Outliers: 4


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-27 22:32 Python dataframe-to-table chi_traffic_2020_Q1 19.764 s 0.187488
2021-09-27 22:29 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.646 s -0.448915
2021-09-27 22:32 Python dataframe-to-table type_strings 0.368 s 0.497403
2021-09-27 22:28 Python csv-read gzip, streaming, fanniemae_2016Q4 14.982 s -0.998664
2021-09-27 22:32 Python dataframe-to-table type_integers 0.011 s 0.192095
2021-09-27 22:49 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 269.002 s 0.155496
2021-09-27 22:29 Python csv-read gzip, streaming, nyctaxi_2010-01 10.632 s -0.439953
2021-09-27 22:32 Python dataframe-to-table type_dict 0.011 s 1.814804
2021-09-27 22:32 Python dataframe-to-table type_nested 2.947 s 0.473435
2021-09-27 22:32 Python dataframe-to-table type_floats 0.012 s -1.959279
2021-09-27 22:27 Python csv-read uncompressed, file, fanniemae_2016Q4 1.199 s -0.460808
2021-09-27 22:32 Python dataframe-to-table type_simple_features 0.906 s 0.529823
2021-09-27 22:29 Python csv-read uncompressed, file, nyctaxi_2010-01 1.029 s -0.147076
2021-09-27 22:27 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.046 s -0.990539
2021-09-27 22:28 Python csv-read gzip, file, fanniemae_2016Q4 6.037 s -1.991837
2021-09-27 22:30 Python csv-read gzip, file, nyctaxi_2010-01 9.040 s 1.392060
2021-09-27 22:32 Python dataset-filter nyctaxi_2010-01 4.368 s -0.444685
2021-09-27 22:36 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.942 s -0.013714
2021-09-27 23:17 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.730 s -0.249442
2021-09-27 23:24 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.122 s 1.047833
2021-09-27 23:25 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.769 s 1.165908
2021-09-28 00:26 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.193 s 0.005906
2021-09-27 22:59 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.311 s -0.023331
2021-09-27 23:18 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.659 s -0.096190
2021-09-27 23:20 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.463 s 1.109508
2021-09-27 23:24 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.493 s 1.971223
2021-09-27 23:26 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.824 s 1.058495
2021-09-27 23:41 R dataframe-to-table type_integers, R 0.085 s -0.394743
2021-09-27 23:41 R dataframe-to-table type_nested, R 0.538 s -0.427904
2021-09-27 23:17 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.255 s -0.840879
2021-09-27 23:22 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.142 s 1.612724
2021-09-27 23:41 R dataframe-to-table type_strings, R 0.489 s 0.365775
2021-09-28 00:08 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.184 s -0.869903
2021-09-28 00:12 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.857 s 1.859590
2021-09-28 00:23 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.727 s 1.006508
2021-09-28 00:27 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.455557
2021-09-28 00:30 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.809 s 0.738860
2021-09-27 22:59 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.159 s 0.715917
2021-09-27 23:19 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.033 s 0.233471
2021-09-28 00:06 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -1.445849
2021-09-28 00:30 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.474 s -0.424353
2021-09-27 23:17 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.834 s -0.799363
2021-09-27 23:23 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.483 s 1.742164
2021-09-28 00:16 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.826 s 0.916926
2021-09-28 00:22 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.529 s 0.737560
2021-09-27 23:03 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.034 s 0.026966
2021-09-27 23:03 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.024 s -0.139964
2021-09-28 00:05 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 8.291 s -5.392420
2021-09-28 00:07 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.921 s -0.298033
2021-09-28 00:27 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.255095
2021-09-27 23:27 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.746 s 0.840194
2021-09-27 23:41 R dataframe-to-table type_floats, R 0.108 s 0.482996
2021-09-27 23:16 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.025 s -1.320955
2021-09-27 23:27 Python file-write lz4, feather, table, nyctaxi_2010-01 1.771 s 2.178640
2021-09-27 23:41 R dataframe-to-table chi_traffic_2020_Q1, R 5.385 s 0.459621
2021-09-27 23:41 R dataframe-to-table type_dict, R 0.042 s 1.324782
2021-09-28 00:07 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.374203
2021-09-28 00:08 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.137 s -0.467588
2021-09-28 00:21 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.857 s 1.583959
2021-09-27 23:21 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.079 s 1.911863
2021-09-27 23:24 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.819 s 2.164503
2021-09-27 23:26 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.303 s 3.387647
2021-09-28 00:14 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.290 s 2.033214
2021-09-28 00:20 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.888 s 0.911088
2021-09-28 00:24 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 0.623627
2021-09-28 00:29 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.055417
2021-09-27 23:16 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.989 s 0.190482
2021-09-28 00:10 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.671 s 1.200511
2021-09-27 23:03 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.017 s 0.234673
2021-09-27 23:17 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.285 s -0.468814
2021-09-27 23:19 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.808 s 1.184198
2021-09-27 23:22 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.434 s 1.894523
2021-09-28 00:05 R dataframe-to-table type_simple_features, R 274.550 s 0.546850
2021-09-28 00:05 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.624 s -6.470803
