Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-02 07:24 R dataframe-to-table type_floats, R 0.108 s 0.584682
2021-10-02 07:49 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.865 s 0.616045
2021-10-02 08:21 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.432311
2021-10-02 08:21 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.209289
2021-10-02 06:27 Python csv-read gzip, streaming, nyctaxi_2010-01 10.839 s -1.406094
2021-10-02 06:51 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.015 s 0.114176
2021-10-02 07:00 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.766 s 1.421397
2021-10-02 07:03 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.178 s -0.418497
2021-10-02 07:09 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.997 s -1.034049
2021-10-02 07:56 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.598 s -0.763748
2021-10-02 08:01 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.544 s 1.233805
2021-10-02 08:03 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.938 s -0.846749
2021-10-02 07:02 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.256 s -1.876362
2021-10-02 07:50 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s -0.050793
2021-10-02 07:52 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.234 s 0.518407
2021-10-02 08:10 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.581 s 1.030793
2021-10-02 08:11 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.607 s 0.927492
2021-10-02 06:29 Python dataframe-to-table type_integers 0.011 s 1.246897
2021-10-02 06:29 Python dataframe-to-table type_nested 2.881 s 1.103374
2021-10-02 06:59 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.712 s 0.299855
2021-10-02 07:01 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.759 s -0.924329
2021-10-02 07:03 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.332 s -1.515945
2021-10-02 07:52 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.132899
2021-10-02 08:21 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.716 s -0.623102
2021-10-02 08:21 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.864878
2021-10-02 06:24 Python csv-read uncompressed, file, fanniemae_2016Q4 1.177 s -0.309870
2021-10-02 06:51 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.039 s -0.027049
2021-10-02 06:59 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.860 s 0.242145
2021-10-02 07:02 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.316 s -1.417630
2021-10-02 07:09 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.929 s -0.174591
2021-10-02 07:10 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.348 s 0.141134
2021-10-02 07:10 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.403 s -0.276536
2021-10-02 07:59 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.117 s -1.169450
2021-10-02 08:04 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.916 s -1.021898
2021-10-02 08:12 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.937 s 0.971035
2021-10-02 06:51 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 0.999 s 0.535142
2021-10-02 06:59 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.656 s -5.347365
2021-10-02 07:00 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.939 s 1.418020
2021-10-02 07:00 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.260 s 1.134327
2021-10-02 07:48 R dataframe-to-table type_simple_features, R 275.239 s -0.595246
2021-10-02 06:29 Python dataframe-to-table type_simple_features 0.918 s -0.507988
2021-10-02 06:46 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.308 s -0.095115
2021-10-02 07:04 Python file-read lz4, feather, table, nyctaxi_2010-01 0.675 s -1.382508
2021-10-02 07:50 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.888 s 2.004826
2021-10-02 07:51 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.620031
2021-10-02 07:59 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.836 s -0.831950
2021-10-02 06:30 Python dataset-filter nyctaxi_2010-01 4.349 s 0.634317
2021-10-02 07:00 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.180 s 1.469910
2021-10-02 07:02 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.966 s -2.084122
2021-10-02 07:07 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.047 s -2.219727
2021-10-02 07:24 R dataframe-to-table chi_traffic_2020_Q1, R 5.350 s 0.917468
2021-10-02 07:48 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.890 s 0.436223
2021-10-02 08:10 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.461562
2021-10-02 08:11 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s -1.043728
2021-10-02 08:12 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.107 s -3.952354
2021-10-02 07:03 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.484 s -1.572828
2021-10-02 07:04 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.392 s -1.087911
2021-10-02 07:11 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.806 s 0.321828
2021-10-02 08:06 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.751 s -0.480414
2021-10-02 08:14 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.501 s 0.091382
2021-10-02 08:21 JavaScript Parse readBatches, tracks 0.000 s 0.325349
2021-10-02 08:21 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.726895
2021-10-02 06:26 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.848 s -1.203792
2021-10-02 07:57 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.612 s -1.012228
2021-10-02 08:09 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.491 s -0.276728
2021-10-02 08:21 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.568 s -0.208840
2021-10-02 08:21 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.999397
2021-10-02 07:24 R dataframe-to-table type_nested, R 0.537 s 0.208136
2021-10-02 08:01 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.405 s -0.679703
2021-10-02 06:29 Python dataframe-to-table type_strings 0.373 s -0.199925
2021-10-02 06:33 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.103 s -0.385735
2021-10-02 07:01 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.955 s -3.108479
2021-10-02 07:04 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.988 s -1.598375
2021-10-02 07:48 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.203 s 0.565011
