Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 10:57 Python csv-read gzip, streaming, fanniemae_2016Q4 14.997 s -0.888247
2021-09-29 10:58 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.629 s -0.324969
2021-09-29 11:36 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.317 s -2.390626
2021-09-29 11:37 Python file-read lz4, feather, table, fanniemae_2016Q4 0.608 s -1.230550
2021-09-29 11:38 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.182 s -1.127874
2021-09-29 11:45 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.794 s 1.097662
2021-09-29 10:59 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s 0.045765
2021-09-29 11:34 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.909 s 0.698078
2021-09-29 11:37 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.998 s 0.289913
2021-09-29 11:39 Python file-read lz4, feather, table, nyctaxi_2010-01 0.666 s 0.809056
2021-09-29 11:42 Python file-write lz4, feather, table, fanniemae_2016Q4 1.160 s 0.098432
2021-09-29 11:43 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.800 s 1.852207
2021-09-29 11:01 Python dataframe-to-table type_simple_features 0.940 s -4.731491
2021-09-29 10:58 Python csv-read gzip, streaming, nyctaxi_2010-01 10.609 s -0.294527
2021-09-29 11:01 Python dataset-filter nyctaxi_2010-01 4.436 s -2.569019
2021-09-29 11:37 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.208 s -6.636311
2021-09-29 10:57 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.281185
2021-09-29 11:01 Python dataframe-to-table type_dict 0.012 s 0.481028
2021-09-29 11:38 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.979 s 0.307687
2021-09-29 11:01 Python dataframe-to-table type_strings 0.370 s 0.202039
2021-09-29 11:09 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.296 s 3.374999
2021-09-29 11:18 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.246 s 0.258130
2021-09-29 11:34 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.669 s 0.567456
2021-09-29 11:43 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.184 s 0.321854
2021-09-29 11:34 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.814 s 0.496007
2021-09-29 11:37 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.061 s -1.485330
2021-09-29 11:36 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.856 s -5.116708
2021-09-29 11:41 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.521 s 1.177951
2021-09-29 11:44 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.769 s 1.000708
2021-09-29 11:01 Python dataframe-to-table type_integers 0.011 s -1.633271
2021-09-29 11:38 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.132 s 0.222045
2021-09-29 11:41 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.435 s 1.427483
2021-09-29 11:46 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.797 s 0.372900
2021-09-29 10:56 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.072 s -0.894764
2021-09-29 11:35 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.134 s -4.470231
2021-09-29 11:36 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.168 s -1.973795
2021-09-29 11:45 Python file-write lz4, feather, table, nyctaxi_2010-01 1.839 s -1.406869
2021-09-29 10:56 Python csv-read uncompressed, file, fanniemae_2016Q4 1.220 s -0.770511
2021-09-29 11:04 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.056 s -0.426176
2021-09-29 11:46 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.207496
2021-09-29 11:35 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.380 s -4.653814
2021-09-29 11:36 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.792 s -4.214123
2021-09-29 11:39 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.077 s 1.463926
2021-09-29 11:42 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.681 s 0.278141
2021-09-29 11:00 Python dataframe-to-table chi_traffic_2020_Q1 19.541 s 1.402401
2021-09-29 11:23 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.011 s 0.328062
2021-09-29 11:37 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.040 s 0.026240
2021-09-29 11:44 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.862 s 2.018573
2021-09-29 11:23 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.063 s -0.356870
2021-09-29 11:45 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.318 s 0.386839
2021-09-29 11:01 Python dataframe-to-table type_nested 2.872 s 4.499546
2021-09-29 11:18 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.057 s -0.046971
2021-09-29 11:23 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.009 s 0.163444
2021-09-29 11:40 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.154 s 1.213465
2021-09-29 11:45 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.356 s -0.433656
2021-09-29 12:24 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.240 s 0.126365
2021-09-29 12:27 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.257 s -0.911486
2021-09-29 12:48 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.473 s 0.080997
2021-09-29 12:56 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.091266
2021-09-29 10:58 Python csv-read uncompressed, file, nyctaxi_2010-01 1.014 s 0.089037
2021-09-29 11:01 Python dataframe-to-table type_floats 0.012 s -0.731058
2021-09-29 11:35 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.969 s -4.821544
2021-09-29 11:36 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.928117
2021-09-29 11:37 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.896 s -5.538986
2021-09-29 11:39 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.624 s 0.282324
2021-09-29 11:42 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.353 s -0.114577
2021-09-29 11:46 Python wide-dataframe use_legacy_dataset=false 0.616 s 0.146555
2021-09-29 12:38 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.829 s 2.610074
2021-09-29 12:48 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.199 s -1.027194
2021-09-29 12:56 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.093194
2021-09-29 11:59 R dataframe-to-table chi_traffic_2020_Q1, R 5.368 s 0.726504
2021-09-29 12:23 R dataframe-to-table type_simple_features, R 274.694 s 0.162801
2021-09-29 12:23 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.243 s 0.108943
