Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 17:17 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.634 s -0.344086
2021-09-29 18:45 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.146 s -1.183354
2021-09-29 18:50 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.286 s 1.261503
2021-09-29 17:18 Python csv-read gzip, streaming, nyctaxi_2010-01 10.612 s -0.301962
2021-09-29 17:20 Python dataframe-to-table chi_traffic_2020_Q1 19.432 s 2.192156
2021-09-29 18:00 Python file-write lz4, feather, table, fanniemae_2016Q4 1.152 s 0.815845
2021-09-29 18:03 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.390 s -2.508307
2021-09-29 18:03 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.272 s 0.712314
2021-09-29 17:21 Python dataframe-to-table type_nested 2.907 s 2.654483
2021-09-29 17:42 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.013 s 0.321895
2021-09-29 17:52 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.854 s 0.301042
2021-09-29 17:54 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.235487
2021-09-29 18:01 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.136 s 0.662102
2021-09-29 18:04 Python wide-dataframe use_legacy_dataset=true 0.396 s -0.421670
2021-09-29 18:46 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.241 s -0.033165
2021-09-29 18:52 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.740 s 1.283618
2021-09-29 17:20 Python dataframe-to-table type_strings 0.365 s 0.731567
2021-09-29 17:53 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.312 s -1.645574
2021-09-29 17:55 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.917 s 0.529754
2021-09-29 17:57 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.135 s 0.146937
2021-09-29 17:58 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.210 s 0.852307
2021-09-29 17:59 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.502 s 1.161009
2021-09-29 18:18 R dataframe-to-table type_dict, R 0.052 s -0.052383
2021-09-29 18:18 R dataframe-to-table type_floats, R 0.109 s -0.252743
2021-09-29 18:46 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.654414
2021-09-29 17:17 Python csv-read gzip, streaming, fanniemae_2016Q4 15.003 s -0.843172
2021-09-29 17:20 Python dataframe-to-table type_dict 0.011 s 1.354422
2021-09-29 17:57 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.075 s 1.363989
2021-09-29 18:02 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.855 s 1.841724
2021-09-29 18:17 R dataframe-to-table type_strings, R 0.494 s -1.210827
2021-09-29 18:18 R dataframe-to-table type_nested, R 0.538 s -0.505151
2021-09-29 18:47 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.972 s -0.231895
2021-09-29 18:43 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.953 s -0.362395
2021-09-29 17:38 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.910 s 0.077676
2021-09-29 17:52 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.013 s -0.001036
2021-09-29 17:54 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.795 s -3.917678
2021-09-29 17:56 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.009 s 0.127017
2021-09-29 18:54 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.577 s 0.916703
2021-09-29 17:38 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.192 s 0.530259
2021-09-29 17:42 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.030 s 0.077360
2021-09-29 17:53 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.039 s -1.417265
2021-09-29 17:54 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.168 s -1.667562
2021-09-29 17:55 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.898 s -9.093881
2021-09-29 17:56 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.023 s 0.890168
2021-09-29 18:00 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.520 s -1.536770
2021-09-29 18:42 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.253 s -0.014985
2021-09-29 17:17 Python csv-read gzip, file, fanniemae_2016Q4 6.041 s -2.372624
2021-09-29 17:52 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.809 s -0.032313
2021-09-29 17:29 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.350 s 2.517394
2021-09-29 18:48 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.539 s -1.023374
2021-09-29 17:16 Python csv-read uncompressed, file, fanniemae_2016Q4 1.166 s 0.085941
2021-09-29 17:18 Python csv-read uncompressed, file, nyctaxi_2010-01 1.017 s 0.040967
2021-09-29 17:53 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.281 s -1.319864
2021-09-29 17:55 Python file-read lz4, feather, table, fanniemae_2016Q4 0.605 s -0.716402
2021-09-29 17:56 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.709312
2021-09-29 18:04 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.643745
2021-09-29 18:48 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.849 s 1.331506
2021-09-29 17:59 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.441 s 1.269993
2021-09-29 18:43 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 1.114456
2021-09-29 17:42 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.008 s 0.186123
2021-09-29 18:02 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.808 s 0.668712
2021-09-29 18:17 R dataframe-to-table chi_traffic_2020_Q1, R 5.355 s 0.946304
2021-09-29 17:21 Python dataframe-to-table type_floats 0.011 s 0.223451
2021-09-29 17:25 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 69.148 s -2.206137
2021-09-29 17:53 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.866 s -1.421166
2021-09-29 18:04 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.810 s 0.236413
2021-09-29 17:57 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.453560
2021-09-29 18:03 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.844 s 0.673909
2021-09-29 17:19 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -0.875217
2021-09-29 17:55 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.048 s -0.202736
2021-09-29 17:57 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.631 s 0.184915
2021-09-29 18:00 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.680 s 0.212020
2021-09-29 18:54 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.402 s 0.103505
2021-09-29 17:16 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.046 s -0.814653
