Outliers: 4


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 04:31 Python csv-read gzip, file, fanniemae_2016Q4 6.030 s -0.098426
2021-10-13 05:08 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.620 s 1.404712
2021-10-13 05:08 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.551 s 1.352137
2021-10-13 05:09 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.087 s -0.874038
2021-10-13 05:17 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.896 s -0.244300
2021-10-13 05:17 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.381 s -1.364348
2021-10-13 05:32 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.748 s 0.714182
2021-10-13 05:39 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.586 s -2.375150
2021-10-13 05:43 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.392 s 0.450961
2021-10-13 05:50 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.474 s 0.891673
2021-10-13 05:54 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s -0.649465
2021-10-13 05:55 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.495 s 0.773928
2021-10-13 06:03 JavaScript Parse readBatches, tracks 0.000 s 0.279288
2021-10-13 06:03 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.609 s -0.264251
2021-10-13 06:03 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.812 s -2.505562
2021-10-13 06:03 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.031970
2021-10-13 06:03 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.256292
2021-10-13 04:31 Python csv-read gzip, streaming, fanniemae_2016Q4 14.861 s -0.110668
2021-10-13 05:08 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.317 s -1.013570
2021-10-13 05:18 Python wide-dataframe use_legacy_dataset=true 0.393 s -0.158855
2021-10-13 05:33 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.037 s -0.910331
2021-10-13 05:38 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.702 s -3.564608
2021-10-13 06:03 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.346655
2021-10-13 06:03 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.621479
2021-10-13 04:35 Python dataframe-to-table type_nested 2.836 s 2.330433
2021-10-13 04:52 Python dataset-read async=True, nyctaxi_multi_ipc_s3 187.994 s -0.173168
2021-10-13 05:08 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.910 s -2.002268
2021-10-13 04:32 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.671 s -0.327198
2021-10-13 05:16 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.867 s -0.254431
2021-10-13 05:31 R dataframe-to-table chi_traffic_2020_Q1, R 3.473 s 0.262351
2021-10-13 05:41 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.132 s -3.286684
2021-10-13 05:44 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.320 s -6.625435
2021-10-13 05:15 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.912 s -1.099815
2021-10-13 05:52 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.840 s 1.003922
2021-10-13 05:52 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.609 s -5.215761
2021-10-13 05:17 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.325 s 0.618623
2021-10-13 04:30 Python csv-read uncompressed, file, fanniemae_2016Q4 1.196 s -0.684349
2021-10-13 04:32 Python csv-read uncompressed, file, nyctaxi_2010-01 1.001 s 0.773364
2021-10-13 04:33 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 0.933675
2021-10-13 04:52 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.159 s 0.269088
2021-10-13 04:35 Python dataframe-to-table type_dict 0.011 s 1.437408
2021-10-13 04:39 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 65.104 s -1.118484
2021-10-13 05:14 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.376 s -0.417461
2021-10-13 05:15 Python file-write lz4, feather, table, fanniemae_2016Q4 1.148 s 0.619958
2021-10-13 05:11 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.818 s 1.143264
2021-10-13 05:16 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.974 s -1.132771
2021-10-13 05:49 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 1.412745
2021-10-13 05:52 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.196 s -1.949906
2021-10-13 05:54 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.116 s -2.249024
2021-10-13 06:03 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.425333
2021-10-13 04:35 Python dataframe-to-table chi_traffic_2020_Q1 19.274 s 0.818661
2021-10-13 05:11 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.286 s 1.260998
2021-10-13 05:13 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.705 s -2.314946
2021-10-13 05:31 R dataframe-to-table type_floats, R 0.013 s 0.830526
2021-10-13 05:34 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.102 s 1.218277
2021-10-13 05:47 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.554 s -1.124503
2021-10-13 05:53 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 3.004 s -3.935780
2021-10-13 06:03 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.596024
2021-10-13 06:03 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.272982
2021-10-13 04:35 Python dataframe-to-table type_floats 0.011 s 0.488330
2021-10-13 05:09 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.687 s 1.051423
2021-10-13 05:09 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.012 s 1.407047
2021-10-13 05:12 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.350 s -2.304482
2021-10-13 05:18 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.255455
2021-10-13 05:31 R dataframe-to-table type_dict, R 0.052 s -0.126246
2021-10-13 05:34 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.222 s 0.782080
2021-10-13 05:45 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.922 s -1.198871
2021-10-13 06:03 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.083657
2021-10-13 06:03 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s 0.049755
2021-10-13 04:32 Python csv-read gzip, streaming, nyctaxi_2010-01 10.659 s -0.381336
2021-10-13 05:07 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.097 s -2.407961
2021-10-13 05:08 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.138 s 0.079099
2021-10-13 05:10 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.059 s -0.594727
