Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 12:33 Python dataset-filter nyctaxi_2010-01 4.315 s 1.775890
2021-10-10 12:50 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.740 s -0.101010
2021-10-10 12:54 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.048 s -0.876509
2021-10-10 12:54 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.023 s -0.038053
2021-10-10 13:04 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.837 s 0.264811
2021-10-10 13:05 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.663 s 0.829794
2021-10-10 13:05 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.212 s 0.478243
2021-10-10 13:06 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.308 s -0.781085
2021-10-10 13:06 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.609 s 4.837116
2021-10-10 13:06 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.125 s 0.884256
2021-10-10 13:06 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.525 s 4.945009
2021-10-10 13:06 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.283 s 0.958330
2021-10-10 13:07 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.687 s 5.345571
2021-10-10 13:07 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.293063
2021-10-10 13:07 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.003 s 5.270047
2021-10-10 13:07 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.050 s -0.230114
2021-10-10 13:08 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.021 s 0.980927
2021-10-10 13:08 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.153 s 1.055421
2021-10-10 13:08 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.170 s 1.226189
2021-10-10 13:09 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.787 s 1.134634
2021-10-10 13:11 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.458 s 0.453447
2021-10-10 13:12 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.348 s -0.070791
2021-10-10 13:13 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.925 s -1.145750
2021-10-10 13:14 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.998 s -2.033435
2021-10-10 13:14 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.936 s -0.447241
2021-10-10 13:15 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.952 s -1.121837
2021-10-10 13:15 Python file-write lz4, feather, table, nyctaxi_2010-01 1.791 s 1.081575
2021-10-10 13:16 Python wide-dataframe use_legacy_dataset=false 0.607 s 3.526364
2021-10-10 13:29 R dataframe-to-table chi_traffic_2020_Q1, R 3.373 s 0.274631
2021-10-10 13:29 R dataframe-to-table type_dict, R 0.049 s 0.125160
2021-10-10 13:29 R dataframe-to-table type_floats, R 0.012 s 1.503555
2021-10-10 13:29 R dataframe-to-table type_nested, R 0.533 s 0.235841
2021-10-10 13:36 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.445 s 1.433914
2021-10-10 13:36 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.200 s 1.478432
2021-10-10 13:37 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.571 s -1.610205
2021-10-10 13:38 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.410 s -1.489723
2021-10-10 13:38 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.045 s 1.585547
2021-10-10 13:38 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.159 s 1.419302
2021-10-10 13:38 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.099 s 1.986672
2021-10-10 13:38 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.217 s 1.398534
2021-10-10 13:38 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -3.562139
2021-10-10 13:39 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.982 s 0.159593
2021-10-10 13:39 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.688 s 0.064179
2021-10-10 13:40 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.488 s 0.534263
2021-10-10 13:43 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.292 s 0.624989
2021-10-10 13:44 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.685 s 0.850260
2021-10-10 13:48 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.192 s 0.537626
2021-10-10 13:50 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.866 s -0.796108
2021-10-10 13:53 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.229 s 1.421833
2021-10-10 13:55 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.592 s -0.674517
2021-10-10 13:56 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.594 s 0.955824
2021-10-10 13:56 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.528 s -1.148427
2021-10-10 13:57 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.667 s -1.280486
2021-10-10 13:58 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.074452
2021-10-10 13:59 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -0.874488
2021-10-10 13:59 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.505 s -0.515715
2021-10-10 14:07 JavaScript Parse Table.from, tracks 0.000 s 0.169346
2021-10-10 14:07 JavaScript Parse serialize, tracks 0.004 s 0.601873
2021-10-10 13:52 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.272 s 3.328530
2021-10-10 14:07 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.138947
2021-10-10 14:07 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.689 s -0.516354
2021-10-10 14:07 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.649 s -0.466111
2021-10-10 14:07 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.112115
2021-10-10 14:07 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.086488
2021-10-10 14:07 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.731 s -1.040605
2021-10-10 14:07 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.714 s 0.141486
2021-10-10 14:07 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.835210
2021-10-10 14:07 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.490226
2021-10-10 14:07 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.935 s -1.545139
2021-10-10 14:07 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.957 s -1.092097
2021-10-10 14:07 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.917924
2021-10-10 14:07 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.468718
2021-10-10 14:07 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.507810
2021-10-10 12:28 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.810 s 1.170729
