Outliers: 5


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-27 20:11 Python csv-read uncompressed, file, fanniemae_2016Q4 1.166 s 0.022214
2021-09-27 20:12 Python csv-read uncompressed, file, nyctaxi_2010-01 1.019 s 0.014940
2021-09-27 20:47 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.019 s 0.197952
2021-09-27 20:58 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.951 s 0.422594
2021-09-27 20:10 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.062 s -1.022699
2021-09-27 20:58 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.875 s 0.201648
2021-09-27 21:04 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.111 s 1.822736
2021-09-27 20:46 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.023 s 0.178627
2021-09-27 20:59 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.285 s -0.454521
2021-09-27 21:00 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.670 s -0.579823
2021-09-27 21:03 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.095 s 1.819993
2021-09-27 20:42 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.192 s 0.551038
2021-09-27 21:07 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.838 s 1.715201
2021-09-27 20:13 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.503662
2021-09-27 21:00 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.895530
2021-09-27 20:12 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.636 s -0.416403
2021-09-27 21:05 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.495 s 1.730719
2021-09-27 20:33 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 266.846 s 0.242313
2021-09-27 21:01 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.884 s 0.893878
2021-09-27 21:02 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.836 s 1.027887
2021-09-27 21:02 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.992 s 0.961499
2021-09-27 20:15 Python dataframe-to-table type_strings 0.367 s 0.634188
2021-09-27 20:16 Python dataframe-to-table type_nested 2.953 s 0.137333
2021-09-27 20:16 Python dataset-filter nyctaxi_2010-01 4.368 s -0.468642
2021-09-27 20:59 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.033 s -1.629056
2021-09-27 20:16 Python dataframe-to-table type_simple_features 0.907 s 0.327166
2021-09-27 20:58 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.724 s 0.364522
2021-09-27 20:59 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.276 s -1.552258
2021-09-27 21:01 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.124 s -1.014132
2021-09-27 20:59 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.863 s -1.838117
2021-09-27 21:00 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.733 s -0.411828
2021-09-27 21:04 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.437 s 1.927132
2021-09-27 20:12 Python csv-read gzip, file, fanniemae_2016Q4 6.032 s -0.871372
2021-09-27 20:15 Python dataframe-to-table type_dict 0.011 s 1.773463
2021-09-27 21:01 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.061 s -1.452849
2021-09-27 20:13 Python csv-read gzip, streaming, nyctaxi_2010-01 10.624 s -0.416057
2021-09-27 21:00 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.809 s -0.203208
2021-09-27 21:02 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.190 s -2.571978
2021-09-27 20:11 Python csv-read gzip, streaming, fanniemae_2016Q4 15.006 s -1.039370
2021-09-27 20:15 Python dataframe-to-table type_integers 0.011 s 0.435916
2021-09-27 21:06 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.571 s 1.380557
2021-09-27 20:19 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 66.824 s -1.729264
2021-09-27 20:15 Python dataframe-to-table chi_traffic_2020_Q1 19.805 s -0.039927
2021-09-27 20:42 Python dataset-read async=True, nyctaxi_multi_ipc_s3 181.151 s 0.801426
2021-09-27 20:47 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.013 s 0.062550
2021-09-27 21:03 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.478 s 1.060664
2021-09-27 21:08 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.892 s 2.144820
2021-09-27 20:15 Python dataframe-to-table type_floats 0.012 s -1.064885
2021-09-27 21:00 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.133 s -0.018604
2021-09-27 21:06 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.165 s 0.670361
2021-09-27 21:08 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.354 s -0.200844
2021-09-27 21:00 Python file-read lz4, feather, table, fanniemae_2016Q4 0.611 s -1.664096
2021-09-27 21:06 Python file-write lz4, feather, table, fanniemae_2016Q4 1.152 s 0.784399
2021-09-27 21:01 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.041 s -0.027472
2021-09-27 21:02 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s -0.009212
2021-09-27 21:05 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.296 s 0.279088
2021-09-27 21:07 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.783 s 1.089695
2021-09-27 21:09 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.285 s 0.713987
2021-09-27 21:09 Python wide-dataframe use_legacy_dataset=false 0.623 s -1.197716
2021-09-27 21:08 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.828 s 1.043322
2021-09-27 21:09 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.745 s 0.859068
2021-09-27 21:09 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.099425
2021-09-27 21:09 Python file-write lz4, feather, table, nyctaxi_2010-01 1.799 s 0.664963
2021-09-27 22:21 JavaScript Parse serialize, tracks 0.005 s -0.530157
2021-09-27 21:52 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.252 s -0.614345
2021-09-27 22:07 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.261 s 0.295746
2021-09-27 22:10 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.970 s 0.787510
2021-09-27 22:21 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -2.067771
2021-09-27 22:00 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.604 s 0.485062
2021-09-27 22:13 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 1.349745
2021-09-27 22:21 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.912 s -1.204639
2021-09-27 21:23 R dataframe-to-table type_strings, R 0.495 s -1.750097
2021-09-27 21:48 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.979528
