Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-30 20:43 Python dataframe-to-table type_simple_features 0.931 s -1.931720
2021-09-30 21:18 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.000 s 0.065638
2021-09-30 20:39 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -0.737388
2021-09-30 20:42 Python dataframe-to-table type_dict 0.012 s -1.700206
2021-09-30 21:21 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.002 s 0.162259
2021-09-30 20:39 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.853 s -1.142772
2021-09-30 20:38 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.836 s -0.516579
2021-09-30 20:38 Python csv-read uncompressed, file, fanniemae_2016Q4 1.189 s -0.299631
2021-09-30 21:22 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.615 s 0.243591
2021-09-30 21:00 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.563 s -0.117063
2021-09-30 20:42 Python dataframe-to-table type_strings 0.368 s 0.348449
2021-09-30 20:42 Python dataframe-to-table type_integers 0.011 s -1.117366
2021-09-30 20:51 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.527 s 1.778389
2021-09-30 21:21 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.030 s 0.455683
2021-09-30 21:21 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.538082
2021-09-30 20:42 Python dataframe-to-table chi_traffic_2020_Q1 19.504 s 1.425823
2021-09-30 21:25 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.615 s 0.584950
2021-09-30 20:39 Python csv-read uncompressed, file, nyctaxi_2010-01 1.021 s -0.047105
2021-09-30 20:43 Python dataframe-to-table type_floats 0.011 s 0.279828
2021-09-30 20:43 Python dataset-filter nyctaxi_2010-01 4.402 s -1.103404
2021-09-30 20:46 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.420 s -0.357948
2021-09-30 21:20 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.225 s -2.894307
2021-09-30 21:23 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.075 s 1.178950
2021-09-30 20:43 Python dataframe-to-table type_nested 2.859 s 2.535976
2021-09-30 21:19 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.317 s -1.280667
2021-09-30 21:20 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.061 s -0.691657
2021-09-30 21:23 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.227 s 0.708993
2021-09-30 21:25 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.801 s -0.728122
2021-09-30 21:04 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.025 s 0.133947
2021-09-30 21:17 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.844 s 0.321340
2021-09-30 21:18 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.755 s 0.174303
2021-09-30 21:18 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.346 s -3.059188
2021-09-30 21:19 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.170 s -1.519356
2021-09-30 21:20 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.282 s 1.356762
2021-09-30 20:41 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s 0.227285
2021-09-30 21:20 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.195611
2021-09-30 21:20 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.921 s -3.072290
2021-09-30 21:22 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.155 s 0.039051
2021-09-30 20:38 Python csv-read gzip, streaming, fanniemae_2016Q4 14.765 s -0.516075
2021-09-30 20:40 Python csv-read gzip, streaming, nyctaxi_2010-01 10.846 s -1.165345
2021-09-30 21:22 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s 0.076861
2021-09-30 21:26 Python file-write lz4, feather, table, fanniemae_2016Q4 1.155 s 0.531856
2021-09-30 21:04 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.022 s 0.178181
2021-09-30 21:04 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.025 s -0.053417
2021-09-30 21:19 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.797 s -2.290910
2021-09-30 21:18 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.107 s -3.156272
2021-09-30 21:19 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.915 s -3.595132
2021-09-30 21:21 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.000 s 0.140214
2021-09-30 21:24 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.440 s 1.093053
2021-09-30 21:25 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.347 s -0.031095
2021-09-30 21:26 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.270 s -0.502671
2021-09-30 21:26 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.865 s 0.123413
2021-09-30 21:29 Python file-write lz4, feather, table, nyctaxi_2010-01 1.802 s 0.483433
2021-09-30 21:28 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.837 s 0.666658
2021-09-30 21:29 Python wide-dataframe use_legacy_dataset=false 0.622 s -0.858742
2021-09-30 21:27 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.796 s 0.684448
2021-09-30 21:27 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.930 s 0.034002
2021-09-30 21:28 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.338 s 0.197582
2021-09-30 21:29 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.826 s 0.106112
2021-09-30 21:28 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.355 s -0.309657
2021-09-30 22:10 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.188 s -0.991284
2021-09-30 22:26 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 0.806236
2021-09-30 22:31 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.593 s 0.073854
2021-09-30 22:32 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.199 s -1.151127
2021-09-30 22:40 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.356704
2021-09-30 21:00 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.487 s -0.981308
2021-09-30 21:19 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.925 s -2.937813
2021-09-30 22:25 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.668 s 1.563321
2021-09-30 21:29 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.064279
2021-09-30 22:28 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.196 s -0.173419
2021-09-30 22:29 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.564 s 1.832672
2021-09-30 22:31 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.211486
2021-09-30 22:40 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.518454
