Outliers: 9


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-07 08:04 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.002584
2021-10-07 08:04 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.002584
2021-10-07 08:06 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.232250
2021-10-07 08:06 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.232250
2021-10-07 08:07 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.169279
2021-10-07 08:07 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.169279
2021-10-07 08:05 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.048475
2021-10-07 08:07 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.297865
2021-10-07 08:08 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.945906
2021-10-07 08:08 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.949527
2021-10-07 08:08 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.949527
2021-10-07 08:09 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.519 s -0.114658
2021-10-07 05:24 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.828 s 0.622861
2021-10-07 05:39 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.362 s 0.590912
2021-10-07 05:48 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.379 s 0.009515
2021-10-07 05:52 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.031 s 0.012512
2021-10-07 06:03 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.839 s 0.300232
2021-10-07 06:04 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.235 s 0.228974
2021-10-07 06:08 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.051 s -0.967672
2021-10-07 05:24 Python csv-read uncompressed, file, fanniemae_2016Q4 1.174 s -0.024341
2021-10-07 05:27 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.422583
2021-10-07 05:29 Python dataframe-to-table type_strings 0.371 s -0.117712
2021-10-07 05:49 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.142 s 0.865946
2021-10-07 05:52 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.032 s 0.063929
2021-10-07 06:04 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.014 s -0.353059
2021-10-07 06:09 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.330 s -1.014072
2021-10-07 05:25 Python csv-read gzip, streaming, fanniemae_2016Q4 14.756 s 0.646309
2021-10-07 05:52 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.014 s 0.111523
2021-10-07 06:04 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.831 s -0.101877
2021-10-07 06:09 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.834735
2021-10-07 05:26 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.668 s -0.114306
2021-10-07 06:05 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.301 s -0.420675
2021-10-07 06:06 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.815 s -1.072766
2021-10-07 06:07 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.244 s -0.815688
2021-10-07 06:09 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.461 s -0.923089
2021-10-07 06:10 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.359243
2021-10-07 05:26 Python csv-read uncompressed, file, nyctaxi_2010-01 0.996 s 1.577677
2021-10-07 06:05 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.860 s -0.637660
2021-10-07 06:05 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.158 s -0.680329
2021-10-07 06:06 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.935 s -0.768585
2021-10-07 06:10 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.977 s -1.001253
2021-10-07 06:47 R dataframe-to-table chi_traffic_2020_Q1, R 303.000 s -869.417605
2021-10-07 05:27 Python csv-read gzip, streaming, nyctaxi_2010-01 10.662 s -0.290294
2021-10-07 06:06 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.279 s 1.917457
2021-10-07 06:11 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.081 s 0.686287
2021-10-07 05:29 Python dataframe-to-table chi_traffic_2020_Q1 19.514 s 0.515989
2021-10-07 06:07 Python file-read lz4, feather, table, fanniemae_2016Q4 0.615 s -2.097741
2021-10-07 06:12 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.292 s 0.190403
2021-10-07 05:29 Python dataframe-to-table type_dict 0.012 s -0.162999
2021-10-07 06:03 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.753 s -0.060127
2021-10-07 06:07 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.089 s -1.666343
2021-10-07 06:08 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.290 s -0.881002
2021-10-07 06:12 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.448 s 0.612569
2021-10-07 05:30 Python dataframe-to-table type_integers 0.011 s 0.652419
2021-10-07 06:13 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.629 s 0.344873
2021-10-07 05:30 Python dataframe-to-table type_floats 0.011 s 0.314430
2021-10-07 06:13 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.286 s 0.346665
2021-10-07 06:14 Python file-write lz4, feather, table, fanniemae_2016Q4 1.165 s -0.195379
2021-10-07 05:30 Python dataframe-to-table type_nested 2.883 s 0.495502
2021-10-07 06:14 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.743 s -0.182253
2021-10-07 05:30 Python dataframe-to-table type_simple_features 0.910 s 0.326179
2021-10-07 06:15 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.239 s -0.265710
2021-10-07 05:31 Python dataset-filter nyctaxi_2010-01 4.356 s 0.507012
2021-10-07 06:15 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.785 s 0.854741
2021-10-07 06:47 R dataframe-to-table chi_traffic_2020_Q1, R 303.000 s -869.417605
2021-10-07 05:34 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 65.933 s -1.245669
2021-10-07 06:03 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.048 s -0.306161
2021-10-07 06:16 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.786 s 0.533957
2021-10-07 06:16 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.850 s 0.821340
2021-10-07 05:25 Python csv-read gzip, file, fanniemae_2016Q4 6.035 s -0.844930
2021-10-07 06:17 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.823 s 0.551553
2021-10-07 06:18 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.352 s -0.084540
2021-10-07 06:18 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.341 s -0.084749
2021-10-07 06:19 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.316919
2021-10-07 06:19 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.806 s 0.004945
2021-10-07 06:20 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.449319
2021-10-07 06:20 Python wide-dataframe use_legacy_dataset=false 0.625 s -1.031602
2021-10-07 06:50 R dataframe-to-table type_integers, R 0.085 s -0.198085
2021-10-07 06:51 R dataframe-to-table type_nested, R 17.625 s -7396.233264
2021-10-07 06:49 R dataframe-to-table type_dict, R 0.059 s -1.010236
2021-10-07 06:49 R dataframe-to-table type_strings, R 17.392 s -7469.415636
2021-10-07 06:50 R dataframe-to-table type_floats, R 0.107 s -0.042485
2021-10-07 07:01 R dataframe-to-table type_simple_features, R 3.333 s 3.980037
2021-10-07 07:01 R dataframe-to-table type_simple_features, R 3.333 s 3.980037
2021-10-07 07:02 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.241 s 0.280822
2021-10-07 07:02 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.241 s 0.280822
2021-10-07 07:03 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.482 s 49.743878
2021-10-07 07:04 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.251 s 0.003851
2021-10-07 07:04 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.473 s 70.274024
2021-10-07 07:04 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.473 s 70.274024
2021-10-07 07:05 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.053179
