Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-12 23:26 Python csv-read gzip, streaming, fanniemae_2016Q4 14.571 s 3.008734
2021-10-12 23:27 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.462 s 1.119011
2021-10-12 23:27 Python csv-read uncompressed, file, nyctaxi_2010-01 1.020 s -0.906404
2021-10-12 23:52 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.083 s -1.538125
2021-10-12 23:52 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.052 s -0.297147
2021-10-13 00:05 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.115455
2021-10-13 00:05 Python file-read lz4, feather, table, fanniemae_2016Q4 0.588 s 1.441853
2021-10-13 00:06 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.036 s 0.243434
2021-10-13 00:06 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.054 s -0.470791
2021-10-13 00:08 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.795 s 1.498860
2021-10-13 00:09 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.523 s -0.797615
2021-10-13 00:12 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.871 s -0.268456
2021-10-13 00:14 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.345 s 0.280196
2021-10-13 00:14 Python file-write lz4, feather, table, nyctaxi_2010-01 1.815 s -0.576257
2021-10-13 00:29 R dataframe-to-table chi_traffic_2020_Q1, R 3.382 s 0.264512
2021-10-13 00:29 R dataframe-to-table type_floats, R 0.013 s 0.876698
2021-10-13 00:29 R dataframe-to-table type_nested, R 0.532 s 0.233294
2021-10-13 00:30 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.218 s 0.321786
2021-10-13 00:31 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.087 s -1.727263
2021-10-13 00:31 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.387 s 0.189719
2021-10-13 00:32 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.045 s 1.246515
2021-10-13 00:32 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.103 s 1.187163
2021-10-13 00:32 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -1.417096
2021-10-13 00:33 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.691 s 0.012972
2021-10-13 00:34 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.540 s -0.006021
2021-10-13 00:36 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.256 s 0.434102
2021-10-13 00:37 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.293 s 0.423652
2021-10-13 00:38 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.698 s 0.573560
2021-10-13 00:39 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.815 s 1.063704
2021-10-13 00:40 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.549 s 0.356067
2021-10-13 00:42 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.896 s -0.571970
2021-10-13 00:44 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.874 s -0.899184
2021-10-13 00:44 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.533 s -0.605522
2021-10-13 00:46 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.723 s -0.652001
2021-10-13 00:47 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.237 s 0.630274
2021-10-13 00:49 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.898 s -0.614403
2021-10-13 00:49 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 0.792715
2021-10-13 00:51 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.767 s -8.986013
2021-10-13 00:51 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.516 s 1.272660
2021-10-13 00:52 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.111 s -0.026392
2021-10-13 00:52 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.492 s -1.412068
2021-10-13 00:53 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.501 s 0.126819
2021-10-13 01:00 JavaScript Parse Table.from, tracks 0.000 s 0.080760
2021-10-13 01:00 JavaScript Parse serialize, tracks 0.005 s 0.094822
2021-10-13 01:00 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.279 s 3.192410
2021-10-13 01:00 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.073264
2021-10-13 01:00 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.676 s 0.403497
2021-10-13 01:00 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.713710
2021-10-13 01:00 JavaScript Parse readBatches, tracks 0.000 s -0.406442
2021-10-13 01:00 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.926 s -0.338127
2021-10-13 01:00 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.819751
2021-10-13 01:00 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500672
2021-10-13 01:00 JavaScript DataFrame Iterate 1,000,000, tracks 0.108 s -100.280277
2021-10-13 01:00 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.130091
2021-10-13 01:00 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.486937
2021-10-13 01:00 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.417670
2021-10-13 01:00 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.602880
2021-10-13 01:00 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.802065
2021-10-13 01:00 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.494 s 0.502534
2021-10-12 23:30 Python dataframe-to-table type_strings 0.372 s 0.044638
2021-10-13 00:05 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.529 s 1.649446
2021-10-13 00:07 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.128 s 1.695079
2021-10-13 00:07 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.273 s 1.447423
2021-10-13 00:13 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.945 s -0.997600
2021-10-13 00:32 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.215 s 0.835481
2021-10-13 00:33 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.983 s 0.200821
2021-10-12 23:47 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.632 s -0.446485
2021-10-13 00:03 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.963 s 0.329093
2021-10-13 00:07 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.169 s 0.172636
2021-10-12 23:28 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.332435
2021-10-12 23:30 Python dataframe-to-table type_nested 2.895 s -0.923357
2021-10-13 00:03 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.629 s 0.983099
2021-10-13 00:05 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.605 s 1.647379
