Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-30 14:15 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.050 s -0.787828
2021-09-30 14:17 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.644 s -0.317555
2021-09-30 14:16 Python csv-read uncompressed, file, fanniemae_2016Q4 1.178 s -0.129797
2021-09-30 14:16 Python csv-read gzip, streaming, fanniemae_2016Q4 15.004 s -0.812553
2021-09-30 14:17 Python csv-read gzip, file, fanniemae_2016Q4 6.036 s -1.236121
2021-09-30 14:17 Python csv-read uncompressed, file, nyctaxi_2010-01 1.013 s 0.092330
2021-09-30 14:18 Python csv-read gzip, streaming, nyctaxi_2010-01 10.622 s -0.277429
2021-09-30 14:18 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.518749
2021-09-30 14:20 Python dataframe-to-table type_floats 0.012 s -2.770750
2021-09-30 14:21 Python dataset-filter nyctaxi_2010-01 4.393 s -0.873939
2021-09-30 14:20 Python dataframe-to-table type_dict 0.012 s 1.262203
2021-09-30 14:20 Python dataframe-to-table type_integers 0.011 s -1.648852
2021-09-30 14:21 Python dataframe-to-table type_nested 2.850 s 3.419690
2021-09-30 14:20 Python dataframe-to-table chi_traffic_2020_Q1 19.463 s 1.753779
2021-09-30 14:20 Python dataframe-to-table type_strings 0.366 s 0.591871
2021-09-30 14:21 Python dataframe-to-table type_simple_features 0.938 s -2.983496
2021-09-30 15:05 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.848 s 1.716122
2021-09-30 14:55 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.711 s 0.375308
2021-09-30 14:57 Python file-read lz4, feather, table, fanniemae_2016Q4 0.617 s -2.913341
2021-09-30 14:24 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.814 s -0.856978
2021-09-30 14:43 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.027 s -0.088354
2021-09-30 14:57 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.791 s -2.553488
2021-09-30 15:59 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.821 s 1.881757
2021-09-30 14:58 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.038 s 0.155334
2021-09-30 15:00 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.639 s 0.147925
2021-09-30 15:03 Python file-write lz4, feather, table, fanniemae_2016Q4 1.155 s 0.472321
2021-09-30 15:06 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.346 s 0.259508
2021-09-30 15:45 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.241 s 0.126142
2021-09-30 16:02 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.661 s 1.893582
2021-09-30 14:28 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.917 s 1.975693
2021-09-30 14:43 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.027 s 0.099464
2021-09-30 14:56 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.031 s -1.044688
2021-09-30 14:56 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.293 s -1.546045
2021-09-30 14:59 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.165 s 0.004309
2021-09-30 15:04 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.814 s 1.148378
2021-09-30 15:47 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.161 s 0.675912
2021-09-30 15:49 R file-read lz4, feather, table, nyctaxi_2010-01, R 1.390 s -142.279636
2021-09-30 15:52 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.285 s 1.102195
2021-09-30 14:55 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.955 s 0.387717
2021-09-30 15:20 R dataframe-to-table type_integers, R 0.083 s 1.026779
2021-09-30 16:03 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.461041
2021-09-30 14:55 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.837 s 0.362258
2021-09-30 14:57 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.170 s -1.605982
2021-09-30 15:20 R dataframe-to-table type_dict, R 0.031 s 1.987393
2021-09-30 15:48 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.265 s -1.317947
2021-09-30 14:38 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.301 s -0.031093
2021-09-30 14:56 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.324 s -2.119713
2021-09-30 15:58 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.218 s 0.781206
2021-09-30 15:02 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.589 s 0.736212
2021-09-30 15:04 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.798 s 0.701266
2021-09-30 15:48 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 0.634488
2021-09-30 14:38 Python dataset-read async=True, nyctaxi_multi_ipc_s3 184.136 s 0.488561
2021-09-30 14:57 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.863 s -3.008371
2021-09-30 15:06 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.375 s -0.107830
2021-09-30 15:06 Python wide-dataframe use_legacy_dataset=true 0.391 s 0.399645
2021-09-30 15:45 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.917 s 0.039963
2021-09-30 15:47 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.052 s 0.633465
2021-09-30 15:48 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.124 s 0.423307
2021-09-30 16:00 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.802 s 1.807030
2021-09-30 16:05 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.494 s -0.690986
2021-09-30 14:57 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.531835
2021-09-30 15:00 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.103 s 1.020661
2021-09-30 15:06 Python wide-dataframe use_legacy_dataset=false 0.623 s -1.041438
2021-09-30 15:46 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.567 s -0.822239
2021-09-30 15:55 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.734 s 1.147076
2021-09-30 14:58 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.015 s 1.329446
2021-09-30 15:02 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.313 s 0.260047
2021-09-30 15:06 Python file-write lz4, feather, table, nyctaxi_2010-01 1.799 s 0.629406
2021-09-30 15:06 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.835 s 0.037072
2021-09-30 15:20 R dataframe-to-table type_strings, R 0.492 s -0.332556
2021-09-30 15:20 R dataframe-to-table type_floats, R 0.108 s 0.438868
2021-09-30 15:44 R dataframe-to-table type_simple_features, R 274.426 s 0.767296
2021-09-30 15:57 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.570 s 0.994387
2021-09-30 14:59 Python file-read lz4, feather, table, nyctaxi_2010-01 0.679 s -1.994813
