Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-01 02:56 Python csv-read gzip, streaming, fanniemae_2016Q4 14.672 s -0.379650
2021-10-01 03:03 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.726 s -0.519358
2021-10-01 03:32 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.110 s 1.435605
2021-10-01 03:38 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.854 s -1.154063
2021-10-01 03:41 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.967 s -0.178717
2021-10-01 03:57 R dataframe-to-table type_integers, R 0.085 s 0.022543
2021-10-01 04:30 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.623 s -1.195037
2021-10-01 04:37 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.906 s -0.486896
2021-10-01 04:39 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.757 s -0.295279
2021-10-01 04:42 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 1.663056
2021-10-01 04:45 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.353 s 1.353168
2021-10-01 04:53 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -3.119425
2021-10-01 04:54 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.101423
2021-10-01 04:54 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 1.398804
2021-10-01 02:57 Python csv-read uncompressed, file, nyctaxi_2010-01 1.022 s -0.060593
2021-10-01 03:00 Python dataframe-to-table type_nested 2.882 s 3.343443
2021-10-01 03:17 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.294 s -0.036658
2021-10-01 03:21 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.045 s -0.144620
2021-10-01 03:21 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.017 s 0.068063
2021-10-01 03:32 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.841 s -2.975749
2021-10-01 03:33 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.940 s -4.806438
2021-10-01 03:34 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.034 s 0.247773
2021-10-01 03:34 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.313 s -1.203833
2021-10-01 03:36 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.376 s -1.053180
2021-10-01 03:39 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.242 s -0.290797
2021-10-01 03:39 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.930 s -0.983302
2021-10-01 03:00 Python dataframe-to-table type_dict 0.012 s 0.412252
2021-10-01 03:00 Python dataset-filter nyctaxi_2010-01 4.343 s 0.605248
2021-10-01 03:31 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.725 s 0.316088
2021-10-01 03:31 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.233 s 0.141022
2021-10-01 03:38 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.418 s -0.603645
2021-10-01 03:40 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.969 s -0.422151
2021-10-01 03:42 Python wide-dataframe use_legacy_dataset=false 0.623 s -1.099621
2021-10-01 03:56 R dataframe-to-table type_strings, R 0.493 s -0.847430
2021-10-01 04:21 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.879 s 0.402079
2021-10-01 04:22 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.855 s 0.672608
2021-10-01 04:27 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.509 s 0.444937
2021-10-01 04:33 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.399 s 0.372621
2021-10-01 04:42 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.185 s 0.573247
2021-10-01 04:42 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.563 s 2.147337
2021-10-01 04:53 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.429749
2021-10-01 04:53 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.654 s -8.333982
2021-10-01 04:53 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.109 s -5.035929
2021-10-01 04:53 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.504453
2021-10-01 02:55 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.769 s -0.409142
2021-10-01 03:00 Python dataframe-to-table type_strings 0.372 s -0.029200
2021-10-01 03:08 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 80.774 s 2.088868
2021-10-01 03:30 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.963 s 0.344027
2021-10-01 03:32 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.822 s -0.080395
2021-10-01 03:32 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.857178
2021-10-01 03:33 Python file-read lz4, feather, table, fanniemae_2016Q4 0.597 s 0.881728
2021-10-01 03:36 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.604 s -0.946297
2021-10-01 03:40 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.991 s -1.069464
2021-10-01 03:42 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.877 s -0.299789
2021-10-01 03:42 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.274031
2021-10-01 03:56 R dataframe-to-table chi_traffic_2020_Q1, R 5.450 s -0.823590
2021-10-01 03:57 R dataframe-to-table type_dict, R 0.063 s -1.431183
2021-10-01 03:57 R dataframe-to-table type_floats, R 0.114 s -1.829440
2021-10-01 03:57 R dataframe-to-table type_nested, R 0.538 s -0.433319
2021-10-01 04:24 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.160 s 0.820247
2021-10-01 04:25 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.273 s -1.681434
2021-10-01 04:31 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.069 s -0.840290
2021-10-01 02:56 Python csv-read gzip, file, fanniemae_2016Q4 6.037 s -1.457192
2021-10-01 03:30 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.903 s 0.092016
2021-10-01 03:37 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.733 s -1.050350
2021-10-01 04:22 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.239377
2021-10-01 04:22 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.557 s 1.330166
2021-10-01 04:24 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.132 s -0.163929
2021-10-01 04:25 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.217525
2021-10-01 04:39 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.852565
2021-10-01 04:43 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.512 s 0.542183
2021-10-01 04:44 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.609 s -0.274583
2021-10-01 04:44 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.589 s 0.226462
2021-10-01 04:46 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.497 s 0.126405
