Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 06:05 Python csv-read gzip, streaming, fanniemae_2016Q4 14.913 s -0.471768
2021-10-08 06:06 Python csv-read uncompressed, file, nyctaxi_2010-01 0.990 s 2.102315
2021-10-08 06:09 Python dataframe-to-table type_strings 0.373 s -0.462269
2021-10-08 06:09 Python dataframe-to-table type_floats 0.011 s 0.241661
2021-10-08 06:42 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.830 s 0.343745
2021-10-08 06:44 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s -0.020489
2021-10-08 06:46 Python file-read lz4, feather, table, nyctaxi_2010-01 0.677 s -1.228273
2021-10-08 06:53 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.338 s -0.084817
2021-10-08 06:53 Python file-write lz4, feather, table, nyctaxi_2010-01 1.807 s 0.161228
2021-10-08 07:33 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.866 s 0.522469
2021-10-08 07:36 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -1.521288
2021-10-08 07:44 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.477876
2021-10-08 07:44 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.310527
2021-10-08 06:06 Python csv-read gzip, file, fanniemae_2016Q4 6.024 s 1.554198
2021-10-08 06:10 Python dataset-filter nyctaxi_2010-01 4.354 s 0.639791
2021-10-08 06:43 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.300 s -0.311071
2021-10-08 06:44 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.798 s -0.628612
2021-10-08 06:49 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.683 s -0.020407
2021-10-08 06:50 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.765 s -0.309890
2021-10-08 06:52 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.844 s 0.771954
2021-10-08 06:09 Python dataframe-to-table type_dict 0.012 s 0.513200
2021-10-08 06:43 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.247 s -0.027754
2021-10-08 06:43 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.834 s -0.151088
2021-10-08 06:46 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.027370
2021-10-08 06:06 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.493 s 1.080916
2021-10-08 06:26 Python dataset-read async=True, nyctaxi_multi_ipc_s3 182.519 s 0.580964
2021-10-08 06:52 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.818 s 0.564621
2021-10-08 06:43 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.843 s -0.195200
2021-10-08 06:04 Python csv-read uncompressed, file, fanniemae_2016Q4 1.187 s -0.768094
2021-10-08 07:44 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.244148
2021-10-08 06:42 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.723 s 0.257498
2021-10-08 06:44 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.926 s -0.486441
2021-10-08 06:53 Python wide-dataframe use_legacy_dataset=true 0.398 s -2.232608
2021-10-08 06:04 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.973 s -0.424115
2021-10-08 06:09 Python dataframe-to-table chi_traffic_2020_Q1 19.477 s 0.536041
2021-10-08 06:30 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.011 s 0.368995
2021-10-08 06:53 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.828 s -0.736920
2021-10-08 06:10 Python dataframe-to-table type_nested 2.873 s 0.622788
2021-10-08 06:42 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.945 s 0.466674
2021-10-08 06:43 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.150 s -0.169809
2021-10-08 06:48 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.438 s 0.566096
2021-10-08 06:52 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.350 s -0.005386
2021-10-08 06:30 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.007 s 0.216020
2021-10-08 06:42 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.012 s -0.267586
2021-10-08 06:44 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.241 s -0.620201
2021-10-08 06:53 Python wide-dataframe use_legacy_dataset=false 0.621 s 0.199019
2021-10-08 06:07 Python csv-read gzip, streaming, nyctaxi_2010-01 10.511 s 0.868386
2021-10-08 06:07 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.340391
2021-10-08 06:13 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.278 s 0.344533
2021-10-08 06:44 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.278 s 1.879861
2021-10-08 06:49 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.382 s -0.348702
2021-10-08 06:51 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.777 s 0.589044
2021-10-08 06:30 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.009 s 0.374519
2021-10-08 06:48 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.298 s 0.092599
2021-10-08 06:51 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.781 s 0.777698
2021-10-08 06:45 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.310 s -0.756308
2021-10-08 06:46 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.469 s -0.800100
2021-10-08 06:09 Python dataframe-to-table type_integers 0.011 s 0.691669
2021-10-08 06:10 Python dataframe-to-table type_simple_features 0.912 s 0.211158
2021-10-08 06:26 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.136 s 0.273266
2021-10-08 06:47 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.983 s -0.864594
2021-10-08 06:44 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.061 s -0.489498
2021-10-08 06:50 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.278 s -0.628566
2021-10-08 07:14 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.241 s 0.126862
2021-10-08 06:47 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.077 s 0.594463
2021-10-08 06:50 Python file-write lz4, feather, table, fanniemae_2016Q4 1.163 s -0.081352
2021-10-08 06:45 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.052 s -0.986763
2021-10-08 06:17 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.638 s 0.472158
2021-10-08 06:45 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.317 s -0.834909
2021-10-08 07:33 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.590 s 0.341402
2021-10-08 07:34 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -0.706600
2021-10-08 07:36 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s -0.004141
2021-10-08 07:17 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.532 s 0.111994
