Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-26 03:22 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.336053
2021-09-26 03:21 Python csv-read uncompressed, file, fanniemae_2016Q4 1.176 s -0.114293
2021-09-26 03:30 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 65.385 s -1.482742
2021-09-26 03:26 Python dataframe-to-table type_nested 2.931 s 1.305699
2021-09-26 03:25 Python dataframe-to-table type_strings 0.367 s 0.691785
2021-09-26 03:22 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.604 s -0.338061
2021-09-26 03:21 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.637 s -0.607937
2021-09-26 03:26 Python dataframe-to-table type_integers 0.011 s 0.350400
2021-09-26 03:26 Python dataframe-to-table type_simple_features 0.904 s 0.817322
2021-09-26 03:23 Python csv-read uncompressed, file, nyctaxi_2010-01 1.022 s -0.026637
2021-09-26 03:23 Python csv-read gzip, streaming, nyctaxi_2010-01 10.559 s -0.207656
2021-09-26 03:26 Python dataset-filter nyctaxi_2010-01 4.368 s -0.462203
2021-09-26 03:50 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 397.766 s -6.030171
2021-09-26 03:24 Python csv-read gzip, file, nyctaxi_2010-01 9.039 s 1.861591
2021-09-26 03:26 Python dataframe-to-table type_floats 0.012 s -1.198991
2021-09-26 03:21 Python csv-read gzip, streaming, fanniemae_2016Q4 14.551 s -0.597771
2021-09-26 03:25 Python dataframe-to-table chi_traffic_2020_Q1 19.745 s 0.286715
2021-09-26 03:26 Python dataframe-to-table type_dict 0.012 s -0.500596
2021-09-26 04:02 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.614 s -1.611508
2021-09-26 04:02 Python dataset-read async=True, nyctaxi_multi_ipc_s3 241.932 s -10.617825
2021-09-26 04:07 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.998 s 0.330309
2021-09-26 04:20 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.104 s 1.021008
2021-09-26 05:11 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.104 s 1.939006
2021-09-26 04:20 Python file-read lz4, feather, table, fanniemae_2016Q4 0.597 s 0.858002
2021-09-26 04:27 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.785 s 1.184115
2021-09-26 04:43 R dataframe-to-table chi_traffic_2020_Q1, R 5.494 s -1.315429
2021-09-26 05:41 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.034388
2021-09-26 05:07 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.218 s 0.703752
2021-09-26 05:10 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.367 s 0.358036
2021-09-26 05:13 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.540 s -1.506104
2021-09-26 05:18 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.839 s -1.898032
2021-09-26 05:30 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.591917
2021-09-26 05:41 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.785 s -0.659127
2021-09-26 04:06 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.050 s -1.580743
2021-09-26 04:19 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.139 s -0.412074
2021-09-26 04:43 R dataframe-to-table type_strings, R 0.493 s -1.290102
2021-09-26 04:22 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.234051
2021-09-26 04:19 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.289 s -0.807822
2021-09-26 04:23 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.076 s 2.480682
2021-09-26 04:29 Python wide-dataframe use_legacy_dataset=false 0.625 s -1.818660
2021-09-26 04:18 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.990 s 0.211260
2021-09-26 04:20 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.097 s -2.142156
2021-09-26 04:21 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.027 s 0.586086
2021-09-26 04:22 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.479 s 1.144967
2021-09-26 04:23 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.107 s 2.206121
2021-09-26 05:15 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.264 s 2.254183
2021-09-26 05:28 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.173 s 1.427045
2021-09-26 04:07 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.050 s -0.313701
2021-09-26 04:25 Python file-write lz4, feather, table, fanniemae_2016Q4 1.158 s 0.263851
2021-09-26 04:29 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.787 s 0.594551
2021-09-26 04:43 R dataframe-to-table type_dict, R 0.054 s -0.198043
2021-09-26 05:10 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.188 s -1.146226
2021-09-26 05:28 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.496 s -1.040806
2021-09-26 05:29 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.696 s 0.666166
2021-09-26 05:41 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.002 s -3.934127
2021-09-26 05:41 JavaScript DataFrame Iterate 1,000,000, tracks 0.054 s -3.186842
2021-09-26 05:11 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.009048
2021-09-26 05:22 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.893 s 0.560276
2021-09-26 05:31 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.884 s 1.125119
2021-09-26 05:31 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.287920
2021-09-26 05:41 JavaScript Parse serialize, tracks 0.005 s -0.564603
2021-09-26 05:41 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.855 s -3.450374
2021-09-26 05:41 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.977 s -2.293927
2021-09-26 04:26 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.090 s 1.497648
2021-09-26 04:19 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.846 s -1.364103
2021-09-26 05:07 R dataframe-to-table type_simple_features, R 275.037 s -0.356497
2021-09-26 05:09 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.288531
2021-09-26 05:29 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.775 s 0.530038
2021-09-26 05:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.491454
2021-09-26 05:41 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.542402
2021-09-26 04:18 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.718 s 0.404305
2021-09-26 04:18 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.037 s -1.835443
2021-09-26 04:19 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.657 s -0.052831
2021-09-26 05:08 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.872 s 1.118511