2021-10-02 07:51 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.167 s 0.482793
2021-10-02 08:21 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.649 s -0.331158
2021-10-02 08:21 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.882444
2021-10-02 08:21 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.981312
2021-10-02 06:29 Python dataframe-to-table type_floats 0.012 s -1.095540
2021-10-02 07:01 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.309 s -2.814200
2021-10-02 07:08 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.973 s -1.655696
2021-10-02 07:10 Python file-write lz4, feather, table, nyctaxi_2010-01 1.802 s 0.485188
2021-10-02 06:26 Python csv-read uncompressed, file, nyctaxi_2010-01 1.002 s 0.952755
2021-10-02 06:27 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.732293
2021-10-02 07:53 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.992 s -1.051736
2021-10-02 07:55 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.164 s -1.043547
2021-10-02 08:10 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.579 s 1.076605
2021-10-02 08:21 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.169598
2021-10-02 06:25 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.662819
2021-10-02 07:06 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.962 s -0.890879
2021-10-02 07:08 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.262 s -0.378855
2021-10-02 07:24 R dataframe-to-table type_dict, R 0.051 s -0.073920
2021-10-02 07:51 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.373 s 0.634022
2021-10-02 08:10 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.999506
2021-10-02 08:21 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.557557
2021-10-02 06:25 Python csv-read gzip, streaming, fanniemae_2016Q4 15.027 s -0.799880
2021-10-02 06:29 Python dataframe-to-table chi_traffic_2020_Q1 19.437 s 1.446526
2021-10-02 07:02 Python file-read lz4, feather, table, fanniemae_2016Q4 0.606 s -0.617740
2021-10-02 07:05 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.602 s -0.907694
2021-10-02 07:07 Python file-write lz4, feather, table, fanniemae_2016Q4 1.227 s -4.973999
2021-10-02 07:53 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.113576
2021-10-02 08:13 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.355 s 0.742756
2021-10-02 08:21 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.090501
2021-10-02 08:21 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.435993
2021-10-02 08:21 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.007683
2021-10-02 06:37 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.905 s 1.120274
2021-10-02 07:11 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.366454
2021-10-02 07:49 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.207 s 0.489288
2021-10-02 08:12 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.676 s -1.233391
2021-10-02 08:21 JavaScript Parse Table.from, tracks 0.000 s 0.786268
2021-10-02 08:21 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.868 s 0.289184
2021-10-02 08:21 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.435043
2021-10-02 06:29 Python dataframe-to-table type_dict 0.012 s 1.214350
2021-10-02 08:08 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.240 s 1.466676
2021-10-02 06:24 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.098 s -0.812680
2021-10-02 06:46 Python dataset-read async=True, nyctaxi_multi_ipc_s3 177.195 s 1.285111
2021-10-02 07:06 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.747 s -1.077147
2021-10-02 07:52 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.136 s -0.445685
2021-10-02 08:13 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.475 s -0.289663
2021-10-02 08:21 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.543582
2021-10-02 08:21 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.722612
2021-10-02 08:21 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.235851
2021-10-02 08:21 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.406945
2021-10-02 07:01 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.118 s 1.033880
2021-10-02 07:02 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.070 s -0.910917
2021-10-02 07:02 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.039 s -0.090012
2021-10-02 07:07 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.390 s -0.326778
2021-10-02 07:10 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.966 s -0.200676
2021-10-02 07:11 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.216236
2021-10-02 07:24 R dataframe-to-table type_strings, R 0.493 s -0.446278
2021-10-02 07:24 R dataframe-to-table type_integers, R 0.085 s -0.899888
2021-10-02 08:05 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.577 s -0.862410
2021-10-02 08:10 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 1.609501
2021-10-02 07:49 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.305728
2021-10-02 07:54 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.553 s -1.437122
2021-10-02 08:02 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.193 s 1.063180
2021-10-02 08:07 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 0.829595
2021-10-02 08:10 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.858 s 1.232174
2021-10-02 08:21 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.538 s -0.515977
2021-10-02 08:11 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.516 s 0.000356
2021-10-02 08:13 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.202 s -2.205900
2021-10-02 08:21 JavaScript Parse serialize, tracks 0.005 s -0.746384
2021-10-02 08:21 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.103249
2021-10-02 08:21 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.739 s 0.044963
2021-10-02 08:21 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.901 s 0.033865
2021-10-02 08:21 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.557557
2021-10-02 08:21 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.446070
2021-10-02 08:21 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.828716
2021-10-02 08:13 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.169 s 0.946607