2021-10-13 05:11 Python file-read lz4, feather, table, nyctaxi_2010-01 0.676 s -0.061855
2021-10-13 05:15 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.322 s 0.248325
2021-10-13 05:32 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.317 s -1.189048
2021-10-13 05:35 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.999 s 0.009510
2021-10-13 05:50 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.265 s -2.809336
2021-10-13 05:55 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.130 s 2.995836
2021-10-13 06:03 JavaScript Parse serialize, tracks 0.004 s 0.624446
2021-10-13 06:03 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.087121
2021-10-13 04:30 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.921 s -0.051040
2021-10-13 04:35 Python dataframe-to-table type_integers 0.011 s -0.037973
2021-10-13 04:56 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.089 s -0.438676
2021-10-13 05:07 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.670 s 0.688527
2021-10-13 05:14 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 14.010 s -1.711411
2021-10-13 05:18 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.754 s 2.066821
2021-10-13 05:41 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.814 s 1.118382
2021-10-13 06:03 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.097304
2021-10-13 06:03 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.453 s 1.205915
2021-10-13 04:43 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.042 s -0.445127
2021-10-13 05:09 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.099537
2021-10-13 05:31 R dataframe-to-table type_integers, R 0.009 s 0.839574
2021-10-13 05:35 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.687 s 0.062443
2021-10-13 05:52 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.601 s -0.427187
2021-10-13 05:54 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.574 s 0.325397
2021-10-13 06:03 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.650 s -0.404289
2021-10-13 06:03 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.672 s 0.431646
2021-10-13 05:07 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.925 s -0.231177
2021-10-13 05:18 Python file-write lz4, feather, table, nyctaxi_2010-01 1.792 s 0.693132
2021-10-13 05:55 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.213 s -2.501206
2021-10-13 06:03 JavaScript Parse Table.from, tracks 0.000 s 0.084655
2021-10-13 06:03 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.808 s 1.970641
2021-10-13 06:03 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.568795
2021-10-13 04:56 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.084 s -1.443040
2021-10-13 05:07 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.947 s 0.426459
2021-10-13 05:34 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -1.334580
2021-10-13 05:51 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.193 s -2.678779
2021-10-13 06:03 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.650139
2021-10-13 05:07 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.320 s -1.899298
2021-10-13 05:10 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.148 s 1.360424
2021-10-13 05:37 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.134 s -2.403691
2021-10-13 05:52 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.054548
2021-10-13 04:56 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.020 s 0.167275
2021-10-13 05:32 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.523 s -3.760742
2021-10-13 05:33 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.486339
2021-10-13 05:34 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.154 s 0.789621
2021-10-13 05:36 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.515 s 0.242713
2021-10-13 06:03 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.426806
2021-10-13 05:32 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.467 s 0.780429
2021-10-13 05:34 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.044 s 1.328137
2021-10-13 05:48 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.763 s -1.577205
2021-10-13 05:53 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.808 s -5.391708
2021-10-13 06:03 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.084401
2021-10-13 04:35 Python dataframe-to-table type_strings 0.368 s 0.325659
2021-10-13 05:10 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.165 s 1.124467
2021-10-13 05:13 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.628 s -1.565068
2021-10-13 05:15 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.844 s 0.112016
2021-10-13 05:51 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.509 s 7.389037
2021-10-13 05:54 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.495 s -1.904905
2021-10-13 06:03 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.912057
2021-10-13 04:35 Python dataset-filter nyctaxi_2010-01 4.405 s -2.889806
2021-10-13 05:09 Python file-read lz4, feather, table, fanniemae_2016Q4 0.607 s -0.341658
2021-10-13 05:10 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.171 s 0.141967
2021-10-13 05:31 R dataframe-to-table type_strings, R 0.495 s 0.229471
2021-10-13 06:03 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.864 s 0.920830
2021-10-13 05:32 R dataframe-to-table type_nested, R 0.543 s 0.230216
2021-10-13 06:03 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.447689
2021-10-13 06:03 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.435382
2021-10-13 06:03 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.686205
2021-10-13 06:03 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s 0.009979
2021-10-13 05:32 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.241 s -0.547061
2021-10-13 05:42 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.646 s -3.646723
2021-10-13 05:46 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.904 s -1.582203
2021-10-13 05:34 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.382 s 0.549877
2021-10-13 05:52 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.534 s -1.541787
2021-10-13 06:03 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.088981