Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 15:25 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.513 s 1.399213
2021-10-08 15:27 Python dataframe-to-table chi_traffic_2020_Q1 19.312 s 1.805755
2021-10-08 15:28 Python dataframe-to-table type_integers 0.011 s 1.438717
2021-10-08 16:02 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.847 s -0.590735
2021-10-08 16:08 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.438 s 1.010054
2021-10-08 16:12 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.318 s 0.470229
2021-10-08 17:09 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 0.895865
2021-10-08 17:10 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.898 s 0.890720
2021-10-08 17:20 JavaScript Parse Table.from, tracks 0.000 s 0.895329
2021-10-08 17:20 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.104249
2021-10-08 17:20 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.165123
2021-10-08 17:20 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.351783
2021-10-08 17:20 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.418509
2021-10-08 17:20 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.488593
2021-10-08 17:20 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.999084
2021-10-08 15:23 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.931 s -0.248426
2021-10-08 15:23 Python csv-read uncompressed, file, fanniemae_2016Q4 1.175 s -0.059292
2021-10-08 15:24 Python csv-read gzip, streaming, fanniemae_2016Q4 14.868 s -0.238982
2021-10-08 15:28 Python dataframe-to-table type_dict 0.012 s 0.119206
2021-10-08 16:02 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.247 s -0.153884
2021-10-08 15:28 Python dataframe-to-table type_strings 0.375 s -0.450386
2021-10-08 15:31 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.207 s -0.152223
2021-10-08 15:49 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.018 s 0.070320
2021-10-08 16:01 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.878 s 0.126523
2021-10-08 16:01 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.756 s -0.022622
2021-10-08 16:09 Python file-write lz4, feather, table, fanniemae_2016Q4 1.154 s 0.645974
2021-10-08 16:05 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.333 s -1.535353
2021-10-08 16:06 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.465 s -1.442166
2021-10-08 16:10 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.267 s -0.381157
2021-10-08 16:06 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.976 s -1.502615
2021-10-08 16:07 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.081 s 1.025553
2021-10-08 15:49 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.028 s 0.070928
2021-10-08 16:01 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.927 s 0.587631
2021-10-08 16:06 Python file-read lz4, feather, table, nyctaxi_2010-01 0.663 s 1.260489
2021-10-08 16:09 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.361 s -0.149834
2021-10-08 15:24 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.796135
2021-10-08 15:28 Python dataframe-to-table type_floats 0.011 s 1.674328
2021-10-08 16:05 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.819835
2021-10-08 16:11 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.772 s 0.867112
2021-10-08 16:12 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.348 s 0.191891
2021-10-08 16:13 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.414990
2021-10-08 15:26 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.306119
2021-10-08 15:36 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.528 s 0.890905
2021-10-08 15:25 Python csv-read gzip, streaming, nyctaxi_2010-01 10.487 s 1.570950
2021-10-08 16:03 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.312 s -1.029903
2021-10-08 16:03 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.148 s -0.432922
2021-10-08 16:04 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.926 s -1.105778
2021-10-08 15:28 Python dataframe-to-table type_simple_features 0.909 s 0.380947
2021-10-08 15:25 Python csv-read uncompressed, file, nyctaxi_2010-01 1.018 s -0.441122
2021-10-08 15:28 Python dataset-filter nyctaxi_2010-01 4.354 s 0.546222
2021-10-08 16:04 Python file-read lz4, feather, table, fanniemae_2016Q4 0.609 s -0.987581
2021-10-08 16:05 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.027 s 0.633645
2021-10-08 16:03 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.794 s -1.297460
2021-10-08 16:03 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.284 s 0.968105
2021-10-08 15:45 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.207 s 0.555201
2021-10-08 16:08 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.672 s 0.356869
2021-10-08 16:10 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.784 s 1.411569
2021-10-08 16:12 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.815 s 0.817037
2021-10-08 16:12 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.414934
2021-10-08 15:28 Python dataframe-to-table type_nested 2.877 s 1.013907
2021-10-08 16:13 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.794 s 0.432561
2021-10-08 16:04 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.247 s -1.336369
2021-10-08 16:03 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.899 s -1.880674
2021-10-08 16:04 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.321 s -1.506976
2021-10-08 15:45 Python dataset-read async=True, nyctaxi_multi_ipc_s3 187.707 s 0.087000
2021-10-08 16:07 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.296 s 0.403382
2021-10-08 16:11 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.854 s 1.296039
2021-10-08 16:04 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.089 s -1.620379
2021-10-08 16:09 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.820 s -0.573568
2021-10-08 15:49 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.043 s -0.077383
2021-10-08 16:02 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.010 s -0.353054
2021-10-08 16:13 Python wide-dataframe use_legacy_dataset=false 0.617 s 0.800291
2021-10-08 16:46 R dataframe-to-table type_simple_features, R 274.760 s 0.460244
2021-10-08 16:52 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.017 s -2.313373