2021-09-29 12:34 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.831 s -0.002471
2021-09-29 12:46 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.359541
2021-09-29 12:26 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.449 s -3.505707
2021-09-29 12:37 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.209 s 1.046546
2021-09-29 12:45 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.907 s 2.398005
2021-09-29 12:45 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.146151
2021-09-29 12:56 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.623549
2021-09-29 12:56 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -1.914347
2021-09-29 12:45 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.578 s 3.049598
2021-09-29 12:56 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.417064
2021-09-29 12:56 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.562785
2021-09-29 12:26 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.064 s -1.419398
2021-09-29 12:56 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.681 s -0.287631
2021-09-29 12:56 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.998493
2021-09-29 12:25 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.566 s -0.787578
2021-09-29 12:40 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.464 s 2.509658
2021-09-29 12:27 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.169 s 0.091634
2021-09-29 12:29 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.538 s -0.997540
2021-09-29 12:48 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.166 s 2.643613
2021-09-29 12:43 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.263 s 0.307399
2021-09-29 12:56 JavaScript Parse Table.from, tracks 0.000 s -0.781881
2021-09-29 12:56 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.625 s -0.210647
2021-09-29 12:56 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.712 s 0.204076
2021-09-29 11:59 R dataframe-to-table type_dict, R 0.028 s 2.687096
2021-09-29 12:27 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.142233
2021-09-29 12:44 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.484 s 1.293148
2021-09-29 12:56 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.605478
2021-09-29 12:24 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.940 s -0.199766
2021-09-29 12:39 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.811 s 2.294269
2021-09-29 12:56 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.058395
2021-09-29 12:56 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.048 s -1.905193
2021-09-29 12:45 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.592 s 2.154345
2021-09-29 12:47 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.624 s -0.261365
2021-09-29 12:47 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.354 s 2.233252
2021-09-29 12:49 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.473 s 0.190050
2021-09-29 11:59 R dataframe-to-table type_integers, R 0.084 s 0.905006
2021-09-29 12:32 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.285 s 1.497245
2021-09-29 12:47 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.440439
2021-09-29 12:56 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -1.788019
2021-09-29 12:56 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.481 s 0.464687
2021-09-29 11:59 R dataframe-to-table type_strings, R 0.490 s 0.346135
2021-09-29 12:34 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.750 s 1.263567
2021-09-29 12:46 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.503 s 1.899309
2021-09-29 12:47 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.969 s 2.675273
2021-09-29 12:30 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.842 s 1.452377
2021-09-29 12:36 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.400 s 0.305109
2021-09-29 12:56 JavaScript Parse readBatches, tracks 0.000 s 0.327044
2021-09-29 12:56 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.012164
2021-09-29 11:59 R dataframe-to-table type_floats, R 0.112 s -1.083165
2021-09-29 12:46 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.604 s 2.632161
2021-09-29 12:56 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.637 s 0.748034
2021-09-29 12:56 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497050
2021-09-29 12:24 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.917 s 0.015401
2021-09-29 12:35 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.615 s 0.172822
2021-09-29 12:45 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.181 s -0.618011
2021-09-29 12:56 JavaScript Parse serialize, tracks 0.005 s -0.694750
2021-09-29 12:56 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.022752
2021-09-29 12:56 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.445864
2021-09-29 12:25 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.964 s -2.274883
2021-09-29 12:27 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.129 s 0.024984
2021-09-29 12:28 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.153985
2021-09-29 12:31 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.289 s 1.295900
2021-09-29 12:42 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.288 s -2.799776
2021-09-29 12:44 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.191 s 0.246001
2021-09-29 12:56 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.579827
2021-09-29 12:56 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.159821
2021-09-29 11:59 R dataframe-to-table type_nested, R 0.542 s -1.778194
2021-09-29 12:24 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -1.095868
2021-09-29 12:28 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.978 s -0.518445
2021-09-29 12:41 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.662 s 2.594632
2021-09-29 12:56 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.515263
2021-09-29 12:56 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.018065
2021-09-29 12:56 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.108626
2021-09-29 12:56 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.880 s 0.074714
2021-09-29 12:56 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.880 s 0.379537
2021-09-29 12:56 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.202522