Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 08:01 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.830 s 0.937609
2021-10-11 08:01 Python csv-read uncompressed, file, fanniemae_2016Q4 1.175 s -0.186701
2021-10-11 08:02 Python csv-read gzip, streaming, fanniemae_2016Q4 14.775 s 0.784465
2021-10-11 08:03 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.625 s -0.037455
2021-10-11 08:03 Python csv-read uncompressed, file, nyctaxi_2010-01 1.004 s 0.663876
2021-10-11 08:06 Python dataframe-to-table chi_traffic_2020_Q1 19.525 s 0.149764
2021-10-11 08:06 Python dataframe-to-table type_strings 0.375 s -0.163185
2021-10-11 08:06 Python dataframe-to-table type_dict 0.012 s 0.688700
2021-10-11 08:06 Python dataframe-to-table type_integers 0.011 s -1.719301
2021-10-11 08:06 Python dataframe-to-table type_floats 0.011 s -0.336961
2021-10-11 08:06 Python dataframe-to-table type_nested 2.872 s 0.344859
2021-10-11 08:06 Python dataframe-to-table type_simple_features 0.927 s -0.594291
2021-10-11 08:07 Python dataset-filter nyctaxi_2010-01 4.328 s 0.971276
2021-10-11 08:23 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.377 s 0.125405
2021-10-11 08:28 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.095 s -0.619459
2021-10-11 08:28 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.996 s 0.427146
2021-10-11 08:37 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.837 s 0.276943
2021-10-11 08:38 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.789 s 0.457063
2021-10-11 08:39 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.616 s 2.360335
2021-10-11 08:39 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.128 s 0.539754
2021-10-11 08:39 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.519 s 2.522013
2021-10-11 08:39 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.294 s -0.653201
2021-10-11 08:39 Python file-read lz4, feather, table, fanniemae_2016Q4 0.591 s 1.832455
2021-10-11 08:40 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.997 s 2.489980
2021-10-11 08:40 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.041 s 0.182701
2021-10-11 08:40 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.206 s 0.897678
2021-10-11 08:40 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.043 s -0.416988
2021-10-11 08:41 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.150 s 1.607955
2021-10-11 08:41 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s -0.111919
2021-10-11 08:41 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.296 s 1.553768
2021-10-11 08:41 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.236858
2021-10-11 08:42 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.795 s 1.612142
2021-10-11 08:43 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.560 s -1.047283
2021-10-11 08:43 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.451 s 0.513231
2021-10-11 08:44 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.929 s -1.068818
2021-10-11 08:44 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.674 s 4.078484
2021-10-11 08:45 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.918 s -0.622409
2021-10-11 08:46 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.863 s -0.064260
2021-10-11 08:47 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.902 s -0.225217
2021-10-11 08:48 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.296 s 2.899061
2021-10-11 08:48 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.296 s 1.535124
2021-10-11 08:48 Python file-write lz4, feather, table, nyctaxi_2010-01 1.771 s 1.985656
2021-10-11 08:48 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.798 s 0.807933
2021-10-11 08:48 Python wide-dataframe use_legacy_dataset=true 0.388 s 2.629196
2021-10-11 09:01 R dataframe-to-table chi_traffic_2020_Q1, R 3.395 s 0.269820
2021-10-11 09:02 R dataframe-to-table type_strings, R 0.492 s 0.231658
2021-10-11 09:02 R dataframe-to-table type_integers, R 0.011 s 1.148607
2021-10-11 09:39 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.608883
2021-10-11 08:47 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.914 s 0.027795
2021-10-11 09:02 R dataframe-to-table type_nested, R 0.538 s 0.233225
2021-10-11 09:08 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.227 s 0.305877
2021-10-11 09:08 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.214 s 0.654192
2021-10-11 09:09 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.460 s 1.105541
2021-10-11 09:09 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.317 s -1.884769
2021-10-11 09:09 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.064 s -2.142517
2021-10-11 09:10 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.155 s 1.119218
2021-10-11 09:11 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -2.013766
2021-10-11 09:11 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.984 s 0.168182
2021-10-11 09:12 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.690 s -0.002304
2021-10-11 09:13 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.831 s 0.673389
2021-10-11 09:15 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.299 s 0.566916
2021-10-11 09:17 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.723 s 0.564444
2021-10-11 09:19 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.387 s 1.432997
2021-10-11 09:20 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.198 s 0.136707
2021-10-11 09:22 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.859 s -0.491973
2021-10-11 09:25 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 2.011928
2021-10-11 09:27 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.478 s 0.885441
2021-10-11 09:28 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 0.465868
2021-10-11 09:28 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.587 s -0.112941
2021-10-11 09:28 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.854 s 0.591305
2021-10-11 09:28 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 0.061522
2021-10-11 09:28 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s 0.063538
2021-10-11 09:29 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.528 s -0.901365
2021-10-11 09:30 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.878 s 1.315629
