Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-06 01:46 Python csv-read gzip, file, fanniemae_2016Q4 6.025 s 1.146751
2021-10-06 01:50 Python dataframe-to-table type_floats 0.011 s 1.534133
2021-10-06 02:08 Python dataset-read async=True, nyctaxi_multi_ipc_s3 194.120 s -0.668225
2021-10-06 01:49 Python dataframe-to-table chi_traffic_2020_Q1 19.677 s 0.020835
2021-10-06 01:46 Python csv-read gzip, streaming, fanniemae_2016Q4 14.891 s -0.355390
2021-10-06 01:50 Python dataframe-to-table type_integers 0.011 s 0.889939
2021-10-06 01:45 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.948 s -0.292483
2021-10-06 01:50 Python dataframe-to-table type_simple_features 0.913 s -0.035756
2021-10-06 01:50 Python dataframe-to-table type_strings 0.372 s -0.005758
2021-10-06 01:50 Python dataset-filter nyctaxi_2010-01 4.356 s 0.455264
2021-10-06 01:58 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.735 s 0.795938
2021-10-06 01:45 Python csv-read uncompressed, file, fanniemae_2016Q4 1.173 s -0.000563
2021-10-06 02:08 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.172 s 0.793347
2021-10-06 01:50 Python dataframe-to-table type_nested 2.873 s 0.952783
2021-10-06 01:47 Python csv-read uncompressed, file, nyctaxi_2010-01 1.013 s -0.002018
2021-10-06 01:48 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.407686
2021-10-06 02:12 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.013 s 0.305284
2021-10-06 01:54 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 67.816 s -1.628648
2021-10-06 02:12 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.026 s -0.064289
2021-10-06 01:47 Python csv-read gzip, streaming, nyctaxi_2010-01 10.536 s 0.896768
2021-10-06 01:50 Python dataframe-to-table type_dict 0.012 s 1.122628
2021-10-06 02:12 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.035 s 0.032211
2021-10-06 01:47 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.515 s 1.137997
2021-10-06 02:22 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.825 s -0.025606
2021-10-06 02:24 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.042 s -0.293753
2021-10-06 02:21 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.851 s 0.260263
2021-10-06 02:23 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.957 s -1.354732
2021-10-06 02:25 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.465 s -1.268975
2021-10-06 02:26 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.977 s -1.331765
2021-10-06 02:29 Python file-write lz4, feather, table, fanniemae_2016Q4 1.147 s 1.102595
2021-10-06 02:46 R dataframe-to-table type_strings, R 0.491 s 0.258535
2021-10-06 03:28 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.634 s 1.758541
2021-10-06 03:43 JavaScript Parse readBatches, tracks 0.000 s 0.098901
2021-10-06 03:43 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.214712
2021-10-06 02:21 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.705 s 0.393796
2021-10-06 02:46 R dataframe-to-table type_floats, R 0.108 s 0.598286
2021-10-06 03:11 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.335205
2021-10-06 03:35 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.188 s 0.793261
2021-10-06 03:43 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.145636
2021-10-06 03:43 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.130380
2021-10-06 02:21 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.983 s 0.180430
2021-10-06 02:21 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.992 s 0.110219
2021-10-06 03:14 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.237 s 0.309623
2021-10-06 03:27 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.463 s 1.236707
2021-10-06 02:22 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.244 s -0.060117
2021-10-06 02:23 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.236 s -0.954757
2021-10-06 02:32 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.341 s 0.264978
2021-10-06 03:10 R dataframe-to-table type_simple_features, R 275.706 s -1.346790
2021-10-06 03:10 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.912 s 0.264232
2021-10-06 03:22 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.550 s 0.886557
2021-10-06 03:32 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 0.718691
2021-10-06 03:34 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.518 s 1.229333
2021-10-06 03:36 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.498 s 0.093073
2021-10-06 03:43 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.266753
2021-10-06 03:43 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.904 s -0.593353
2021-10-06 03:43 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.518053
2021-10-06 03:43 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.139528
2021-10-06 02:25 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.312 s -1.265366
2021-10-06 02:32 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.824 s 0.164989
2021-10-06 03:13 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.172 s 0.142774
2021-10-06 03:15 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.095820
2021-10-06 02:30 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.777 s 0.741816
2021-10-06 03:11 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.898 s 0.266865
2021-10-06 02:32 Python file-write lz4, feather, table, nyctaxi_2010-01 1.803 s 0.455392
2021-10-06 03:29 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.278 s 1.753255
2021-10-06 03:35 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.485 s -2.189903
2021-10-06 03:43 JavaScript Parse serialize, tracks 0.005 s 0.441734
2021-10-06 02:27 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.450 s 0.810726
2021-10-06 02:29 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.298 s -0.614939
2021-10-06 03:13 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.393 s -0.530608
2021-10-06 03:32 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.585 s 0.698433
2021-10-06 03:43 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.136589
2021-10-06 03:43 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.590540
2021-10-06 03:43 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.569797
