Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 13:23 Python csv-read uncompressed, file, fanniemae_2016Q4 1.188 s -0.822257
2021-10-08 13:25 Python csv-read gzip, streaming, nyctaxi_2010-01 10.500 s 1.255857
2021-10-08 13:27 Python dataframe-to-table type_integers 0.011 s 1.270758
2021-10-08 13:28 Python dataframe-to-table type_simple_features 0.907 s 0.585804
2021-10-08 13:28 Python dataset-filter nyctaxi_2010-01 4.355 s 0.462596
2021-10-08 13:44 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.677 s -2.625723
2021-10-08 13:48 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.017 s 0.285640
2021-10-08 13:59 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.829 s 0.370178
2021-10-08 14:00 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.260 s -0.482088
2021-10-08 14:01 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.304 s -0.677143
2021-10-08 14:01 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.161 s -0.997290
2021-10-08 14:02 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s -0.115642
2021-10-08 14:04 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.506 s -1.402794
2021-10-08 14:07 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.757 s -0.131385
2021-10-08 14:07 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.269374
2021-10-08 14:08 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.234 s -0.053901
2021-10-08 14:11 Python wide-dataframe use_legacy_dataset=false 0.615 s 1.199659
2021-10-08 14:24 R dataframe-to-table type_dict, R 0.063 s -1.334594
2021-10-08 15:01 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.455 s 1.466986
2021-10-08 15:07 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.518 s -0.148272
2021-10-08 15:17 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.165256
2021-10-08 14:01 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.187703
2021-10-08 14:04 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.415600
2021-10-08 14:10 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.837 s 0.615811
2021-10-08 14:10 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.361 s -0.639516
2021-10-08 14:10 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.351 s 0.165652
2021-10-08 13:23 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.938 s -0.259882
2021-10-08 14:01 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.856 s -0.842011
2021-10-08 14:07 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.659 s 0.409808
2021-10-08 14:09 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.850 s 1.173749
2021-10-08 13:24 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.506 s 1.275117
2021-10-08 14:01 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.888 s -1.469493
2021-10-08 14:05 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.079 s 0.964295
2021-10-08 13:24 Python csv-read uncompressed, file, nyctaxi_2010-01 1.013 s -0.027362
2021-10-08 14:05 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.301 s 0.380249
2021-10-08 14:10 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.307927
2021-10-08 14:24 R dataframe-to-table chi_traffic_2020_Q1, R 3.396 s 9.733718
2021-10-08 13:35 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.281 s 0.844438
2021-10-08 13:27 Python dataframe-to-table chi_traffic_2020_Q1 19.298 s 1.518807
2021-10-08 13:28 Python dataframe-to-table type_nested 2.882 s 0.837011
2021-10-08 14:11 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.808 s 0.301236
2021-10-08 15:17 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.900198
2021-10-08 13:27 Python dataframe-to-table type_dict 0.012 s 0.140845
2021-10-08 14:02 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.253 s -1.292178
2021-10-08 13:27 Python dataframe-to-table type_strings 0.375 s -0.323928
2021-10-08 13:44 Python dataset-read async=True, nyctaxi_multi_ipc_s3 173.745 s 1.707544
2021-10-08 14:03 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.179 s -0.507920
2021-10-08 13:48 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.011 s 0.344655
2021-10-08 14:03 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.036 s -0.010121
2021-10-08 14:04 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.995 s -1.362908
2021-10-08 14:24 R dataframe-to-table type_floats, R 0.013 s 9.610510
2021-10-08 13:28 Python dataframe-to-table type_floats 0.011 s 1.647855
2021-10-08 13:48 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.021 s 0.010078
2021-10-08 13:24 Python csv-read gzip, file, fanniemae_2016Q4 6.026 s 1.017190
2021-10-08 13:26 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.435772
2021-10-08 13:31 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.293 s -0.157796
2021-10-08 14:11 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.239710
2021-10-08 14:01 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.806 s -1.293887
2021-10-08 14:02 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.057 s -0.410465
2021-10-08 14:09 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.803 s 0.599315
2021-10-08 13:23 Python csv-read gzip, streaming, fanniemae_2016Q4 14.882 s -0.315260
2021-10-08 13:59 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.004 s 0.022553
2021-10-08 14:03 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.320 s -1.245840
2021-10-08 14:03 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.311 s -1.206377
2021-10-08 14:06 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.441 s 0.917796
2021-10-08 14:07 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.337 s 0.000442
2021-10-08 14:00 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.734 s 0.189266
2021-10-08 14:00 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.022 s -0.657888
2021-10-08 14:02 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.926 s -0.957871
2021-10-08 14:08 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.789 s 1.152384
2021-10-08 14:25 R dataframe-to-table type_nested, R 0.539 s -0.716506
2021-10-08 14:24 R dataframe-to-table type_strings, R 0.490 s 0.898655
2021-10-08 14:24 R dataframe-to-table type_integers, R 0.010 s 9.964615
2021-10-08 14:48 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.118 s 0.807345
