Outliers: 5


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-28 14:42 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.707 s -1.741026
2021-09-28 14:25 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.193 s 0.548950
2021-09-28 14:42 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.312 s -2.414723
2021-09-28 14:43 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.799 s 0.778102
2021-09-28 14:40 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.508 s -2.605518
2021-09-28 14:41 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.033 s -1.454203
2021-09-28 14:42 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.879 s -2.186559
2021-09-28 14:43 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.847 s 0.929222
2021-09-28 14:04 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s -0.017206
2021-09-28 14:08 Python dataframe-to-table type_integers 0.011 s 0.054709
2021-09-28 14:04 Python csv-read gzip, streaming, fanniemae_2016Q4 15.011 s -0.941904
2021-09-28 14:03 Python csv-read uncompressed, file, fanniemae_2016Q4 1.180 s -0.152236
2021-09-28 14:30 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.004 s 0.436164
2021-09-28 14:08 Python dataframe-to-table type_dict 0.012 s 0.251087
2021-09-28 14:30 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.010 s 0.374211
2021-09-28 14:25 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.832 s -0.246720
2021-09-28 14:42 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.167 s -2.199661
2021-09-28 14:06 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s -0.042069
2021-09-28 14:40 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.937 s 0.514007
2021-09-28 14:41 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.280 s -1.571624
2021-09-28 14:08 Python dataframe-to-table type_simple_features 0.912 s -0.543275
2021-09-28 14:16 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.020 s 5.727640
2021-09-28 14:43 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.131 s -1.789038
2021-09-28 14:08 Python dataframe-to-table type_nested 2.960 s -0.251900
2021-09-28 14:42 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.286 s 0.695428
2021-09-28 14:03 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.068 s -0.927900
2021-09-28 14:05 Python csv-read uncompressed, file, nyctaxi_2010-01 1.009 s 0.175301
2021-09-28 14:44 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.182 s -1.117112
2021-09-28 14:05 Python csv-read gzip, streaming, nyctaxi_2010-01 10.663 s -0.528218
2021-09-28 14:08 Python dataframe-to-table type_strings 0.364 s 0.928017
2021-09-28 14:44 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.035 s 0.153366
2021-09-28 14:08 Python dataframe-to-table type_floats 0.012 s -0.755288
2021-09-28 14:08 Python dataset-filter nyctaxi_2010-01 4.396 s -1.359834
2021-09-28 14:11 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.705 s -0.820442
2021-09-28 14:04 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.675 s -0.531519
2021-09-28 14:42 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.766 s -1.923108
2021-09-28 14:44 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.872 s 0.772478
2021-09-28 14:45 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.954 s 1.026816
2021-09-28 14:46 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.133 s 1.399231
2021-09-28 14:45 Python file-read lz4, feather, table, nyctaxi_2010-01 0.662 s 1.544550
2021-09-28 14:45 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.481 s 0.940315
2021-09-28 14:47 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.425 s 1.632495
2021-09-28 14:46 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.090 s 1.480254
2021-09-28 14:47 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.454 s 1.592153
2021-09-28 14:48 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.440 s -0.894261
2021-09-28 15:36 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.525 s -0.546951
2021-09-28 14:48 Python file-write lz4, feather, table, fanniemae_2016Q4 1.183 s -2.129325
2021-09-28 14:48 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.486 s 1.872552
2021-09-28 15:32 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.409 s -1.677935
2021-09-28 15:45 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.835 s 2.901728
2021-09-28 15:06 R dataframe-to-table type_floats, R 0.108 s 0.461755
2021-09-28 16:02 JavaScript Parse Table.from, tracks 0.000 s -0.180891
2021-09-28 14:07 Python dataframe-to-table chi_traffic_2020_Q1 19.774 s 0.110506
2021-09-28 14:30 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.019 s -0.069093
2021-09-28 14:41 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.702 s 0.435584
2021-09-28 14:43 Python file-read lz4, feather, table, fanniemae_2016Q4 0.602 s -0.156578
2021-09-28 14:43 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.022 s 0.718639
2021-09-28 14:49 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.775 s 2.782194
2021-09-28 15:06 R dataframe-to-table type_integers, R 0.083 s 1.440016
2021-09-28 14:50 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.721 s 1.400439
2021-09-28 14:52 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.026901
2021-09-28 15:31 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.949 s -0.298310
2021-09-28 16:03 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.550044
2021-09-28 14:51 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.303 s 3.061979
2021-09-28 14:51 Python file-write lz4, feather, table, nyctaxi_2010-01 1.860 s -2.663943
2021-09-28 15:06 R dataframe-to-table type_strings, R 0.491 s -0.106405
2021-09-28 15:30 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.247 s 0.061443
2021-09-28 15:32 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.921 s -0.316904
2021-09-28 15:42 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.581 s 0.933016
2021-09-28 16:03 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.639 s 0.667761
2021-09-28 16:03 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.028 s -3.170939
2021-09-28 14:49 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.075 s 1.310198
2021-09-28 15:54 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.300469