2021-09-29 17:20 Python dataframe-to-table type_integers 0.011 s -1.904587
2021-09-29 17:54 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.879 s -6.760635
2021-09-29 18:41 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.239 s 0.143412
2021-09-29 18:43 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.921 s -0.336795
2021-09-29 17:21 Python dataset-filter nyctaxi_2010-01 4.393 s -1.039600
2021-09-29 18:43 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.564 s -0.272975
2021-09-29 18:44 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.055 s 0.211112
2021-09-29 18:03 Python file-write lz4, feather, table, nyctaxi_2010-01 1.861 s -2.575271
2021-09-29 18:41 R dataframe-to-table type_simple_features, R 274.656 s 0.291685
2021-09-29 18:53 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.827 s 0.874244
2021-09-29 17:21 Python dataframe-to-table type_simple_features 0.931 s -3.919714
2021-09-29 18:47 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.681 s -0.919658
2021-09-29 17:55 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.219 s -11.052140
2021-09-29 18:01 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.803 s 1.555381
2021-09-29 18:18 R dataframe-to-table type_integers, R 0.085 s -0.143609
2021-09-29 18:42 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.941 s -0.221084
2021-09-29 18:44 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.387 s -0.544955
2021-09-29 18:45 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.199 s -1.695348
2021-09-29 18:55 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.219 s 0.825003
2021-09-29 18:56 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.820 s 2.363412
2021-09-29 18:57 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.808 s 2.073240
2021-09-29 19:14 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.996 s -1.943739
2021-09-29 18:58 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.463 s 2.189815
2021-09-29 19:14 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.070796
2021-09-29 19:04 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.443369
2021-09-29 19:06 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.197 s 0.636390
2021-09-29 19:14 JavaScript Parse Table.from, tracks 0.000 s 0.194634
2021-09-29 19:14 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -3.744586
2021-09-29 19:14 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.170931
2021-09-29 19:14 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.194059
2021-09-29 19:00 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.286 s -1.517359
2021-09-29 19:07 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.497 s 0.133959
2021-09-29 19:14 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.346316
2021-09-29 19:03 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.566 s 2.597765
2021-09-29 19:14 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.307 s 3.958091
2021-09-29 19:14 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.760 s -0.079159
2021-09-29 19:03 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.581 s 2.077088
2021-09-29 19:06 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.102 s -2.353222
2021-09-29 19:14 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.590158
2021-09-29 19:00 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.680 s 1.786389
2021-09-29 19:14 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.189300
2021-09-29 19:14 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.134796
2021-09-29 19:04 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.506 s 1.335362
2021-09-29 19:14 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.032798
2021-09-29 19:05 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.509 s 1.433391
2021-09-29 19:06 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.472 s 0.195664
2021-09-29 19:14 JavaScript Parse readBatches, tracks 0.000 s 0.021201
2021-09-29 19:14 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.039750
2021-09-29 19:14 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.555945
2021-09-29 19:05 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.607 s 0.070563
2021-09-29 19:06 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.349 s 1.849348
2021-09-29 19:03 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.875 s 2.140608
2021-09-29 19:03 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.275576
2021-09-29 19:01 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.254 s 0.974056
2021-09-29 19:03 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.195 s -0.036477
2021-09-29 19:14 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-29 19:14 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.102147
2021-09-29 19:02 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.488 s 0.321061
2021-09-29 19:05 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.950 s 1.906471
2021-09-29 19:14 JavaScript Parse serialize, tracks 0.005 s -0.786332
2021-09-29 19:14 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.000924
2021-09-29 19:14 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.963 s -1.837331
2021-09-29 19:14 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.368027
2021-09-29 19:07 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.141 s 1.979155
2021-09-29 19:14 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.421 s 3.574006
2021-09-29 19:14 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.584 s -1.188276
2021-09-29 19:14 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.711 s -0.580040
2021-09-29 19:14 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.355218
2021-09-29 19:14 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.014956
2021-09-29 19:04 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.600 s 1.902930
2021-09-29 19:14 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -3.590957
2021-09-29 19:14 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.990821
2021-09-29 19:14 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-29 19:14 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.002602
2021-09-29 18:51 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.289 s 1.422044