Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-30 03:36 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.245844
2021-09-30 03:39 Python dataframe-to-table type_strings 0.367 s 0.572397
2021-09-30 03:44 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 72.340 s -2.817469
2021-09-30 03:37 Python csv-read gzip, streaming, nyctaxi_2010-01 10.661 s -0.502827
2021-09-30 03:40 Python dataset-filter nyctaxi_2010-01 4.400 s -1.246049
2021-09-30 03:36 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.664 s -0.465883
2021-09-30 03:34 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.052 s -0.812852
2021-09-30 03:39 Python dataframe-to-table type_dict 0.012 s 0.089970
2021-09-30 03:40 Python dataframe-to-table type_nested 2.864 s 4.857079
2021-09-30 03:39 Python dataframe-to-table chi_traffic_2020_Q1 19.580 s 1.249948
2021-09-30 03:40 Python dataframe-to-table type_simple_features 0.936 s -4.515327
2021-09-30 03:36 Python csv-read uncompressed, file, nyctaxi_2010-01 1.013 s 0.107467
2021-09-30 03:37 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.363008
2021-09-30 03:39 Python dataframe-to-table type_integers 0.011 s -1.666897
2021-09-30 03:34 Python csv-read uncompressed, file, fanniemae_2016Q4 1.173 s -0.027740
2021-09-30 03:35 Python csv-read gzip, streaming, fanniemae_2016Q4 14.995 s -0.825771
2021-09-30 03:39 Python dataframe-to-table type_floats 0.012 s -0.985701
2021-09-30 03:48 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.180 s 2.398870
2021-09-30 04:26 Python file-write lz4, feather, table, fanniemae_2016Q4 1.157 s 0.350032
2021-09-30 04:21 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.982 s 0.234988
2021-09-30 04:25 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.772 s -0.488879
2021-09-30 04:18 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.965 s 0.326179
2021-09-30 04:19 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.176 s -2.080458
2021-09-30 04:04 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.042 s -0.083414
2021-09-30 04:21 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.607699
2021-09-30 04:18 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.040 s -1.403399
2021-09-30 04:42 R dataframe-to-table chi_traffic_2020_Q1, R 5.386 s 0.359720
2021-09-30 05:18 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.830 s 0.158374
2021-09-30 05:29 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.514 s 0.227478
2021-09-30 05:08 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.924 s -0.450998
2021-09-30 05:10 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.175 s -0.253982
2021-09-30 05:11 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.244 s -0.218673
2021-09-30 05:28 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.599483
2021-09-30 04:25 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.605 s 0.689296
2021-09-30 04:26 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.794 s 1.689964
2021-09-30 04:28 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.854 s 0.599669
2021-09-30 04:29 Python wide-dataframe use_legacy_dataset=true 0.398 s -0.791417
2021-09-30 04:43 R dataframe-to-table type_nested, R 0.536 s 0.413922
2021-09-30 05:11 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.421986
2021-09-30 05:29 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.607 s 1.774889
2021-09-30 04:20 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.027 s 0.549547
2021-09-30 04:27 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.856 s 1.781130
2021-09-30 04:19 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.854 s -1.034983
2021-09-30 04:23 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.084 s 1.274166
2021-09-30 04:28 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.346 s 0.261026
2021-09-30 04:29 Python file-write lz4, feather, table, nyctaxi_2010-01 1.796 s 0.783971
2021-09-30 05:12 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.671 s 1.240814
2021-09-30 05:23 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.468 s 1.969398
2021-09-30 05:25 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -1.306095
2021-09-30 05:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.434771
2021-09-30 04:18 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.732 s 0.293900
2021-09-30 04:04 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.039 s -0.075357
2021-09-30 04:21 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.047 s -0.588379
2021-09-30 04:24 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.262 s 0.610928
2021-09-30 04:19 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.871 s -5.117445
2021-09-30 04:20 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.628307
2021-09-30 04:22 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s -0.047046
2021-09-30 04:26 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.197 s 0.128233
2021-09-30 04:42 R dataframe-to-table type_integers, R 0.085 s -0.681832
2021-09-30 05:07 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.918 s 0.010207
2021-09-30 03:58 Python dataset-read async=True, nyctaxi_multi_ipc_s3 207.796 s -2.512680
2021-09-30 04:04 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.020 s 0.012268
2021-09-30 04:17 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.848 s 0.326287
2021-09-30 04:22 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.122 s 0.203939
2021-09-30 04:43 R dataframe-to-table type_floats, R 0.109 s -0.134257
2021-09-30 05:06 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.254 s -0.015134
2021-09-30 05:22 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.810 s 1.954455
2021-09-30 05:24 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.677 s 1.818449
2021-09-30 05:28 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.581 s 1.995718
2021-09-30 04:42 R dataframe-to-table type_dict, R 0.028 s 3.060588
2021-09-30 05:09 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.046 s 1.673851
2021-09-30 05:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.347 s 1.849750
2021-09-30 04:20 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.221 s -7.188142
2021-09-30 04:21 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.936 s 0.454377