2021-10-10 12:28 Python csv-read uncompressed, file, fanniemae_2016Q4 1.156 s 1.061836
2021-10-10 12:29 Python csv-read gzip, streaming, fanniemae_2016Q4 14.754 s 0.995670
2021-10-10 12:29 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.755 s -0.851613
2021-10-10 12:30 Python csv-read uncompressed, file, nyctaxi_2010-01 1.020 s -0.767694
2021-10-10 12:30 Python csv-read gzip, streaming, nyctaxi_2010-01 10.750 s -1.087285
2021-10-10 12:32 Python dataframe-to-table type_strings 0.368 s 0.336277
2021-10-10 12:32 Python dataframe-to-table type_dict 0.011 s 1.331744
2021-10-10 12:33 Python dataframe-to-table type_integers 0.011 s -1.533240
2021-10-10 12:33 Python dataframe-to-table type_nested 2.872 s 0.254471
2021-10-10 12:33 Python dataframe-to-table type_simple_features 0.925 s -0.421404
2021-10-10 14:07 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.731502
2021-10-10 14:07 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.709540
2021-10-10 14:07 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.113348
2021-10-10 14:07 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.506415
2021-10-10 14:07 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.383619
2021-10-10 14:07 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.759712
2021-10-10 14:07 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.610811
2021-10-10 14:07 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.771229
2021-10-10 12:29 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.716303
2021-10-10 12:33 Python dataframe-to-table type_floats 0.011 s -0.315781
2021-10-10 13:10 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.535 s -1.108330
2021-10-10 13:15 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.348 s 0.207033
2021-10-10 13:16 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.893 s -2.524294
2021-10-10 13:41 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.842 s 0.612786
2021-10-10 13:45 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.811 s 2.693575
2021-10-10 13:49 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.896 s -0.594844
2021-10-10 13:56 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 0.848708
2021-10-10 13:56 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.262222
2021-10-10 13:09 Python file-read lz4, feather, table, nyctaxi_2010-01 0.682 s -1.961455
2021-10-10 13:46 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.534 s 1.082619
2021-10-10 13:57 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.896 s 0.724089
2021-10-10 13:58 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s -0.599218
2021-10-10 13:58 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.492 s -1.917436
2021-10-10 13:59 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.159 s 0.986595
2021-10-10 14:07 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.106060
2021-10-10 14:07 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.671132
2021-10-10 14:07 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.599 s -1.443704
2021-10-10 12:54 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.068 s -0.336134
2021-10-10 13:09 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.267 s 1.315860
2021-10-10 13:36 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.205 s 0.532936
2021-10-10 13:36 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.465 s 1.396136
2021-10-10 13:36 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.318 s -3.363549
2021-10-10 13:37 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.074 s -3.466452
2021-10-10 13:47 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.392 s 1.202595
2021-10-10 13:51 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.532 s -0.555846
2021-10-10 13:57 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -0.390240
2021-10-10 14:07 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.189566
2021-10-10 12:31 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -1.305787
2021-10-10 13:12 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.108 s -3.414356
2021-10-10 13:29 R dataframe-to-table type_integers, R 0.010 s 1.500872
2021-10-10 13:55 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.150 s 1.902393
2021-10-10 12:40 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.140 s 0.248360
2021-10-10 13:10 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.081 s 0.608606
2021-10-10 13:52 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.725 s -0.760156
2021-10-10 14:07 JavaScript Parse readBatches, tracks 0.000 s 1.075612
2021-10-10 14:07 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.762593
2021-10-10 14:07 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.193514
2021-10-10 12:32 Python dataframe-to-table chi_traffic_2020_Q1 19.620 s -0.135025
2021-10-10 13:05 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.973 s 0.316371
2021-10-10 13:06 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.777 s 0.911777
2021-10-10 13:13 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.563 s -4.832990
2021-10-10 13:16 Python wide-dataframe use_legacy_dataset=true 0.388 s 3.576184
2021-10-10 13:35 R dataframe-to-table type_simple_features, R 3.337 s 1.218176
2021-10-10 13:54 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.471 s 2.239408
2021-10-10 13:55 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.995 s -2.871331
2021-10-10 12:36 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.796 s 0.852513
2021-10-10 13:04 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.973 s 0.314426
2021-10-10 13:07 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.136 s 1.105350
2021-10-10 13:29 R dataframe-to-table type_strings, R 0.487 s 0.234173
2021-10-10 13:42 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.246 s 0.713889
2021-10-10 13:55 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.576 s -0.317286
2021-10-10 12:50 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.243 s -0.285419
2021-10-10 13:12 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.931 s -1.348265
2021-10-10 13:12 Python file-write lz4, feather, table, fanniemae_2016Q4 1.158 s 0.245320
2021-10-10 13:15 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.340 s 0.761265