Outliers: 5


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 08:57 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.878 s 0.474413
2021-10-10 08:57 Python csv-read gzip, streaming, fanniemae_2016Q4 14.819 s 0.372491
2021-10-10 08:58 Python csv-read gzip, file, fanniemae_2016Q4 6.034 s -0.711064
2021-10-10 08:58 Python csv-read uncompressed, file, nyctaxi_2010-01 0.999 s 1.145204
2021-10-10 08:59 Python csv-read gzip, streaming, nyctaxi_2010-01 10.631 s -0.216343
2021-10-10 10:25 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.587 s -0.196608
2021-10-10 10:25 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 0.185212
2021-10-10 10:25 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.188 s -1.033329
2021-10-10 10:26 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.541 s -3.120357
2021-10-10 10:26 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.612 s 0.225664
2021-10-10 10:27 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.558 s 0.483532
2021-10-10 10:27 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.023865
2021-10-10 10:28 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.210 s -1.289012
2021-10-10 10:28 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.170 s 0.299092
2021-10-10 10:36 JavaScript Parse serialize, tracks 0.005 s -0.590021
2021-10-10 10:36 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.199598
2021-10-10 10:36 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.655302
2021-10-10 10:36 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.640 s -0.470067
2021-10-10 10:36 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.233506
2021-10-10 10:36 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.872 s 0.136112
2021-10-10 10:36 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.902 s 0.062223
2021-10-10 10:36 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.519115
2021-10-10 10:36 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.094396
2021-10-10 10:36 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.170821
2021-10-10 10:36 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.381405
2021-10-10 10:36 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.126935
2021-10-10 10:36 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.520860
2021-10-10 10:36 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.782098
2021-10-10 10:36 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.518 s -0.032554
2021-10-10 08:59 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 0.902312
2021-10-10 09:01 Python dataframe-to-table type_dict 0.011 s 1.366310
2021-10-10 09:01 Python dataframe-to-table type_integers 0.011 s -1.618601
2021-10-10 09:01 Python dataframe-to-table type_floats 0.011 s -0.323320
2021-10-10 09:02 Python dataframe-to-table type_nested 2.885 s -0.342096
2021-10-10 09:02 Python dataframe-to-table type_simple_features 0.928 s -0.558483
2021-10-10 09:02 Python dataset-filter nyctaxi_2010-01 4.313 s 1.912306
2021-10-10 09:19 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.201 s 0.243300
2021-10-10 09:23 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.041 s -0.385353
2021-10-10 09:34 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.952 s 0.424310
2021-10-10 09:34 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.699 s 0.493306
2021-10-10 09:34 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.011 s -0.607459
2021-10-10 09:35 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.263 s -0.756860
2021-10-10 09:35 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.845 s -0.794945
2021-10-10 09:36 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.120 s 1.157867
2021-10-10 09:36 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.283 s 1.020162
2021-10-10 09:37 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.035 s 0.412461
2021-10-10 09:38 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.148 s 1.073095
2021-10-10 09:39 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.766 s 1.293726
2021-10-10 09:39 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.079 s 0.629988
2021-10-10 09:41 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.342 s -0.015471
2021-10-10 09:42 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.100 s -3.926472
2021-10-10 09:42 Python file-write lz4, feather, table, fanniemae_2016Q4 1.149 s 0.897799
2021-10-10 09:42 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.557 s -5.906171
2021-10-10 09:43 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.989 s -2.116782
2021-10-10 09:43 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.973 s -1.771214
2021-10-10 09:44 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.031 s -1.909137
2021-10-10 09:44 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.917 s -0.619423
2021-10-10 09:45 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.338 s 0.854129
2021-10-10 09:45 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.402 s -1.271170
2021-10-10 09:45 Python file-write lz4, feather, table, nyctaxi_2010-01 1.807 s 0.169576
2021-10-10 09:45 Python wide-dataframe use_legacy_dataset=true 0.391 s 2.129170
2021-10-10 09:45 Python wide-dataframe use_legacy_dataset=false 0.606 s 4.172378
2021-10-10 09:59 R dataframe-to-table type_strings, R 0.493 s 0.233977
2021-10-10 09:59 R dataframe-to-table type_dict, R 0.042 s 1.039646
2021-10-10 10:05 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.211 s 0.476895
2021-10-10 10:05 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.223 s 0.384780
2021-10-10 10:06 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.319 s -3.953057
2021-10-10 10:06 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.048 s -3.161679
2021-10-10 10:06 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.558 s 0.913958
2021-10-10 10:07 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.422 s -2.369730
2021-10-10 10:07 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.105 s 1.654347
2021-10-10 10:08 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.207 s 1.457587
2021-10-10 10:09 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.701 s -0.041104
2021-10-10 10:12 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.250 s 0.693466
