Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-04 09:50 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.109 s -1.377105
2021-10-04 09:50 Python csv-read uncompressed, file, fanniemae_2016Q4 1.180 s -0.417608
2021-10-04 10:13 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.298 s -0.318107
2021-10-04 10:28 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.107 s 1.544145
2021-10-04 10:28 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.286 s 0.620928
2021-10-04 09:51 Python csv-read gzip, streaming, fanniemae_2016Q4 15.046 s -1.357545
2021-10-04 09:55 Python dataframe-to-table type_integers 0.011 s 1.534058
2021-10-04 09:56 Python dataset-filter nyctaxi_2010-01 4.357 s 0.313922
2021-10-04 10:26 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.793 s 0.569792
2021-10-04 10:30 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.043 s -0.320525
2021-10-04 09:52 Python csv-read gzip, file, fanniemae_2016Q4 6.034 s -0.875694
2021-10-04 09:53 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.855233
2021-10-04 10:28 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.826 s -0.810002
2021-10-04 10:29 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.336 s -1.471237
2021-10-04 09:53 Python csv-read gzip, streaming, nyctaxi_2010-01 10.850 s -1.609859
2021-10-04 10:28 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.251 s 1.455909
2021-10-04 10:29 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.258 s -1.714886
2021-10-04 09:52 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.874 s -1.455381
2021-10-04 10:27 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.944 s 1.210000
2021-10-04 10:30 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.304 s -1.342487
2021-10-04 09:52 Python csv-read uncompressed, file, nyctaxi_2010-01 1.036 s -2.179946
2021-10-04 10:03 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.209 s 1.021922
2021-10-04 10:17 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.026 s 0.103862
2021-10-04 10:17 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.998 s 0.357101
2021-10-04 10:27 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.174 s 1.528994
2021-10-04 09:55 Python dataframe-to-table type_dict 0.012 s 0.926475
2021-10-04 10:31 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.494 s -1.552141
2021-10-04 09:55 Python dataframe-to-table chi_traffic_2020_Q1 19.305 s 2.053190
2021-10-04 10:27 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.681 s 0.477233
2021-10-04 10:27 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.768 s 1.268280
2021-10-04 10:29 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.996 s 1.837180
2021-10-04 09:55 Python dataframe-to-table type_strings 0.372 s -0.022980
2021-10-04 09:59 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 57.097 s 0.670934
2021-10-04 10:13 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.136 s 1.012027
2021-10-04 10:26 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.031 s -0.165399
2021-10-04 09:55 Python dataframe-to-table type_floats 0.011 s 0.659641
2021-10-04 10:17 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.048 s -0.147939
2021-10-04 10:29 Python file-read lz4, feather, table, fanniemae_2016Q4 0.602 s 0.049789
2021-10-04 09:56 Python dataframe-to-table type_nested 2.858 s 1.553983
2021-10-04 10:28 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.767 s -0.963403
2021-10-04 10:29 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.922 s -1.183921
2021-10-04 09:56 Python dataframe-to-table type_simple_features 0.911 s 0.126710
2021-10-04 10:30 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.169 s 1.551220
2021-10-04 10:31 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.997 s -1.552254
2021-10-04 10:32 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.611 s -0.894446
2021-10-04 10:32 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.383 s -0.965726
2021-10-04 10:33 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.742 s -0.989477
2021-10-04 10:34 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.981 s -0.926269
2021-10-04 10:34 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.672 s 7.491127
2021-10-04 10:35 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.315 s -0.809919
2021-10-04 10:34 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.711 s 0.137158
2021-10-04 10:34 Python file-write lz4, feather, table, fanniemae_2016Q4 1.157 s 0.435343
2021-10-04 10:51 R dataframe-to-table type_dict, R 0.051 s -0.095339
2021-10-04 11:22 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.167 s -1.006410
2021-10-04 11:32 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.574 s -0.811618
2021-10-04 11:48 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.400617
2021-10-04 10:31 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s -0.128633
2021-10-04 10:35 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.924 s -0.757119
2021-10-04 10:36 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.920 s -0.104759
2021-10-04 11:15 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.202 s 0.572059
2021-10-04 11:16 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.225 s 0.282032
2021-10-04 11:36 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.488 s 0.359305
2021-10-04 10:38 Python wide-dataframe use_legacy_dataset=true 0.397 s -1.578126
2021-10-04 11:40 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.201 s -1.521237
2021-10-04 11:48 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.419062
2021-10-04 11:48 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.799420
2021-10-04 11:48 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.120810
2021-10-04 11:48 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.267191
2021-10-04 11:20 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.232 s 0.594453
2021-10-04 11:35 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.235 s 1.779505
2021-10-04 11:48 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.698717
2021-10-04 10:51 R dataframe-to-table type_strings, R 0.493 s -0.487846
2021-10-04 11:24 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.624 s -1.034998
2021-10-04 11:28 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s -0.075685