2021-09-26 05:14 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.833 s 2.246082
2021-09-26 05:41 JavaScript Parse readBatches, tracks 0.000 s 0.649609
2021-09-26 04:19 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.740 s -0.874213
2021-09-26 04:22 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.950 s 1.223346
2021-09-26 04:25 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.354 s -0.115427
2021-09-26 04:43 R dataframe-to-table type_floats, R 0.113 s -1.109181
2021-09-26 05:12 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.975 s -0.607171
2021-09-26 05:19 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.557 s 1.508247
2021-09-26 05:33 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.818 s 0.045589
2021-09-26 05:41 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.457764
2021-09-26 05:41 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.020638
2021-09-26 04:20 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.809 s -0.243058
2021-09-26 04:21 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.840 s 1.159770
2021-09-26 04:28 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.810 s 1.278503
2021-09-26 05:11 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.258 s -0.996375
2021-09-26 05:41 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.145960
2021-09-26 05:41 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.532 s 0.131612
2021-09-26 04:21 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.845 s 1.069258
2021-09-26 05:08 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.220 s 0.681955
2021-09-26 05:09 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.914 s 0.021054
2021-09-26 05:26 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.286 s -1.492113
2021-09-26 05:30 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.979 s 0.367310
2021-09-26 05:32 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.392 s 0.274593
2021-09-26 05:41 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.668 s -6.370135
2021-09-26 05:41 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.723006
2021-09-26 04:17 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.839 s 0.390107
2021-09-26 05:08 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.279271
2021-09-26 05:12 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.181021
2021-09-26 05:27 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.248 s 1.200982
2021-09-26 05:30 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.512 s 0.421819
2021-09-26 05:41 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.632257
2021-09-26 05:41 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.183861
2021-09-26 05:41 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.048 s -3.171770
2021-09-26 04:28 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.286 s 0.777344
2021-09-26 05:21 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.225 s 0.741406
2021-09-26 05:25 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.714 s 1.272836
2021-09-26 05:29 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.758 s 0.759879
2021-09-26 05:31 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.597 s 0.104394
2021-09-26 05:41 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.367975
2021-09-26 05:20 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.688199
2021-09-26 05:41 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.618437
2021-09-26 05:41 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.657 s -2.404265
2021-09-26 04:18 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.266 s -1.354092
2021-09-26 04:21 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.170 s 1.280781
2021-09-26 04:27 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.902 s 2.331864
2021-09-26 04:28 Python file-write lz4, feather, table, nyctaxi_2010-01 1.808 s 0.263227
2021-09-26 05:07 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.863 s 1.301315
2021-09-26 05:23 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.852 s 1.623361
2021-09-26 04:20 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.293 s -0.344732
2021-09-26 04:24 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.432 s 2.474530
2021-09-26 04:25 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.470 s 2.195838
2021-09-26 04:25 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.583 s 1.488268
2021-09-26 04:28 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.348 s 0.334208
2021-09-26 04:43 R dataframe-to-table type_integers, R 0.087 s -1.548639
2021-09-26 04:43 R dataframe-to-table type_nested, R 0.535 s 0.132834
2021-09-26 05:18 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.696 s 2.425776
2021-09-26 05:41 JavaScript Parse Table.from, tracks 0.000 s 0.460486
2021-09-26 05:41 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.002 s -3.045174
2021-09-26 05:41 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.050 s -3.064136
2021-09-26 04:26 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.843 s 2.039047
2021-09-26 04:29 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.134701
2021-09-26 05:32 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.469 s 0.932892
2021-09-26 05:41 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.542402
2021-09-26 05:16 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.293 s 2.148134
2021-09-26 05:41 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.152905
2021-09-26 05:41 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.028 s -1.577842
2021-09-26 05:10 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.048 s 1.480891
2021-09-26 05:24 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.525 s 0.697038
2021-09-26 05:41 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.142712
2021-09-26 05:29 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.495221
2021-09-26 05:32 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 1.406615
2021-09-26 05:33 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.485 s 0.209555
2021-09-26 05:41 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.585193
2021-09-26 05:41 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.687139
2021-09-26 05:41 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.507562