Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-01 09:49 Python file-read lz4, feather, table, fanniemae_2016Q4 0.607 s -0.844818
2021-10-01 10:12 R dataframe-to-table type_floats, R 0.113 s -1.299443
2021-10-01 10:36 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.195 s 0.619370
2021-10-01 10:59 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.606 s 0.136978
2021-10-01 11:01 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.197 s 0.122147
2021-10-01 11:08 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.496357
2021-10-01 11:08 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.526686
2021-10-01 09:10 Python csv-read uncompressed, file, fanniemae_2016Q4 1.215 s -0.703450
2021-10-01 09:50 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.310 s -1.291843
2021-10-01 09:57 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.997 s -1.179084
2021-10-01 09:12 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.682071
2021-10-01 09:14 Python dataframe-to-table chi_traffic_2020_Q1 19.684 s 0.333183
2021-10-01 09:32 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.152 s 0.794369
2021-10-01 09:51 Python file-read lz4, feather, table, nyctaxi_2010-01 0.674 s -1.216611
2021-10-01 09:55 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.228 s -4.014786
2021-10-01 09:58 Python wide-dataframe use_legacy_dataset=true 0.397 s -0.572803
2021-10-01 10:38 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.063 s -1.157988
2021-10-01 11:08 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.626671
2021-10-01 09:10 Python csv-read gzip, streaming, fanniemae_2016Q4 14.874 s -0.498930
2021-10-01 09:56 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.938 s -0.280016
2021-10-01 10:40 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.151381
2021-10-01 10:41 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.682 s 0.032997
2021-10-01 09:36 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.016 s 0.280574
2021-10-01 10:11 R dataframe-to-table type_strings, R 0.490 s 0.186883
2021-10-01 10:44 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.610 s -0.883025
2021-10-01 11:08 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.678 s 0.102480
2021-10-01 11:08 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.686442
2021-10-01 11:08 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.632217
2021-10-01 11:08 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.504508
2021-10-01 11:08 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.067086
2021-10-01 09:11 Python csv-read uncompressed, file, nyctaxi_2010-01 1.007 s 0.196268
2021-10-01 09:47 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.846 s 0.324973
2021-10-01 09:49 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.048 s -0.126481
2021-10-01 09:51 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.958 s -1.478509
2021-10-01 09:52 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.411 s -1.367981
2021-10-01 10:55 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.259 s 0.351642
2021-10-01 11:08 JavaScript Parse serialize, tracks 0.005 s -0.369032
2021-10-01 11:08 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.230 s 3.089972
2021-10-01 11:08 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.914 s -0.249875
2021-10-01 09:15 Python dataset-filter nyctaxi_2010-01 4.356 s 0.326391
2021-10-01 09:48 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.263 s 1.036679
2021-10-01 09:48 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.760 s -1.182745
2021-10-01 09:50 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s -0.050228
2021-10-01 10:39 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.135 s -0.354182
2021-10-01 10:40 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.224 s 0.999133
2021-10-01 11:08 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.630535
2021-10-01 09:15 Python dataframe-to-table type_simple_features 0.910 s 0.235801
2021-10-01 10:12 R dataframe-to-table type_nested, R 0.536 s 0.385023
2021-10-01 10:48 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.568 s 0.900260
2021-10-01 10:50 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.198 s 1.135716
2021-10-01 09:09 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.939 s -0.494899
2021-10-01 09:14 Python dataframe-to-table type_dict 0.012 s 0.413107
2021-10-01 09:47 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.010 s -0.006740
2021-10-01 09:47 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.951 s 1.158112
2021-10-01 09:48 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.793 s 0.787492
2021-10-01 09:53 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.754 s -1.268054
2021-10-01 09:55 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.303 s -0.790725
2021-10-01 09:55 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.936 s -1.179543
2021-10-01 09:58 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.379476
2021-10-01 10:54 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.761 s -0.595853
2021-10-01 10:57 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.580 s 1.344956
2021-10-01 10:57 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.576 s 1.364622
2021-10-01 11:08 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.819 s 1.486889
2021-10-01 09:48 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.120 s 0.963300
2021-10-01 10:41 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.973 s -0.092068
2021-10-01 10:42 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.520 s 0.027403
2021-10-01 10:48 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.399 s 0.415666
2021-10-01 11:00 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.348 s 1.140255
2021-10-01 11:08 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.728158
2021-10-01 11:08 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.630592
2021-10-01 09:18 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.664 s -0.346080
2021-10-01 09:49 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.246 s -2.375824
2021-10-01 10:37 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.866 s 0.594423
2021-10-01 10:46 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.065 s -0.965939
