 JavaScript Parse serialize, tracks 0.005 s -0.614647
2021-10-07 21:39 JavaScript Parse readBatches, tracks 0.000 s 0.017847
2021-10-07 21:38 JavaScript Parse Table.from, tracks 0.000 s -0.220788
2021-10-07 21:40 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.897378
2021-10-07 21:42 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.610 s -0.379193
2021-10-07 21:42 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.631 s -0.378699
2021-10-07 21:41 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.904434
2021-10-07 21:40 JavaScript Parse serialize, tracks 0.005 s -0.614647
2021-10-07 21:43 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.688486
2021-10-07 21:41 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.904434
2021-10-07 21:40 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.897378
2021-10-07 21:44 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.704 s -0.279608
2021-10-07 21:44 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.638142
2021-10-07 21:43 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.688486
2021-10-07 21:45 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.872 s 0.154119
2021-10-07 21:42 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.610 s -0.379193
2021-10-07 21:44 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.865586
2021-10-07 21:45 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.553649
2021-10-07 21:44 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.743 s 0.022985
2021-10-07 21:45 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.961 s -1.185452
2021-10-07 21:43 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.728256
2021-10-07 21:46 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.044987
2021-10-07 21:45 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497361
2021-10-07 21:46 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.042843
2021-10-07 21:45 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.615049
2021-10-07 21:46 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.073728
2021-10-07 21:46 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497361
2021-10-07 21:47 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.323229
2021-10-07 21:48 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.335606
2021-10-07 21:47 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.530113
2021-10-07 21:47 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.530113
2021-10-07 21:48 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.335606
2021-10-07 21:49 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.136557
2021-10-07 21:49 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.817510
2021-10-07 21:49 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.136557
2021-10-07 21:49 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.817510
2021-10-07 21:52 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.530 s -0.276988
2021-10-07 21:51 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.905757
2021-10-07 21:50 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.734883
2021-10-07 21:50 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.734883
2021-10-07 21:51 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.909810
2021-10-07 21:51 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.909810
2021-10-07 19:42 Python csv-read uncompressed, file, fanniemae_2016Q4 1.166 s 0.481999
2021-10-07 19:47 Python dataframe-to-table chi_traffic_2020_Q1 19.444 s 0.749674
2021-10-07 19:43 Python csv-read gzip, streaming, fanniemae_2016Q4 14.896 s -0.339134
2021-10-07 19:43 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.641227
2021-10-07 19:47 Python dataframe-to-table type_strings 0.375 s -0.888280
2021-10-07 19:44 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.497 s 1.143689
2021-10-07 19:44 Python csv-read gzip, streaming, nyctaxi_2010-01 10.487 s 1.156537
2021-10-07 19:47 Python dataframe-to-table type_dict 0.011 s 1.109381
2021-10-07 19:48 Python dataset-filter nyctaxi_2010-01 4.348 s 0.900090
2021-10-07 20:06 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.210 s 0.468913
2021-10-07 19:44 Python csv-read uncompressed, file, nyctaxi_2010-01 1.031 s -1.683637
2021-10-07 19:47 Python dataframe-to-table type_integers 0.011 s 1.029965
2021-10-07 19:45 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.692799
2021-10-07 19:48 Python dataframe-to-table type_floats 0.011 s 0.522715
2021-10-07 19:48 Python dataframe-to-table type_nested 2.887 s 0.341474
2021-10-07 19:52 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 67.350 s -1.491440
2021-10-07 19:56 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.593 s 0.551719
2021-10-07 20:05 Python dataset-read async=True, nyctaxi_multi_ipc_s3 182.001 s 0.727783
2021-10-07 20:10 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.026 s 0.092442
2021-10-07 20:22 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.974 s 0.234225
2021-10-07 20:25 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.244 s -0.752648
2021-10-07 20:30 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.682 s 0.006823
2021-10-07 20:32 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.772 s 0.718769
2021-10-07 20:35 Python wide-dataframe use_legacy_dataset=false 0.622 s 0.073598
2021-10-07 20:49 R dataframe-to-table chi_traffic_2020_Q1, R 3.424 s 0.252686