2021-10-08 15:06 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.866 s 0.930403
2021-10-08 15:06 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.210982
2021-10-08 15:17 JavaScript Parse serialize, tracks 0.005 s 0.366091
2021-10-08 15:17 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.007496
2021-10-08 15:17 JavaScript Parse readBatches, tracks 0.000 s -2.574530
2021-10-08 15:17 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.543688
2021-10-08 15:17 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.921 s -0.945755
2021-10-08 15:17 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.553649
2021-10-08 15:17 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.104040
2021-10-08 15:17 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.924391
2021-10-08 14:51 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.848 s 0.920932
2021-10-08 14:54 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.291 s 0.977828
2021-10-08 14:59 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.820 s 1.400315
2021-10-08 14:45 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.911 s 0.267962
2021-10-08 14:46 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.920 s 0.026481
2021-10-08 14:46 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.909 s 0.594484
2021-10-08 14:47 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.379 s 0.236772
2021-10-08 15:09 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s 0.442721
2021-10-08 14:48 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.151 s 1.414204
2021-10-08 14:50 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.673 s 0.140334
2021-10-08 14:53 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.260 s 0.991637
2021-10-08 15:05 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.482 s 1.642807
2021-10-08 14:46 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.286 s 2.073103
2021-10-08 14:46 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.555 s 1.587144
2021-10-08 14:50 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.977 s 0.126602
2021-10-08 15:03 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.434848
2021-10-08 15:06 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.169 s 1.081092
2021-10-08 15:09 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.209 s -4.815831
2021-10-08 14:49 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.223 s 1.070270
2021-10-08 14:56 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.832 s -0.120027
2021-10-08 15:00 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.772 s 1.771035
2021-10-08 15:02 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.638 s 1.749275
2021-10-08 15:07 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.613 s -0.627206
2021-10-08 14:50 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.512 s 0.716085
2021-10-08 15:10 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.229 s 0.674331
2021-10-08 15:10 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.513 s 0.054991
2021-10-08 14:45 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.240 s 0.121282
2021-10-08 14:47 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.048 s 1.467742
2021-10-08 14:49 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.618872
2021-10-08 14:58 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.192 s 0.864929
2021-10-08 15:07 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 0.826200
2021-10-08 15:08 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.904 s 0.813930
2021-10-08 15:09 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.485 s -2.344370
2021-10-08 15:17 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.110498
2021-10-08 15:06 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 0.857454
2021-10-08 15:17 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.646 s -0.316050
2021-10-08 15:17 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.699554
2021-10-08 15:17 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.432954
2021-10-08 14:57 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.397 s 0.768920
2021-10-08 15:07 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.624203
2021-10-08 15:08 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.589 s 0.106837
2021-10-08 15:17 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.736 s 0.071270
2021-10-08 15:17 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.561211
2021-10-08 15:17 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.877 s 0.575949
2021-10-08 15:04 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.244 s 0.851028
2021-10-08 15:17 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.728 s -0.784028
2021-10-08 15:17 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.024060
2021-10-08 15:17 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.493 s 0.245259
2021-10-08 14:44 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.247 s 0.224332
2021-10-08 14:55 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.714 s 1.044717
2021-10-08 15:17 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.900198
2021-10-08 15:17 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.323062
2021-10-08 15:17 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.813540
2021-10-08 14:44 R dataframe-to-table type_simple_features, R 274.781 s 0.426964
2021-10-08 14:57 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.554 s 0.776063
2021-10-08 15:17 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.136651
2021-10-08 15:17 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.832553
2021-10-08 15:06 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.588 s 0.722132
2021-10-08 15:17 JavaScript Parse Table.from, tracks 0.000 s -2.925131
2021-10-08 15:08 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -3.940021
2021-10-08 15:17 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.115507
2021-10-08 15:17 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.661 s -0.419123
2021-10-08 15:17 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.662286
2021-10-08 15:17 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.586588
2021-10-08 15:17 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.345038