Outliers: 5


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 11:22 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.484 s 1.684421
2021-10-08 11:23 Python csv-read gzip, streaming, nyctaxi_2010-01 10.463 s 1.846568
2021-10-08 11:33 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.439 s 0.934520
2021-10-08 11:59 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.234 s 0.118583
2021-10-08 12:01 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.245 s -1.374628
2021-10-08 13:16 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.058714
2021-10-08 11:48 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.008 s 0.203883
2021-10-08 11:25 Python dataframe-to-table chi_traffic_2020_Q1 21.216 s -6.870505
2021-10-08 11:25 Python dataframe-to-table type_strings 0.414 s -5.307067
2021-10-08 11:26 Python dataset-filter nyctaxi_2010-01 4.354 s 0.499614
2021-10-08 12:03 Python file-read lz4, feather, table, nyctaxi_2010-01 0.666 s 0.715660
2021-10-08 12:07 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.785 s 1.432234
2021-10-08 12:00 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.804 s -1.517508
2021-10-08 12:07 Python file-write lz4, feather, table, fanniemae_2016Q4 1.157 s 0.456965
2021-10-08 12:08 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.784 s 0.805854
2021-10-08 11:43 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.124 s 1.120690
2021-10-08 12:06 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.315 s 0.172492
2021-10-08 12:23 R dataframe-to-table type_strings, R 0.489 s 1.168335
2021-10-08 11:22 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.620506
2021-10-08 11:25 Python dataframe-to-table type_integers 0.011 s 1.274352
2021-10-08 12:02 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.320 s -1.436043
2021-10-08 12:02 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.192979
2021-10-08 12:09 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.804 s 0.912366
2021-10-08 12:24 R dataframe-to-table type_nested, R 0.540 s -0.905687
2021-10-08 11:21 Python csv-read gzip, streaming, fanniemae_2016Q4 14.865 s -0.244275
2021-10-08 11:47 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.003 s 0.487512
2021-10-08 11:22 Python csv-read uncompressed, file, nyctaxi_2010-01 1.000 s 1.234658
2021-10-08 12:06 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.743 s 0.084120
2021-10-08 12:09 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.329 s 0.379575
2021-10-08 12:23 R dataframe-to-table type_dict, R 0.060 s -0.962408
2021-10-08 12:02 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.000 s 2.292834
2021-10-08 12:03 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.468 s -1.426725
2021-10-08 12:03 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.969 s -1.437421
2021-10-08 12:04 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.285 s 0.474778
2021-10-08 12:09 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.347 s 0.265828
2021-10-08 11:20 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.921 s -0.208610
2021-10-08 11:21 Python csv-read uncompressed, file, fanniemae_2016Q4 1.188 s -0.847520
2021-10-08 12:00 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.814 s 0.184884
2021-10-08 11:25 Python dataframe-to-table type_floats 0.012 s -1.035856
2021-10-08 12:02 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.360 s -1.610225
2021-10-08 12:06 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.814 s -0.546294
2021-10-08 12:07 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.250 s -0.239277
2021-10-08 11:58 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.816 s 0.440726
2021-10-08 11:59 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.997 s -0.045203
2021-10-08 12:10 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.843 s 0.013709
2021-10-08 12:23 R dataframe-to-table chi_traffic_2020_Q1, R 3.406 s 35.728630
2021-10-08 12:00 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.292 s -0.209568
2021-10-08 12:08 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.853 s 1.356385
2021-10-08 12:10 Python wide-dataframe use_legacy_dataset=false 0.627 s -1.470827
2021-10-08 11:23 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.368510
2021-10-08 12:23 R dataframe-to-table type_integers, R 0.010 s 74.091730
2021-10-08 11:26 Python dataframe-to-table type_simple_features 0.909 s 0.354445
2021-10-08 11:29 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.284 s 0.026005
2021-10-08 12:00 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.151 s -0.597257
2021-10-08 11:25 Python dataframe-to-table type_dict 0.012 s 0.811917
2021-10-08 12:01 Python file-read lz4, feather, table, fanniemae_2016Q4 0.597 s 0.904508
2021-10-08 12:10 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.887090
2021-10-08 12:23 R dataframe-to-table type_floats, R 0.012 s 37.418030
2021-10-08 11:26 Python dataframe-to-table type_nested 2.910 s 0.304934
2021-10-08 11:43 Python dataset-read async=True, nyctaxi_multi_ipc_s3 190.702 s -0.258029
2021-10-08 11:47 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.023 s 0.148754
2021-10-08 12:00 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.509071
2021-10-08 12:01 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.934 s -1.281699
2021-10-08 11:58 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.967 s 0.305671
2021-10-08 12:04 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.095 s 0.972752
2021-10-08 12:00 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.866 s -1.385653
2021-10-08 11:59 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.786 s -0.176782
2021-10-08 12:01 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.006 s 1.410735
2021-10-08 12:45 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.913 s 0.107635
2021-10-08 12:05 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.457 s 0.922844
2021-10-08 13:08 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -3.944314
2021-10-08 12:47 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.129 s 0.009155
2021-10-08 13:07 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -4.007764
2021-10-08 13:09 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.174 s 0.852920
