Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-28 03:03 Python dataframe-to-table chi_traffic_2020_Q1 19.647 s 0.838527
2021-09-28 03:51 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.006 s 1.945319
2021-09-28 03:57 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.812 s 0.840093
2021-09-28 03:49 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.246 s -0.504879
2021-09-28 03:58 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.841 s 0.912836
2021-09-28 03:03 Python dataframe-to-table type_floats 0.011 s -0.020813
2021-09-28 03:48 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.781 s 0.135488
2021-09-28 03:35 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.014 s 0.287786
2021-09-28 03:59 Python file-write lz4, feather, table, nyctaxi_2010-01 1.802 s 0.472188
2021-09-28 03:30 Python dataset-read async=True, nyctaxi_multi_ipc_s3 182.814 s 0.589887
2021-09-28 03:49 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.283 s -0.319488
2021-09-28 03:50 Python file-read lz4, feather, table, fanniemae_2016Q4 0.598 s 0.563193
2021-09-28 03:59 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.327 s 0.372671
2021-09-28 03:04 Python dataframe-to-table type_simple_features 0.907 s 0.352128
2021-09-28 03:52 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.475 s 1.023279
2021-09-28 03:59 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.087321
2021-09-28 03:00 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.695 s -0.627211
2021-09-28 03:01 Python csv-read gzip, streaming, nyctaxi_2010-01 10.634 s -0.443382
2021-09-28 03:07 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 55.956 s 0.604190
2021-09-28 03:35 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 0.997 s 0.529894
2021-09-28 03:03 Python dataframe-to-table type_dict 0.011 s 1.783167
2021-09-28 03:00 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -1.038085
2021-09-28 03:59 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.729508
2021-09-28 03:00 Python csv-read uncompressed, file, nyctaxi_2010-01 1.014 s 0.100862
2021-09-28 03:03 Python dataframe-to-table type_strings 0.369 s 0.395503
2021-09-28 03:04 Python dataframe-to-table type_nested 2.953 s 0.145464
2021-09-28 03:53 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.080 s 1.782156
2021-09-28 03:54 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.429 s 1.818361
2021-09-28 02:58 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.043 s -0.960022
2021-09-28 03:48 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.008 s -0.723603
2021-09-28 03:49 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.827 s -0.546461
2021-09-28 03:03 Python dataframe-to-table type_integers 0.011 s 0.618047
2021-09-28 03:30 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.250 s 0.249494
2021-09-28 03:50 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.811 s -0.391862
2021-09-28 03:58 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.357 s -0.419375
2021-09-28 03:56 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.129 s 0.950063
2021-09-28 02:59 Python csv-read uncompressed, file, fanniemae_2016Q4 1.206 s -0.558365
2021-09-28 03:20 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 267.140 s 0.229481
2021-09-28 03:35 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.002 s 0.269897
2021-09-28 03:48 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.840 s 0.360879
2021-09-28 03:49 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.134 s -0.092944
2021-09-28 03:51 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.181 s -0.826810
2021-09-28 03:55 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.451 s 1.803395
2021-09-28 03:59 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.755 s 0.753638
2021-09-28 03:04 Python dataset-filter nyctaxi_2010-01 4.371 s -0.563659
2021-09-28 03:49 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.739 s -0.770297
2021-09-28 03:55 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.377 s -0.372537
2021-09-28 03:01 Python csv-read gzip, file, nyctaxi_2010-01 9.049 s -1.163616
2021-09-28 03:51 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.851 s 0.980184
2021-09-28 03:56 Python file-write lz4, feather, table, fanniemae_2016Q4 1.163 s -0.279656
2021-09-28 03:57 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.901 s 1.630353
2021-09-28 02:59 Python csv-read gzip, streaming, fanniemae_2016Q4 14.984 s -0.973021
2021-09-28 03:52 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.019 s 0.808559
2021-09-28 03:48 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.983 s 0.226189
2021-09-28 03:50 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.111 s 0.353095
2021-09-28 03:51 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.787 s 1.182528
2021-09-28 03:52 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s -0.062530
2021-09-28 03:56 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.826 s 1.858047
2021-09-28 03:50 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.943259
2021-09-28 03:50 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.051 s -0.316577
2021-09-28 03:54 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.138 s 1.558576
2021-09-28 04:13 R dataframe-to-table type_nested, R 0.538 s -0.450419
2021-09-28 04:12 R dataframe-to-table chi_traffic_2020_Q1, R 5.393 s 0.302504
2021-09-28 04:13 R dataframe-to-table type_floats, R 0.108 s 0.473134
2021-09-28 05:10 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.627 s -0.191092
2021-09-28 05:10 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.965164
2021-09-28 04:13 R dataframe-to-table type_integers, R 0.084 s 0.925418
2021-09-28 04:37 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.604 s -4.419562
2021-09-28 04:39 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.061 s -0.953842
2021-09-28 04:13 R dataframe-to-table type_dict, R 0.042 s 1.232063
2021-09-28 03:50 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.655 s 0.091624
2021-09-28 03:56 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.637 s 0.751175
2021-09-28 04:59 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.177 s -0.032831
2021-09-28 04:13 R dataframe-to-table type_strings, R 0.490 s 0.114467
