Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-28 00:43 Python csv-read uncompressed, file, fanniemae_2016Q4 1.178 s -0.147005
2021-09-28 00:48 Python dataframe-to-table type_strings 0.366 s 0.704915
2021-09-28 01:31 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.729 s 0.341805
2021-09-28 01:31 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.257 s -0.888819
2021-09-28 01:32 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.727 s -0.052842
2021-09-28 00:43 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.024 s -0.952829
2021-09-28 01:33 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.842 s 1.029571
2021-09-28 00:49 Python dataframe-to-table type_simple_features 0.906 s 0.582984
2021-09-28 00:48 Python dataframe-to-table chi_traffic_2020_Q1 19.724 s 0.411571
2021-09-28 01:19 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.031 s 0.073124
2021-09-28 01:32 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.284 s -0.349991
2021-09-28 01:37 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.469 s 1.761770
2021-09-28 01:41 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.826 s 1.027072
2021-09-28 01:41 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.284 s 0.704473
2021-09-28 01:56 R dataframe-to-table type_strings, R 0.493 s -0.880790
2021-09-28 02:20 R dataframe-to-table type_simple_features, R 273.903 s 1.903476
2021-09-28 02:20 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 8.289 s -4.646676
2021-09-28 01:56 R dataframe-to-table chi_traffic_2020_Q1, R 5.382 s 0.494290
2021-09-28 01:38 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.442 s -0.923608
2021-09-28 01:34 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.040 s -0.202869
2021-09-28 01:35 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.078 s 1.864159
2021-09-28 00:46 Python csv-read gzip, streaming, nyctaxi_2010-01 10.621 s -0.396759
2021-09-28 01:35 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.481 s 1.011165
2021-09-28 00:48 Python dataframe-to-table type_floats 0.011 s 0.012584
2021-09-28 01:32 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.862 s -1.741353
2021-09-28 01:35 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.997 s 0.912030
2021-09-28 01:40 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.787 s 1.025368
2021-09-28 00:49 Python dataframe-to-table type_nested 2.948 s 0.391956
2021-09-28 01:30 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.896 s 0.101733
2021-09-28 00:45 Python csv-read uncompressed, file, nyctaxi_2010-01 1.015 s 0.079829
2021-09-28 00:48 Python dataframe-to-table type_integers 0.011 s 0.247579
2021-09-28 01:05 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 272.137 s 0.029920
2021-09-28 01:19 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.027 s 0.063313
2021-09-28 01:34 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.375915
2021-09-28 01:40 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.893 s 1.935714
2021-09-28 01:41 Python file-write lz4, feather, table, nyctaxi_2010-01 1.806 s 0.264567
2021-09-28 01:56 R dataframe-to-table type_dict, R 0.042 s 1.236343
2021-09-28 02:20 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.611 s -5.161158
2021-09-28 02:21 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.019155
2021-09-28 01:33 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.809 s -0.219370
2021-09-28 01:37 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.429 s 1.879543
2021-09-28 01:38 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.080871
2021-09-28 01:42 Python wide-dataframe use_legacy_dataset=false 0.620 s -0.639815
2021-09-28 00:48 Python dataframe-to-table type_dict 0.012 s -1.112423
2021-09-28 01:42 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.047275
2021-09-28 00:45 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.629 s -0.380855
2021-09-28 01:39 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.125 s 1.001955
2021-09-28 01:42 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.747 s 0.821726
2021-09-28 00:45 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s -0.597336
2021-09-28 01:32 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.675 s -0.810201
2021-09-28 01:56 R dataframe-to-table type_floats, R 0.108 s 0.302963
2021-09-28 00:44 Python csv-read gzip, streaming, fanniemae_2016Q4 14.959 s -0.959675
2021-09-28 00:52 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 53.613 s 1.120231
2021-09-28 01:33 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.109 s 0.621152
2021-09-28 01:39 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.832 s 1.751665
2021-09-28 01:41 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.350 s -0.012430
2021-09-28 00:49 Python dataset-filter nyctaxi_2010-01 4.373 s -0.614856
2021-09-28 01:31 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.023 s -1.239504
2021-09-28 01:32 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.136 s -0.245477
2021-09-28 01:33 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.073 s -1.098423
2021-09-28 01:14 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.464 s -0.884110
2021-09-28 01:19 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.018 s -0.042207
2021-09-28 01:30 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.027 s -0.051198
2021-09-28 01:32 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.494737
2021-09-28 01:34 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.876 s 0.838717
2021-09-28 01:36 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.155 s 1.508883
2021-09-28 01:56 R dataframe-to-table type_nested, R 0.538 s -0.557452
2021-09-28 02:22 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.921 s -0.279877
2021-09-28 00:46 Python csv-read gzip, file, nyctaxi_2010-01 9.053 s -2.259814
2021-09-28 01:14 Python dataset-read async=True, nyctaxi_multi_ipc_s3 178.501 s 1.101881
2021-09-28 01:35 Python file-read lz4, feather, table, nyctaxi_2010-01 0.661 s 1.951463
2021-09-28 02:20 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.580 s -4.664000
2021-09-28 01:33 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.160608
2021-09-28 01:56 R dataframe-to-table type_integers, R 0.084 s 0.932569
