Outliers: 4


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-30 10:43 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.013 s -0.710073
2021-09-30 10:45 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.901 s -5.860290
2021-09-30 10:45 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s -0.297584
2021-09-30 10:42 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.839 s 0.386456
2021-09-30 10:45 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.216 s -7.183559
2021-09-30 10:45 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.040 s 0.046482
2021-09-30 10:01 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.825 s -0.627633
2021-09-30 10:03 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.822 s -1.082347
2021-09-30 10:44 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.167 s -1.897145
2021-09-30 10:04 Python csv-read gzip, file, nyctaxi_2010-01 9.049 s -0.957125
2021-09-30 10:44 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.848 s -4.810802
2021-09-30 10:02 Python csv-read gzip, streaming, fanniemae_2016Q4 14.748 s -0.621812
2021-09-30 10:31 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.034 s 0.016676
2021-09-30 10:02 Python csv-read uncompressed, file, fanniemae_2016Q4 1.182 s -0.189812
2021-09-30 10:06 Python dataframe-to-table chi_traffic_2020_Q1 19.458 s 1.875786
2021-09-30 10:03 Python csv-read gzip, file, fanniemae_2016Q4 6.036 s -1.566608
2021-09-30 10:06 Python dataframe-to-table type_strings 0.367 s 0.493779
2021-09-30 10:07 Python dataframe-to-table type_nested 2.846 s 5.911562
2021-09-30 10:06 Python dataframe-to-table type_floats 0.012 s -0.863744
2021-09-30 10:31 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.036 s -0.034552
2021-09-30 10:47 Python file-read lz4, feather, table, nyctaxi_2010-01 0.672 s -0.560117
2021-09-30 10:06 Python dataframe-to-table type_integers 0.011 s -1.871270
2021-09-30 10:07 Python dataset-filter nyctaxi_2010-01 4.404 s -1.518014
2021-09-30 10:03 Python csv-read uncompressed, file, nyctaxi_2010-01 1.018 s 0.020953
2021-09-30 10:46 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.063 s -1.572683
2021-09-30 10:04 Python csv-read gzip, streaming, nyctaxi_2010-01 10.818 s -1.109249
2021-09-30 10:26 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.866 s -3.140626
2021-09-30 10:44 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.783 s -3.901205
2021-09-30 10:10 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.996 s -0.627789
2021-09-30 10:15 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 92.179 s 3.187538
2021-09-30 10:44 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.853 s -1.154630
2021-09-30 10:48 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.076 s 1.478472
2021-09-30 10:44 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.315 s -2.287563
2021-09-30 10:47 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.124 s 0.254381
2021-09-30 10:06 Python dataframe-to-table type_dict 0.012 s 0.807895
2021-09-30 10:07 Python dataframe-to-table type_simple_features 0.943 s -5.188895
2021-09-30 10:26 Python dataset-read async=True, nyctaxi_multi_ipc_s3 211.932 s -2.913586
2021-09-30 10:46 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.170 s 1.296121
2021-09-30 10:43 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.692 s 0.468963
2021-09-30 10:44 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.335177
2021-09-30 10:46 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.976 s 0.322096
2021-09-30 10:42 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.957 s 0.383771
2021-09-30 10:31 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.030 s -0.118270
2021-09-30 10:47 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.686 s 0.011474
2021-09-30 10:48 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.266 s 0.699940
2021-09-30 10:50 Python file-write lz4, feather, table, fanniemae_2016Q4 1.158 s 0.258192
2021-09-30 10:49 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.439 s 1.396289
2021-09-30 10:49 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.638 s 0.644776
2021-09-30 10:50 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.828 s -0.853146
2021-09-30 10:51 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.792 s 2.013881
2021-09-30 10:50 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.386 s -0.394764
2021-09-30 10:51 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.210 s 0.098212
2021-09-30 10:52 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.828 s 0.608634
2021-09-30 10:53 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.379 s -0.087751
2021-09-30 11:07 R dataframe-to-table type_floats, R 0.110 s -0.551802
2021-09-30 11:36 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.254 s -0.768618
2021-09-30 10:53 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.365 s -0.953882
2021-09-30 11:07 R dataframe-to-table type_integers, R 0.084 s 1.041356
2021-09-30 11:46 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.820 s 2.868516
2021-09-30 12:04 JavaScript Parse Table.from, tracks 0.000 s 0.502368
2021-09-30 10:43 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.266 s -0.970910
2021-09-30 10:46 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.947 s 0.488625
2021-09-30 10:54 Python wide-dataframe use_legacy_dataset=false 0.626 s -1.745437
2021-09-30 11:37 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.679 s -0.362202
2021-09-30 12:04 JavaScript Parse serialize, tracks 0.005 s -0.740007
2021-09-30 10:54 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.219054
2021-09-30 11:32 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.929 s -0.093640
2021-09-30 11:37 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.527 s -0.515726
2021-09-30 11:38 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.847 s 1.414713
2021-09-30 11:52 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.492 s -0.379067
2021-09-30 12:04 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.895 s -0.273284
2021-09-30 10:52 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.859 s 2.071632
2021-09-30 11:34 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.063 s -1.120925
