Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-12 10:52 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.073 s -1.367817
2021-10-12 11:02 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.769 s 0.656495
2021-10-12 11:06 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.202291
2021-10-12 11:37 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.696 s -0.054064
2021-10-12 11:40 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.246 s 0.597598
2021-10-12 11:40 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.295 s 0.494086
2021-10-12 11:42 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.815 s 1.228034
2021-10-12 11:44 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.381 s 1.943219
2021-10-12 11:51 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.238 s 0.533803
2021-10-12 11:53 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 0.885251
2021-10-12 11:57 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.490 s 1.289285
2021-10-12 12:04 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.132735
2021-10-12 12:04 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.215042
2021-10-12 12:04 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.520 s 0.051405
2021-10-12 10:26 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.836 s 0.918426
2021-10-12 11:02 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.111 s -3.596950
2021-10-12 11:03 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.605 s 1.849318
2021-10-12 11:11 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.832 s 0.268195
2021-10-12 10:29 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.259093
2021-10-12 10:48 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.340 s -0.385486
2021-10-12 11:10 Python file-write lz4, feather, table, fanniemae_2016Q4 1.140 s 1.270220
2021-10-12 11:03 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.941 s -3.453791
2021-10-12 11:03 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.368 s -3.767025
2021-10-12 11:05 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.000 s 2.002557
2021-10-12 11:07 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.805 s 1.541193
2021-10-12 10:28 Python csv-read gzip, streaming, nyctaxi_2010-01 10.920 s -2.898496
2021-10-12 11:13 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.795 s 0.887682
2021-10-12 12:04 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.815193
2021-10-12 11:04 Python file-read lz4, feather, table, fanniemae_2016Q4 0.608 s -0.906706
2021-10-12 11:09 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.839 s -0.400057
2021-10-12 11:11 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.848 s 0.118773
2021-10-12 11:13 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.336 s 0.393501
2021-10-12 10:53 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.073 s -0.626726
2021-10-12 11:04 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.007 s 1.757869
2021-10-12 11:06 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s 0.246275
2021-10-12 11:08 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.488 s -0.484159
2021-10-12 11:12 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.910 s 0.035945
2021-10-12 10:27 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.910 s -2.002315
2021-10-12 11:04 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.680 s 1.866940
2021-10-12 11:05 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.103 s 2.294138
2021-10-12 10:28 Python csv-read uncompressed, file, nyctaxi_2010-01 1.006 s 0.397017
2021-10-12 11:08 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.441 s 0.512606
2021-10-12 11:13 Python file-write lz4, feather, table, nyctaxi_2010-01 1.798 s 0.405901
2021-10-12 10:31 Python dataframe-to-table type_dict 0.011 s 1.263700
2021-10-12 10:34 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.391 s 0.276948
2021-10-12 11:03 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.304 s -1.108113
2021-10-12 11:04 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.544 s 1.671278
2021-10-12 11:09 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.352 s -0.242323
2021-10-12 10:31 Python dataframe-to-table type_strings 0.369 s 0.266461
2021-10-12 10:31 Python dataframe-to-table type_floats 0.011 s 0.670189
2021-10-12 10:31 Python dataframe-to-table type_simple_features 0.934 s -0.917521
2021-10-12 10:27 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.425350
2021-10-12 11:02 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.585 s -2.804504
2021-10-12 10:39 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.841 s -0.329225
2021-10-12 11:10 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.896 s -0.312335
2021-10-12 10:27 Python csv-read gzip, streaming, fanniemae_2016Q4 14.775 s 0.844079
2021-10-12 10:30 Python dataframe-to-table chi_traffic_2020_Q1 19.582 s -0.006102
2021-10-12 10:48 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.270 s 0.194790
2021-10-12 11:03 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.129 s 0.487684
2021-10-12 11:10 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.430 s -0.874172
2021-10-12 11:12 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.416 s -3.023279
2021-10-12 10:26 Python csv-read uncompressed, file, fanniemae_2016Q4 1.462 s -17.982333
2021-10-12 10:53 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.088 s -0.469844
2021-10-12 11:04 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.579129
2021-10-12 11:04 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.054 s -0.392116
2021-10-12 11:05 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.126 s 1.892214
2021-10-12 11:06 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.267 s 1.955982
2021-10-12 11:07 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.082 s 0.524918
2021-10-12 10:31 Python dataframe-to-table type_integers 0.011 s -0.170543
2021-10-12 10:31 Python dataframe-to-table type_nested 2.857 s 1.253430
2021-10-12 10:31 Python dataset-filter nyctaxi_2010-01 4.366 s -1.212137
2021-10-12 11:02 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 2.410 s -6.395733
2021-10-12 11:12 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.907 s -0.314439
