Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 19:29 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.725454
2021-10-13 19:54 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.048 s 0.152972
2021-10-13 20:06 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.526 s 1.297918
2021-10-13 20:07 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.009 s 1.182770
2021-10-13 20:09 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.329 s -2.792307
2021-10-13 20:12 Python file-write lz4, feather, table, fanniemae_2016Q4 1.144 s 0.895293
2021-10-13 20:12 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.352 s 0.009375
2021-10-13 20:13 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.893 s -1.054079
2021-10-13 20:14 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.946 s -0.802738
2021-10-13 20:14 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.891 s -0.360526
2021-10-13 20:15 Python file-write lz4, feather, table, nyctaxi_2010-01 1.797 s 0.333646
2021-10-13 20:15 Python wide-dataframe use_legacy_dataset=true 0.391 s 0.606290
2021-10-13 20:15 Python wide-dataframe use_legacy_dataset=false 0.615 s 0.677815
2021-10-13 20:33 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.995 s 0.065290
2021-10-13 20:34 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.529 s 0.090303
2021-10-13 21:00 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.368922
2021-10-13 21:00 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.128311
2021-10-13 21:01 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.815862
2021-10-13 21:01 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.500 s 0.370920
2021-10-13 19:31 Python dataframe-to-table type_strings 0.367 s 0.338046
2021-10-13 20:06 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.684 s 0.910814
2021-10-13 20:07 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.080 s -0.696328
2021-10-13 19:27 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s -0.300835
2021-10-13 19:26 Python csv-read uncompressed, file, fanniemae_2016Q4 1.117 s 1.670005
2021-10-13 19:31 Python dataframe-to-table type_dict 0.011 s 1.179890
2021-10-13 19:49 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.150 s 0.277041
2021-10-13 20:05 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.868 s -0.733092
2021-10-13 20:06 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.601 s 1.301454
2021-10-13 20:06 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.116 s 1.430738
2021-10-13 20:04 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.017 s -0.263875
2021-10-13 20:11 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.976 s -1.472192
2021-10-13 20:05 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.279 s 0.572836
2021-10-13 20:38 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.158 s -3.371798
2021-10-13 20:50 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.594 s 0.552155
2021-10-13 21:00 JavaScript Parse serialize, tracks 0.005 s -0.744397
2021-10-13 19:49 Python dataset-read async=True, nyctaxi_multi_ipc_s3 192.038 s -0.743448
2021-10-13 20:04 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.717 s 0.204444
2021-10-13 20:15 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.761 s 1.516200
2021-10-13 20:47 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.264 s -1.771377
2021-10-13 20:50 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.531 s -0.887762
2021-10-13 21:00 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.123967
2021-10-13 19:31 Python dataframe-to-table chi_traffic_2020_Q1 19.259 s 0.795404
2021-10-13 19:39 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.879 s -0.278963
2021-10-13 20:06 Python file-read lz4, feather, table, fanniemae_2016Q4 0.600 s 0.328782
2021-10-13 20:08 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.298 s 0.956343
2021-10-13 20:09 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.790 s 1.258934
2021-10-13 20:10 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.706 s -3.056729
2021-10-13 20:29 R dataframe-to-table type_strings, R 0.494 s 0.229672
2021-10-13 20:30 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.691 s 0.590356
2021-10-13 20:37 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.562 s -2.847093
2021-10-13 20:45 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.524 s -0.596533
2021-10-13 21:00 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.626 s -0.301443
2021-10-13 21:00 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.717 s -0.632586
2021-10-13 21:00 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.802 s 2.067907
2021-10-13 21:00 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s 0.045883
2021-10-13 20:04 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.812 s 0.399212
2021-10-13 19:35 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.013 s -0.126404
2021-10-13 20:04 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.971 s 0.296849
2021-10-13 20:06 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.280 s 0.153683
2021-10-13 20:46 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.275 s 0.772348
2021-10-13 20:48 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.470 s 1.073698
2021-10-13 20:49 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.808 s 1.757948
2021-10-13 20:49 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.127203
2021-10-13 20:52 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.359 s 1.443423
2021-10-13 21:00 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.669 s 0.430168
2021-10-13 21:01 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.826445
2021-10-13 19:26 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.619 s 3.366514
2021-10-13 20:40 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.661 s -2.513953
2021-10-13 21:00 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.552588
2021-10-13 21:00 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.357651
2021-10-13 19:29 Python csv-read gzip, streaming, nyctaxi_2010-01 10.474 s 0.909701
2021-10-13 19:32 Python dataset-filter nyctaxi_2010-01 4.370 s -0.713128
2021-10-13 19:54 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.086 s -1.293676
