Outliers: 7


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-07 08:27 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.848 s 0.502413
2021-10-07 08:28 Python csv-read uncompressed, file, fanniemae_2016Q4 1.193 s -1.103873
2021-10-07 08:29 Python csv-read gzip, streaming, fanniemae_2016Q4 14.796 s 0.387596
2021-10-07 08:29 Python csv-read gzip, file, fanniemae_2016Q4 6.036 s -1.028459
2021-10-07 08:30 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.683 s -0.220746
2021-10-07 08:31 Python csv-read gzip, streaming, nyctaxi_2010-01 10.675 s -0.389600
2021-10-07 08:30 Python csv-read uncompressed, file, nyctaxi_2010-01 1.017 s -0.385384
2021-10-07 08:31 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.268336
2021-10-07 08:34 Python dataframe-to-table type_nested 2.883 s 0.482759
2021-10-07 08:33 Python dataframe-to-table chi_traffic_2020_Q1 19.488 s 0.606778
2021-10-07 08:42 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.146 s 0.590500
2021-10-07 08:52 Python dataset-read async=True, nyctaxi_multi_ipc_s3 190.188 s -0.206319
2021-10-07 08:57 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.041 s -0.154667
2021-10-07 09:07 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.830 s 0.350444
2021-10-07 08:33 Python dataframe-to-table type_strings 0.372 s -0.356532
2021-10-07 08:34 Python dataframe-to-table type_integers 0.011 s 0.897837
2021-10-07 08:52 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.130 s 0.917048
2021-10-07 08:57 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.049 s -0.173410
2021-10-07 09:07 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.952 s 0.416360
2021-10-07 08:33 Python dataframe-to-table type_dict 0.012 s -0.344753
2021-10-07 08:57 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.013 s 0.127442
2021-10-07 09:07 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.814 s -0.664934
2021-10-07 08:34 Python dataframe-to-table type_floats 0.011 s 0.446834
2021-10-07 08:34 Python dataframe-to-table type_simple_features 0.910 s 0.348374
2021-10-07 09:07 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.002 s -0.046184
2021-10-07 08:35 Python dataset-filter nyctaxi_2010-01 4.350 s 0.779730
2021-10-07 09:08 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.249 s -0.101170
2021-10-07 08:38 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.721 s -0.440448
2021-10-07 09:08 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.823 s 0.110818
2021-10-07 09:08 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.297 s -0.237603
2021-10-07 09:09 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.874 s -0.836372
2021-10-07 09:12 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.345 s -1.109778
2021-10-07 09:09 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.152 s -0.372775
2021-10-07 09:09 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.807 s -0.924354
2021-10-07 09:11 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.234 s -0.630968
2021-10-07 09:10 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.279 s 1.858655
2021-10-07 09:10 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.914 s -0.426365
2021-10-07 09:10 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s 0.027918
2021-10-07 09:11 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.051 s -0.067510
2021-10-07 09:12 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.033 s 0.198785
2021-10-07 09:12 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.312 s -0.913220
2021-10-07 09:12 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.182 s -1.142561
2021-10-07 09:13 Python file-read lz4, feather, table, nyctaxi_2010-01 0.673 s -0.898428
2021-10-07 09:15 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.281 s 0.230023
2021-10-07 09:17 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.336 s -0.021195
2021-10-07 09:14 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.970 s -0.951428
2021-10-07 09:14 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.074 s 0.718280
2021-10-07 09:16 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.442 s 0.637607
2021-10-07 09:17 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.628 s 0.337985
2021-10-07 09:18 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.239 s -0.288723
2021-10-07 09:50 R dataframe-to-table chi_traffic_2020_Q1, R 303.276 s -7.148580
2021-10-07 09:51 R dataframe-to-table type_strings, R 17.441 s -10.129456
2021-10-07 10:03 R dataframe-to-table type_simple_features, R 3.309 s 3.429377
2021-10-07 10:04 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.502 s 7.003438
2021-10-07 09:18 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.746 s -0.220423
2021-10-07 09:52 R dataframe-to-table type_dict, R 0.062 s -1.342666
2021-10-07 09:52 R dataframe-to-table type_floats, R 0.108 s -0.105330
2021-10-07 10:03 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.245 s 0.252402
2021-10-07 10:07 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.505761
2021-10-07 09:18 Python file-write lz4, feather, table, fanniemae_2016Q4 1.161 s 0.081822
2021-10-07 09:52 R dataframe-to-table type_integers, R 0.085 s -0.188108
2021-10-07 10:04 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.476 s 9.856054
2021-10-07 09:53 R dataframe-to-table type_nested, R 17.384 s -9.907261
2021-10-07 09:19 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.777 s 0.966833
2021-10-07 10:04 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.236 s 0.190040
2021-10-07 09:13 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.466 s -0.926397
2021-10-07 09:21 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.811 s 0.711814
2021-10-07 10:04 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.384676
2021-10-07 09:20 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.780 s 0.612393
2021-10-07 10:05 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.927 s -0.470409
2021-10-07 10:05 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.426715
2021-10-07 09:20 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.842 s 0.926046
2021-10-07 10:06 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.396 s -0.671186
2021-10-07 09:21 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.356 s -0.289248
2021-10-07 10:06 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.534296
2021-10-07 10:10 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.846 s 0.641699
2021-10-07 09:22 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.329 s 0.082025
2021-10-07 10:06 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.180 s 9.984799
2021-10-07 10:07 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.263 s 9.974881