2021-09-30 15:01 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.277 s 0.505580
2021-09-30 15:03 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.749 s -0.293479
2021-09-30 15:46 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.901 s 0.610059
2021-09-30 15:50 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.513 s 0.269971
2021-09-30 14:59 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.020 s 0.073128
2021-09-30 15:01 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.433 s 1.210560
2021-09-30 15:44 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.248 s 0.045346
2021-09-30 16:05 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.190 s 0.302745
2021-09-30 14:58 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.040 s 0.027832
2021-09-30 15:03 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.204 s 0.089189
2021-09-30 15:05 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.842 s 0.657921
2021-09-30 15:44 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.923 s -0.011648
2021-09-30 15:55 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.827 s 0.811982
2021-09-30 14:56 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.865 s -1.260782
2021-09-30 14:43 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.002 s 0.447760
2021-09-30 14:58 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.238 s -4.093789
2021-09-30 15:49 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.997 s -1.545431
2021-09-30 14:59 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.883108
2021-09-30 15:20 R dataframe-to-table type_nested, R 0.540 s -1.120811
2021-09-30 15:51 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.844 s 1.187471
2021-09-30 15:57 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.056516
2021-09-30 15:45 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.287 s 1.514155
2021-09-30 15:47 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.387 s -0.461763
2021-09-30 16:04 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.270 s -0.205932
2021-09-30 16:06 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.543111
2021-09-30 16:06 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.428963
2021-09-30 16:07 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.597 s 1.477003
2021-09-30 14:57 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.906 s -3.386499
2021-09-30 16:06 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.577 s 1.740803
2021-09-30 16:06 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.886 s 1.724633
2021-09-30 16:06 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.569 s 1.982914
2021-09-30 16:07 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.514 s 0.174165
2021-09-30 16:07 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.504803
2021-09-30 16:08 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.509 s 1.330390
2021-09-30 16:08 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.957 s 1.461357
2021-09-30 16:08 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.031633
2021-09-30 16:08 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.348 s 1.449815
2021-09-30 16:10 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.486 s 0.145612
2021-09-30 16:09 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.199 s -1.153694
2021-09-30 15:20 R dataframe-to-table chi_traffic_2020_Q1, R 5.389 s 0.274342
2021-09-30 16:09 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.474 s -0.526432
2021-09-30 16:09 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.160 s 1.451274
2021-09-30 16:17 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.448860
2021-09-30 16:17 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.387226
2021-09-30 16:17 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.089796
2021-09-30 16:17 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.984938
2021-09-30 16:17 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -2.136529
2021-09-30 16:17 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497361
2021-09-30 16:17 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.106493
2021-09-30 16:17 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.035838
2021-09-30 16:17 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.513673
2021-09-30 16:17 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -2.480817
2021-09-30 16:17 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.594461
2021-09-30 16:17 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.571651
2021-09-30 16:17 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.034973
2021-09-30 16:17 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.028 s -1.557273
2021-09-30 16:17 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.717 s 0.208378
2021-09-30 16:17 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.951061
2021-09-30 16:17 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.639 s -0.202578
2021-09-30 16:17 JavaScript Parse Table.from, tracks 0.000 s -1.115460
2021-09-30 16:17 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.436978
2021-09-30 16:17 JavaScript Parse readBatches, tracks 0.000 s -1.345761
2021-09-30 16:17 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.691 s -0.127805
2021-09-30 16:17 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.917 s -0.719218
2021-09-30 16:17 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 2.052282
2021-09-30 16:17 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.380430
2021-09-30 16:17 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.897 s 0.109621
2021-09-30 16:17 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.146638
2021-09-30 16:17 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.521 s -0.156548
2021-09-30 16:17 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578334
2021-09-30 16:17 JavaScript Parse serialize, tracks 0.005 s -0.786700
2021-09-30 16:17 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.659 s -0.358068
2021-09-30 16:17 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.006692
2021-09-30 15:53 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.303 s 1.119357
2021-09-30 16:01 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.473 s 1.576852