2021-10-07 07:05 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.053179
2021-10-07 07:06 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.911 s 0.468358
2021-10-07 07:08 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.395 s -0.629791
2021-10-07 07:06 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.911 s 0.468358
2021-10-07 07:07 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.562 s 0.325677
2021-10-07 07:11 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.004 s -1.068269
2021-10-07 07:08 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.056 s 0.035827
2021-10-07 07:09 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.163 s 756.892108
2021-10-07 07:10 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.985560
2021-10-07 07:09 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.130 s -0.292139
2021-10-07 07:09 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.234 s 707.017082
2021-10-07 07:11 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.683 s -0.003797
2021-10-07 07:16 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.310 s 0.444826
2021-10-07 07:12 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.519 s 0.773764
2021-10-07 07:14 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.859 s 0.573118
2021-10-07 07:19 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.792 s 0.300322
2021-10-07 07:17 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.294 s 0.685288
2021-10-07 07:17 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.294 s 0.685288
2021-10-07 07:19 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.792 s 0.300322
2021-10-07 07:20 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.832 s -0.077778
2021-10-07 07:27 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.808 s 0.588717
2021-10-07 07:23 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.688091
2021-10-07 07:22 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.590 s -0.385267
2021-10-07 07:23 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.688091
2021-10-07 07:24 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.255 s -1.644346
2021-10-07 07:25 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.818 s 1.053920
2021-10-07 07:27 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.808 s 0.588717
2021-10-07 07:29 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.457 s 0.985332
2021-10-07 07:29 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.457 s 0.985332
2021-10-07 07:30 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.652 s 0.955524
2021-10-07 07:30 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.652 s 0.955524
2021-10-07 07:32 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -1.167055
2021-10-07 07:32 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -1.167055
2021-10-07 07:33 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.267 s -1.153926
2021-10-07 07:33 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.267 s -1.153926
2021-10-07 07:36 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.587 s 0.521592
2021-10-07 07:35 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.488 s 0.370667
2021-10-07 07:36 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.194 s -0.975878
2021-10-07 07:38 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.287858
2021-10-07 07:40 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.887 s 0.629639
2021-10-07 07:37 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.861 s 0.675070
2021-10-07 07:40 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.525 s 1.042002
2021-10-07 07:37 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.579 s 0.545948
2021-10-07 07:39 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.521 s -0.578351
2021-10-07 07:39 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -0.883628
2021-10-07 07:39 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.605 s 0.582940
2021-10-07 07:38 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.187 s -1.260930
2021-10-07 07:41 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s 0.145508
2021-10-07 07:41 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.109 s -1.256898
2021-10-07 07:41 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.489 s -2.498092
2021-10-07 07:42 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -1.342339
2021-10-07 07:42 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.200 s 0.554242
2021-10-07 07:42 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.501 s -0.556465
2021-10-07 07:50 JavaScript Parse Table.from, tracks 0.000 s 0.224509
2021-10-07 07:51 JavaScript Parse readBatches, tracks 0.000 s 0.786705
2021-10-07 07:50 JavaScript Parse Table.from, tracks 0.000 s 0.224509
2021-10-07 07:51 JavaScript Parse readBatches, tracks 0.000 s 0.786705
2021-10-07 07:52 JavaScript Parse serialize, tracks 0.005 s 0.395160
2021-10-07 07:53 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.138349
2021-10-07 07:52 JavaScript Parse serialize, tracks 0.005 s 0.395160
2021-10-07 07:53 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.138349
2021-10-07 07:53 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.026 s -0.247189
2021-10-07 07:55 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.705860
2021-10-07 07:53 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.026 s -0.247189
2021-10-07 07:54 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.578 s -0.242686
2021-10-07 07:55 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.705860
2021-10-07 07:54 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.578 s -0.242686
2021-10-07 07:55 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.549 s -0.212629
2021-10-07 07:56 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.503468
2021-10-07 07:59 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.456532
2021-10-07 07:57 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.701 s -0.297804
2021-10-07 07:56 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.503468
2021-10-07 07:57 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.701 s -0.297804
2021-10-07 07:57 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.709 s 0.220478
2021-10-07 07:58 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.806620
2021-10-07 07:58 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.806620
2021-10-07 08:01 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.503980
2021-10-07 07:59 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.857 s 0.554731
2021-10-07 08:00 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.503980
2021-10-07 07:59 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.896 s 0.207680
2021-10-07 08:00 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.619586
2021-10-07 07:59 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.537150
2021-10-07 08:00 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.503980
2021-10-07 08:01 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.503980
2021-10-07 08:05 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.048475
2021-10-07 08:02 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.167335
2021-10-07 08:02 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.167335
2021-10-07 08:02 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.107143
2021-10-07 08:03 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.201658
2021-10-07 08:02 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.107143
2021-10-07 08:03 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.201658
2021-10-07 08:08 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.458801