Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 05:26 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.082 s -1.580413
2021-10-10 05:28 Python csv-read gzip, file, fanniemae_2016Q4 6.030 s 0.220822
2021-10-10 05:29 Python csv-read gzip, streaming, nyctaxi_2010-01 10.872 s -2.079432
2021-10-10 05:31 Python dataframe-to-table chi_traffic_2020_Q1 19.551 s 0.085734
2021-10-10 05:31 Python dataframe-to-table type_strings 0.368 s 0.362786
2021-10-10 05:31 Python dataframe-to-table type_floats 0.011 s 1.271538
2021-10-10 05:32 Python dataframe-to-table type_nested 2.879 s 0.072421
2021-10-10 05:53 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.088 s -3.397788
2021-10-10 05:53 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.022 s -0.047740
2021-10-10 06:04 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.961 s 0.689152
2021-10-10 06:05 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.832 s 0.904804
2021-10-10 06:05 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.773 s 0.546764
2021-10-10 06:06 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.922 s 0.170662
2021-10-10 06:06 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.003 s 1.776561
2021-10-10 06:08 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.455 s -0.350857
2021-10-10 06:08 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s 0.011736
2021-10-10 06:08 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.970 s -0.463191
2021-10-10 06:09 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.068 s 0.703252
2021-10-10 06:11 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.434 s -0.737819
2021-10-10 06:12 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.872 s -0.474962
2021-10-10 06:14 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.342 s 0.649230
2021-10-10 06:14 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.362 s -0.275692
2021-10-10 06:14 Python file-write lz4, feather, table, nyctaxi_2010-01 1.815 s -0.233471
2021-10-10 06:15 Python wide-dataframe use_legacy_dataset=false 0.624 s -0.408477
2021-10-10 06:28 R dataframe-to-table type_strings, R 0.495 s 0.230794
2021-10-10 06:28 R dataframe-to-table type_dict, R 0.049 s 0.152716
2021-10-10 06:28 R dataframe-to-table type_floats, R 0.013 s 1.679241
2021-10-10 06:34 R dataframe-to-table type_simple_features, R 3.316 s 1.343384
2021-10-10 06:34 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.230 s 0.285997
2021-10-10 06:35 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.212 s 0.950767
2021-10-10 06:35 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.438 s 1.560812
2021-10-10 06:36 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.046 s -3.741838
2021-10-10 06:36 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.390 s -0.133960
2021-10-10 06:37 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.044 s 1.944633
2021-10-10 06:37 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.183 s 1.575659
2021-10-10 06:37 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.247 s 1.547928
2021-10-10 06:37 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.218 s -5.950504
2021-10-10 06:38 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.976 s 0.229830
2021-10-10 06:39 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.504 s 0.382698
2021-10-10 06:39 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.837 s 0.645218
2021-10-10 06:47 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.895 s -0.631000
2021-10-10 06:48 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.859 s -0.711784
2021-10-10 06:49 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.528 s -0.540331
2021-10-10 06:51 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.731 s -0.935064
2021-10-10 06:51 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.274 s 3.586986
2021-10-10 06:52 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.230 s 1.488821
2021-10-10 06:54 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.156 s 1.606137
2021-10-10 06:54 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.587 s -0.232156
2021-10-10 06:54 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.998 s -3.067940
2021-10-10 06:54 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.101 s -4.212738
2021-10-10 06:55 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.186 s -0.765347
2021-10-10 06:55 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 0.645969
2021-10-10 06:56 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.132936
2021-10-10 06:56 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.876 s 1.603889
2021-10-10 06:56 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.513 s 1.188865
2021-10-10 06:57 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.132062
2021-10-10 06:57 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.488 s -1.313145
2021-10-10 06:58 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.159 s 0.919374
2021-10-10 07:05 JavaScript Parse Table.from, tracks 0.000 s 0.199462
2021-10-10 06:43 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.698 s 0.774963
2021-10-10 07:05 JavaScript Parse serialize, tracks 0.005 s -0.382550
2021-10-10 07:05 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.864090
2021-10-10 07:05 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.604 s -0.368326
2021-10-10 07:05 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.550 s -0.302905
2021-10-10 07:05 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.637 s 0.989930
2021-10-10 07:05 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.698 s 0.260797
2021-10-10 07:05 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.262985
2021-10-10 07:05 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.838 s 1.400424
2021-10-10 07:05 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574060
2021-10-10 07:05 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.504203
2021-10-10 07:05 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -2.286135
2021-10-10 07:05 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.190895
2021-10-10 07:05 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.131396
2021-10-10 07:06 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.751966