2021-10-06 02:24 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.058 s -0.421939
2021-10-06 02:32 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.226966
2021-10-06 03:34 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -5.033294
2021-10-06 03:43 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.775 s -0.701766
2021-10-06 03:43 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.720 s 0.156878
2021-10-06 03:43 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.334689
2021-10-06 03:43 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.604220
2021-10-06 02:22 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.307 s -0.786351
2021-10-06 02:24 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.279 s -1.148415
2021-10-06 02:46 R dataframe-to-table type_dict, R 0.050 s 0.033438
2021-10-06 03:14 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.112 s 1.221121
2021-10-06 02:26 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.087 s 0.864180
2021-10-06 02:31 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.358 s -0.489138
2021-10-06 03:13 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.066 s -1.635321
2021-10-06 03:31 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.492 s -0.276225
2021-10-06 03:43 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -2.090713
2021-10-06 03:43 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.524 s -0.288904
2021-10-06 02:28 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.305 s 0.224186
2021-10-06 02:32 Python wide-dataframe use_legacy_dataset=false 0.623 s -0.583742
2021-10-06 03:24 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.188 s 1.033909
2021-10-06 03:30 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.252 s 0.257965
2021-10-06 02:46 R dataframe-to-table type_nested, R 0.541 s -1.565995
2021-10-06 03:34 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s 0.338572
2021-10-06 03:43 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.097570
2021-10-06 03:19 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.294 s 0.905837
2021-10-06 03:23 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.404 s -0.376199
2021-10-06 03:43 JavaScript Parse Table.from, tracks 0.000 s 0.594849
2021-10-06 03:43 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.284862
2021-10-06 03:43 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.841 s 1.446347
2021-10-06 02:23 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.796 s -1.062391
2021-10-06 02:25 Python file-read lz4, feather, table, nyctaxi_2010-01 0.661 s 1.586206
2021-10-06 02:30 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.779 s 1.255388
2021-10-06 02:31 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.840 s 1.277798
2021-10-06 02:46 R dataframe-to-table chi_traffic_2020_Q1, R 5.374 s 0.434476
2021-10-06 03:11 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.260 s -0.104894
2021-10-06 03:21 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.830 s 0.365430
2021-10-06 02:22 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.869 s -1.088800
2021-10-06 02:23 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.391085
2021-10-06 02:28 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.721 s 0.098947
2021-10-06 02:29 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.826 s -0.605651
2021-10-06 03:17 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.849 s 0.855349
2021-10-06 03:18 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.283 s 0.834690
2021-10-06 03:33 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.957072
2021-10-06 03:43 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.518053
2021-10-06 03:43 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.589549
2021-10-06 02:22 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.154 s -0.619545
2021-10-06 02:23 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.294 s -0.557096
2021-10-06 02:31 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.823 s 0.682439
2021-10-06 02:46 R dataframe-to-table type_integers, R 0.084 s 0.419413
2021-10-06 03:12 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.916 s 0.183394
2021-10-06 03:21 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.721 s 0.946052
2021-10-06 03:35 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.211 s -5.484165
2021-10-06 03:43 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.693 s -0.173060
2021-10-06 02:25 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.179 s -0.434972
2021-10-06 03:32 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.871 s 0.854156
2021-10-06 03:32 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.421723
2021-10-06 03:43 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.036541
2021-10-06 02:27 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.285 s 0.410945
2021-10-06 03:10 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.240 s 0.284103
2021-10-06 03:12 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.568 s -0.917194
2021-10-06 03:15 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.017 s -2.160341
2021-10-06 03:14 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 0.775437
2021-10-06 03:16 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.524 s 0.223022
2021-10-06 03:25 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.819 s 1.336416
2021-10-06 03:32 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.591 s 0.841213
2021-10-06 03:34 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.901 s 0.808083
2021-10-06 03:43 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.378784
2021-10-06 03:26 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.781 s 1.508075
2021-10-06 03:31 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.169 s 1.124595
2021-10-06 03:32 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.851654
2021-10-06 03:33 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.522 s -0.732942
2021-10-06 03:43 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.190732
2021-10-06 03:43 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.651 s -0.350448
2021-10-06 03:43 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.447346
2021-10-06 03:43 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.028682