2021-10-01 10:56 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.481 s 1.694709
2021-10-01 10:58 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.176 s 0.037154
2021-10-01 10:58 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.513 s 0.355136
2021-10-01 09:50 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.027 s 0.672318
2021-10-01 10:39 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.187 s -0.747484
2021-10-01 10:53 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.576 s -0.775040
2021-10-01 09:11 Python csv-read gzip, file, fanniemae_2016Q4 6.023 s 1.518080
2021-10-01 09:22 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.148 s 1.425095
2021-10-01 09:32 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.287 s -0.131487
2021-10-01 09:47 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.681 s 0.497760
2021-10-01 09:49 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.293 s -0.321683
2021-10-01 09:49 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.950 s -2.639793
2021-10-01 09:55 Python file-write lz4, feather, table, fanniemae_2016Q4 1.220 s -5.300251
2021-10-01 09:57 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.385 s -2.082347
2021-10-01 10:35 R dataframe-to-table type_simple_features, R 275.661 s -1.562050
2021-10-01 10:35 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.615 s -3.442826
2021-10-01 10:37 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.927 s -0.431955
2021-10-01 10:38 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.661697
2021-10-01 09:14 Python dataframe-to-table type_floats 0.011 s 1.917010
2021-10-01 09:54 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.371 s -0.206321
2021-10-01 10:12 R dataframe-to-table type_integers, R 0.085 s -0.315670
2021-10-01 10:36 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 8.302 s -3.326201
2021-10-01 11:00 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.102 s -1.363008
2021-10-01 11:08 JavaScript Parse readBatches, tracks 0.000 s 0.275669
2021-10-01 11:08 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.249 s 3.013501
2021-10-01 09:53 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.594 s -0.966681
2021-10-01 10:37 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.687301
2021-10-01 10:38 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.383 s -0.018294
2021-10-01 10:58 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.472232
2021-10-01 10:58 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.601 s 1.111758
2021-10-01 11:08 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.503980
2021-10-01 09:14 Python dataframe-to-table type_integers 0.011 s 1.631157
2021-10-01 09:36 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.009 s 0.405663
2021-10-01 09:36 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.006 s 0.230056
2021-10-01 09:47 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.185 s 1.486748
2021-10-01 09:48 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.833 s -1.320096
2021-10-01 09:54 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.973 s -1.026175
2021-10-01 09:57 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.965 s -0.229444
2021-10-01 10:42 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.170 s -1.237760
2021-10-01 10:57 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.999 s 1.091734
2021-10-01 11:01 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.499 s 0.102818
2021-10-01 11:08 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.583984
2021-10-01 11:08 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.208645
2021-10-01 11:08 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.473 s 0.491968
2021-10-01 09:11 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.491 s 0.469656
2021-10-01 09:12 Python csv-read gzip, streaming, nyctaxi_2010-01 10.475 s 0.485499
2021-10-01 09:14 Python dataframe-to-table type_strings 0.370 s 0.160337
2021-10-01 09:15 Python dataframe-to-table type_nested 2.874 s 1.639065
2021-10-01 09:50 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.308 s -1.316946
2021-10-01 09:51 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.464 s -1.531353
2021-10-01 09:58 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.373 s -0.062170
2021-10-01 09:58 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.802 s 0.342330
2021-10-01 09:58 Python wide-dataframe use_legacy_dataset=false 0.619 s -0.000543
2021-10-01 10:11 R dataframe-to-table chi_traffic_2020_Q1, R 5.443 s -0.728324
2021-10-01 10:12 R dataframe-to-table type_dict, R 0.063 s -1.341164
2021-10-01 10:45 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.626 s -1.255183
2021-10-01 10:54 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.357939
2021-10-01 10:57 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.186 s 0.495449
2021-10-01 10:59 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.933 s 1.128253
2021-10-01 11:08 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.093271
2021-10-01 11:08 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.000061
2021-10-01 11:08 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.074702
2021-10-01 10:47 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.831 s 0.010093
2021-10-01 10:52 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.888 s -0.295706
2021-10-01 11:01 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.155 s 1.134419
2021-10-01 11:08 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.503980
2021-10-01 11:08 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.207741
2021-10-01 11:08 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.469379
2021-10-01 10:50 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.940 s -0.810283
2021-10-01 10:59 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.583 s 0.199760
2021-10-01 11:00 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.478 s -1.599678
2021-10-01 11:08 JavaScript Parse Table.from, tracks 0.000 s 0.439699
2021-10-01 11:08 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.034 s -3.435286
2021-10-01 11:08 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.694 s 0.303698
2021-10-01 11:08 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.603070