Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 22:57 Python csv-read gzip, streaming, fanniemae_2016Q4 14.764 s 0.996201
2021-10-11 22:59 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.615356
2021-10-11 23:36 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.793 s 0.526989
2021-10-11 23:37 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.286 s -0.047582
2021-10-11 23:38 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.294 s -0.678350
2021-10-11 23:38 Python file-read lz4, feather, table, fanniemae_2016Q4 0.597 s 0.983232
2021-10-12 00:40 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.531085
2021-10-12 00:40 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.077 s -3.624158
2021-10-12 00:40 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.019741
2021-10-12 00:40 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.392065
2021-10-12 00:40 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.013589
2021-10-11 23:43 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.165 s 0.814662
2021-10-11 23:09 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.888 s -0.362033
2021-10-11 23:23 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.071 s -0.265742
2021-10-11 23:37 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.122 s 0.868659
2021-10-11 23:01 Python dataframe-to-table type_floats 0.011 s -0.508660
2021-10-11 23:23 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.049 s -0.494969
2021-10-11 23:36 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.712 s 0.334494
2021-10-11 23:36 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.059 s -2.361776
2021-10-11 23:38 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.522 s 2.022820
2021-10-11 23:01 Python dataframe-to-table chi_traffic_2020_Q1 19.488 s 0.263120
2021-10-11 23:01 Python dataframe-to-table type_nested 2.876 s 0.158651
2021-10-11 23:05 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 66.063 s -1.400507
2021-10-11 23:01 Python dataframe-to-table type_strings 0.366 s 0.525839
2021-10-11 23:02 Python dataset-filter nyctaxi_2010-01 4.371 s -1.573832
2021-10-11 23:23 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.035 s -0.108216
2021-10-11 23:37 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.625 s 1.814233
2021-10-11 23:38 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.999 s 2.000048
2021-10-11 23:39 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.001 s 2.000915
2021-10-11 23:01 Python dataframe-to-table type_dict 0.011 s 0.894758
2021-10-11 23:38 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.706 s 1.772024
2021-10-11 23:38 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.029 s 0.619917
2021-10-11 23:39 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.137 s 1.959282
2021-10-11 23:47 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.805 s 0.570240
2021-10-11 23:01 Python dataframe-to-table type_integers 0.011 s -1.748357
2021-10-11 23:39 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.168 s 1.340576
2021-10-11 23:43 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.883 s -0.704985
2021-10-12 00:40 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.024 s 0.170013
2021-10-11 23:02 Python dataframe-to-table type_simple_features 0.928 s -0.643764
2021-10-11 23:36 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.977 s 0.257869
2021-10-11 23:39 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.139 s 1.857456
2021-10-11 23:42 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.433 s 0.615954
2021-10-11 22:56 Python csv-read uncompressed, file, fanniemae_2016Q4 1.154 s 1.065137
2021-10-11 22:58 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.653 s -0.293834
2021-10-11 22:59 Python csv-read gzip, streaming, nyctaxi_2010-01 10.616 s -0.305365
2021-10-11 23:37 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.900 s -2.585445
2021-10-11 23:44 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.487 s -1.482480
2021-10-11 23:46 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.351 s 0.026875
2021-10-11 22:57 Python csv-read gzip, file, fanniemae_2016Q4 6.026 s 0.998836
2021-10-11 23:19 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.176 s 0.253198
2021-10-11 23:46 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.911 s -0.352542
2021-10-11 22:56 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.817 s 1.168064
2021-10-11 23:40 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.287 s 1.855369
2021-10-11 23:40 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s 0.080557
2021-10-11 23:47 Python wide-dataframe use_legacy_dataset=true 0.388 s 2.070170
2021-10-11 23:40 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.777 s 2.017553
2021-10-11 23:42 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.573 s -1.080275
2021-10-11 23:47 Python wide-dataframe use_legacy_dataset=false 0.617 s 0.732577
2021-10-11 23:19 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.405 s -0.258250
2021-10-11 23:41 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.074 s 0.621660
2021-10-11 23:44 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.944 s -0.718550
2021-10-11 23:47 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.297 s 1.515263
2021-10-11 22:58 Python csv-read uncompressed, file, nyctaxi_2010-01 0.994 s 1.518357
2021-10-11 23:37 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.305 s -2.256002
2021-10-12 00:02 R dataframe-to-table chi_traffic_2020_Q1, R 3.366 s 0.268366
2021-10-11 23:44 Python file-write lz4, feather, table, fanniemae_2016Q4 1.157 s 0.160454
2021-10-11 23:44 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.840 s 0.183934
2021-10-12 00:40 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.828 s -3.248236
2021-10-11 23:45 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.864 s -0.067929
2021-10-11 23:46 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.916 s -0.021775
2021-10-11 23:47 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.055708
2021-10-12 00:03 R dataframe-to-table type_integers, R 0.010 s 1.041609
2021-10-12 00:02 R dataframe-to-table type_strings, R 0.491 s 0.231914
2021-10-12 00:02 R dataframe-to-table type_dict, R 0.052 s 0.112356
