Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 23:07 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.874 s 0.492151
2021-10-10 23:07 Python csv-read gzip, streaming, fanniemae_2016Q4 14.989 s -1.375991
2021-10-10 23:08 Python csv-read gzip, file, fanniemae_2016Q4 6.030 s 0.047345
2021-10-10 23:08 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.625 s -0.050664
2021-10-10 23:08 Python csv-read uncompressed, file, nyctaxi_2010-01 1.008 s 0.290983
2021-10-10 23:11 Python dataframe-to-table chi_traffic_2020_Q1 19.493 s 0.231220
2021-10-10 23:11 Python dataframe-to-table type_strings 0.366 s 0.551419
2021-10-10 23:11 Python dataframe-to-table type_integers 0.011 s -1.905600
2021-10-10 23:11 Python dataframe-to-table type_floats 0.011 s -0.263675
2021-10-10 23:12 Python dataframe-to-table type_simple_features 0.928 s -0.626511
2021-10-10 23:12 Python dataset-filter nyctaxi_2010-01 4.312 s 1.934267
2021-10-10 23:29 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.204 s 0.239635
2021-10-10 23:34 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.083 s -2.161610
2021-10-10 23:34 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.066 s -0.275516
2021-10-10 23:34 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.514 s -9.285802
2021-10-10 23:44 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.786 s 0.598271
2021-10-10 23:44 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.781 s -0.327874
2021-10-10 23:45 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.207 s 0.524119
2021-10-10 23:45 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.791 s 0.458888
2021-10-10 23:45 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.265 s 1.057232
2021-10-10 23:45 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.634 s 2.718299
2021-10-10 23:45 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.123 s 0.828521
2021-10-10 23:46 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.570 s 2.488631
2021-10-10 23:46 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.085512
2021-10-10 23:46 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.995 s 3.155212
2021-10-10 23:46 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.037 s 0.342238
2021-10-10 23:47 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.169 s 1.105169
2021-10-10 23:47 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.061 s -1.484011
2021-10-10 23:47 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.132 s 1.548820
2021-10-10 23:48 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.171 s 0.815310
2021-10-10 23:50 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.453 s 0.495459
2021-10-10 23:51 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.958 s -1.392347
2021-10-10 23:51 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.525 s -1.436914
2021-10-10 23:52 Python file-write lz4, feather, table, fanniemae_2016Q4 1.150 s 0.744214
2021-10-10 23:53 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.925 s -0.931765
2021-10-10 23:54 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.019 s -1.540894
2021-10-10 23:54 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.910 s -0.393152
2021-10-10 23:54 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.359 s -0.339585
2021-10-10 23:55 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.360 s -0.146857
2021-10-10 23:55 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.840 s -0.560518
2021-10-10 23:55 Python wide-dataframe use_legacy_dataset=false 0.611 s 2.221159
2021-10-11 00:10 R dataframe-to-table chi_traffic_2020_Q1, R 3.405 s 0.272366
2021-10-11 00:10 R dataframe-to-table type_strings, R 0.486 s 0.234364
2021-10-11 00:10 R dataframe-to-table type_dict, R 0.054 s -0.389761
2021-10-11 00:10 R dataframe-to-table type_integers, R 0.010 s 1.302397
2021-10-11 00:10 R dataframe-to-table type_floats, R 0.013 s 1.295980
2021-10-11 00:10 R dataframe-to-table type_nested, R 0.531 s 0.236276
2021-10-11 00:16 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.222 s 0.334968
2021-10-11 00:17 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.215 s 0.636016
2021-10-11 00:18 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.553 s 1.836711
2021-10-11 00:18 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.050 s 0.811244
2021-10-11 00:19 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.166 s 1.234627
2021-10-11 00:19 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.215 s -2.185476
2021-10-11 00:20 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.687 s 0.082821
2021-10-11 00:21 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.522 s 0.181431
2021-10-11 00:23 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.321 s 0.173841
2021-10-11 00:25 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.737 s 0.478485
2021-10-11 00:26 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.813 s 2.073142
2021-10-11 00:27 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.578 s -0.460822
2021-10-11 00:27 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.389 s 1.378680
2021-10-11 00:29 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.889 s -0.371311
2021-10-11 00:30 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.885 s -1.094670
2021-10-10 23:55 Python file-write lz4, feather, table, nyctaxi_2010-01 1.791 s 1.014030
2021-10-11 00:31 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.525 s -0.357637
2021-10-11 00:33 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.740 s -0.978651
2021-10-11 00:36 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.182 s -0.700956
2021-10-11 00:36 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.576 s -0.193535
2021-10-11 00:37 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.275015
2021-10-11 00:37 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.620 s -1.019988
2021-10-11 00:38 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.877 s 1.393232
2021-10-11 00:38 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.580 s 0.215418
2021-10-11 00:38 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.279122
2021-10-11 00:39 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s -0.464579
2021-10-11 00:39 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.488 s -1.031212
