Outliers: 6


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-07 15:47 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.621 s -0.357597
2021-10-07 15:48 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.716368
2021-10-07 15:47 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.621 s -0.357597
2021-10-07 15:49 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.775007
2021-10-07 15:49 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.737 s -0.948158
2021-10-07 15:49 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.775007
2021-10-07 15:50 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.132987
2021-10-07 15:50 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.778 s -0.182324
2021-10-07 15:53 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.103775
2021-10-07 15:53 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.136214
2021-10-07 15:51 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.867 s 0.278267
2021-10-07 15:52 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.553649
2021-10-07 15:52 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.899 s 0.118162
2021-10-07 15:51 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.606469
2021-10-07 15:52 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.631425
2021-10-07 15:54 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.378470
2021-10-07 15:53 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.057364
2021-10-07 15:53 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.823647
2021-10-07 15:52 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.057364
2021-10-07 15:55 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.367619
2021-10-07 15:54 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.067961
2021-10-07 15:55 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.812471
2021-10-07 15:56 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.487 s 0.561382
2021-10-07 15:55 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.610682
2021-10-07 15:56 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.866717
2021-10-07 15:56 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.875111
2021-10-07 13:34 Python csv-read gzip, streaming, fanniemae_2016Q4 14.903 s -0.378244
2021-10-07 13:36 Python csv-read gzip, streaming, nyctaxi_2010-01 10.489 s 1.157376
2021-10-07 13:33 Python csv-read uncompressed, file, fanniemae_2016Q4 1.150 s 1.423420
2021-10-07 13:33 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.979 s -0.454350
2021-10-07 13:35 Python csv-read gzip, file, fanniemae_2016Q4 6.039 s -1.867778
2021-10-07 13:35 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.492 s 1.200434
2021-10-07 13:35 Python csv-read uncompressed, file, nyctaxi_2010-01 1.018 s -0.495641
2021-10-07 13:38 Python dataframe-to-table chi_traffic_2020_Q1 19.396 s 0.964282
2021-10-07 13:39 Python dataframe-to-table type_integers 0.011 s 0.890650
2021-10-07 13:39 Python dataframe-to-table type_floats 0.011 s 1.101091
2021-10-07 13:37 Python csv-read gzip, file, nyctaxi_2010-01 9.040 s 1.613676
2021-10-07 13:40 Python dataframe-to-table type_simple_features 0.914 s -0.136839
2021-10-07 13:39 Python dataframe-to-table type_dict 0.012 s -0.065823
2021-10-07 13:39 Python dataframe-to-table type_strings 0.370 s -0.069086
2021-10-07 13:40 Python dataframe-to-table type_nested 2.875 s 0.662771
2021-10-07 13:40 Python dataset-filter nyctaxi_2010-01 4.353 s 0.655850
2021-10-07 13:44 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 73.418 s -3.126763
2021-10-07 13:48 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 72.707 s 0.674800
2021-10-07 14:02 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.043 s -0.181775
2021-10-07 14:14 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.853 s 0.222280
2021-10-07 14:16 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.795 s -0.687246
2021-10-07 14:17 Python file-read lz4, feather, table, fanniemae_2016Q4 0.607 s -0.708523
2021-10-07 14:24 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.765 s 0.839788
2021-10-07 14:25 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.823 s 0.559152
2021-10-07 14:27 Python wide-dataframe use_legacy_dataset=false 0.627 s -1.307344
2021-10-07 14:03 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.016 s 0.294009
2021-10-07 14:14 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.603 s -5.161722
2021-10-07 14:17 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.086 s -1.490701
2021-10-07 14:26 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.357 s -0.539670
2021-10-07 14:03 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.008 s 0.207465
2021-10-07 14:14 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.708 s 0.387777
2021-10-07 14:14 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.998 s 0.068233
2021-10-07 14:15 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.260 s -0.380966
2021-10-07 14:15 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.847 s -0.483123
2021-10-07 14:16 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.946 s -0.914991
2021-10-07 14:17 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.252 s -0.898444
2021-10-07 14:15 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.295 s -0.154369
2021-10-07 14:16 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.156 s -0.570597
2021-10-07 14:16 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.299 s -1.369636
2021-10-07 14:26 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.347 s 0.210633
2021-10-07 14:15 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.869 s -0.735115
2021-10-07 14:19 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.466 s -0.890010
2021-10-07 14:23 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.318 s -1.157233
2021-10-07 14:17 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.294 s -0.842810
2021-10-07 14:18 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.292 s -0.787037
2021-10-07 14:22 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.336 s -0.031780
2021-10-07 14:18 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.057 s -1.331488
2021-10-07 14:56 R dataframe-to-table type_integers, R 0.082 s -0.010521
2021-10-07 14:18 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s 0.109854
2021-10-07 14:27 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.809 s -0.253504
2021-10-07 14:19 Python file-read lz4, feather, table, nyctaxi_2010-01 0.665 s 0.729737
2021-10-07 14:26 Python file-write lz4, feather, table, nyctaxi_2010-01 1.833 s -1.326509
