Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-06 22:36 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.377203
2021-10-06 22:40 Python dataframe-to-table type_dict 0.012 s 1.030039
2021-10-06 22:40 Python dataframe-to-table type_strings 0.371 s 0.036513
2021-10-06 22:35 Python csv-read uncompressed, file, fanniemae_2016Q4 1.181 s -0.435766
2021-10-06 22:38 Python csv-read gzip, streaming, nyctaxi_2010-01 10.475 s 1.298518
2021-10-06 22:40 Python dataframe-to-table type_floats 0.011 s 0.501810
2021-10-06 22:35 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.960 s -0.349595
2021-10-06 22:44 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.094 s 0.415055
2021-10-06 22:36 Python csv-read gzip, streaming, fanniemae_2016Q4 14.897 s -0.367520
2021-10-06 22:37 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.490 s 1.231037
2021-10-06 22:40 Python dataframe-to-table type_integers 0.011 s 1.122408
2021-10-06 22:37 Python csv-read uncompressed, file, nyctaxi_2010-01 0.997 s 1.556797
2021-10-06 22:38 Python csv-read gzip, file, nyctaxi_2010-01 9.049 s -1.291291
2021-10-06 22:40 Python dataframe-to-table chi_traffic_2020_Q1 19.688 s -0.147121
2021-10-06 22:40 Python dataframe-to-table type_nested 2.882 s 0.576470
2021-10-06 22:40 Python dataframe-to-table type_simple_features 0.913 s -0.026985
2021-10-06 22:41 Python dataset-filter nyctaxi_2010-01 4.351 s 0.731231
2021-10-06 22:48 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.350 s 0.638557
2021-10-06 23:12 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.465 s -3.644936
2021-10-06 23:15 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.037 s 0.459009
2021-10-06 23:19 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.450 s 0.650720
2021-10-06 23:38 R dataframe-to-table type_dict, R 0.050 s 0.113128
2021-10-06 23:38 R dataframe-to-table type_floats, R 0.112 s -0.339829
2021-10-07 00:12 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.658 s -1.099036
2021-10-06 23:02 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.514 s -7.064282
2021-10-06 23:12 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.741 s 0.063184
2021-10-06 23:17 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.085 s 0.716087
2021-10-06 23:23 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.359 s -0.126706
2021-10-07 00:07 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.011079
2021-10-07 00:10 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.591 s 0.508501
2021-10-07 00:10 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.572 s 0.688272
2021-10-07 00:22 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.182547
2021-10-06 23:24 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.803 s 0.187501
2021-10-06 23:51 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.050 s 1.175682
2021-10-07 00:13 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.111 s -1.807602
2021-10-06 23:12 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.008 s -0.022242
2021-10-06 23:15 Python file-read lz4, feather, table, fanniemae_2016Q4 0.604 s -0.219376
2021-10-06 23:16 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.656015
2021-10-06 23:50 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.558 s 0.999601
2021-10-07 00:12 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.787758
2021-10-07 00:22 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.583 s -0.244125
2021-10-06 23:19 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.668 s 0.209807
2021-10-06 23:48 R dataframe-to-table type_simple_features, R 3.335 s 5.744726
2021-10-07 00:11 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.524 s -1.076348
2021-10-06 22:58 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.763 s -0.378235
2021-10-06 23:02 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 0.986 s 0.733063
2021-10-06 23:14 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.152 s -0.409412
2021-10-06 23:14 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.808 s -1.013805
2021-10-06 23:15 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.315 s -1.052325
2021-10-06 23:38 R dataframe-to-table type_strings, R 0.487 s 1.827557
2021-10-06 23:53 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.255 s -0.781563
2021-10-07 00:07 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.651 s 1.047427
2021-10-07 00:08 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.246 s 0.590792
2021-10-06 23:20 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.827 s -0.784190
2021-10-06 23:54 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.673 s 0.150963
2021-10-07 00:11 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.182 s -0.487502
2021-10-07 00:22 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -2.100072
2021-10-06 23:14 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.418143
2021-10-06 23:18 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.338 s 0.023612
2021-10-06 23:21 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.767 s 1.188741
2021-10-07 00:22 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.610 s -0.341240
2021-10-07 00:22 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.684 s 0.017314
2021-10-06 23:13 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.251 s -0.150968
2021-10-06 23:17 Python file-read lz4, feather, table, nyctaxi_2010-01 0.661 s 1.562234
2021-10-06 23:17 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 8.033 s -1.314085
2021-10-07 00:22 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.903 s -0.567098
2021-10-06 23:20 Python file-write lz4, feather, table, fanniemae_2016Q4 1.149 s 0.981348
2021-10-06 23:52 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.187 s -0.798944
2021-10-06 23:54 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.001 s -0.915223
2021-10-06 23:15 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.041 s -0.263900
2021-10-06 23:22 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.783 s 0.590828
2021-10-07 00:02 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.226 s -0.350878
2021-10-07 00:10 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.172 s 0.723131
2021-10-07 00:22 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.739 s 0.033933
2021-10-06 23:16 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.380 s -1.305219
2021-10-06 23:21 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.300 s -0.782683
2021-10-07 00:10 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.628989