2021-10-07 20:50 R dataframe-to-table type_floats, R 0.013 s 5.688440
2021-10-07 20:56 R dataframe-to-table type_simple_features, R 3.403 s 2.876781
2021-10-07 20:56 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.241 s 0.285728
2021-10-07 20:57 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.237 s 0.172226
2021-10-07 19:42 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.972 s -0.407981
2021-10-07 19:48 Python dataframe-to-table type_simple_features 0.911 s 0.303990
2021-10-07 20:10 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.034 s 0.039020
2021-10-07 20:22 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.777 s -0.298940
2021-10-07 20:22 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.259 s -0.352940
2021-10-07 20:25 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.023 s 1.134993
2021-10-07 20:31 Python file-write lz4, feather, table, fanniemae_2016Q4 1.168 s -0.369991
2021-10-07 20:33 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.839 s 0.925770
2021-10-07 20:34 Python file-write lz4, feather, table, nyctaxi_2010-01 1.820 s -0.572690
2021-10-07 20:49 R dataframe-to-table type_strings, R 0.485 s 0.200980
2021-10-07 20:49 R dataframe-to-table type_integers, R 0.010 s 5.716814
2021-10-07 20:56 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.476 s 4.926783
2021-10-07 21:02 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.530 s 0.233780
2021-10-07 20:10 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.026 s -0.054130
2021-10-07 20:23 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.826 s 0.043947
2021-10-07 20:25 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.924 s -0.533910
2021-10-07 20:27 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.981 s -0.939325
2021-10-07 20:50 R dataframe-to-table type_nested, R 0.539 s 0.199691
2021-10-07 20:57 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.488 s 4.391498
2021-10-07 20:59 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.556 s 1.476331
2021-10-07 20:59 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.055 s 0.319452
2021-10-07 21:05 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.263 s 0.696667
2021-10-07 20:24 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.862 s -0.598494
2021-10-07 20:26 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.323 s -0.907057
2021-10-07 20:57 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.540470
2021-10-07 21:07 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.694 s 0.895756
2021-10-07 20:24 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.146 s -0.016740
2021-10-07 20:30 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.342 s -0.066462
2021-10-07 20:34 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.524496
2021-10-07 20:58 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.954 s -1.998382
2021-10-07 20:24 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.804 s -0.825001
2021-10-07 20:59 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.373 s 0.887422
2021-10-07 20:24 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.248033
2021-10-07 21:00 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.174 s 4.925540
2021-10-07 20:25 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.291782
2021-10-07 20:26 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.708841
2021-10-07 20:27 Python file-read lz4, feather, table, nyctaxi_2010-01 0.676 s -1.370992
2021-10-07 21:00 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.147 s -1.734148
2021-10-07 20:25 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.293 s -0.818599
2021-10-07 21:00 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.227 s 4.932904
2021-10-07 20:22 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.000 s 0.012453
2021-10-07 20:25 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.060 s -1.536325
2021-10-07 20:34 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.803 s -0.039253
2021-10-07 21:00 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.162893
2021-10-07 20:27 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.467 s -0.874857
2021-10-07 20:33 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.353 s -0.119468
2021-10-07 20:34 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.338 s -0.137705
2021-10-07 21:01 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.990 s -0.201329
2021-10-07 20:28 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.085 s 0.593187
2021-10-07 20:49 R dataframe-to-table type_dict, R 0.052 s -0.173953
2021-10-07 21:01 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.095714
2021-10-07 20:28 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.280 s 0.206638
2021-10-07 20:29 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.444 s 0.580707
2021-10-07 20:31 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.766 s -0.366398
2021-10-07 21:03 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.864 s 0.476101
2021-10-07 20:31 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.276 s -0.674995
2021-10-07 20:33 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.798 s 0.916134
2021-10-07 20:22 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.802 s 0.514887
2021-10-07 20:23 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.298 s -0.278533
2021-10-07 20:31 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.776 s 0.930690