2021-09-30 04:27 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.809 s 0.655659
2021-09-30 05:15 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.310 s 1.097220
2021-09-30 03:58 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.465 s -1.007242
2021-09-30 04:24 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.439 s 1.267068
2021-09-30 04:28 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.398 s -0.291217
2021-09-30 05:08 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 1.051814
2021-09-30 05:12 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.971 s -0.201411
2021-09-30 05:20 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.244 s 0.273089
2021-09-30 04:19 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.789 s -3.422682
2021-09-30 04:20 Python file-read lz4, feather, table, fanniemae_2016Q4 0.597 s 0.826334
2021-09-30 05:08 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.877094
2021-09-30 05:12 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.532 s -0.705343
2021-09-30 05:15 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.289 s 1.384955
2021-09-30 05:30 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.943 s 1.821107
2021-09-30 04:18 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.280 s -1.267346
2021-09-30 04:19 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.312 s -1.654298
2021-09-30 04:22 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.631 s 0.186523
2021-09-30 04:29 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.848 s -0.072581
2021-09-30 05:13 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.846 s 1.326684
2021-09-30 04:20 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.905 s -6.884354
2021-09-30 04:29 Python wide-dataframe use_legacy_dataset=false 0.617 s 0.184697
2021-09-30 05:06 R dataframe-to-table type_simple_features, R 274.710 s 0.174626
2021-09-30 05:07 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.916 s 0.039137
2021-09-30 05:28 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.886 s 2.041378
2021-09-30 05:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.606 s 0.146204
2021-09-30 04:42 R dataframe-to-table type_strings, R 0.489 s 0.601192
2021-09-30 05:07 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.258 s -0.072953
2021-09-30 05:19 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.407 s -0.727756
2021-09-30 05:26 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.266 s 0.085534
2021-09-30 05:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.052110
2021-09-30 05:09 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.387 s -0.559020
2021-09-30 05:21 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.821 s 2.269976
2021-09-30 05:27 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.489 s 0.236669
2021-09-30 05:30 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.526 s 1.137829
2021-09-30 05:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.152 s 1.844087
2021-09-30 05:17 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.740 s 1.254010
2021-09-30 05:31 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.198 s -0.349007
2021-09-30 05:10 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.131 s -0.042950
2021-09-30 05:28 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.195 s -0.052540
2021-09-30 05:28 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.576 s 2.351814
2021-09-30 05:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.475 s -0.675301
2021-09-30 05:32 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.487 s 0.153124
2021-09-30 05:39 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.555941
2021-09-30 04:25 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.338 s 0.041840
2021-09-30 05:19 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.603 s 0.371036
2021-09-30 05:39 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.553459
2021-09-30 05:39 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.409649
2021-09-30 05:39 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.379053
2021-09-30 05:39 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.518657
2021-09-30 05:39 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.061164
2021-09-30 05:39 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.809 s -0.356138
2021-09-30 05:39 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.629 s -0.260247
2021-09-30 05:39 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.145863
2021-09-30 05:39 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.336197
2021-09-30 05:39 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.019417
2021-09-30 05:39 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.747 s -1.233249
2021-09-30 05:39 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.884 s 0.016066
2021-09-30 05:39 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.494149
2021-09-30 05:39 JavaScript Parse readBatches, tracks 0.000 s -0.654139
2021-09-30 05:39 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.128304
2021-09-30 05:39 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.338541
2021-09-30 05:39 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.020482
2021-09-30 05:39 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.020482
2021-09-30 05:39 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.589580
2021-09-30 05:39 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.301749
2021-09-30 05:39 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.479466
2021-09-30 05:39 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.439 s 1.175620
2021-09-30 05:39 JavaScript Parse Table.from, tracks 0.000 s -0.803892
2021-09-30 05:39 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.623 s -0.161307
2021-09-30 05:39 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.421058
2021-09-30 05:39 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-30 05:39 JavaScript Parse serialize, tracks 0.005 s 0.360429
2021-09-30 05:39 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.952 s -1.008505
2021-09-30 05:39 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.510310
2021-09-30 05:39 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.606267