2021-10-06 23:14 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.939 s -0.892718
2021-10-06 23:38 R dataframe-to-table type_nested, R 0.540 s -0.673267
2021-10-06 23:49 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.906 s 0.322769
2021-10-06 23:50 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.893 s 1.612496
2021-10-06 22:58 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.175 s 0.681045
2021-10-06 23:52 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.118 s 0.739444
2021-10-06 23:58 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.298 s 0.714503
2021-10-07 00:01 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.600 s -0.598258
2021-10-07 00:04 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.790 s 1.047393
2021-10-06 23:13 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.298 s -0.337720
2021-10-07 00:11 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.597 s 0.687241
2021-10-07 00:13 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.485 s -1.734938
2021-10-06 23:16 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.501 s -1.162918
2021-10-06 23:53 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -0.963435
2021-10-06 23:23 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.821 s 0.579006
2021-10-07 00:09 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.492 s -0.317443
2021-10-07 00:22 JavaScript Parse serialize, tracks 0.005 s 0.367442
2021-10-07 00:23 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.549823
2021-10-07 00:24 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500638
2021-10-06 23:02 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.511 s -7.517169
2021-10-06 23:15 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.251 s -0.989694
2021-10-07 00:00 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.839 s -1.452159
2021-10-07 00:22 JavaScript Parse readBatches, tracks 0.000 s 0.410347
2021-10-06 23:13 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.995 s 0.111132
2021-10-06 23:13 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.823 s 0.092898
2021-10-06 23:14 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.866 s -0.802702
2021-10-06 23:22 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.838 s 1.064688
2021-10-06 23:23 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.247058
2021-10-06 23:48 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.289 s -0.104696
2021-10-06 23:51 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.373 s 0.793115
2021-10-06 23:59 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.739 s 0.688883
2021-10-07 00:12 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.876 s 0.689982
2021-10-07 00:14 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.199 s 0.620208
2021-10-06 23:49 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.245 s 0.076317
2021-10-06 23:50 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.665191
2021-10-06 23:55 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.847 s 0.709384
2021-10-07 00:03 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.826 s 0.939146
2021-10-07 00:10 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.872 s 0.694777
2021-10-07 00:13 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s 0.337756
2021-10-07 00:22 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.165541
2021-10-06 23:20 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.323 s 0.076716
2021-10-06 23:23 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.349 s 0.091973
2021-10-06 23:24 Python wide-dataframe use_legacy_dataset=false 0.623 s -0.343669
2021-10-06 23:38 R dataframe-to-table type_integers, R 0.085 s -0.233053
2021-10-06 23:55 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.522 s 0.501501
2021-10-07 00:21 JavaScript Parse Table.from, tracks 0.000 s 0.542219
2021-10-07 00:22 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.110634
2021-10-07 00:24 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.324323
2021-10-06 23:24 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.400961
2021-10-06 23:38 R dataframe-to-table chi_traffic_2020_Q1, R 5.518 s -0.515042
2021-10-06 23:57 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.288 s 0.641377
2021-10-07 00:05 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.455 s 1.105918
2021-10-07 00:14 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.512 s 0.052895
2021-10-07 00:22 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.297309
2021-10-07 00:22 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.574798
2021-10-06 23:50 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.901 s 0.242329
2021-10-07 00:14 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.207 s -1.685640
2021-10-07 00:22 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.945 s -0.840903
2021-10-07 00:23 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.631425
2021-10-07 00:23 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500638
2021-10-07 00:24 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.192128
2021-10-07 00:24 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.192128
2021-10-07 00:25 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.176753
2021-10-07 00:25 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.176753
2021-10-07 00:25 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.176753
2021-10-07 00:27 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.455074
2021-10-07 00:27 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.448243
2021-10-07 00:27 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 1.041107
2021-10-07 00:28 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.592205
2021-10-07 00:28 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.163357
2021-10-07 00:29 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.289658
2021-10-07 00:29 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.289658
2021-10-07 00:29 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.109905
2021-10-07 00:29 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.109905
2021-10-07 00:30 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.096747
2021-10-07 00:30 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.096747
2021-10-07 00:31 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.498 s 0.229992
2021-10-07 00:31 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.498 s 0.229992
2021-10-07 00:01 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.412 s -1.996881