2021-10-07 10:11 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.298 s 0.508366
2021-10-07 09:22 Python file-write lz4, feather, table, nyctaxi_2010-01 1.827 s -1.001588
2021-10-07 10:06 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.106 s 1.718241
2021-10-07 09:22 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.800 s 0.079032
2021-10-07 09:23 Python wide-dataframe use_legacy_dataset=true 0.392 s 1.183529
2021-10-07 10:08 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.989 s -0.200672
2021-10-07 09:23 Python wide-dataframe use_legacy_dataset=false 0.626 s -1.162318
2021-10-07 10:08 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.679 s 0.060795
2021-10-07 10:09 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.519 s 0.759486
2021-10-07 10:12 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.297 s 0.642851
2021-10-07 10:17 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.591 s -0.448268
2021-10-07 10:14 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.760 s 0.492644
2021-10-07 10:17 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.591 s -0.448268
2021-10-07 10:15 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.830 s 0.459582
2021-10-07 10:19 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.232 s -0.743324
2021-10-07 10:18 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.669566
2021-10-07 10:22 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.799 s 0.770095
2021-10-07 10:21 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.818 s 1.020799
2021-10-07 10:23 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.459 s 0.928761
2021-10-07 10:25 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.661 s 0.755534
2021-10-07 10:26 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.537952
2021-10-07 10:27 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.262 s -0.782311
2021-10-07 10:28 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.487 s 0.626443
2021-10-07 10:28 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.487 s 0.626443
2021-10-07 10:30 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.192 s -0.819311
2021-10-07 10:30 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 0.539975
2021-10-07 10:32 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.223611
2021-10-07 10:30 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.192 s -0.819311
2021-10-07 10:31 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.861 s 0.659857
2021-10-07 10:34 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.618 s -1.160979
2021-10-07 10:32 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.851834
2021-10-07 10:34 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.883 s 0.616516
2021-10-07 10:35 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.109 s -1.252101
2021-10-07 10:31 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.568 s 0.674000
2021-10-07 10:33 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.591 s 0.654356
2021-10-07 10:33 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.532 s -2.313731
2021-10-07 10:35 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.481 s -0.849970
2021-10-07 10:35 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.720 s -2.121263
2021-10-07 10:35 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s 0.290306
2021-10-07 10:37 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.507 s -1.117473
2021-10-07 10:36 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -1.260516
2021-10-07 10:36 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.173 s 0.632609
2021-10-07 10:44 JavaScript Parse Table.from, tracks 0.000 s 0.586031
2021-10-07 10:47 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.194335
2021-10-07 10:44 JavaScript Parse Table.from, tracks 0.000 s 0.586031
2021-10-07 10:46 JavaScript Parse readBatches, tracks 0.000 s 0.416259
2021-10-07 10:47 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.205690
2021-10-07 10:44 JavaScript Parse Table.from, tracks 0.000 s 0.586031
2021-10-07 10:46 JavaScript Parse readBatches, tracks 0.000 s 0.416259
2021-10-07 10:46 JavaScript Parse serialize, tracks 0.005 s -0.687187
2021-10-07 10:47 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.194335
2021-10-07 10:49 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.643934
2021-10-07 10:48 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.625 s -0.348408
2021-10-07 10:47 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.205690
2021-10-07 10:48 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.625 s -0.348408
2021-10-07 10:49 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.501 s -0.111839
2021-10-07 10:49 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.653599
2021-10-07 10:50 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.769 s -1.596891
2021-10-07 10:52 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.619586
2021-10-07 10:50 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.664 s 0.483209
2021-10-07 10:51 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.431554
2021-10-07 10:51 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.865 s 0.349998
2021-10-07 10:51 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.157722
2021-10-07 10:52 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.537150
2021-10-07 10:52 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.816 s 1.896578
2021-10-07 10:53 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500638
2021-10-07 10:52 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.619586
2021-10-07 10:57 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.500 s 0.250759
2021-10-07 10:54 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.911034
2021-10-07 10:55 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.136567
2021-10-07 10:56 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.666889
2021-10-07 10:56 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.436590
2021-10-07 10:53 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500638
2021-10-07 10:54 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500638
2021-10-07 10:57 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.855531
2021-10-07 10:57 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.270844
2021-10-07 10:57 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.876441
2021-10-07 10:54 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500638
2021-10-07 10:55 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.108496
2021-10-07 10:56 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.241614
2021-10-07 10:57 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.555264
2021-10-07 10:57 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.101352