2021-10-04 11:15 R dataframe-to-table type_simple_features, R 276.503 s -2.980997
2021-10-04 11:19 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.106 s 1.790409
2021-10-04 11:38 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 0.933525
2021-10-04 11:39 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.557 s 0.653365
2021-10-04 11:48 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.095449
2021-10-04 11:48 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.192110
2021-10-04 11:48 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.004976
2021-10-04 10:36 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.988 s -0.836273
2021-10-04 10:37 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.298 s 0.634136
2021-10-04 11:18 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.691359
2021-10-04 11:21 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.670 s 0.197657
2021-10-04 11:48 JavaScript Parse readBatches, tracks 0.000 s -0.293124
2021-10-04 11:17 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.931 s -0.571459
2021-10-04 11:28 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.557 s 0.873985
2021-10-04 11:48 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.285450
2021-10-04 11:48 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.549823
2021-10-04 10:37 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.308 s 2.639671
2021-10-04 11:17 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.558 s 1.086205
2021-10-04 11:29 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.184 s 1.254019
2021-10-04 11:48 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.615 s -0.244220
2021-10-04 10:38 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.788 s 0.481046
2021-10-04 11:26 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.036 s -0.669045
2021-10-04 11:34 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 0.937474
2021-10-04 11:41 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.502 s 0.085543
2021-10-04 11:48 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.136103
2021-10-04 11:16 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.872 s 0.539179
2021-10-04 11:24 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.577 s -0.622324
2021-10-04 11:37 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.583 s 0.936900
2021-10-04 11:37 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.570 s 1.053839
2021-10-04 11:48 JavaScript Parse Table.from, tracks 0.000 s -0.172403
2021-10-04 11:48 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.110654
2021-10-04 11:48 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -0.850880
2021-10-04 10:38 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.184512
2021-10-04 10:51 R dataframe-to-table type_floats, R 0.106 s 1.145001
2021-10-04 11:20 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.995 s -1.044674
2021-10-04 11:37 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.168 s 1.407792
2021-10-04 11:48 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.002633
2021-10-04 10:37 Python file-write lz4, feather, table, nyctaxi_2010-01 1.793 s 1.027272
2021-10-04 11:19 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.186 s -0.657566
2021-10-04 11:21 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.543 s -0.860783
2021-10-04 11:27 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.834 s -0.416702
2021-10-04 11:30 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.948 s -1.075927
2021-10-04 11:38 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -1.068237
2021-10-04 11:39 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.944 s 0.911755
2021-10-04 11:48 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.127120
2021-10-04 11:16 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.900 s 0.355232
2021-10-04 11:39 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.106 s -2.478958
2021-10-04 11:48 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.910 s -0.160569
2021-10-04 11:48 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.488983
2021-10-04 10:37 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.956 s -0.116778
2021-10-04 10:51 R dataframe-to-table chi_traffic_2020_Q1, R 5.308 s 1.623477
2021-10-04 11:40 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.480 s -1.247082
2021-10-04 11:48 JavaScript Parse serialize, tracks 0.005 s 0.386789
2021-10-04 11:48 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.597 s -0.271679
2021-10-04 11:48 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.716 s 0.174997
2021-10-04 11:18 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.372 s 0.677427
2021-10-04 11:38 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.519 s -0.304304
2021-10-04 11:41 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.170 s 0.890831
2021-10-04 11:38 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.618 s -1.555039
2021-10-04 11:48 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -0.905250
2021-10-04 10:51 R dataframe-to-table type_integers, R 0.086 s -2.095462
2021-10-04 10:52 R dataframe-to-table type_nested, R 0.540 s -0.945779
2021-10-04 11:16 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.027245
2021-10-04 11:31 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.899 s -0.685565
2021-10-04 11:37 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.866 s 1.106885
2021-10-04 11:40 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.357 s 0.601419
2021-10-04 11:48 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.371580
2021-10-04 11:20 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.547415
2021-10-04 11:33 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.758 s -0.705608
2021-10-04 11:37 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.176434
2021-10-04 11:48 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.688 s -0.084880
2021-10-04 11:48 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.834 s 1.056353
2021-10-04 11:48 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.549823
2021-10-04 11:48 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.066100
2021-10-04 11:48 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.505 s -0.020923