2021-09-28 01:38 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.567 s 1.327184
2021-09-28 02:23 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.182 s -0.787853
2021-09-28 02:21 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 8.340 s -5.627221
2021-09-28 02:22 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.397 s -1.059424
2021-09-28 02:22 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.569 s -1.316525
2021-09-28 02:23 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.043 s 2.396580
2021-09-28 02:23 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.134 s -0.251255
2021-09-28 02:24 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.053634
2021-09-28 02:24 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.243 s -0.113679
2021-09-28 02:25 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.171047
2021-09-28 02:25 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.993 s -1.415060
2021-09-28 02:53 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.674 s -0.244573
2021-09-28 02:26 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.512 s 0.062102
2021-09-28 02:41 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.199 s -0.413761
2021-09-28 02:45 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.392 s 0.223171
2021-09-28 02:53 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.154075
2021-09-28 02:53 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-28 02:27 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.825 s 2.153841
2021-09-28 02:36 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.869 s 1.159508
2021-09-28 02:37 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.525 s 0.901431
2021-09-28 02:28 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.277 s 1.850780
2021-09-28 02:45 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.469 s 1.019517
2021-09-28 02:53 JavaScript Parse readBatches, tracks 0.000 s -0.007091
2021-09-28 02:33 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.406 s -0.719363
2021-09-28 02:42 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.605413
2021-09-28 02:53 JavaScript Parse serialize, tracks 0.005 s -0.703617
2021-09-28 02:53 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.613 s -0.134612
2021-09-28 02:53 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.710 s 0.216786
2021-09-28 02:53 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.604844
2021-09-28 02:53 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.048 s -1.846260
2021-09-28 02:43 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.354722
2021-09-28 02:44 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.186053
2021-09-28 02:53 JavaScript Parse Table.from, tracks 0.000 s -0.842766
2021-09-28 02:53 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-28 02:32 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.580 s 0.959892
2021-09-28 02:41 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.695 s 0.696440
2021-09-28 02:42 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.757 s 0.450156
2021-09-28 02:42 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.979 s 0.243307
2021-09-28 02:53 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.078797
2021-09-28 02:38 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.728 s 1.009700
2021-09-28 02:53 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.050381
2021-09-28 02:44 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.862 s 1.117747
2021-09-28 02:29 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.287 s 2.036584
2021-09-28 02:31 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.834 s -0.805693
2021-09-28 02:53 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.931 s -0.637681
2021-09-28 02:53 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -2.859103
2021-09-28 02:40 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.270 s -0.261702
2021-09-28 02:40 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.492 s -0.250591
2021-09-28 02:46 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.494 s 0.173533
2021-09-28 02:53 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.508944
2021-09-28 02:43 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.520 s -0.627810
2021-09-28 02:45 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.807 s 0.914966
2021-09-28 02:39 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.248577
2021-09-28 02:53 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.149346
2021-09-28 02:53 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.539280
2021-09-28 02:42 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.770 s 0.379905
2021-09-28 02:44 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.522 s 1.138127
2021-09-28 02:53 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.653 s 0.428088
2021-09-28 02:53 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.061249
2021-09-28 02:53 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.566637
2021-09-28 02:53 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.508 s 0.060011
2021-09-28 02:31 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.741 s 1.783089
2021-09-28 02:34 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.224 s 0.728418
2021-09-28 02:42 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.175 s 0.232453
2021-09-28 02:53 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.723006
2021-09-28 02:35 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.888 s 0.944588
2021-09-28 02:53 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.014540
2021-09-28 02:45 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.200 s -1.201011
2021-09-28 02:53 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.137537
2021-09-28 02:53 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -0.824487
2021-09-28 02:53 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.632257
2021-09-28 02:53 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.861 s 0.532018
2021-09-28 02:53 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.177002
2021-09-28 02:53 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.028 s -2.259721
2021-09-28 02:53 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.097695