2021-10-01 04:53 JavaScript Parse Table.from, tracks 0.000 s 0.167046
2021-10-01 04:53 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.432 s 3.039297
2021-10-01 04:53 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.172700
2021-10-01 04:53 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.605386
2021-10-01 04:38 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.578 s -0.770267
2021-10-01 04:43 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.279575
2021-10-01 04:53 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.937 s -5.280034
2021-10-01 04:53 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.997 s -2.799851
2021-10-01 04:53 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.485725
2021-10-01 04:54 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.511415
2021-10-01 04:54 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.721 s -3.492253
2021-10-01 03:32 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.265 s 1.057090
2021-10-01 04:41 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.487 s 0.457258
2021-10-01 04:42 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.008 s 1.519432
2021-10-01 04:44 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.907 s 1.629720
2021-10-01 04:45 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.478 s -1.860567
2021-10-01 04:46 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.175 s 1.557705
2021-10-01 04:53 JavaScript Parse readBatches, tracks 0.000 s 0.309328
2021-10-01 04:54 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.606348
2021-10-01 04:54 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s -0.042794
2021-10-01 04:54 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.946165
2021-10-01 03:21 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.057 s -0.276640
2021-10-01 03:31 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.984 s 0.240974
2021-10-01 03:34 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.253 s -0.901522
2021-10-01 03:41 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.355 s 0.048400
2021-10-01 04:21 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.206 s 0.488348
2021-10-01 04:26 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.306952
2021-10-01 04:32 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.832 s -0.220390
2021-10-01 04:33 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.579 s 0.749191
2021-10-01 04:45 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.586981
2021-10-01 04:54 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.633602
2021-10-01 04:54 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.960998
2021-10-01 03:00 Python dataframe-to-table type_integers 0.011 s 1.623711
2021-10-01 04:53 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.366577
2021-10-01 04:53 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.605386
2021-10-01 04:53 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.399853
2021-10-01 03:00 Python dataframe-to-table type_simple_features 0.912 s -0.484454
2021-10-01 03:33 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.239 s -4.837123
2021-10-01 03:42 Python file-write lz4, feather, table, nyctaxi_2010-01 1.845 s -1.661936
2021-10-01 04:20 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.205 s 0.475173
2021-10-01 04:35 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.945 s -0.849157
2021-10-01 04:45 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.197 s 0.433818
2021-10-01 04:53 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -3.239061
2021-10-01 04:53 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.218745
2021-10-01 02:59 Python dataframe-to-table chi_traffic_2020_Q1 20.034 s -1.414292
2021-10-01 03:32 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.751 s -1.933004
2021-10-01 04:20 R dataframe-to-table type_simple_features, R 275.296 s -0.899935
2021-10-01 04:22 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.923 s -0.281944
2021-10-01 04:26 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.965 s 0.253580
2021-10-01 04:27 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.168 s -1.172976
2021-10-01 04:43 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.591 s 1.658104
2021-10-01 04:54 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.972667
2021-10-01 03:00 Python dataframe-to-table type_floats 0.012 s -0.358482
2021-10-01 04:29 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.617 s -0.770890
2021-10-01 02:56 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.677 s -0.489683
2021-10-01 02:57 Python csv-read gzip, streaming, nyctaxi_2010-01 10.665 s -0.486669
2021-10-01 03:17 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.647 s 0.108986
2021-10-01 03:33 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.053 s -0.370528
2021-10-01 03:35 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.460 s -1.407257
2021-10-01 03:39 Python file-write lz4, feather, table, fanniemae_2016Q4 1.150 s 0.997331
2021-10-01 04:23 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.534265
2021-10-01 04:35 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.213 s 0.846342
2021-10-01 04:43 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.603201
2021-10-01 03:35 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.973 s -1.432185
2021-10-01 03:38 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.982 s -0.997599
2021-10-01 04:53 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.317036
2021-10-01 02:55 Python csv-read uncompressed, file, fanniemae_2016Q4 1.168 s 0.061536
2021-10-01 02:58 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 0.930220
2021-10-01 03:34 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.923002
2021-10-01 03:35 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.331363
2021-10-01 03:41 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.354 s -0.249002
2021-10-01 04:23 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.373 s 0.293745
2021-10-01 04:40 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.245 s 1.459340
2021-10-01 04:53 JavaScript Parse serialize, tracks 0.004 s 0.793112
2021-10-01 04:53 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.334 s 3.282836