Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-09 02:22 Python csv-read gzip, file, fanniemae_2016Q4 6.034 s -0.699580
2021-10-09 02:22 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.721 s -0.635036
2021-10-09 02:22 Python csv-read uncompressed, file, nyctaxi_2010-01 1.010 s 0.269706
2021-10-09 02:25 Python dataframe-to-table type_integers 0.011 s 0.447665
2021-10-09 02:58 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.857 s 0.195113
2021-10-09 03:00 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.279 s 0.622064
2021-10-09 03:00 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.129 s 0.894516
2021-10-09 03:01 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.245 s -0.474132
2021-10-09 03:02 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.272 s -0.400846
2021-10-09 03:02 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.344 s -0.717936
2021-10-09 03:06 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.380 s -0.302431
2021-10-09 03:07 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.298 s -0.704811
2021-10-09 03:08 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.942 s -0.746420
2021-10-09 03:10 Python wide-dataframe use_legacy_dataset=true 0.394 s 0.381496
2021-10-09 03:33 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.401494
2021-10-09 03:34 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.681 s 0.108837
2021-10-09 03:51 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.575 s 0.179578
2021-10-09 03:51 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.182 s -0.233966
2021-10-09 03:51 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.601 s 0.198299
2021-10-09 03:51 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.525 s -0.793759
2021-10-09 03:52 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.432278
2021-10-09 04:02 JavaScript Parse Table.from, tracks 0.000 s 0.598531
2021-10-09 04:02 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.752 s -0.692286
2021-10-09 04:02 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.634176
2021-10-09 04:02 JavaScript Parse serialize, tracks 0.005 s -0.761085
2021-10-09 04:02 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.924 s -0.368027
2021-10-09 04:02 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.068861
2021-10-09 04:02 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.345852
2021-10-09 02:34 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.863 s 0.221801
2021-10-09 03:00 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.766 s 0.339012
2021-10-09 02:20 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.747 s 1.982114
2021-10-09 02:25 Python dataframe-to-table chi_traffic_2020_Q1 22.309 s -11.376009
2021-10-09 02:26 Python dataframe-to-table type_floats 0.011 s 0.222330
2021-10-09 02:26 Python dataframe-to-table type_nested 2.993 s -3.633860
2021-10-09 02:26 Python dataframe-to-table type_simple_features 1.071 s -17.672310
2021-10-09 02:26 Python dataset-filter nyctaxi_2010-01 4.341 s 1.046727
2021-10-09 03:09 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.948 s -1.240487
2021-10-09 03:10 Python wide-dataframe use_legacy_dataset=false 0.613 s 2.414067
2021-10-09 02:47 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.055 s -1.804624
2021-10-09 03:00 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.858 s -0.180229
2021-10-09 03:06 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.672 s 0.217593
2021-10-09 02:29 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.500 s -0.497100
2021-10-09 02:21 Python csv-read gzip, streaming, fanniemae_2016Q4 14.670 s 2.014805
2021-10-09 02:25 Python dataframe-to-table type_strings 0.488 s -21.285365
2021-10-09 02:43 Python dataset-read async=True, nyctaxi_multi_ipc_s3 187.318 s -0.089932
2021-10-09 03:00 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.779 s 1.108072
2021-10-09 03:07 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.768 s -0.094717
2021-10-09 03:24 R dataframe-to-table type_floats, R 0.013 s 2.241739
2021-10-09 03:24 R dataframe-to-table chi_traffic_2020_Q1, R 3.410 s 0.275688
2021-10-09 02:23 Python csv-read gzip, streaming, nyctaxi_2010-01 10.646 s -0.321968
2021-10-09 02:23 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.735374
2021-10-09 02:59 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.196 s 1.074524
2021-10-09 03:24 R dataframe-to-table type_dict, R 0.050 s 0.084379
2021-10-09 03:24 R dataframe-to-table type_integers, R 0.010 s 2.238636
2021-10-09 03:47 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.715 s -0.657866
2021-10-09 03:48 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.645502
2021-10-09 03:51 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.863 s 0.309322
2021-10-09 04:02 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.260169
2021-10-09 04:02 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.652 s 0.714882
2021-10-09 02:25 Python dataframe-to-table type_dict 0.012 s 0.825748
2021-10-09 02:59 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.977 s 0.247990
2021-10-09 02:59 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.794 s -0.410343
2021-10-09 03:01 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.293 s -0.423359
2021-10-09 03:24 R dataframe-to-table type_nested, R 0.539 s 0.231965
2021-10-09 02:21 Python csv-read uncompressed, file, fanniemae_2016Q4 1.149 s 1.612458
2021-10-09 02:59 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.948 s 1.171746
2021-10-09 03:04 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.090 s 0.520195
2021-10-09 04:02 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.479885
2021-10-09 03:24 R dataframe-to-table type_strings, R 0.488 s 0.231401
2021-10-09 03:33 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.384 s 0.273309
2021-10-09 03:33 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.110 s 1.404989
2021-10-09 02:47 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.023 s 0.209395
2021-10-09 03:01 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.042 s 0.207287
2021-10-09 03:05 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.435 s 0.610955
2021-10-09 03:10 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.893 s -2.921052
