Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-06 17:04 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 64.319 s -0.829323
2021-10-06 18:09 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.905 s 0.326927
2021-10-06 18:10 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.972 s -0.537290
2021-10-06 18:11 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.376 s 0.580910
2021-10-06 16:57 Python csv-read gzip, file, fanniemae_2016Q4 6.038 s -1.663660
2021-10-06 17:01 Python dataframe-to-table type_simple_features 0.910 s 0.277158
2021-10-06 17:35 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.153 s -0.510318
2021-10-06 17:36 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.943 s -1.019097
2021-10-06 17:37 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.044 s -0.447044
2021-10-06 17:43 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.857 s 0.858758
2021-10-06 17:44 Python file-write lz4, feather, table, nyctaxi_2010-01 1.812 s -0.121328
2021-10-06 17:18 Python dataset-read async=True, nyctaxi_multi_ipc_s3 190.071 s -0.155800
2021-10-06 17:44 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.356 s -0.394888
2021-10-06 18:11 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.051 s 0.936250
2021-10-06 16:57 Python csv-read uncompressed, file, nyctaxi_2010-01 1.022 s -0.843222
2021-10-06 17:22 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.003 s 0.285157
2021-10-06 17:35 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.866 s -0.892690
2021-10-06 17:36 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.297 s -1.081392
2021-10-06 18:14 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.039 s -3.273123
2021-10-06 17:09 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 84.291 s 0.687655
2021-10-06 16:56 Python csv-read gzip, streaming, fanniemae_2016Q4 14.731 s 0.669961
2021-10-06 16:57 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.719 s -0.478735
2021-10-06 16:58 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -1.190931
2021-10-06 17:00 Python dataframe-to-table type_strings 0.375 s -0.696794
2021-10-06 17:00 Python dataframe-to-table chi_traffic_2020_Q1 19.513 s 0.678409
2021-10-06 17:00 Python dataframe-to-table type_floats 0.011 s 0.409594
2021-10-06 17:22 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.025 s 0.115412
2021-10-06 17:00 Python dataframe-to-table type_dict 0.012 s 0.385717
2021-10-06 17:34 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.846 s -0.542442
2021-10-06 17:39 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.101 s 0.668227
2021-10-06 17:45 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.589026
2021-10-06 17:58 R dataframe-to-table type_integers, R 0.085 s -1.170833
2021-10-06 17:33 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.827 s -0.791010
2021-10-06 17:43 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.793 s 0.550470
2021-10-06 18:11 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.897 s 1.313929
2021-10-06 18:13 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.248 s -0.365586
2021-10-06 17:01 Python dataframe-to-table type_nested 2.884 s 0.593522
2021-10-06 17:22 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.032 s 0.053139
2021-10-06 17:42 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.262 s -0.354343
2021-10-06 18:17 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.290 s 0.691738
2021-10-06 17:34 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.295 s -0.217293
2021-10-06 17:38 Python file-read lz4, feather, table, nyctaxi_2010-01 0.674 s -1.028287
2021-10-06 17:41 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.775 s -0.350912
2021-10-06 17:45 Python wide-dataframe use_legacy_dataset=false 0.620 s 0.431968
2021-10-06 17:01 Python dataset-filter nyctaxi_2010-01 4.363 s 0.176883
2021-10-06 17:33 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.861 s 0.147748
2021-10-06 17:44 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.355 s 0.019413
2021-10-06 17:59 R dataframe-to-table type_floats, R 0.113 s -1.355902
2021-10-06 18:08 R dataframe-to-table type_simple_features, R 3.319 s 492.752272
2021-10-06 16:55 Python csv-read uncompressed, file, fanniemae_2016Q4 1.188 s -0.840975
2021-10-06 16:58 Python csv-read gzip, streaming, nyctaxi_2010-01 10.705 s -0.624738
2021-10-06 17:34 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.247 s -0.118583
2021-10-06 17:37 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.313 s -1.111209
2021-10-06 17:39 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.332 s 0.118882
2021-10-06 18:15 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.552 s -1.062330
2021-10-06 17:36 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.023 s 1.090482
2021-10-06 17:37 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.183 s -1.389029
2021-10-06 18:10 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.480838
2021-10-06 18:12 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.126 s 0.042897
2021-10-06 17:00 Python dataframe-to-table type_integers 0.011 s 1.152140
2021-10-06 17:18 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.282 s 0.056366
2021-10-06 17:58 R dataframe-to-table type_dict, R 0.050 s -0.007464
2021-10-06 17:38 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.949 s -1.045214
2021-10-06 18:11 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.568 s -0.919014
2021-10-06 16:55 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.814 s 0.571367
2021-10-06 17:33 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.110 s -0.762921
2021-10-06 17:35 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.800 s -0.977760
2021-10-06 17:40 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.453 s 0.692392
2021-10-06 17:41 Python file-write lz4, feather, table, fanniemae_2016Q4 1.162 s -0.002357
2021-10-06 17:35 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.292 s -0.180355
2021-10-06 17:36 Python file-read lz4, feather, table, fanniemae_2016Q4 0.600 s 0.471443
2021-10-06 17:36 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.250 s -1.061378
2021-10-06 17:44 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.839 s 0.478012