2021-09-30 22:40 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.003 s 6.797250
2021-09-30 22:40 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -1.639308
2021-09-30 22:40 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.304404
2021-09-30 21:43 R dataframe-to-table chi_traffic_2020_Q1, R 5.351 s 0.974457
2021-09-30 22:07 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.931 s -0.091815
2021-09-30 21:43 R dataframe-to-table type_floats, R 0.107 s 0.737816
2021-09-30 21:43 R dataframe-to-table type_strings, R 0.489 s 0.725774
2021-09-30 22:10 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.122 s 0.629695
2021-09-30 22:27 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.252 s 1.068349
2021-09-30 22:32 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.162 s 1.304459
2021-09-30 22:40 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.653 s 0.535417
2021-09-30 22:40 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.240 s 4.348419
2021-09-30 22:09 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.556 s 1.304183
2021-09-30 22:16 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.303 s 1.040757
2021-09-30 22:21 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.224 s 0.608800
2021-09-30 22:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.475 s -0.543428
2021-09-30 22:40 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.877653
2021-09-30 21:43 R dataframe-to-table type_nested, R 0.538 s -0.496061
2021-09-30 22:07 R dataframe-to-table type_simple_features, R 275.809 s -1.854678
2021-09-30 22:08 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.286 s 1.934766
2021-09-30 22:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.249363
2021-09-30 22:40 JavaScript Parse readBatches, tracks 0.000 s 4.784225
2021-09-30 22:08 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.244 s 0.087025
2021-09-30 22:11 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 0.651740
2021-09-30 22:40 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.357867
2021-09-30 22:40 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497361
2021-09-30 22:40 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.914506
2021-09-30 22:40 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.703196
2021-09-30 22:12 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.671 s 0.178114
2021-09-30 22:40 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.837842
2021-09-30 22:40 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.283 s 0.821122
2021-09-30 22:40 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.216065
2021-09-30 22:40 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578334
2021-09-30 22:40 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.585820
2021-09-30 22:40 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.808408
2021-09-30 22:11 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.234 s 0.432550
2021-09-30 22:15 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.279 s 1.050134
2021-09-30 22:18 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.744 s 0.998300
2021-09-30 22:20 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.402 s -0.104349
2021-09-30 22:27 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.494 s -0.724457
2021-09-30 22:29 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.507221
2021-09-30 22:30 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.445718
2021-09-30 22:40 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.955390
2021-09-30 22:07 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.246 s 0.067054
2021-09-30 22:18 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.839 s -1.802056
2021-09-30 22:40 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.314320
2021-09-30 22:08 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.938 s -0.154318
2021-09-30 22:09 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.919 s -0.125212
2021-09-30 22:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.350 s 1.211928
2021-09-30 22:10 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.682989
2021-09-30 22:30 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.523 s -0.920201
2021-09-30 22:40 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.303 s 0.691786
2021-09-30 22:40 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.710904
2021-09-30 22:23 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.796 s 1.764280
2021-09-30 21:43 R dataframe-to-table type_dict, R 0.051 s -0.181445
2021-09-30 22:13 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.531 s -0.526706
2021-09-30 21:43 R dataframe-to-table type_integers, R 0.084 s 0.777791
2021-09-30 22:22 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.832 s 1.475266
2021-09-30 22:28 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.578 s 1.600189
2021-09-30 22:40 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.855 s 0.965780
2021-09-30 22:09 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.401 s -1.036655
2021-09-30 22:12 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.984 s -0.786203
2021-09-30 22:40 JavaScript Parse Table.from, tracks 0.000 s 3.888079
2021-09-30 22:40 JavaScript Parse serialize, tracks 0.005 s -0.695388
2021-09-30 22:40 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.577 s 1.952180
2021-09-30 22:40 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.801 s 1.968338
2021-09-30 22:40 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.480942
2021-09-30 22:40 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.003 s 6.796368
2021-09-30 22:14 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.861 s 0.973025
2021-09-30 22:24 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.462 s 1.688598
2021-09-30 22:32 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.500 s 0.110113
2021-09-30 22:19 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.569 s 0.968939
2021-09-30 22:30 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.974 s 1.308967
2021-09-30 22:29 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.885 s 1.578833
2021-09-30 22:29 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.598 s 1.326850
2021-09-30 22:40 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.444717