2021-09-27 21:53 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.679 s -0.311003
2021-09-27 22:04 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.516 s 1.140343
2021-09-27 21:23 R dataframe-to-table type_floats, R 0.108 s 0.468271
2021-09-27 21:50 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.411 s -1.811375
2021-09-27 22:10 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.518 s -0.311212
2021-09-27 22:21 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.360363
2021-09-27 22:21 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.929889
2021-09-27 21:48 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 8.303 s -8.319920
2021-09-27 21:47 R dataframe-to-table type_simple_features, R 274.708 s 0.235781
2021-09-27 21:56 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.288 s 1.839430
2021-09-27 22:02 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.892 s 0.739404
2021-09-27 21:49 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.571777
2021-09-27 21:23 R dataframe-to-table chi_traffic_2020_Q1, R 5.349 s 1.020492
2021-09-27 21:48 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 8.385 s -9.441826
2021-09-27 21:24 R dataframe-to-table type_nested, R 0.536 s -0.017594
2021-09-27 22:13 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.815 s 0.235717
2021-09-27 22:21 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.929 s -1.082034
2021-09-27 21:50 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.072 s -3.160865
2021-09-27 22:12 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.100 s 1.077141
2021-09-27 22:09 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.192 s 0.064912
2021-09-27 22:21 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.724 s 0.128146
2021-09-27 22:21 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.622277
2021-09-27 22:21 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.121811
2021-09-27 21:23 R dataframe-to-table type_dict, R 0.041 s 1.386204
2021-09-27 21:53 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.549 s -1.679572
2021-09-27 22:01 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.225 s 0.717469
2021-09-27 22:03 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.861 s 1.391156
2021-09-27 22:09 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.695 s 0.728710
2021-09-27 22:21 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.922 s -0.462073
2021-09-27 22:21 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-27 21:49 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.911 s 0.139387
2021-09-27 21:48 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.610 s -8.281761
2021-09-27 21:51 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.111 s 1.407463
2021-09-27 22:00 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.688214
2021-09-27 22:05 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.722 s 1.141130
2021-09-27 22:21 JavaScript Parse readBatches, tracks 0.000 s -0.709398
2021-09-27 22:21 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.021 s 2.539310
2021-09-27 22:21 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-27 22:21 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.051893
2021-09-27 22:21 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -2.005857
2021-09-27 22:08 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.479 s 2.224678
2021-09-27 22:21 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.985337
2021-09-27 21:52 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.436743
2021-09-27 21:58 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.730 s 1.947664
2021-09-27 22:21 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.926259
2021-09-27 21:23 R dataframe-to-table type_integers, R 0.083 s 1.156150
2021-09-27 21:47 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.608 s -8.097830
2021-09-27 22:12 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.478 s -1.594881
2021-09-27 22:21 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.202956
2021-09-27 22:21 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.494177
2021-09-27 21:51 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.170 s -0.069543
2021-09-27 22:21 JavaScript Parse Table.from, tracks 0.000 s -0.507192
2021-09-27 21:52 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.994 s -1.517967
2021-09-27 22:06 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.095434
2021-09-27 22:09 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.752 s 0.843840
2021-09-27 21:54 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.832 s 2.136750
2021-09-27 21:58 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.827 s 0.747446
2021-09-27 22:13 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.488 s 0.188957
2021-09-27 22:21 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -2.021758
2021-09-27 21:56 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.286 s 2.102399
2021-09-27 22:21 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.021 s 2.531624
2021-09-27 22:21 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.389747
2021-09-27 22:21 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.515 s -0.068661
2021-09-27 22:10 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.269929
2021-09-27 22:11 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.917 s 0.543471
2021-09-27 22:12 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.392 s 0.436311
2021-09-27 22:21 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.132836
2021-09-27 22:09 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.588 s 0.998613
2021-09-27 22:09 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.104 s -5.390055
2021-09-27 22:10 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.608 s -0.206562
2021-09-27 22:11 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.521 s 1.211370
2021-09-27 22:21 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 6.280 s -2.321804
2021-09-27 22:21 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.705 s -0.509085
2021-09-27 22:21 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-27 22:21 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.089055
2021-09-27 22:21 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.425831