2021-10-13 00:06 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.108 s 1.928520
2021-10-13 00:29 R dataframe-to-table type_strings, R 0.490 s 0.230902
2021-10-13 00:49 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.583 s -1.251685
2021-10-13 00:50 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.192 s -1.310182
2021-10-12 23:52 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.057 s -0.045198
2021-10-12 23:27 Python csv-read gzip, streaming, nyctaxi_2010-01 10.444 s 1.200426
2021-10-12 23:30 Python dataframe-to-table type_dict 0.011 s 0.979021
2021-10-13 00:05 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.204 s -3.689059
2021-10-13 00:11 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.470 s -1.134298
2021-10-13 00:29 R dataframe-to-table type_dict, R 0.053 s -0.376410
2021-10-13 00:30 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.320 s -1.536163
2021-10-13 00:50 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 0.904807
2021-10-13 01:00 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.680 s 0.118755
2021-10-13 01:00 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.168350
2021-10-13 01:00 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.806617
2021-10-13 00:08 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.086 s 0.408080
2021-10-13 00:11 Python file-write lz4, feather, table, fanniemae_2016Q4 1.148 s 0.650564
2021-10-13 00:13 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.928 s -0.312131
2021-10-13 00:14 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.338 s 0.298134
2021-10-13 00:14 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.813 s 0.234068
2021-10-13 00:40 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.382 s 1.690866
2021-10-13 00:49 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.175 s -0.572476
2021-10-13 01:00 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.831 s 1.340625
2021-10-12 23:30 Python dataframe-to-table type_floats 0.011 s 0.633732
2021-10-13 00:04 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.265 s 0.962786
2021-10-13 00:10 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.879 s -0.739941
2021-10-13 00:11 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.971 s -0.913470
2021-10-13 00:30 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.277 s -2.346182
2021-10-13 00:31 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.556 s 1.075577
2021-10-13 00:04 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.895 s -1.842837
2021-10-13 00:07 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.187421
2021-10-13 00:09 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.434 s 0.501181
2021-10-13 00:42 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.181 s 0.896448
2021-10-13 00:50 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.532 s -1.310888
2021-10-13 00:51 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.930 s -1.540655
2021-10-13 01:00 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.787069
2021-10-13 01:00 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.735291
2021-10-12 23:30 Python dataframe-to-table type_integers 0.011 s -0.171881
2021-10-13 00:52 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.365 s -1.300200
2021-10-13 01:00 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -2.717847
2021-10-13 01:00 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.197960
2021-10-13 01:00 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.793890
2021-10-12 23:29 Python dataframe-to-table chi_traffic_2020_Q1 19.600 s -0.065061
2021-10-12 23:47 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.332 s 0.157760
2021-10-13 00:10 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.980 s 1.747524
2021-10-13 00:29 R dataframe-to-table type_integers, R 0.010 s 0.892147
2021-10-12 23:25 Python csv-read uncompressed, file, fanniemae_2016Q4 1.163 s 0.320265
2021-10-12 23:26 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.703974
2021-10-13 00:04 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.071 s -2.052176
2021-10-13 00:14 Python wide-dataframe use_legacy_dataset=true 0.388 s 1.887160
2021-10-12 23:25 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.935 s -0.250403
2021-10-12 23:30 Python dataset-filter nyctaxi_2010-01 4.375 s -1.639731
2021-10-13 00:04 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.311 s -1.863829
2021-10-13 00:06 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.007 s 1.551489
2021-10-13 00:12 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.852 s -0.116511
2021-10-13 00:30 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.450 s 0.837639
2021-10-13 00:32 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.155 s 0.842354
2021-10-13 00:35 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.824 s 0.584454
2021-10-13 00:49 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.621 s -3.240772
2021-10-13 00:52 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.210 s -1.332129
2021-10-13 01:00 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.088990
2021-10-12 23:33 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.055 s -0.498749
2021-10-13 00:03 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.781 s 0.586289
2021-10-13 00:05 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.684 s 1.128415
2021-10-13 00:15 Python wide-dataframe use_legacy_dataset=false 0.606 s 2.487399
2021-10-13 00:30 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.453 s 0.859031
2021-10-13 01:00 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.479885
2021-10-13 01:00 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.513010
2021-10-12 23:38 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.038 s -0.477660
2021-10-13 00:46 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 1.456449
2021-10-13 00:48 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.472 s 1.176507
2021-10-13 00:53 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.199 s -1.438432
2021-10-13 01:00 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.253 s 3.064570
2021-10-13 01:00 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.555415