2021-10-08 16:49 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.566 s -0.395161
2021-10-08 16:49 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.380 s 0.244354
2021-10-08 16:51 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.342803
2021-10-08 16:57 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.723 s 1.069834
2021-10-08 17:01 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.187 s 1.120105
2021-10-08 17:05 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.673822
2021-10-08 17:10 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.999117
2021-10-08 17:09 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.512 s 0.657219
2021-10-08 17:12 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.507 s 0.070083
2021-10-08 17:20 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.232495
2021-10-08 16:48 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.572222
2021-10-08 17:20 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.527821
2021-10-08 17:20 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.527821
2021-10-08 16:26 R dataframe-to-table chi_traffic_2020_Q1, R 3.394 s 36.957685
2021-10-08 16:26 R dataframe-to-table type_strings, R 0.492 s -0.041301
2021-10-08 16:27 R dataframe-to-table type_floats, R 0.012 s 37.410292
2021-10-08 17:03 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.782 s 1.898652
2021-10-08 17:11 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.482 s -1.770821
2021-10-08 17:20 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.561549
2021-10-08 16:26 R dataframe-to-table type_dict, R 0.060 s -1.000768
2021-10-08 16:27 R dataframe-to-table type_integers, R 0.010 s 73.567139
2021-10-08 17:20 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.595208
2021-10-08 16:47 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.246 s 0.060914
2021-10-08 16:58 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.833 s -0.367111
2021-10-08 17:20 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.149130
2021-10-08 17:20 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.542319
2021-10-08 17:20 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.941371
2021-10-08 17:20 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.249011
2021-10-08 17:20 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.009471
2021-10-08 16:27 R dataframe-to-table type_nested, R 0.542 s -1.870460
2021-10-08 16:49 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.925 s -0.318456
2021-10-08 17:20 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.536 s -0.479180
2021-10-08 17:06 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.242 s 1.155300
2021-10-08 16:47 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.251 s 0.204738
2021-10-08 17:11 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.359 s 0.479809
2021-10-08 17:20 JavaScript Parse readBatches, tracks 0.000 s 0.381593
2021-10-08 17:20 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.331756
2021-10-08 17:20 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.684 s 0.364464
2021-10-08 17:20 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.410380
2021-10-08 16:48 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.968 s -0.461273
2021-10-08 17:01 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.821 s 1.583042
2021-10-08 17:05 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.635 s 2.210379
2021-10-08 16:59 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.399 s 0.353778
2021-10-08 17:07 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.491 s -0.190226
2021-10-08 17:08 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.590 s 0.733299
2021-10-08 17:08 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.868 s 0.985547
2021-10-08 17:09 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.990018
2021-10-08 17:20 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.591 s -0.194369
2021-10-08 16:52 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.685 s -0.009019
2021-10-08 16:59 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.545 s 1.107813
2021-10-08 17:08 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.577 s 0.867026
2021-10-08 16:55 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.251 s 1.134527
2021-10-08 16:56 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.303 s 0.975637
2021-10-08 17:12 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.194 s 0.781364
2021-10-08 17:20 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.127794
2021-10-08 17:20 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.675 s 0.168456
2021-10-08 17:20 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.583832
2021-10-08 17:20 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.899 s -0.416591
2021-10-08 16:53 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.544 s -0.831924
2021-10-08 17:09 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.355909
2021-10-08 17:12 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -3.417442
2021-10-08 17:20 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.128926
2021-10-08 16:47 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.923 s 0.195635
2021-10-08 16:50 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.138 s -0.681617
2021-10-08 16:51 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.216 s 1.515993
2021-10-08 16:54 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.849 s 0.980033
2021-10-08 17:20 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.810 s 2.028059
2021-10-08 16:50 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.139 s 2.300961
2021-10-08 17:04 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.458 s 1.590125
2021-10-08 17:11 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.111 s -4.207486
2021-10-08 17:20 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.576 s -0.231891
2021-10-08 16:49 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.052 s 0.818663
2021-10-08 17:08 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.165 s 1.601637
2021-10-08 17:10 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.517 s 1.243408
2021-10-08 17:20 JavaScript Parse serialize, tracks 0.005 s -0.552004