2021-10-12 11:13 Python wide-dataframe use_legacy_dataset=false 0.611 s 1.726933
2021-10-12 11:13 Python wide-dataframe use_legacy_dataset=true 0.390 s 1.164609
2021-10-12 11:34 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.568 s -1.115953
2021-10-12 11:35 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.161 s 0.935225
2021-10-12 11:36 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.215 s 0.927722
2021-10-12 11:33 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.222 s 0.329530
2021-10-12 11:46 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.898 s -0.528521
2021-10-12 11:53 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.161 s 1.070451
2021-10-12 11:56 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.163 s 0.880709
2021-10-12 12:04 JavaScript Parse Table.from, tracks 0.000 s 0.827645
2021-10-12 11:27 R dataframe-to-table type_floats, R 0.013 s 0.972188
2021-10-12 11:35 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.050 s 0.598348
2021-10-12 11:35 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.109 s 0.882771
2021-10-12 12:04 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.075929
2021-10-12 11:38 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.839 s 0.529535
2021-10-12 11:53 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.600 s -1.331858
2021-10-12 11:55 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.109 s 0.229454
2021-10-12 11:55 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s -0.281331
2021-10-12 12:04 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.678 s -0.451155
2021-10-12 12:04 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.085268
2021-10-12 11:55 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.629 s -0.495839
2021-10-12 12:04 JavaScript Parse readBatches, tracks 0.000 s 0.321122
2021-10-12 12:04 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.623 s 1.265582
2021-10-12 12:04 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.300997
2021-10-12 11:27 R dataframe-to-table type_dict, R 0.051 s 0.156090
2021-10-12 11:36 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.218 s -1.795013
2021-10-12 11:52 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.476 s 0.853218
2021-10-12 12:04 JavaScript Parse serialize, tracks 0.005 s -0.782650
2021-10-12 12:04 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.742 s 0.036585
2021-10-12 11:33 R dataframe-to-table type_simple_features, R 3.349 s 0.815651
2021-10-12 11:45 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.182 s 0.828067
2021-10-12 12:04 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.524825
2021-10-12 11:27 R dataframe-to-table type_integers, R 0.010 s 0.989005
2021-10-12 11:27 R dataframe-to-table type_nested, R 0.537 s 0.232169
2021-10-12 11:36 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.968 s 0.374191
2021-10-12 11:47 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.857 s -0.462293
2021-10-12 12:04 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.673 s -0.481117
2021-10-12 11:34 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.320 s -1.663735
2021-10-12 11:34 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.467 s 0.924160
2021-10-12 11:44 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.546 s 0.496628
2021-10-12 11:50 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 1.666294
2021-10-12 11:54 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.590 s 1.447303
2021-10-12 12:04 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.878 s 0.616173
2021-10-12 11:37 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.543 s -0.038407
2021-10-12 12:04 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.535349
2021-10-12 12:04 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -1.566109
2021-10-12 12:04 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.805564
2021-10-12 11:26 R dataframe-to-table chi_traffic_2020_Q1, R 3.366 s 0.266290
2021-10-12 11:33 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.498 s 0.938155
2021-10-12 11:53 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.877 s -0.000389
2021-10-12 12:04 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.587000
2021-10-12 11:54 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.530 s -1.057421
2021-10-12 11:55 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.875 s 1.259024
2021-10-12 11:34 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.054 s -1.413300
2021-10-12 11:53 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.083827
2021-10-12 12:04 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s 0.040232
2021-10-12 12:04 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.483237
2021-10-12 11:35 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.406 s -0.878047
2021-10-12 11:42 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.693 s 0.685238
2021-10-12 11:56 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.489 s -0.857398
2021-10-12 11:56 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -0.536346
2021-10-12 12:04 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.103743
2021-10-12 12:04 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.799036
2021-10-12 11:26 R dataframe-to-table type_strings, R 0.489 s 0.231226
2021-10-12 12:04 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.560825
2021-10-12 11:33 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.215 s 0.553186
2021-10-12 12:04 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.514424
2021-10-12 12:04 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.318239
2021-10-12 12:04 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.195337
2021-10-12 12:04 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.851 s 0.768753
2021-10-12 12:04 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.140926
2021-10-12 11:48 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.527 s -0.397921
2021-10-12 11:49 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.729 s -0.718564
2021-10-12 11:53 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 0.194955
2021-10-12 11:54 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.619 s -0.575336
2021-10-12 12:04 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.092533