Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-07 18:51 R dataframe-to-table type_strings, R 0.493 s 0.224378
2021-10-07 18:51 R dataframe-to-table type_dict, R 0.049 s 0.151061
2021-10-07 18:51 R dataframe-to-table type_integers, R 0.009 s 2.869269
2021-10-07 18:50 R dataframe-to-table chi_traffic_2020_Q1, R 3.436 s 0.271178
2021-10-07 18:52 R dataframe-to-table type_floats, R 0.013 s 2.844826
2021-10-07 18:52 R dataframe-to-table type_nested, R 0.540 s 0.225522
2021-10-07 19:00 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.478 s 2.600068
2021-10-07 18:58 R dataframe-to-table type_simple_features, R 3.345 s 2.014738
2021-10-07 19:00 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.240 s 0.152626
2021-10-07 18:58 R dataframe-to-table type_simple_features, R 3.345 s 2.014738
2021-10-07 18:59 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.251 s 0.224022
2021-10-07 19:00 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.478 s 2.600068
2021-10-07 19:00 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.240 s 0.152626
2021-10-07 19:01 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.482 s 2.493683
2021-10-07 19:02 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.105237
2021-10-07 19:01 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.482 s 2.493683
2021-10-07 19:02 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.105237
2021-10-07 19:03 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.926 s -0.329954
2021-10-07 19:03 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.926 s -0.329954
2021-10-07 19:04 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.558 s 1.089277
2021-10-07 19:04 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.558 s 1.089277
2021-10-07 19:05 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.402 s -0.953703
2021-10-07 19:05 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.402 s -0.953703
2021-10-07 19:09 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.681 s 0.101629
2021-10-07 19:07 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.139 s -1.093206
2021-10-07 19:06 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.064 s -1.260127
2021-10-07 19:06 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.064 s -1.260127
2021-10-07 19:08 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.280483
2021-10-07 19:08 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.233 s 2.495498
2021-10-07 19:07 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.166 s 2.589767
2021-10-07 19:13 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.317 s 0.245073
2021-10-07 19:14 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.302 s 0.525573
2021-10-07 19:09 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.972 s 0.274003
2021-10-07 19:10 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.508 s 0.354595
2021-10-07 19:12 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.858 s 0.470857
2021-10-07 19:16 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.764 s 0.329229
2021-10-07 19:17 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.831 s 0.166599
2021-10-07 19:20 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.395 s 1.379913
2021-10-07 19:19 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.583 s -0.369052
2021-10-07 19:23 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.802 s 0.480014
2021-10-07 19:25 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.458 s 0.739897
2021-10-07 19:27 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.292 s -4.604162
2021-10-07 19:26 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.673 s 0.267175
2021-10-07 19:28 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.265 s -1.200236
2021-10-07 19:30 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.198 s -1.387765
2021-10-07 19:33 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.591 s 0.466443
2021-10-07 19:29 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.491 s -0.203872
2021-10-07 19:30 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.605 s -0.083145
2021-10-07 19:31 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.889 s 0.365789
2021-10-07 19:32 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.581 s 0.280095
2021-10-07 19:36 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.211 s -2.031540
2021-10-07 19:32 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.139459
2021-10-07 19:34 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.961 s 0.377187
2021-10-07 19:35 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.488 s -1.813131
2021-10-07 19:32 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.193 s -2.079974
2021-10-07 19:33 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.522 s -0.605343
2021-10-07 19:33 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.719 s -17.189328
2021-10-07 19:34 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.548 s 0.649130
2021-10-07 19:35 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s -0.076015
2021-10-07 19:36 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.506 s -0.764234
2021-10-07 19:35 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.609972
2021-10-07 19:36 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.175 s 0.417490
2021-10-07 19:21 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.224 s -0.733564
2021-10-07 19:22 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.822 s 0.756468