2021-09-28 04:39 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.940 s -1.096233
2021-09-28 04:43 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.535 s -1.017341
2021-09-28 05:10 JavaScript Parse serialize, tracks 0.005 s -0.732634
2021-09-28 04:41 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.224 s 0.866354
2021-09-28 05:00 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.392806
2021-09-28 05:10 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-28 05:10 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.205908
2021-09-28 04:45 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.292 s 1.726954
2021-09-28 04:46 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.288 s 1.999977
2021-09-28 04:53 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.856 s 1.665988
2021-09-28 05:10 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.880 s 0.070370
2021-09-28 05:10 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.045884
2021-09-28 05:10 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.832495
2021-09-28 04:58 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.694 s 0.720061
2021-09-28 05:01 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.115242
2021-09-28 05:10 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.678628
2021-09-28 04:44 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.825 s 2.122794
2021-09-28 04:48 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.838 s -1.606580
2021-09-28 04:56 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.253 s 0.917170
2021-09-28 04:59 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.756 s 0.710243
2021-09-28 05:03 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.503 s 0.155375
2021-09-28 05:10 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.026935
2021-09-28 05:10 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.098827
2021-09-28 05:10 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.961747
2021-09-28 04:36 R dataframe-to-table type_simple_features, R 274.414 s 0.788996
2021-09-28 04:38 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.291 s -2.299619
2021-09-28 04:40 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.129 s 0.108200
2021-09-28 04:42 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.673 s 0.939122
2021-09-28 04:55 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.716 s 1.450264
2021-09-28 05:02 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.473 s -0.013833
2021-09-28 05:10 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.737722
2021-09-28 04:37 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 8.325 s -4.558870
2021-09-28 04:39 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.391 s -0.744740
2021-09-28 04:42 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.979 s -0.682070
2021-09-28 05:10 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.149829
2021-09-28 05:10 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.188177
2021-09-28 04:41 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.012647
2021-09-28 04:52 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.890 s 0.949017
2021-09-28 04:37 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.719 s -5.918182
2021-09-28 04:51 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.221 s 0.812720
2021-09-28 04:54 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.526 s 0.927791
2021-09-28 05:10 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.641394
2021-09-28 05:10 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.515089
2021-09-28 05:10 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.523 s -0.201029
2021-09-28 04:50 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.406 s -0.628589
2021-09-28 05:02 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 1.080798
2021-09-28 05:01 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.391 s 0.610851
2021-09-28 05:02 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.809 s 0.734483
2021-09-28 05:10 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.556668
2021-09-28 05:10 JavaScript Parse Table.from, tracks 0.000 s 0.836322
2021-09-28 05:10 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.600360
2021-09-28 04:38 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 8.259 s -3.899827
2021-09-28 04:40 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.186 s -1.033097
2021-09-28 04:57 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.487 s 0.578354
2021-09-28 05:01 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.697 s -1.585002
2021-09-28 04:49 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.632 s -0.075748
2021-09-28 04:56 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 0.593065
2021-09-28 04:58 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.772 s 0.393770
2021-09-28 04:59 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.486979
2021-09-28 04:59 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.975 s 0.472634
2021-09-28 05:10 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.622277
2021-09-28 04:39 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.598384
2021-09-28 04:58 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.194 s -0.000977
2021-09-28 04:59 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.507 s 1.104219
2021-09-28 05:00 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.863 s 1.070543
2021-09-28 05:10 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.126700
2021-09-28 05:10 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.185607
2021-09-28 04:47 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.731 s 1.821758
2021-09-28 05:10 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.680 s 0.386054
2021-09-28 05:10 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.910 s -0.236315
2021-09-28 05:10 JavaScript Parse readBatches, tracks 0.000 s 1.018200
2021-09-28 05:10 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.681 s -0.285973
2021-09-28 05:10 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.510310
2021-09-28 05:10 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.663 s 0.241208
2021-09-28 05:10 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292