2021-10-13 20:11 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.301 s 0.061138
2021-10-13 20:15 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.284 s 1.919913
2021-10-13 20:32 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.154 s 0.640186
2021-10-13 20:34 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.111 s -2.880490
2021-10-13 20:51 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.926 s -0.828157
2021-10-13 19:27 Python csv-read gzip, streaming, fanniemae_2016Q4 14.556 s 3.080824
2021-10-13 19:28 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.489 s 0.876295
2021-10-13 19:31 Python dataframe-to-table type_integers 0.011 s -0.289450
2021-10-13 19:31 Python dataframe-to-table type_floats 0.011 s 0.658201
2021-10-13 20:08 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s 0.063749
2021-10-13 20:10 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.628 s -1.627671
2021-10-13 20:14 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.339 s 0.536959
2021-10-13 20:30 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.207 s 1.214087
2021-10-13 20:33 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.689 s 0.046178
2021-10-13 20:39 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.813 s 1.090725
2021-10-13 21:00 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -1.425316
2021-10-13 20:08 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.169 s 0.916916
2021-10-13 20:13 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.835 s 0.087399
2021-10-13 20:30 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.320 s -1.156536
2021-10-13 20:32 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.209 s 0.636700
2021-10-13 20:50 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.196 s -1.623549
2021-10-13 21:00 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.087051
2021-10-13 21:00 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.133234
2021-10-13 21:00 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.705978
2021-10-13 21:00 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.560825
2021-10-13 19:28 Python csv-read uncompressed, file, nyctaxi_2010-01 1.009 s 0.022889
2021-10-13 19:31 Python dataframe-to-table type_nested 2.848 s 1.517264
2021-10-13 20:29 R dataframe-to-table type_nested, R 0.538 s 0.231632
2021-10-13 20:40 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.387 s 0.985668
2021-10-13 20:52 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.491 s -0.689110
2021-10-13 19:54 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.051 s -0.222631
2021-10-13 20:07 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.111 s 1.567583
2021-10-13 20:31 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.037 s -0.737937
2021-10-13 21:00 JavaScript Parse readBatches, tracks 0.000 s -2.062508
2021-10-13 20:05 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.319 s -1.551321
2021-10-13 20:12 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.827 s 0.273591
2021-10-13 20:29 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.502 s -2.093793
2021-10-13 20:31 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.612003
2021-10-13 20:36 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.698 s -3.180995
2021-10-13 20:44 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.896 s -1.390597
2021-10-13 20:46 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.753 s -1.298301
2021-10-13 21:00 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.682878
2021-10-13 20:29 R dataframe-to-table type_integers, R 0.009 s 0.688635
2021-10-13 21:00 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.223611
2021-10-13 20:07 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.026 s 0.334890
2021-10-13 20:08 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 0.290662
2021-10-13 20:29 R dataframe-to-table type_dict, R 0.061 s -1.762643
2021-10-13 20:29 R dataframe-to-table type_floats, R 0.013 s 0.679313
2021-10-13 20:53 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.492 s 0.905527
2021-10-13 21:00 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.056093
2021-10-13 21:00 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.368405
2021-10-13 20:49 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.475 s 5.624815
2021-10-13 20:52 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.116 s -2.248098
2021-10-13 20:53 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.101 s 3.031592
2021-10-13 21:00 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.080797
2021-10-13 20:30 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.448 s 0.639176
2021-10-13 20:32 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.046 s 0.862310
2021-10-13 20:42 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.891 s -0.648453
2021-10-13 21:00 JavaScript Parse Table.from, tracks 0.000 s -1.571880
2021-10-13 21:00 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.124465
2021-10-13 20:32 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.218 s -1.208124
2021-10-13 20:42 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.299 s -2.568814
2021-10-13 20:49 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.196 s -2.162696
2021-10-13 20:52 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.214 s -2.023575
2021-10-13 21:00 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.830 s 1.466898
2021-10-13 20:31 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.382 s 0.593560
2021-10-13 20:49 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.582 s -0.756279
2021-10-13 21:00 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.456403
2021-10-13 21:01 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.028 s -2.709377
2021-10-13 20:29 R dataframe-to-table chi_traffic_2020_Q1, R 3.516 s 0.259214
2021-10-13 20:32 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.125 s -0.522094
2021-10-13 20:51 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.812 s -2.838327
2021-10-13 20:51 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.668 s -1.141881
2021-10-13 21:00 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.520 s -0.077275