Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 23:37 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.446567
2021-09-29 23:41 Python dataframe-to-table type_strings 0.371 s -0.000640
2021-09-29 23:38 Python csv-read uncompressed, file, nyctaxi_2010-01 1.002 s 0.294342
2021-09-29 23:36 Python csv-read uncompressed, file, fanniemae_2016Q4 1.148 s 0.389514
2021-09-30 00:03 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.010 s 0.359306
2021-09-30 00:03 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.009 s 0.176702
2021-09-29 23:41 Python dataframe-to-table type_nested 2.902 s 2.380748
2021-09-29 23:41 Python dataframe-to-table type_simple_features 0.912 s -0.471533
2021-09-29 23:41 Python dataframe-to-table type_floats 0.011 s 1.756443
2021-09-30 00:03 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.015 s 0.276503
2021-09-29 23:38 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.676 s -0.485398
2021-09-29 23:41 Python dataframe-to-table type_integers 0.011 s 1.617174
2021-09-29 23:42 Python dataset-filter nyctaxi_2010-01 4.348 s 0.459586
2021-09-30 00:14 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.055 s -0.275484
2021-09-29 23:41 Python dataframe-to-table chi_traffic_2020_Q1 19.524 s 1.448683
2021-09-29 23:41 Python dataframe-to-table type_dict 0.012 s 0.823711
2021-09-29 23:59 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.126 s 0.891971
2021-09-29 23:45 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 64.752 s -1.163305
2021-09-29 23:36 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.752 s -0.389055
2021-09-29 23:37 Python csv-read gzip, streaming, fanniemae_2016Q4 14.679 s -0.387638
2021-09-29 23:49 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.856 s 2.074659
2021-09-29 23:39 Python csv-read gzip, file, nyctaxi_2010-01 9.040 s 1.400185
2021-09-30 00:14 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.721 s 0.334353
2021-09-29 23:39 Python csv-read gzip, streaming, nyctaxi_2010-01 10.676 s -0.532300
2021-09-29 23:59 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.193 s 0.164430
2021-09-30 00:14 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.800 s 0.547307
2021-09-30 00:15 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.940 s 1.501739
2021-09-30 00:15 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.765 s 1.545885
2021-09-30 00:16 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.816 s -2.273313
2021-09-30 00:15 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.180 s 1.703997
2021-09-30 00:17 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.018 s 0.892607
2021-09-30 00:16 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.111 s 1.396444
2021-09-30 00:15 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.259 s 1.412719
2021-09-30 00:16 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.766 s -2.291264
2021-09-30 00:16 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.930 s -4.431909
2021-09-30 00:16 Python file-read lz4, feather, table, fanniemae_2016Q4 0.602 s -0.143951
2021-09-30 00:18 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.308 s -1.182721
2021-09-30 00:16 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.286 s 0.721969
2021-09-30 00:25 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.352 s -0.150422
2021-09-30 00:17 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.254 s -5.444703
2021-09-30 00:17 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.047 s -0.617559
2021-09-30 00:17 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.286 s -1.037128
2021-09-30 01:03 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.400 s 0.208126
2021-09-30 00:24 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.932 s -0.177853
2021-09-30 01:02 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.587 s 0.582549
2021-09-30 01:08 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.756 s -0.271974
2021-09-30 01:13 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.457450
2021-09-30 01:14 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.518 s 1.269046
2021-09-30 01:23 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.013 s -2.738565
2021-09-30 00:18 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.963739
2021-09-30 00:21 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.981 s -0.992040
2021-09-30 00:23 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.277 s -0.583929
2021-09-30 00:24 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.023 s -1.702862
2021-09-30 00:53 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.129 s 0.038481
2021-09-30 01:14 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.068090
2021-09-30 01:23 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.977280
2021-09-30 00:19 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.388 s -1.144373
2021-09-30 01:09 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.277969
2021-09-30 01:12 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.575 s 2.002414
2021-09-30 01:23 JavaScript Parse serialize, tracks 0.005 s -0.712208
2021-09-30 01:23 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.615398
2021-09-30 01:23 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.137783
2021-09-30 01:23 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.878125
2021-09-30 00:18 Python file-read lz4, feather, table, nyctaxi_2010-01 0.674 s -1.003706
2021-09-30 00:50 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.882 s 0.379068
2021-09-30 00:54 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.016889
2021-09-30 00:55 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.965 s 0.279381
2021-09-30 01:11 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.583 s 1.712517
2021-09-30 00:18 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.462 s -1.416278
2021-09-30 00:19 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.945 s -1.305754
2021-09-30 01:23 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.141797
2021-09-30 00:20 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.607 s -0.958079
2021-09-30 01:01 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.846 s -3.024348
2021-09-30 00:25 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.362 s -0.000299