2021-10-10 07:06 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.096138
2021-10-10 07:06 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.536075
2021-10-10 07:06 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.179609
2021-10-10 07:06 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.287702
2021-10-10 07:06 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.272616
2021-10-10 06:04 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.797 s 0.480965
2021-10-10 06:05 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.284 s 0.847099
2021-10-10 06:06 Python file-read lz4, feather, table, fanniemae_2016Q4 0.596 s 1.262036
2021-10-10 06:06 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.270 s -0.179167
2021-10-10 06:14 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.905 s -0.493028
2021-10-10 05:39 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.759 s -0.060086
2021-10-10 06:10 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.426 s 0.696457
2021-10-10 06:28 R dataframe-to-table type_integers, R 0.010 s 1.677456
2021-10-10 06:35 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.453 s 1.592903
2021-10-10 06:37 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.106 s 1.603468
2021-10-10 07:05 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.529419
2021-10-10 05:27 Python csv-read gzip, streaming, fanniemae_2016Q4 15.128 s -2.673917
2021-10-10 05:31 Python dataframe-to-table type_integers 0.011 s 0.678739
2021-10-10 05:32 Python dataframe-to-table type_simple_features 0.961 s -2.492270
2021-10-10 05:32 Python dataset-filter nyctaxi_2010-01 4.320 s 1.643799
2021-10-10 05:53 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.089 s -0.635820
2021-10-10 06:04 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.693 s 0.557302
2021-10-10 06:10 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.570 s 1.071025
2021-10-10 06:28 R dataframe-to-table chi_traffic_2020_Q1, R 3.357 s 0.274897
2021-10-10 06:11 Python file-write lz4, feather, table, fanniemae_2016Q4 1.146 s 1.137484
2021-10-10 06:13 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.963 s -0.955257
2021-10-10 06:41 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.247 s 0.723346
2021-10-10 06:53 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.468 s 3.296843
2021-10-10 05:31 Python dataframe-to-table type_dict 0.012 s 0.176944
2021-10-10 06:03 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.874 s 0.025605
2021-10-10 06:07 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.328 s -0.495592
2021-10-10 06:55 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.527 s -1.055325
2021-10-10 07:05 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.741732
2021-10-10 05:28 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.781 s -1.060378
2021-10-10 05:28 Python csv-read uncompressed, file, nyctaxi_2010-01 1.009 s 0.208757
2021-10-10 05:49 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.260 s 0.201912
2021-10-10 06:09 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.224 s 0.936338
2021-10-10 06:13 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.882 s -0.595699
2021-10-10 06:15 Python wide-dataframe use_legacy_dataset=true 0.392 s 1.182383
2021-10-10 06:57 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.207 s -0.719778
2021-10-10 07:05 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.881 s -0.067996
2021-10-10 07:05 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-10 07:06 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.745291
2021-10-10 07:06 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.147950
2021-10-10 07:06 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.479 s 0.632050
2021-10-10 05:26 Python csv-read uncompressed, file, fanniemae_2016Q4 1.163 s 0.685155
2021-10-10 05:29 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 0.979897
2021-10-10 06:05 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.266 s 1.089646
2021-10-10 06:05 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.118 s 1.338709
2021-10-10 06:06 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.223 s 0.405958
2021-10-10 06:11 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.758 s 0.254115
2021-10-10 06:15 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.899 s -3.308023
2021-10-10 06:36 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.557 s 1.052371
2021-10-10 05:35 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.713 s 0.595673
2021-10-10 06:07 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.985 s 3.414872
2021-10-10 06:12 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.221 s 0.852920
2021-10-10 06:35 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.319 s -5.184813
2021-10-10 06:44 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.809 s 3.795111
2021-10-10 06:45 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.377 s 4.025387
2021-10-10 06:47 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.177 s 1.373280
2021-10-10 07:05 JavaScript Parse readBatches, tracks 0.000 s -0.268665
2021-10-10 07:05 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.866458
2021-10-10 05:49 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.733 s -0.244379
2021-10-10 06:04 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.271 s -0.904002
2021-10-10 06:42 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.285 s 0.680730
2021-10-10 06:45 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.553 s 0.509222
2021-10-10 06:03 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.605 s -2.878967
2021-10-10 06:07 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.171 s 1.078224
2021-10-10 06:28 R dataframe-to-table type_nested, R 0.531 s 0.235144
2021-10-10 06:38 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.690 s 0.041958
2021-10-10 06:54 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.565 s 1.426746
2021-10-10 06:57 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.359 s -0.282197
2021-10-10 06:58 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.483 s 1.578284
2021-10-10 07:05 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.753010
2021-10-10 07:05 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.644112