2021-10-06 17:45 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.806 s 0.220575
2021-10-06 17:58 R dataframe-to-table chi_traffic_2020_Q1, R 5.530 s -2.547366
2021-10-06 18:12 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.193 s -1.212556
2021-10-06 17:40 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.669 s 0.251854
2021-10-06 18:16 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.848 s 0.770294
2021-10-06 18:13 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.224919
2021-10-06 18:14 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.670 s 0.194667
2021-10-06 18:18 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.307 s 0.723268
2021-10-06 18:19 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.775 s 0.538509
2021-10-06 18:20 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.827 s 0.854694
2021-10-06 18:22 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.402 s -0.100196
2021-10-06 18:21 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.572 s 0.282851
2021-10-06 18:23 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.217 s 0.074909
2021-10-06 18:25 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.786 s 1.238442
2021-10-06 17:34 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.032 s -0.873778
2021-10-06 18:24 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.825 s 1.070622
2021-10-06 17:38 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.475 s -1.153401
2021-10-06 17:41 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.392 s -0.408758
2021-10-06 17:42 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.823 s 0.458884
2021-10-06 17:58 R dataframe-to-table type_strings, R 0.491 s 0.247244
2021-10-06 17:59 R dataframe-to-table type_nested, R 0.539 s -0.377486
2021-10-06 18:35 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.522 s 0.021760
2021-10-06 18:09 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.246 s 0.240277
2021-10-06 18:09 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.241 s 0.123297
2021-10-06 18:27 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.643 s 1.346193
2021-10-06 18:31 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.598 s 0.741913
2021-10-06 18:32 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.521 s -0.518830
2021-10-06 18:29 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.487 s 0.625503
2021-10-06 18:30 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 0.662599
2021-10-06 18:33 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s 0.310488
2021-10-06 18:42 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.628971
2021-10-06 18:33 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -2.693143
2021-10-06 18:42 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.038609
2021-10-06 18:42 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.173459
2021-10-06 18:31 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.868 s 0.766836
2021-10-06 18:33 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.529 s 1.059267
2021-10-06 18:42 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.677298
2021-10-06 18:42 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.933904
2021-10-06 18:29 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.245 s 0.692919
2021-10-06 18:31 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.642069
2021-10-06 18:42 JavaScript Parse serialize, tracks 0.002 s 5.211831
2021-10-06 18:34 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -2.441934
2021-10-06 18:42 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.893299
2021-10-06 18:42 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.619415
2021-10-06 18:42 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.149391
2021-10-06 18:42 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.045633
2021-10-06 18:42 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.951030
2021-10-06 18:34 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.174 s 0.767997
2021-10-06 18:42 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.783 s -0.212291
2021-10-06 18:42 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.903 s 0.045255
2021-10-06 18:42 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.493828
2021-10-06 18:42 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.785466
2021-10-06 18:28 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.321345
2021-10-06 18:42 JavaScript Parse Table.from, tracks 0.000 s 0.937932
2021-10-06 18:42 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.878901
2021-10-06 18:42 JavaScript Parse readBatches, tracks 0.000 s 0.738836
2021-10-06 18:42 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.583 s -0.223223
2021-10-06 18:26 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.461 s 1.098312
2021-10-06 18:31 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.577 s 0.689137
2021-10-06 18:32 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.889 s 0.747164
2021-10-06 18:42 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.493828
2021-10-06 18:42 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.284535
2021-10-06 18:42 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.346118
2021-10-06 18:42 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.541528
2021-10-06 18:32 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s -0.649190
2021-10-06 18:42 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.645 s -0.408296
2021-10-06 18:42 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.330782
2021-10-06 18:42 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.490 s 0.374846
2021-10-06 18:42 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.561549
2021-10-06 18:42 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.016041
2021-10-06 18:30 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.179 s 0.258217
2021-10-06 18:31 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.277898
2021-10-06 18:33 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.480 s -0.895521
2021-10-06 18:42 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.320723
2021-10-06 18:42 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.711 s -0.524033
2021-10-06 18:42 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.860 s 0.509407