Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-06 20:42 Python csv-read gzip, streaming, fanniemae_2016Q4 14.734 s 0.665961
2021-10-06 20:43 Python csv-read uncompressed, file, nyctaxi_2010-01 1.010 s 0.268985
2021-10-06 20:44 Python csv-read gzip, streaming, nyctaxi_2010-01 10.681 s -0.438386
2021-10-06 20:47 Python dataframe-to-table type_nested 2.877 s 0.726822
2021-10-06 20:54 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.170 s 0.669046
2021-10-06 20:46 Python dataframe-to-table type_dict 0.012 s 0.113637
2021-10-06 20:47 Python dataframe-to-table type_simple_features 0.916 s -0.346065
2021-10-06 20:43 Python csv-read gzip, file, fanniemae_2016Q4 6.034 s -0.795141
2021-10-06 20:46 Python dataframe-to-table type_strings 0.374 s -0.652643
2021-10-06 20:42 Python csv-read uncompressed, file, fanniemae_2016Q4 1.162 s 0.598878
2021-10-06 20:41 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.804 s 0.645082
2021-10-06 20:46 Python dataframe-to-table chi_traffic_2020_Q1 19.503 s 0.690331
2021-10-06 20:43 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.691 s -0.267278
2021-10-06 20:45 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -0.888187
2021-10-06 20:46 Python dataframe-to-table type_floats 0.011 s 0.532630
2021-10-06 20:46 Python dataframe-to-table type_integers 0.011 s 1.005020
2021-10-06 20:47 Python dataset-filter nyctaxi_2010-01 4.351 s 0.705157
2021-10-06 20:50 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.694 s 0.514162
2021-10-06 21:04 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 2.456 s -7.726657
2021-10-06 21:04 Python dataset-read async=True, nyctaxi_multi_ipc_s3 185.851 s 0.329440
2021-10-06 21:26 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.292 s 0.267237
2021-10-06 21:21 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.154 s -0.535801
2021-10-06 21:23 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.033 s 0.182513
2021-10-06 21:28 Python file-write lz4, feather, table, fanniemae_2016Q4 1.160 s 0.139911
2021-10-06 21:58 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.929 s -0.579901
2021-10-06 22:13 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.458 s 1.072485
2021-10-06 22:18 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.179 s 0.209164
2021-10-06 22:18 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 0.614081
2021-10-06 22:29 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.679 s 0.096201
2021-10-06 21:21 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.300 s -1.386394
2021-10-06 21:28 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.237 s -0.146713
2021-10-06 21:46 R dataframe-to-table type_dict, R 0.052 s -0.087771
2021-10-06 21:58 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.291 s -1.969253
2021-10-06 21:59 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.057 s -0.099977
2021-10-06 21:22 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.935 s -0.845280
2021-10-06 21:31 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.330 s 0.229512
2021-10-06 21:58 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s 0.142075
2021-10-06 21:23 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.286 s -0.911451
2021-10-06 21:30 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.843 s 1.013418
2021-10-06 21:46 R dataframe-to-table type_floats, R 0.113 s -0.384292
2021-10-06 21:56 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.250 s 0.212136
2021-10-06 22:01 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.248 s -0.378365
2021-10-06 22:29 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.311 s 2.633003
2021-10-06 21:24 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.462 s -1.016114
2021-10-06 21:31 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.810 s 0.125641
2021-10-06 21:57 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.244 s 0.084911
2021-10-06 22:00 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.119 s 0.617879
2021-10-06 21:31 Python wide-dataframe use_legacy_dataset=false 0.622 s -0.237159
2021-10-06 21:46 R dataframe-to-table type_nested, R 0.537 s 0.371874
2021-10-06 21:56 R dataframe-to-table type_simple_features, R 3.333 s 7.070563
2021-10-06 21:57 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.898 s 0.276487
2021-10-06 22:16 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.250 s 0.303749
2021-10-06 22:19 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.182 s -0.449611
2021-10-06 22:29 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.061207
2021-10-06 21:20 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.710 s 0.377878
2021-10-06 21:22 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.819 s -1.214277
2021-10-06 21:27 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.287 s 0.347056
2021-10-06 21:29 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.780 s 0.620720
2021-10-06 22:20 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.886 s 0.700957
2021-10-06 22:29 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.935 s -0.647994
2021-10-06 22:29 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500638
2021-10-06 21:23 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.334 s -1.160668
2021-10-06 21:30 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.822 s 0.585664
2021-10-06 22:02 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.014 s -1.691348
2021-10-06 22:06 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.295 s 0.748802
2021-10-06 22:12 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.784 s 1.168467
2021-10-06 22:29 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.126710
2021-10-06 21:27 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.676 s 0.185771
2021-10-06 22:02 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.668 s 0.221011
2021-10-06 22:07 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.751 s 0.633964
2021-10-06 22:19 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.522 s -0.776320
2021-10-06 21:20 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.056 s -1.410804
2021-10-06 21:31 Python file-write lz4, feather, table, nyctaxi_2010-01 1.808 s 0.111448
2021-10-06 21:46 R dataframe-to-table chi_traffic_2020_Q1, R 5.515 s -0.509105
