Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-27 13:21 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.652 s -0.611483
2021-09-27 13:26 Python dataframe-to-table type_strings 0.365 s 0.887299
2021-09-27 13:23 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.606 s -0.340307
2021-09-27 13:23 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.078944
2021-09-27 13:24 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 0.902775
2021-09-27 13:27 Python dataset-filter nyctaxi_2010-01 4.365 s -0.351160
2021-09-27 13:53 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.846 s -0.398167
2021-09-27 13:24 Python csv-read gzip, streaming, nyctaxi_2010-01 10.571 s -0.251175
2021-09-27 13:22 Python csv-read gzip, streaming, fanniemae_2016Q4 14.585 s -0.620683
2021-09-27 13:26 Python dataframe-to-table type_dict 0.012 s -0.527821
2021-09-27 13:26 Python dataframe-to-table type_floats 0.011 s -0.083912
2021-09-27 13:27 Python dataframe-to-table type_simple_features 0.909 s 0.019578
2021-09-27 13:22 Python csv-read uncompressed, file, fanniemae_2016Q4 1.188 s -0.282541
2021-09-27 13:26 Python dataframe-to-table chi_traffic_2020_Q1 19.740 s 0.307529
2021-09-27 13:26 Python dataframe-to-table type_integers 0.011 s 0.146840
2021-09-27 13:58 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.020 s -0.064976
2021-09-27 13:53 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.202 s 0.510357
2021-09-27 13:23 Python csv-read uncompressed, file, nyctaxi_2010-01 1.017 s 0.043417
2021-09-27 13:30 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.880 s -0.238951
2021-09-27 13:58 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.003 s 0.465459
2021-09-27 13:58 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.017 s 0.261944
2021-09-27 13:27 Python dataframe-to-table type_nested 2.950 s 0.295731
2021-09-27 13:44 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 271.022 s 0.078144
2021-09-27 15:04 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.994 s -1.545745
2021-09-27 15:15 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.847 s 1.813210
2021-09-27 14:14 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.029 s 0.844103
2021-09-27 15:02 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.046 s 1.856402
2021-09-27 15:10 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.721 s 2.132393
2021-09-27 14:13 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.110 s 0.455722
2021-09-27 14:13 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.855 s 1.061986
2021-09-27 14:16 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.096 s 2.086027
2021-09-27 14:18 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.500 s 2.132198
2021-09-27 14:21 Python wide-dataframe use_legacy_dataset=false 0.629 s -2.440780
2021-09-27 14:11 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.279 s 0.012307
2021-09-27 14:17 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.706 s 6.621467
2021-09-27 14:18 Python file-write lz4, feather, table, fanniemae_2016Q4 1.148 s 1.172550
2021-09-27 14:21 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.223 s 1.226842
2021-09-27 14:21 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.734 s 0.971391
2021-09-27 14:12 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.842 s -3.702855
2021-09-27 14:15 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.069 s 2.340580
2021-09-27 14:19 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.763 s 1.296396
2021-09-27 14:21 Python wide-dataframe use_legacy_dataset=true 0.397 s -0.547695
2021-09-27 15:23 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.612 s -0.176180
2021-09-27 14:12 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.732 s -0.371686
2021-09-27 14:35 R dataframe-to-table chi_traffic_2020_Q1, R 5.474 s -0.994153
2021-09-27 14:35 R dataframe-to-table type_dict, R 0.054 s -0.134374
2021-09-27 14:35 R dataframe-to-table type_floats, R 0.113 s -1.013455
2021-09-27 15:01 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.918 s -0.158223
2021-09-27 15:02 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.107 s 1.666428
2021-09-27 15:11 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.558 s 1.444552
2021-09-27 15:17 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.715 s 1.271277
2021-09-27 15:21 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.601 s 1.021911
2021-09-27 15:24 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.199 s -0.334260
2021-09-27 15:32 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.560370
2021-09-27 14:11 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.264 s -1.232932
2021-09-27 14:12 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.132 s 0.007563
2021-09-27 15:01 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.362 s 0.636765
2021-09-27 15:19 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.493 s -0.469200
2021-09-27 15:21 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.969 s 0.794755
2021-09-27 15:24 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.475 s -0.636494
2021-09-27 14:12 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.293 s -0.356381
2021-09-27 14:20 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.809 s 1.239998
2021-09-27 15:23 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.100 s 0.833026
2021-09-27 15:32 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.590263
2021-09-27 15:32 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.391574
2021-09-27 14:13 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.057 s -0.619700
2021-09-27 14:14 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.183 s -1.294865
2021-09-27 14:59 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.224 s 0.415755
2021-09-27 15:00 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.214 s 1.002826
2021-09-27 15:00 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.150298
2021-09-27 15:21 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.748 s 1.002229
2021-09-27 15:22 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.436591
2021-09-27 15:23 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.870 s 1.179807
2021-09-27 15:32 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.690113