2021-10-08 07:18 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.862 s 0.428328
2021-10-08 07:21 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.303 s 0.501729
2021-10-08 07:20 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.266 s 0.622286
2021-10-08 07:33 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.636090
2021-10-08 07:35 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.521 s 1.096429
2021-10-08 07:36 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.484 s -1.222988
2021-10-08 07:31 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.241 s 0.773813
2021-10-08 07:34 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.599 s 0.482678
2021-10-08 07:13 R dataframe-to-table type_simple_features, R 3.387 s 2.319947
2021-10-08 07:30 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.086024
2021-10-08 07:34 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.522 s -0.596389
2021-10-08 07:35 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -1.457089
2021-10-08 07:23 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.838 s -1.400119
2021-10-08 07:35 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.895 s 0.490593
2021-10-08 07:32 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.498 s -1.562190
2021-10-08 07:37 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.525 s -2.695954
2021-10-08 07:36 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.194 s 0.425884
2021-10-08 07:22 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.720 s 0.646829
2021-10-08 07:29 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.640 s 1.044829
2021-10-08 07:33 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.172 s 0.592640
2021-10-08 07:06 R dataframe-to-table chi_traffic_2020_Q1, R 3.406 s 0.251371
2021-10-08 07:07 R dataframe-to-table type_floats, R 0.013 s 3.421960
2021-10-08 07:07 R dataframe-to-table type_strings, R 0.488 s 0.199958
2021-10-08 07:13 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.475 s 3.211218
2021-10-08 07:15 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.404 s -1.187655
2021-10-08 07:16 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.133 s -0.542524
2021-10-08 07:44 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.739325
2021-10-08 07:44 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.837 s 1.010519
2021-10-08 07:44 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.537150
2021-10-08 07:15 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s 0.043617
2021-10-08 07:24 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.414 s -2.346872
2021-10-08 07:26 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.826 s 0.721249
2021-10-08 07:44 JavaScript Parse Table.from, tracks 0.000 s 1.999226
2021-10-08 07:44 JavaScript Parse serialize, tracks 0.004 s 0.498466
2021-10-08 07:44 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.231577
2021-10-08 07:44 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.330 s 0.184296
2021-10-08 07:44 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.795094
2021-10-08 07:44 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.123025
2021-10-08 07:07 R dataframe-to-table type_integers, R 0.010 s 3.428614
2021-10-08 07:13 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.233 s 0.352847
2021-10-08 07:14 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.311432
2021-10-08 07:14 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.967 s -2.974123
2021-10-08 07:15 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.168 s 3.199179
2021-10-08 07:17 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.689 s 0.033268
2021-10-08 07:44 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.303 s 4.127858
2021-10-08 07:07 R dataframe-to-table type_dict, R 0.052 s -0.118780
2021-10-08 07:07 R dataframe-to-table type_nested, R 0.538 s 0.200150
2021-10-08 07:44 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.610610
2021-10-08 07:44 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.592958
2021-10-08 07:44 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.298185
2021-10-08 07:44 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.141201
2021-10-08 07:16 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.379643
2021-10-08 07:44 JavaScript Parse readBatches, tracks 0.000 s 2.121325
2021-10-08 07:44 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.228866
2021-10-08 07:44 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.399 s 0.091989
2021-10-08 07:44 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.816504
2021-10-08 07:44 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.667 s 0.403458
2021-10-08 07:44 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.238511
2021-10-08 07:14 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.485 s 3.029384
2021-10-08 07:17 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.986 s 0.103565
2021-10-08 07:28 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.457 s 0.817105
2021-10-08 07:15 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.044 s 2.071647
2021-10-08 07:25 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.190 s 1.125520
2021-10-08 07:44 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.655 s 0.538947
2021-10-08 07:44 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.125828
2021-10-08 07:44 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.863 s 0.901798
2021-10-08 07:16 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.233 s 3.200108
2021-10-08 07:27 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.805 s 0.482582
2021-10-08 07:33 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.412274
2021-10-08 07:44 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.003 s 9.802288
2021-10-08 07:44 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.393867
2021-10-08 07:44 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.075157
2021-10-08 07:24 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.580 s -0.272333
2021-10-08 07:33 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 0.486949
2021-10-08 07:44 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.480942
2021-10-08 07:44 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.003 s 9.665797