2021-10-09 03:01 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.934 s -0.412343
2021-10-09 03:01 Python file-read lz4, feather, table, fanniemae_2016Q4 0.597 s 1.015148
2021-10-09 03:07 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.840 s -0.120319
2021-10-09 03:08 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.941 s -1.536440
2021-10-09 03:10 Python file-write lz4, feather, table, nyctaxi_2010-01 1.823 s -0.670824
2021-10-09 03:03 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.491 s -0.714763
2021-10-09 03:09 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.372 s -1.160713
2021-10-09 02:43 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.148 s 0.271481
2021-10-09 03:10 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.422 s -1.891842
2021-10-09 02:47 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.996 s 0.386573
2021-10-09 03:02 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.180 s -0.854347
2021-10-09 03:05 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.278 s 0.397247
2021-10-09 03:07 Python file-write lz4, feather, table, fanniemae_2016Q4 1.167 s -0.362590
2021-10-09 03:02 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.047 s -0.655804
2021-10-09 03:03 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.334261
2021-10-09 03:03 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.966 s -0.583783
2021-10-09 03:34 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.981 s 0.161544
2021-10-09 03:35 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.519 s 0.233929
2021-10-09 03:50 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.169 s 0.781705
2021-10-09 03:53 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -1.221381
2021-10-09 03:53 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s -0.340338
2021-10-09 04:02 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.543209
2021-10-09 04:02 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -2.438291
2021-10-09 04:02 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.272831
2021-10-09 03:31 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.205 s 0.784029
2021-10-09 03:31 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.444 s 2.029261
2021-10-09 03:31 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.466887
2021-10-09 03:33 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.171 s 2.070529
2021-10-09 03:49 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.237 s 0.976555
2021-10-09 03:53 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.590 s 0.069500
2021-10-09 04:02 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.253504
2021-10-09 04:02 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.189605
2021-10-09 04:02 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 0.801020
2021-10-09 03:50 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.590 s -0.028020
2021-10-09 04:02 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.597 s -0.432069
2021-10-09 04:02 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.864 s 0.364992
2021-10-09 03:32 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.927 s -0.261476
2021-10-09 03:49 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.486 s 0.699256
2021-10-09 04:02 JavaScript Parse readBatches, tracks 0.000 s 0.102000
2021-10-09 04:02 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.527413
2021-10-09 04:02 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.587000
2021-10-09 04:02 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.174363
2021-10-09 03:31 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.447 s 2.091662
2021-10-09 03:43 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.197 s 0.599168
2021-10-09 04:02 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.525537
2021-10-09 04:02 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.278220
2021-10-09 03:32 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.381164
2021-10-09 03:33 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.061 s -0.764502
2021-10-09 03:33 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.228 s 2.017706
2021-10-09 03:51 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.008633
2021-10-09 03:52 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.907 s 0.222715
2021-10-09 04:02 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.129990
2021-10-09 04:02 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.234877
2021-10-09 04:02 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.669408
2021-10-09 03:31 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.226 s 0.361598
2021-10-09 04:02 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.724 s 0.087539
2021-10-09 04:02 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-09 04:02 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.720903
2021-10-09 04:02 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.513 s 0.040107
2021-10-09 03:30 R dataframe-to-table type_simple_features, R 3.308 s 1.688427
2021-10-09 03:36 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.835 s 0.660389
2021-10-09 03:37 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.232 s 0.877016
2021-10-09 03:39 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.705 s 0.766144
2021-10-09 03:54 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.183 s 0.156000
2021-10-09 03:40 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.494165
2021-10-09 03:41 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.539 s 1.170253
2021-10-09 03:45 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.863 s -0.863033
2021-10-09 03:46 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.538 s -0.825692
2021-10-09 03:53 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.487 s -1.379912
2021-10-09 03:38 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.302 s 0.543721
2021-10-09 03:42 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.406 s -0.870272
2021-10-09 03:44 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.897 s -0.769611
2021-10-09 03:54 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -1.065352
2021-10-09 03:54 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.502 s -0.344940