2021-10-10 10:12 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.299 s 0.578764
2021-10-10 10:14 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.809 s 3.312586
2021-10-10 10:16 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.512 s 1.874111
2021-10-10 10:16 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.397 s 0.544242
2021-10-10 10:17 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.199 s 0.237874
2021-10-10 10:20 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.531 s -0.560122
2021-10-10 10:21 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.725 s -0.777200
2021-10-10 10:22 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.274 s 3.141351
2021-10-10 10:23 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.233 s 1.120162
2021-10-10 10:24 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.468 s 2.985259
2021-10-10 09:35 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.279 s 0.458114
2021-10-10 09:36 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.529 s 7.305850
2021-10-10 09:40 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.438 s 0.612226
2021-10-10 09:58 R dataframe-to-table chi_traffic_2020_Q1, R 3.383 s 0.276328
2021-10-10 09:59 R dataframe-to-table type_integers, R 0.010 s 1.567533
2021-10-10 10:06 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.449 s 1.458821
2021-10-10 10:09 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.546 s -0.036736
2021-10-10 09:01 Python dataframe-to-table chi_traffic_2020_Q1 19.504 s 0.189321
2021-10-10 09:40 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.524 s -1.072830
2021-10-10 09:59 R dataframe-to-table type_floats, R 0.013 s 1.568908
2021-10-10 10:05 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.443 s 1.495020
2021-10-10 10:07 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.157 s 1.479405
2021-10-10 10:08 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.974 s 0.265791
2021-10-10 10:10 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.850 s 0.558447
2021-10-10 10:14 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.733 s 0.530986
2021-10-10 10:18 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.901 s -0.701042
2021-10-10 10:27 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.874 s 1.624799
2021-10-10 10:27 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.359 s -0.225004
2021-10-10 10:28 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.491 s -1.782235
2021-10-10 09:01 Python dataframe-to-table type_strings 0.365 s 0.578922
2021-10-10 09:59 R dataframe-to-table type_nested, R 0.536 s 0.236361
2021-10-10 10:08 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -3.990190
2021-10-10 10:25 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.854 s 0.658417
2021-10-10 10:25 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.596390
2021-10-10 10:29 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.491 s 0.822380
2021-10-10 08:57 Python csv-read uncompressed, file, fanniemae_2016Q4 1.163 s 0.676616
2021-10-10 09:36 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.010 s 7.806849
2021-10-10 10:05 R dataframe-to-table type_simple_features, R 3.318 s 1.262564
2021-10-10 10:36 JavaScript Parse readBatches, tracks 0.000 s 1.082975
2021-10-10 10:36 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.438829
2021-10-10 10:36 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.565084
2021-10-10 10:36 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.002905
2021-10-10 10:36 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.217455
2021-10-10 08:58 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.625 s 0.006985
2021-10-10 09:23 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.066 s -0.311165
2021-10-10 09:34 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.751 s 0.838918
2021-10-10 09:36 Python file-read lz4, feather, table, fanniemae_2016Q4 0.604 s -0.149687
2021-10-10 09:37 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.029 s 0.433540
2021-10-10 09:38 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.181 s -0.973538
2021-10-10 09:38 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.331504
2021-10-10 09:45 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.888 s -2.436492
2021-10-10 10:36 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.028 s -0.781390
2021-10-10 10:36 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.623 s -0.395325
2021-10-10 10:36 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.766998
2021-10-10 10:36 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.639 s 0.883553
2021-10-10 10:36 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.180362
2021-10-10 10:36 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.002694
2021-10-10 10:36 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.236069
2021-10-10 09:05 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.810 s 0.879125
2021-10-10 10:07 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.043 s 2.008932
2021-10-10 10:26 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 1.341200
2021-10-10 09:09 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.313 s 0.159416
2021-10-10 09:36 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.725 s 6.643782
2021-10-10 09:41 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.878 s -1.001168
2021-10-10 10:19 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.850 s -0.477257
2021-10-10 10:25 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.155 s 1.634427
2021-10-10 10:36 JavaScript Parse Table.from, tracks 0.000 s 1.304460
2021-10-10 10:36 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.676 s 0.403102
2021-10-10 10:36 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.184828
2021-10-10 09:19 Python dataset-read async=True, nyctaxi_multi_ipc_s3 192.763 s -0.763072
2021-10-10 09:23 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.097 s -3.469571
2021-10-10 09:35 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.597 s 7.725813
2021-10-10 09:37 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.149 s 0.951534
2021-10-10 09:38 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.277 s 1.201387
2021-10-10 10:36 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.816012