2021-09-27 14:20 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.883 s 2.777181
2021-09-27 15:23 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.392 s 0.244709
2021-09-27 15:32 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.652 s -0.199309
2021-09-27 14:10 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.809 s 0.515447
2021-09-27 14:11 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.827 s -0.661742
2021-09-27 14:35 R dataframe-to-table type_integers, R 0.085 s -0.505259
2021-09-27 15:03 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.284 s -2.379603
2021-09-27 15:05 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.539 s -1.357920
2021-09-27 15:12 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.396 s 0.999401
2021-09-27 15:32 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.231400
2021-09-27 14:10 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.694 s 0.489466
2021-09-27 14:12 Python file-read lz4, feather, table, fanniemae_2016Q4 0.602 s -0.116396
2021-09-27 14:13 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.042 s -0.329618
2021-09-27 14:14 Python file-read lz4, feather, table, nyctaxi_2010-01 0.683 s -2.908469
2021-09-27 14:35 R dataframe-to-table type_strings, R 0.491 s -0.200247
2021-09-27 14:35 R dataframe-to-table type_nested, R 0.534 s 0.763770
2021-09-27 15:02 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.175 s -0.377171
2021-09-27 15:18 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.332293
2021-09-27 15:32 JavaScript Parse serialize, tracks 0.005 s -0.766886
2021-09-27 15:32 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.592 s -0.080740
2021-09-27 14:16 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.419 s 2.378089
2021-09-27 15:01 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.508436
2021-09-27 14:14 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.825 s 1.115877
2021-09-27 14:59 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.862 s 1.290409
2021-09-27 15:13 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.207 s 1.103093
2021-09-27 15:21 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.183205
2021-09-27 15:22 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.508 s 0.903141
2021-09-27 15:32 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.109545
2021-09-27 14:10 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.951 s 0.437675
2021-09-27 14:11 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.009 s -0.846939
2021-09-27 14:15 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.489 s 1.058562
2021-09-27 14:18 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.056 s 1.754512
2021-09-27 14:19 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.808 s 2.945655
2021-09-27 15:04 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.681 s -0.783508
2021-09-27 15:20 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.173 s 1.366240
2021-09-27 15:21 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.373141
2021-09-27 15:32 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.542402
2021-09-27 15:32 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.114154
2021-09-27 15:32 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.086078
2021-09-27 15:32 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.949859
2021-09-27 15:19 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.244 s 1.527071
2021-09-27 15:32 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.703335
2021-09-27 14:17 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.504 s 1.856661
2021-09-27 14:20 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.307 s 3.607481
2021-09-27 15:03 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.086639
2021-09-27 15:16 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.524 s 0.790449
2021-09-27 15:32 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.784167
2021-09-27 14:12 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.663 s -0.271360
2021-09-27 14:21 Python file-write lz4, feather, table, nyctaxi_2010-01 1.784 s 1.556327
2021-09-27 15:00 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.882 s 0.708785
2021-09-27 15:08 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.293 s 2.101951
2021-09-27 15:32 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.717228
2021-09-27 15:32 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.676430
2021-09-27 14:59 R dataframe-to-table type_simple_features, R 274.520 s 0.561553
2021-09-27 15:32 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.617 s 1.079422
2021-09-27 15:06 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.846 s 2.059597
2021-09-27 15:07 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.254 s 2.229575
2021-09-27 15:32 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.707 s 0.224742
2021-09-27 15:32 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.507 s 0.033813
2021-09-27 15:32 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.794 s 2.196191
2021-09-27 15:10 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.836 s -1.292579
2021-09-27 15:32 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.176003
2021-09-27 15:32 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.313901
2021-09-27 15:14 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.893 s 0.633780
2021-09-27 15:32 JavaScript Parse readBatches, tracks 0.000 s -0.040254
2021-09-27 15:32 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.306475
2021-09-27 15:20 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.695 s 0.699955
2021-09-27 15:25 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.508 s 0.166616
2021-09-27 15:32 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.542402
2021-09-27 15:32 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.076614
2021-09-27 15:24 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.806 s 1.003567
2021-09-27 15:32 JavaScript Parse Table.from, tracks 0.000 s 0.126678
2021-09-27 15:32 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.872 s 0.505960
2021-09-27 15:32 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.964051
2021-09-27 15:32 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.623034