2021-09-30 00:40 R dataframe-to-table type_floats, R 0.112 s -1.298678
2021-09-30 00:52 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.397 s -0.840605
2021-09-30 01:14 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.472 s 0.350269
2021-09-30 01:23 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.806531
2021-09-30 00:23 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.975 s -1.812152
2021-09-30 00:25 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.977 s -0.244890
2021-09-30 00:51 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.856 s 0.666191
2021-09-30 00:52 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.932 s -0.729804
2021-09-30 01:10 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.245 s 1.417925
2021-09-30 01:14 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.350 s 1.508359
2021-09-30 00:57 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.166 s -1.161525
2021-09-30 00:59 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.626 s -1.217108
2021-09-30 01:06 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.900 s -0.354415
2021-09-30 01:13 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.517 s -0.208614
2021-09-30 01:23 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.689 s -0.446606
2021-09-30 00:26 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.823 s 0.134066
2021-09-30 00:39 R dataframe-to-table type_dict, R 0.054 s -0.417120
2021-09-30 01:15 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.159 s 1.611497
2021-09-30 01:23 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.825114
2021-09-30 01:23 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.474558
2021-09-30 01:23 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.727718
2021-09-30 01:23 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.615398
2021-09-30 01:23 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.136797
2021-09-30 00:26 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.175868
2021-09-30 00:51 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.268646
2021-09-30 01:12 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.961 s 1.641852
2021-09-30 01:23 JavaScript Parse Table.from, tracks 0.000 s -1.767576
2021-09-30 01:23 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.851497
2021-09-30 01:23 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.504453
2021-09-30 01:23 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.640023
2021-09-30 00:40 R dataframe-to-table type_nested, R 0.537 s -0.269967
2021-09-30 01:23 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.500355
2021-09-30 00:50 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.210 s 0.428373
2021-09-30 01:15 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.197 s 0.537964
2021-09-30 00:22 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.824 s -0.928440
2021-09-30 00:39 R dataframe-to-table type_strings, R 0.490 s 0.132017
2021-09-30 00:50 R dataframe-to-table type_simple_features, R 3.264 s 523.808802
2021-09-30 00:53 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.178 s -0.322914
2021-09-30 01:23 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.805 s -0.704315
2021-09-30 00:21 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.738 s -1.084797
2021-09-30 00:55 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.670 s 1.457978
2021-09-30 01:10 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.491 s -0.168691
2021-09-30 01:12 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.309202
2021-09-30 01:23 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.880995
2021-09-30 00:22 Python file-write lz4, feather, table, fanniemae_2016Q4 1.162 s -0.125671
2021-09-30 00:26 Python wide-dataframe use_legacy_dataset=false 0.617 s 0.279197
2021-09-30 00:53 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.059 s -0.635665
2021-09-30 00:54 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.274 s -1.760248
2021-09-30 01:05 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.938 s -0.692489
2021-09-30 01:12 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.317409
2021-09-30 01:23 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.485725
2021-09-30 01:23 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.592903
2021-09-30 00:22 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.376 s -0.254149
2021-09-30 00:39 R dataframe-to-table chi_traffic_2020_Q1, R 5.469 s -1.156511
2021-09-30 00:50 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.208 s 0.468997
2021-09-30 00:52 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.586 s -4.896030
2021-09-30 00:56 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.508 s 0.477230
2021-09-30 00:58 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.606 s -0.711415
2021-09-30 01:07 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.579 s -0.795464
2021-09-30 01:12 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.591 s 1.658693
2021-09-30 01:13 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.924 s 1.622007
2021-09-30 01:15 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.496 s 0.128223
2021-09-30 00:25 Python file-write lz4, feather, table, nyctaxi_2010-01 1.798 s 0.702710
2021-09-30 01:01 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.058 s -0.776893
2021-09-30 01:23 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.470732
2021-09-30 01:23 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.573 s -1.074383
2021-09-30 00:40 R dataframe-to-table type_integers, R 0.084 s 0.814189
2021-09-30 01:23 JavaScript Parse readBatches, tracks 0.000 s -2.024691
2021-09-30 01:23 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.638 s -0.216176
2021-09-30 01:23 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.715 s -0.770969
2021-09-30 01:23 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.884 s -0.045802
2021-09-30 01:23 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.973794
2021-09-30 01:04 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.217 s 0.740898
2021-09-30 01:11 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.178 s 1.101108
2021-09-30 01:23 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.501805