2021-10-12 00:03 R dataframe-to-table type_floats, R 0.013 s 1.023880
2021-10-12 00:03 R dataframe-to-table type_nested, R 0.537 s 0.233296
2021-10-12 00:23 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.856 s -0.437379
2021-10-12 00:26 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.274 s 1.442565
2021-10-12 00:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.199 s -2.395352
2021-10-12 00:30 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s 0.268612
2021-10-12 00:40 JavaScript Parse readBatches, tracks 0.000 s 0.325568
2021-10-12 00:28 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.470 s 1.574473
2021-10-12 00:40 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.164191
2021-10-12 00:40 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.770342
2021-10-12 00:40 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.815318
2021-10-12 00:40 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.946 s -1.827744
2021-10-12 00:40 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-12 00:40 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.071499
2021-10-12 00:40 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.411476
2021-10-12 00:40 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.610768
2021-10-12 00:10 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.564 s -0.295928
2021-10-12 00:40 JavaScript Parse serialize, tracks 0.005 s -0.645849
2021-10-12 00:40 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.907219
2021-10-12 00:10 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.319 s -1.743322
2021-10-12 00:40 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.521760
2021-10-12 00:40 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.775937
2021-10-12 00:40 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.641125
2021-10-12 00:09 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.481 s 0.993627
2021-10-12 00:10 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.463 s 0.974080
2021-10-12 00:10 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.065 s -1.716643
2021-10-12 00:16 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.300 s 0.491119
2021-10-12 00:40 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.671518
2021-10-12 00:40 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.232265
2021-10-12 00:28 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.172 s -0.064456
2021-10-12 00:30 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.535 s -1.818642
2021-10-12 00:40 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.550 s -0.243212
2021-10-12 00:09 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.216 s 0.499540
2021-10-12 00:20 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.384 s 1.639713
2021-10-12 00:30 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 0.392863
2021-10-12 00:40 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.577 s -0.256032
2021-10-12 00:40 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.749070
2021-10-12 00:40 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.735462
2021-10-12 00:13 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.486 s 0.520175
2021-10-12 00:14 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.837 s 0.583659
2021-10-12 00:24 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.534 s -0.477667
2021-10-12 00:40 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.652 s -6.029018
2021-10-12 00:40 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.631 s -1.867461
2021-10-12 00:18 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.703 s 0.638302
2021-10-12 00:25 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.726 s -0.627951
2021-10-12 00:31 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s 0.059333
2021-10-12 00:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s -0.531742
2021-10-12 00:40 JavaScript Parse Table.from, tracks 0.000 s 0.837190
2021-10-12 00:40 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.237841
2021-10-12 00:29 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.873 s 0.100271
2021-10-12 00:29 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 0.782262
2021-10-12 00:32 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.162 s 0.947790
2021-10-12 00:11 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.052 s 0.433434
2021-10-12 00:12 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -1.632556
2021-10-12 00:32 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s 0.255653
2021-10-12 00:12 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.218 s 0.976119
2021-10-12 00:29 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.571 s 0.508527
2021-10-12 00:31 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.613 s -0.217974
2021-10-12 00:32 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.492 s -1.569149
2021-10-12 00:33 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.487 s 1.544234
2021-10-12 00:16 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.247 s 0.627502
2021-10-12 00:09 R dataframe-to-table type_simple_features, R 3.337 s 0.858859
2021-10-12 00:27 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.241 s 0.127439
2021-10-12 00:11 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.154 s 0.986324
2021-10-12 00:12 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.958 s 0.485951
2021-10-12 00:21 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.182 s 0.861788
2021-10-12 00:13 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.685 s 0.058083
2021-10-12 00:22 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.891 s -0.353267
2021-10-12 00:29 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.601 s -1.533592
2021-10-12 00:31 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.869 s 1.632155
2021-10-12 00:09 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.221 s 0.352722
2021-10-12 00:11 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.405 s -0.860218
2021-10-12 00:11 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.110 s 0.837146
2021-10-12 00:18 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.810 s 1.843494
2021-10-12 00:20 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.552 s 0.262290