2021-10-11 00:39 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -0.642622
2021-10-11 00:40 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.168 s 0.503786
2021-10-11 00:40 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.495 s 0.505912
2021-10-11 00:48 JavaScript Parse readBatches, tracks 0.000 s -1.329806
2021-10-11 00:48 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.182131
2021-10-11 00:48 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.166634
2021-10-11 00:48 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.597 s -0.335271
2021-10-11 00:48 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.738 s -0.666401
2021-10-11 00:48 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.190690
2021-10-11 00:48 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.164951
2021-10-11 00:48 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.817 s -2.663045
2021-10-11 00:48 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.685 s -6.321128
2021-10-11 00:48 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.002 s -3.084241
2021-10-11 00:48 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.964 s -2.270924
2021-10-11 00:48 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.569442
2021-10-11 00:48 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.613656
2021-10-11 00:48 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.525547
2021-10-11 00:48 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.045317
2021-10-11 00:48 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.133767
2021-10-11 00:48 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.192256
2021-10-11 00:48 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.570215
2021-10-11 00:48 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.617851
2021-10-11 00:48 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.931829
2021-10-11 00:48 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.743016
2021-10-11 00:48 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.722265
2021-10-11 00:48 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.692 s -2.704564
2021-10-10 23:09 Python csv-read gzip, streaming, nyctaxi_2010-01 10.611 s -0.131939
2021-10-10 23:19 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.920 s 0.377429
2021-10-10 23:48 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s 0.182000
2021-10-10 23:52 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.526 s -2.641526
2021-10-11 00:18 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.016 s -1.543683
2021-10-11 00:19 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.101 s 1.656408
2021-10-11 00:36 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.566730
2021-10-11 00:37 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.532 s -1.499109
2021-10-10 23:46 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.685 s 3.106418
2021-10-11 00:17 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.458 s 1.219993
2021-10-11 00:24 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.313 s 0.469679
2021-10-11 00:34 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.257 s -1.238956
2021-10-11 00:35 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.469 s 2.133130
2021-10-11 00:48 JavaScript Parse Table.from, tracks 0.000 s -0.602981
2021-10-11 00:48 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.975 s -1.316611
2021-10-11 00:48 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.188598
2021-10-10 23:15 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.299 s -0.267302
2021-10-10 23:48 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.287 s 1.423730
2021-10-10 23:48 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.786 s 1.416540
2021-10-10 23:49 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.102 s 0.456740
2021-10-11 00:17 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.450 s 1.248196
2021-10-11 00:33 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.272 s 2.699970
2021-10-11 00:48 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.002 s -3.976555
2021-10-11 00:48 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.294520
2021-10-10 23:07 Python csv-read uncompressed, file, fanniemae_2016Q4 1.154 s 1.200844
2021-10-10 23:10 Python csv-read gzip, file, nyctaxi_2010-01 9.049 s -1.583398
2021-10-10 23:11 Python dataframe-to-table type_dict 0.012 s 0.406466
2021-10-10 23:44 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.944 s 0.449628
2021-10-11 00:19 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.210 s 1.221846
2021-10-11 00:37 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 1.050872
2021-10-10 23:12 Python dataframe-to-table type_nested 2.865 s 0.701349
2021-10-10 23:50 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.541 s -1.017845
2021-10-10 23:53 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.919 s -0.965029
2021-10-11 00:16 R dataframe-to-table type_simple_features, R 3.408 s 1.070315
2021-10-11 00:17 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.320 s -2.566795
2021-10-11 00:20 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.997 s 0.005330
2021-10-11 00:48 JavaScript Parse serialize, tracks 0.004 s 0.543280
2021-10-11 00:48 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.486534
2021-10-11 00:48 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.770098
2021-10-10 23:29 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.208 s -0.810821
2021-10-10 23:44 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.971 s 0.265124
2021-10-10 23:46 Python file-read lz4, feather, table, fanniemae_2016Q4 0.604 s -0.186430
2021-10-10 23:52 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.210 s -3.347681
2021-10-10 23:55 Python wide-dataframe use_legacy_dataset=true 0.387 s 3.509144
2021-10-11 00:18 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.372 s 0.998943
2021-10-11 00:21 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.846 s 0.574723
2021-10-11 00:28 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.211 s -0.489667
2021-10-11 00:36 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.593 s -0.733994
2021-10-11 00:36 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.857 s 0.566708