Outliers: 10


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-12 23:18 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.370233
2021-10-12 23:18 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.199482
2021-10-12 21:47 Python dataframe-to-table type_strings 0.367 s 0.415582
2021-10-12 21:47 Python dataframe-to-table type_dict 0.012 s 0.091452
2021-10-12 22:26 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.117 s 0.112983
2021-10-12 23:07 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.499107
2021-10-12 23:18 JavaScript Parse readBatches, tracks 0.000 s -0.408862
2021-10-12 23:18 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.659 s 0.529420
2021-10-12 23:18 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.447792
2021-10-12 23:18 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.317711
2021-10-12 23:18 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.261612
2021-10-12 23:18 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.258675
2021-10-12 23:18 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.466 s 0.985950
2021-10-12 23:18 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574060
2021-10-12 21:42 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.886 s 0.308146
2021-10-12 21:44 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.504529
2021-10-12 21:45 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.625958
2021-10-12 22:27 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.434 s 0.500772
2021-10-12 22:51 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.988 s 0.131299
2021-10-12 23:02 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.534 s -0.619632
2021-10-12 23:07 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.192 s -1.215811
2021-10-12 23:18 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.711 s 0.209645
2021-10-12 22:09 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.047 s -0.224728
2021-10-12 22:47 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.232 s 0.112321
2021-10-12 22:54 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.254 s 0.462561
2021-10-12 21:43 Python csv-read gzip, streaming, fanniemae_2016Q4 14.820 s 0.325845
2021-10-12 22:22 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.179 s -2.356478
2021-10-12 22:29 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.961 s -0.840718
2021-10-12 22:30 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.855 s -0.164485
2021-10-12 22:47 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.458 s 0.865093
2021-10-12 22:49 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.160 s 0.849182
2021-10-12 22:58 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.392 s 0.471111
2021-10-12 21:44 Python csv-read uncompressed, file, nyctaxi_2010-01 1.022 s -1.060549
2021-10-12 21:45 Python csv-read gzip, streaming, nyctaxi_2010-01 10.657 s -0.400196
2021-10-12 22:09 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.091 s -1.840464
2021-10-12 22:29 Python file-write lz4, feather, table, fanniemae_2016Q4 1.145 s 0.927597
2021-10-12 22:54 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.282 s 0.529085
2021-10-12 22:56 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.686 s 0.680274
2021-10-12 23:18 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.547272
2021-10-12 23:18 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.454164
2021-10-12 21:47 Python dataframe-to-table chi_traffic_2020_Q1 19.399 s 0.497652
2021-10-12 22:23 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.432 s -15.752166
2021-10-12 22:31 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.906 s -0.377074
2021-10-12 22:47 R dataframe-to-table type_nested, R 0.533 s 0.234487
2021-10-12 22:50 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -1.406227
2021-10-12 22:51 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.523 s 0.160736
2021-10-12 23:03 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.723 s -0.660572
2021-10-12 22:21 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.916 s 0.563726
2021-10-12 22:27 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.513 s -0.731744
2021-10-12 22:32 Python wide-dataframe use_legacy_dataset=true 0.388 s 1.808023
2021-10-12 22:49 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.404 s -0.693792
2021-10-12 22:50 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.108 s 0.844083
2021-10-12 22:05 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.129 s 0.084926
2021-10-12 22:21 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.838 s 0.266617
2021-10-12 22:21 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.056 s -1.717275
2021-10-12 21:55 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.979 s -0.433553
2021-10-12 22:05 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.368 s 0.135614
2021-10-12 22:23 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.702 s 0.169704
2021-10-12 22:32 Python file-write lz4, feather, table, nyctaxi_2010-01 1.800 s 0.277431
2021-10-12 22:33 Python wide-dataframe use_legacy_dataset=false 0.613 s 1.243610
2021-10-12 22:46 R dataframe-to-table chi_traffic_2020_Q1, R 3.352 s 0.266372
2021-10-12 22:47 R dataframe-to-table type_integers, R 0.010 s 0.900486
2021-10-12 22:48 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.317 s -1.291101
2021-10-12 22:51 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.690 s 0.022731
2021-10-12 22:50 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.216 s 0.842661
2021-10-12 23:18 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.580 s -0.190720
2021-10-12 21:51 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 64.687 s -0.982767
2021-10-12 22:21 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.678 s 0.593743
2021-10-12 22:52 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.834 s 0.496235
2021-10-12 23:01 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.866 s -0.731734
2021-10-12 23:09 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -0.451501
2021-10-12 23:11 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.504 s -0.254218
2021-10-12 23:18 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.085895