2021-10-06 21:57 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.891 s 0.435217
2021-10-06 21:20 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.976 s 0.220412
2021-10-06 22:22 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.192 s 0.661133
2021-10-06 21:22 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.298 s -1.247128
2021-10-06 21:22 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.059 s -0.415444
2021-10-06 21:24 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.168 s 1.865310
2021-10-06 22:18 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.577 s 0.637633
2021-10-06 22:29 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.794241
2021-10-06 22:29 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.926283
2021-10-06 21:09 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.024 s 0.182243
2021-10-06 21:29 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.778 s 1.045823
2021-10-06 21:59 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.391 s -0.329127
2021-10-06 22:00 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.176 s -0.105634
2021-10-06 21:09 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.034 s -0.040095
2021-10-06 21:20 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.818 s 0.414943
2021-10-06 21:21 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.882 s -1.055786
2021-10-06 21:25 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.949 s -0.967875
2021-10-06 21:26 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.442 s 0.723798
2021-10-06 22:21 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.209 s -2.250641
2021-10-06 22:29 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.020 s -9.610431
2021-10-06 22:29 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.935467
2021-10-06 21:46 R dataframe-to-table type_strings, R 0.490 s 0.673178
2021-10-06 22:11 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.818 s 1.118366
2021-10-06 22:15 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.637 s 1.364016
2021-10-06 22:29 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.523566
2021-10-06 22:29 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500638
2021-10-06 22:29 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.133644
2021-10-06 22:29 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s 0.009353
2021-10-06 21:09 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.015 s 0.108691
2021-10-06 21:21 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.887 s -1.526302
2021-10-06 21:22 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.256 s -1.081152
2021-10-06 21:28 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.753 s -0.199704
2021-10-06 21:31 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.752696
2021-10-06 22:08 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.453657
2021-10-06 22:17 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.486 s 0.835497
2021-10-06 22:18 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.871 s 0.711304
2021-10-06 22:22 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.512 s 0.051529
2021-10-06 22:29 JavaScript Parse Table.from, tracks 0.000 s 0.205032
2021-10-06 22:29 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -2.507515
2021-10-06 22:29 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.104232
2021-10-06 22:29 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.214131
2021-10-06 21:25 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.089 s 0.705439
2021-10-06 22:29 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.685 s 0.347828
2021-10-06 22:29 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.374934
2021-10-06 21:21 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.304 s -0.584467
2021-10-06 21:22 Python file-read lz4, feather, table, fanniemae_2016Q4 0.606 s -0.522719
2021-10-06 21:24 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 0.961993
2021-10-06 21:30 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.365 s -0.868093
2021-10-06 21:46 R dataframe-to-table type_integers, R 0.085 s -0.235444
2021-10-06 22:01 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.230939
2021-10-06 22:05 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.286 s 0.663632
2021-10-06 22:29 JavaScript Parse readBatches, tracks 0.000 s -0.893256
2021-10-06 22:29 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.549823
2021-10-06 22:29 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.048114
2021-10-06 22:29 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.517417
2021-10-06 22:29 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.372828
2021-10-06 22:09 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.400 s 0.373779
2021-10-06 22:15 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.460471
2021-10-06 22:09 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.573 s 0.202056
2021-10-06 22:20 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.580 s 0.184076
2021-10-06 22:21 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.365 s 0.151996
2021-10-06 22:02 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.543 s -0.632287
2021-10-06 22:03 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.846 s 0.731652
2021-10-06 22:18 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.381580
2021-10-06 22:21 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -2.227478
2021-10-06 22:29 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.475 s 0.651302
2021-10-06 22:10 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.224 s -0.224876
2021-10-06 22:29 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.534 s 2.145212
2021-10-06 22:29 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.854 s 0.652976
2021-10-06 22:19 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.599 s 0.690328
2021-10-06 22:20 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.621 s -1.828159
2021-10-06 22:21 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.486 s -2.068141
2021-10-06 22:29 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.179497
2021-10-06 22:29 JavaScript Parse serialize, tracks 0.005 s -0.045269
2021-10-06 22:29 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.631425