2021-10-07 15:08 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.485 s 4.933665
2021-10-07 15:10 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.397 s -0.663584
2021-10-07 15:13 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.535 s -0.086363
2021-10-07 13:58 Python dataset-read async=True, nyctaxi_multi_ipc_s3 179.949 s 0.985563
2021-10-07 14:20 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.980 s -0.954433
2021-10-07 14:57 R dataframe-to-table type_nested, R 17.345 s -5.674923
2021-10-07 15:10 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.062 s -0.943951
2021-10-07 13:58 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.066 s 1.260319
2021-10-07 14:20 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.077 s 0.670362
2021-10-07 14:24 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.782 s 0.854002
2021-10-07 14:21 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.290 s 0.155446
2021-10-07 14:22 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.688 s -0.026468
2021-10-07 14:54 R dataframe-to-table chi_traffic_2020_Q1, R 301.895 s -4.991333
2021-10-07 15:12 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.669 s 0.204139
2021-10-07 15:14 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.849 s 0.597124
2021-10-07 14:21 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.443 s 0.599841
2021-10-07 14:25 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.839 s 0.931799
2021-10-07 14:27 Python wide-dataframe use_legacy_dataset=true 0.394 s 0.248159
2021-10-07 15:08 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.254 s -0.033850
2021-10-07 14:23 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.818 s -0.808782
2021-10-07 14:55 R dataframe-to-table type_strings, R 17.208 s -5.714687
2021-10-07 14:23 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.232142
2021-10-07 14:55 R dataframe-to-table type_dict, R 0.053 s -0.304268
2021-10-07 15:07 R dataframe-to-table type_simple_features, R 3.254 s 3.035466
2021-10-07 15:15 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.255 s 0.768158
2021-10-07 15:18 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.720 s 0.727399
2021-10-07 15:21 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.103069
2021-10-07 15:22 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.186 s 1.323906
2021-10-07 15:23 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.817 s 1.000748
2021-10-07 15:24 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.781 s 1.086286
2021-10-07 15:25 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.470 s 0.683519
2021-10-07 15:27 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.632 s 1.321806
2021-10-07 15:28 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -1.071017
2021-10-07 15:30 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.485 s 0.968985
2021-10-07 15:34 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.111 s -1.510034
2021-10-07 15:34 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s 0.109131
2021-10-07 14:56 R dataframe-to-table type_floats, R 0.107 s -0.027941
2021-10-07 15:07 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.250 s 0.212620
2021-10-07 15:19 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.828 s 0.800042
2021-10-07 15:20 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.547 s 1.045262
2021-10-07 15:29 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.235 s 1.404453
2021-10-07 15:31 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.171 s 0.805927
2021-10-07 15:31 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.587 s 0.489688
2021-10-07 15:07 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.484 s 5.698990
2021-10-07 15:31 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.861 s 0.627365
2021-10-07 15:08 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.924267
2021-10-07 15:32 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.590 s 0.351305
2021-10-07 15:09 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.920 s -0.007116
2021-10-07 15:32 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.300445
2021-10-07 15:32 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.599 s 0.577026
2021-10-07 15:09 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.937174
2021-10-07 15:32 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.824357
2021-10-07 15:33 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.518 s -0.140790
2021-10-07 15:10 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.176 s 5.715577
2021-10-07 15:33 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.617 s -0.996034
2021-10-07 15:11 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.125 s 0.064455
2021-10-07 15:33 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.892 s 0.582219
2021-10-07 15:11 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.251 s 5.711971
2021-10-07 15:34 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.520 s 1.111154
2021-10-07 15:11 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.136711
2021-10-07 15:16 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.301 s 0.587704
2021-10-07 15:34 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.483 s -1.227698
2021-10-07 15:12 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.984 s 0.116041
2021-10-07 15:35 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -1.310367
2021-10-07 15:35 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.200 s 0.504514
2021-10-07 15:36 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.508 s -1.206503
2021-10-07 15:44 JavaScript Parse readBatches, tracks 0.000 s 1.096654
2021-10-07 15:43 JavaScript Parse Table.from, tracks 0.000 s 0.933748
2021-10-07 15:44 JavaScript Parse readBatches, tracks 0.000 s 1.096654
2021-10-07 15:45 JavaScript Parse serialize, tracks 0.005 s -0.734895
2021-10-07 15:43 JavaScript Parse Table.from, tracks 0.000 s 0.933748
2021-10-07 15:49 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.737 s -0.948158
2021-10-07 15:45 JavaScript Parse serialize, tracks 0.005 s -0.734895
2021-10-07 15:47 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.565 s -0.281109
2021-10-07 15:45 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.912332
2021-10-07 15:45 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.912332
2021-10-07 15:46 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.916903
2021-10-07 15:46 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.916903
2021-10-07 15:48 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.716368
2021-10-07 15:54 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.505646