2021-10-12 21:43 Python csv-read uncompressed, file, fanniemae_2016Q4 1.155 s 0.551438
2021-10-12 22:24 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.330 s -16.575029
2021-10-12 22:25 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 9.023 s -6.974624
2021-10-12 22:49 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.557 s 1.010587
2021-10-12 22:49 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.049 s 0.721171
2021-10-12 22:58 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.550 s 0.305734
2021-10-12 23:09 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.609 s -0.197061
2021-10-12 23:09 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s -0.794929
2021-10-12 23:10 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.209 s -1.094789
2021-10-12 21:44 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.676 s -0.352419
2021-10-12 21:47 Python dataframe-to-table type_floats 0.011 s 0.275353
2021-10-12 22:23 Python file-read uncompressed, feather, table, fanniemae_2016Q4 3.807 s -214.125880
2021-10-12 22:23 Python file-read lz4, feather, table, fanniemae_2016Q4 0.690 s -14.328714
2021-10-12 22:26 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 8.111 s -2.281702
2021-10-12 22:47 R dataframe-to-table type_strings, R 0.493 s 0.231099
2021-10-12 22:48 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.061 s -1.358690
2021-10-12 23:08 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.773 s -35.512619
2021-10-12 23:10 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.499 s -3.280102
2021-10-12 21:48 Python dataset-filter nyctaxi_2010-01 4.367 s -1.204632
2021-10-12 22:24 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.528 s -3.306613
2021-10-12 22:32 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.794 s 0.875975
2021-10-12 23:07 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.623 s -3.732105
2021-10-12 23:18 JavaScript Parse Table.from, tracks 0.000 s -0.261265
2021-10-12 23:18 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.410778
2021-10-12 21:47 Python dataframe-to-table type_integers 0.011 s -0.191096
2021-10-12 22:23 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.074 s 0.952114
2021-10-12 22:25 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.962 s -140.331888
2021-10-12 22:47 R dataframe-to-table type_dict, R 0.053 s -0.283339
2021-10-12 22:47 R dataframe-to-table type_floats, R 0.013 s 0.884790
2021-10-12 22:48 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.212 s 0.758242
2021-10-12 23:07 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.170 s 0.050844
2021-10-12 23:07 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.899 s -0.655007
2021-10-12 23:18 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.075028
2021-10-12 23:18 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.448677
2021-10-12 21:48 Python dataframe-to-table type_nested 2.868 s 0.567641
2021-10-12 22:25 Python file-read lz4, feather, table, nyctaxi_2010-01 0.995 s -50.003654
2021-10-12 22:31 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.924 s -0.250170
2021-10-12 22:32 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.356 s -0.194707
2021-10-12 23:00 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.891 s -0.455742
2021-10-12 23:18 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-12 22:22 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.759 s 0.325288
2021-10-12 22:25 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.406 s -1.574915
2021-10-12 22:28 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.889 s -0.822851
2021-10-12 22:29 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.471 s -1.161002
2021-10-12 22:32 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.330 s 0.535574
2021-10-12 22:48 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.474 s 0.837489
2021-10-12 22:57 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.814 s 1.246069
2021-10-12 23:07 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.576 s -0.130709
2021-10-12 23:18 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.630 s -0.361538
2021-10-12 23:18 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.130816
2021-10-12 22:28 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.334 s -0.164683
2021-10-12 23:08 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.592 s 1.108184
2021-10-12 23:18 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.843785
2021-10-12 23:18 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.862 s 0.524562
2021-10-12 23:18 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.278622
2021-10-12 22:22 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.488 s -12.903995
2021-10-12 22:23 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 6.191 s -11.783269
2021-10-12 22:30 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.869 s -0.246467
2021-10-12 23:04 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.274 s 1.309572
2021-10-12 23:18 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.424547
2021-10-12 23:18 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.504203
2021-10-12 22:09 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.084 s -0.400316
2021-10-12 22:21 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.300 s -1.625468
2021-10-12 22:22 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.905 s -2.146354
2021-10-12 22:59 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.202 s -0.265236
2021-10-12 23:09 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.914 s -0.757421
2021-10-12 23:18 JavaScript Parse serialize, tracks 0.004 s 0.607676
2021-10-12 23:18 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.365028
2021-10-12 23:05 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.253 s -1.336966
2021-10-12 23:06 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.472 s 1.100565
2021-10-12 23:08 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.528 s -0.729932
2021-10-12 23:18 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.861972
2021-10-12 23:10 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.164 s 0.821768
2021-10-12 23:18 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.860 s 0.976929