2021-09-28 00:17 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.575 s 1.076100
2021-09-28 00:26 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.696 s 0.679416
2021-09-28 00:18 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.394 s 1.349928
2021-09-28 00:19 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.221 s 0.790924
2021-09-28 00:27 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.589 s 0.975107
2021-09-28 00:29 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.396 s -0.819109
2021-09-27 23:16 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.865 s 0.245178
2021-09-27 23:18 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.085 s -1.551853
2021-09-27 23:26 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.248 s 0.981263
2021-09-28 00:06 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 8.326 s -6.621144
2021-09-28 00:11 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.512 s 0.055878
2021-09-28 00:13 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.297 s 1.743841
2021-09-27 23:19 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.813 s 1.104817
2021-09-27 23:20 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.978 s 1.009122
2021-09-27 23:24 Python file-write lz4, feather, table, fanniemae_2016Q4 1.150 s 0.971784
2021-09-27 23:27 Python wide-dataframe use_legacy_dataset=true 0.393 s -0.019993
2021-09-28 00:05 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.640 s -6.765526
2021-09-27 23:17 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.138 s -0.345995
2021-09-27 23:18 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.806 s 0.071960
2021-09-27 23:25 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.886 s 2.230562
2021-09-28 00:08 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.062 s -1.222476
2021-09-28 00:27 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.768 s 0.443468
2021-09-28 00:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.496 s 0.172829
2021-09-28 00:10 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.977 s -0.586911
2021-09-28 00:38 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.705 s -0.510167
2021-09-28 00:38 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.592816
2021-09-27 23:16 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.726 s 0.354041
2021-09-27 23:18 Python file-read lz4, feather, table, fanniemae_2016Q4 0.594 s 1.385331
2021-09-27 23:20 Python file-read lz4, feather, table, nyctaxi_2010-01 0.666 s 0.760879
2021-09-27 23:23 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.333 s -0.021753
2021-09-28 00:07 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.404 s -1.399357
2021-09-28 00:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.197 s 0.655952
2021-09-27 23:18 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.295 s -0.634298
2021-09-27 23:18 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.120 s -0.505812
2021-09-28 00:28 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.606 s 0.191218
2021-09-27 23:19 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.180 s -0.613839
2021-09-28 00:38 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-28 00:38 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.529 s -0.285531
2021-09-27 23:27 Python wide-dataframe use_legacy_dataset=false 0.614 s 0.435388
2021-09-28 00:15 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.719 s 1.965126
2021-09-28 00:25 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.261 s 0.367712
2021-09-28 00:29 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.543 s 0.839116
2021-09-28 00:38 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.563 s 0.041255
2021-09-28 00:38 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.048254
2021-09-28 00:09 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.223 s 0.948985
2021-09-28 00:38 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.642 s -0.140519
2021-09-28 00:09 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.572748
2021-09-28 00:28 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.522 s -0.864917
2021-09-28 00:38 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.276660
2021-09-28 00:25 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.497 s -1.288213
2021-09-28 00:27 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.972 s 0.661868
2021-09-28 00:29 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.877 s 0.976974
2021-09-28 00:38 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -0.943540
2021-09-28 00:38 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.121811
2021-09-28 00:38 JavaScript Parse readBatches, tracks 0.000 s -1.049910
2021-09-28 00:38 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.873 s 0.241079
2021-09-28 00:38 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-28 00:38 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.067812
2021-09-28 00:38 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.075696
2021-09-28 00:38 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -0.904991
2021-09-28 00:38 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.123282
2021-09-28 00:38 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.070795
2021-09-28 00:38 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.595160
2021-09-28 00:38 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.538958
2021-09-28 00:38 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.297323
2021-09-28 00:38 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.292198
2021-09-28 00:38 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.872 s 0.517711
2021-09-28 00:38 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.071844
2021-09-28 00:38 JavaScript Parse serialize, tracks 0.005 s -0.730863
2021-09-28 00:38 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.739 s 0.046939
2021-09-28 00:38 JavaScript Parse Table.from, tracks 0.000 s -0.183948
2021-09-28 00:38 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.622277
2021-09-28 00:38 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.801508
2021-09-28 00:38 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-28 00:38 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.850598