2021-09-30 11:53 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.194 s 0.050316
2021-09-30 11:55 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.982 s 2.667306
2021-09-30 11:56 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.148 s 2.725883
2021-09-30 12:04 JavaScript Parse readBatches, tracks 0.000 s 0.657981
2021-09-30 12:04 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.263477
2021-09-30 11:51 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.273 s -0.392032
2021-09-30 10:53 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.835 s 0.820927
2021-09-30 11:40 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.297 s 1.401947
2021-09-30 11:53 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.581 s 2.348974
2021-09-30 11:54 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.604 s 2.630551
2021-09-30 12:04 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.079083
2021-09-30 12:04 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.160526
2021-09-30 11:34 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.389 s -0.644112
2021-09-30 11:35 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.189 s -1.170462
2021-09-30 11:42 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.262977
2021-09-30 11:53 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.579 s 3.045640
2021-09-30 11:54 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.606 s 0.104225
2021-09-30 11:55 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.582 s 0.363235
2021-09-30 12:04 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.080629
2021-09-30 12:04 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497050
2021-09-30 11:40 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.291 s 1.283829
2021-09-30 11:44 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.582 s 0.836169
2021-09-30 11:54 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.530 s -1.873624
2021-09-30 10:54 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.884 s -0.310375
2021-09-30 11:32 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.927 s -0.068422
2021-09-30 11:45 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.219 s 0.843461
2021-09-30 11:50 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.438800
2021-09-30 12:04 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.622605
2021-09-30 12:04 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.605478
2021-09-30 11:53 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.877 s 2.483064
2021-09-30 12:04 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.120259
2021-09-30 12:04 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.068481
2021-09-30 12:04 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.541680
2021-09-30 12:04 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.623549
2021-09-30 12:04 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.695060
2021-09-30 12:04 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.986702
2021-09-30 11:07 R dataframe-to-table type_dict, R 0.027 s 2.809342
2021-09-30 11:44 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.156039
2021-09-30 11:47 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.814 s 2.229519
2021-09-30 12:04 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.719 s -0.410615
2021-09-30 12:04 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.121535
2021-09-30 11:33 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.566 s -0.733928
2021-09-30 11:53 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.230172
2021-09-30 11:53 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.179 s -0.299261
2021-09-30 11:55 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.806763
2021-09-30 11:35 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.124 s 0.409972
2021-09-30 11:36 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.195957
2021-09-30 11:56 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.199 s -0.862000
2021-09-30 10:53 Python file-write lz4, feather, table, nyctaxi_2010-01 1.808 s 0.173991
2021-09-30 11:31 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.253 s 0.004495
2021-09-30 11:32 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 1.049742
2021-09-30 11:42 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.765 s 1.175548
2021-09-30 12:04 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.515263
2021-09-30 11:07 R dataframe-to-table chi_traffic_2020_Q1, R 5.385 s 0.422243
2021-09-30 11:33 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.956 s -1.920927
2021-09-30 11:31 R dataframe-to-table type_simple_features, R 275.003 s -0.433707
2021-09-30 11:36 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.978 s -0.539249
2021-09-30 12:04 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.089192
2021-09-30 12:04 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.729 s -0.867547
2021-09-30 12:04 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.613600
2021-09-30 11:07 R dataframe-to-table type_strings, R 0.492 s -0.573963
2021-09-30 11:08 R dataframe-to-table type_nested, R 0.534 s 0.837300
2021-09-30 11:32 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.264 s -0.123242
2021-09-30 11:48 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.469 s 2.382492
2021-09-30 11:49 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.666 s 2.477107
2021-09-30 11:56 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.475 s -0.664552
2021-09-30 11:57 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.498 s 0.141685
2021-09-30 12:04 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.173141
2021-09-30 11:55 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.349 s 2.569367
2021-09-30 12:04 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.426643
2021-09-30 12:04 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.982459
2021-09-30 12:04 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.661 s -0.330815
2021-09-30 12:04 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.746 s 0.018084
2021-09-30 12:04 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.862 s 0.724358
2021-09-30 12:04 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.044676
2021-09-30 12:04 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.511 s -0.004827