2021-09-28 16:03 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.596 s -0.118086
2021-09-28 14:51 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.779 s 1.259658
2021-09-28 15:06 R dataframe-to-table type_dict, R 0.054 s -0.301868
2021-09-28 15:33 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.108 s 1.624451
2021-09-28 15:41 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.832 s -0.340852
2021-09-28 15:44 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.223 s 0.811328
2021-09-28 15:48 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.667 s 3.025435
2021-09-28 15:52 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.542276
2021-09-28 16:03 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.125034
2021-09-28 16:03 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.110547
2021-09-28 15:32 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.573 s -2.267519
2021-09-28 15:42 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.094434
2021-09-28 16:03 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.497443
2021-09-28 15:06 R dataframe-to-table type_nested, R 0.538 s -0.287840
2021-09-28 15:31 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.338199
2021-09-28 15:34 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.251 s -0.598894
2021-09-28 15:49 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.257 s 0.655322
2021-09-28 15:53 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.660176
2021-09-28 15:55 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 1.393368
2021-09-28 16:03 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.676430
2021-09-28 15:51 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.875 s 2.878668
2021-09-28 15:52 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.177 s -0.079157
2021-09-28 15:53 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.989 s 5.917697
2021-09-28 16:03 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.894 s 0.085866
2021-09-28 15:34 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -0.887226
2021-09-28 15:52 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.527 s -1.549881
2021-09-28 16:03 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.632257
2021-09-28 16:03 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.399932
2021-09-28 14:52 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.754 s 0.724916
2021-09-28 15:50 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.493 s -0.575466
2021-09-28 16:02 JavaScript Parse serialize, tracks 0.005 s 0.450602
2021-09-28 16:03 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.661 s 0.484481
2021-09-28 16:03 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.872069
2021-09-28 14:50 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.847 s 2.786101
2021-09-28 15:33 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.182 s -0.789250
2021-09-28 15:40 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.768 s 1.372181
2021-09-28 15:51 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.197 s -0.231861
2021-09-28 15:52 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.604 s 5.716739
2021-09-28 15:55 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.495 s 0.155507
2021-09-28 16:03 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.477876
2021-09-28 14:52 Python wide-dataframe use_legacy_dataset=false 0.624 s -1.429226
2021-09-28 15:29 R dataframe-to-table type_simple_features, R 274.912 s -0.277963
2021-09-28 15:35 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.997 s -1.572446
2021-09-28 16:03 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.554 s 0.081964
2021-09-28 15:30 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.242 s 0.102339
2021-09-28 15:39 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.304 s 1.604623
2021-09-28 16:03 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.551624
2021-09-28 16:03 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.435248
2021-09-28 16:03 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.073360
2021-09-28 15:32 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.058 s -0.389606
2021-09-28 15:55 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.155 s 6.035154
2021-09-28 16:03 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 1.050145
2021-09-28 14:51 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.258 s 0.873437
2021-09-28 15:05 R dataframe-to-table chi_traffic_2020_Q1, R 5.363 s 0.739037
2021-09-28 15:30 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.916 s 0.027117
2021-09-28 15:38 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.294 s 1.502141
2021-09-28 15:54 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.473 s -0.004456
2021-09-28 16:02 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.024 s -0.052552
2021-09-28 16:03 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.439 s 1.179559
2021-09-28 15:35 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.682 s -1.109502
2021-09-28 15:37 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.848 s 1.660761
2021-09-28 15:47 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.466 s 2.895927
2021-09-28 15:51 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.582 s 4.261835
2021-09-28 16:02 JavaScript Parse readBatches, tracks 0.000 s -0.025057
2021-09-28 16:03 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.488800
2021-09-28 15:48 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.041331
2021-09-28 15:54 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.349 s 5.386247
2021-09-28 16:02 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.024 s -0.063438
2021-09-28 16:03 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.198011
2021-09-28 15:53 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.654 s -0.755034
2021-09-28 15:46 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.804 s 3.289046
2021-09-28 15:51 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 3.005822
2021-09-28 16:03 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.845 s 0.969178
2021-09-28 16:03 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.911297
2021-09-28 16:03 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.326842
2021-09-28 16:03 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.494149