2021-10-11 09:30 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s -0.303902
2021-10-11 09:39 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.565084
2021-10-11 09:39 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.908616
2021-10-11 09:39 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.776013
2021-10-11 09:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.489 s -0.961664
2021-10-11 09:31 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -0.103404
2021-10-11 09:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.161 s 0.984271
2021-10-11 09:32 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.506 s -0.439199
2021-10-11 09:39 JavaScript Parse Table.from, tracks 0.000 s 5.023187
2021-10-11 09:39 JavaScript Parse serialize, tracks 0.004 s 0.679944
2021-10-11 09:39 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.805136
2021-10-11 09:39 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.569 s -0.333764
2021-10-11 09:39 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.512378
2021-10-11 09:39 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.649 s 0.630094
2021-10-11 09:39 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.234636
2021-10-11 09:39 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.845 s 0.816685
2021-10-11 09:39 JavaScript DataFrame Iterate 1,000,000, tracks 0.056 s -9.497985
2021-10-11 09:39 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.237149
2021-10-11 09:39 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.010226
2021-10-11 09:39 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.064973
2021-10-11 09:39 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.819175
2021-10-11 09:39 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.664584
2021-10-11 09:39 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.651689
2021-10-11 09:39 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.181039
2021-10-11 08:38 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.970 s 0.211965
2021-10-11 08:46 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.826 s 0.387317
2021-10-11 09:02 R dataframe-to-table type_floats, R 0.013 s 1.163643
2021-10-11 09:30 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.584 s 0.170048
2021-10-11 08:04 Python csv-read gzip, streaming, nyctaxi_2010-01 10.610 s -0.169665
2021-10-11 08:04 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 1.020529
2021-10-11 09:10 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.048 s 1.061221
2021-10-11 09:12 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.511 s 0.285758
2021-10-11 09:21 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.895 s -0.433023
2021-10-11 09:39 JavaScript Parse readBatches, tracks 0.000 s 4.776685
2021-10-11 09:39 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.822642
2021-10-11 09:39 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.692 s -0.552577
2021-10-11 09:39 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -1.806649
2021-10-11 09:10 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.393 s -0.265893
2021-10-11 09:10 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.105 s 1.230629
2021-10-11 09:11 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.218 s 1.106322
2021-10-11 09:15 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.268 s 0.539371
2021-10-11 09:23 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.531 s -0.427667
2021-10-11 09:26 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.248 s -0.576147
2021-10-11 09:39 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.207836
2021-10-11 09:39 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.172885
2021-10-11 09:39 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.356 s 2.510809
2021-10-11 08:02 Python csv-read gzip, file, fanniemae_2016Q4 6.032 s -0.371709
2021-10-11 08:27 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.053 s -0.758315
2021-10-11 09:02 R dataframe-to-table type_dict, R 0.051 s 0.105071
2021-10-11 09:24 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.727 s -0.647484
2021-10-11 08:10 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 57.862 s 1.071413
2021-10-11 08:37 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.955 s 0.388875
2021-10-11 08:38 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.273 s 0.726761
2021-10-11 08:39 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.695 s 2.325716
2021-10-11 08:48 Python wide-dataframe use_legacy_dataset=false 0.608 s 2.652897
2021-10-11 09:08 R dataframe-to-table type_simple_features, R 3.353 s 0.974087
2021-10-11 09:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.231917
2021-10-11 08:14 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.898 s 0.410572
2021-10-11 08:37 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.688 s 0.573582
2021-10-11 08:38 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.211 s 0.353274
2021-10-11 09:08 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.450 s 1.137236
2021-10-11 09:09 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s -0.178816
2021-10-11 09:19 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.569 s -0.222985
2021-10-11 09:29 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.590 s 1.616952
2021-10-11 09:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.617 s -0.327493
2021-10-11 08:23 Python dataset-read async=True, nyctaxi_multi_ipc_s3 190.169 s -0.613361
2021-10-11 08:42 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.084 s 0.590708
2021-10-11 08:45 Python file-write lz4, feather, table, fanniemae_2016Q4 1.133 s 1.793199
2021-10-11 08:45 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.496 s -1.886984
2021-10-11 09:17 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.813 s 1.838957
2021-10-11 09:28 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.345458
2021-10-11 09:39 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.509908
2021-10-11 09:39 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.708 s 0.217429
2021-10-11 09:39 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.187803
2021-10-11 09:39 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.902 s 0.070537