2021-10-08 12:10 Python file-write lz4, feather, table, nyctaxi_2010-01 1.802 s 0.527996
2021-10-08 12:49 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.992 s -0.895780
2021-10-08 13:06 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.896197
2021-10-08 13:08 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s 0.346825
2021-10-08 13:06 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.516 s 0.098068
2021-10-08 13:06 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.599 s 0.888360
2021-10-08 12:43 R dataframe-to-table type_simple_features, R 275.188 s -0.345623
2021-10-08 12:44 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.959 s -0.073757
2021-10-08 12:45 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.562 s 0.173703
2021-10-08 12:48 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.194 s 2.350846
2021-10-08 12:52 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.276 s 1.031025
2021-10-08 13:03 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.254 s 0.239431
2021-10-08 13:09 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.520 s 0.035641
2021-10-08 13:16 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.537150
2021-10-08 13:16 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.395619
2021-10-08 13:16 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.647 s -0.382034
2021-10-08 13:16 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.149612
2021-10-08 13:16 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.723 s 0.150569
2021-10-08 13:16 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -1.844792
2021-10-08 13:16 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.749466
2021-10-08 13:16 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.341021
2021-10-08 13:16 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.465 s 0.705891
2021-10-08 12:55 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.828 s 0.633801
2021-10-08 13:02 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.637 s 2.286237
2021-10-08 13:05 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.589 s 0.784218
2021-10-08 13:05 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.435997
2021-10-08 13:16 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.861437
2021-10-08 13:16 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -2.161846
2021-10-08 12:46 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.370 s 0.757677
2021-10-08 13:04 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.488 s 0.463991
2021-10-08 13:16 JavaScript Parse Table.from, tracks 0.000 s 1.244690
2021-10-08 13:16 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.553649
2021-10-08 13:05 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.951 s 0.816686
2021-10-08 13:16 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.853295
2021-10-08 13:16 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.014926
2021-10-08 13:16 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.665 s 0.356513
2021-10-08 13:16 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.520377
2021-10-08 13:16 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.862 s 0.444635
2021-10-08 13:16 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.157352
2021-10-08 13:16 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.123330
2021-10-08 13:16 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.063777
2021-10-08 12:45 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.754155
2021-10-08 12:46 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.610050
2021-10-08 12:48 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.245 s -0.097503
2021-10-08 12:50 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.848 s 1.027975
2021-10-08 12:53 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.290 s 1.093126
2021-10-08 12:56 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.547 s 1.034008
2021-10-08 12:57 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.188 s 1.082981
2021-10-08 13:16 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.164867
2021-10-08 12:43 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.277 s 0.000000
2021-10-08 12:56 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.081811
2021-10-08 13:05 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 1.388971
2021-10-08 13:16 JavaScript Parse readBatches, tracks 0.000 s 0.709093
2021-10-08 13:16 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.514 s -0.011231
2021-10-08 13:16 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.427962
2021-10-08 13:16 JavaScript Parse serialize, tracks 0.005 s -0.661920
2021-10-08 13:16 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.897 s 0.162351
2021-10-08 13:00 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.450 s 1.828992
2021-10-08 13:16 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.206963
2021-10-08 12:59 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.782 s 2.013211
2021-10-08 13:05 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 0.946018
2021-10-08 13:16 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.586588
2021-10-08 13:16 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.537150
2021-10-08 12:44 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.252 s -0.005789
2021-10-08 12:45 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.924 s -0.214931
2021-10-08 12:49 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.107434
2021-10-08 12:58 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.821 s 1.654054
2021-10-08 13:07 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.891 s 0.903117
2021-10-08 13:07 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.526 s 1.100624
2021-10-08 13:08 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.483 s -1.935806
2021-10-08 12:47 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.204 s -1.783887
2021-10-08 12:50 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.520 s 0.299596
2021-10-08 12:54 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.770 s 0.843571
2021-10-08 13:02 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.301105
2021-10-08 13:07 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.617 s -1.359699