Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-30 18:58 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.011 s 0.148259
2021-09-30 19:12 Python file-read lz4, feather, table, fanniemae_2016Q4 0.608 s -1.213917
2021-09-30 18:40 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.411 s -0.494828
2021-09-30 19:11 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.275 s -1.120895
2021-09-30 18:33 Python csv-read uncompressed, file, nyctaxi_2010-01 1.021 s -0.034954
2021-09-30 18:36 Python dataframe-to-table type_nested 2.844 s 5.493041
2021-09-30 19:14 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.404453
2021-09-30 19:20 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.905 s 0.253845
2021-09-30 18:34 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.440853
2021-09-30 18:36 Python dataframe-to-table type_integers 0.011 s -1.416821
2021-09-30 18:58 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.049 s -0.219746
2021-09-30 19:19 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.822 s 0.569247
2021-09-30 19:35 R dataframe-to-table type_integers, R 0.083 s 1.728576
2021-09-30 18:31 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.838 s -0.583534
2021-09-30 19:10 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.816 s 0.475959
2021-09-30 19:21 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.289684
2021-09-30 18:31 Python csv-read uncompressed, file, fanniemae_2016Q4 1.170 s 0.018048
2021-09-30 19:11 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.314 s -1.814019
2021-09-30 19:21 Python wide-dataframe use_legacy_dataset=false 0.625 s -1.573807
2021-09-30 19:35 R dataframe-to-table type_strings, R 0.492 s -0.637171
2021-09-30 18:36 Python dataframe-to-table type_dict 0.012 s 0.410527
2021-09-30 19:10 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.735 s 0.277023
2021-09-30 19:14 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.991 s 0.210108
2021-09-30 18:36 Python dataframe-to-table type_strings 0.370 s 0.180174
2021-09-30 18:32 Python csv-read gzip, streaming, fanniemae_2016Q4 14.767 s -0.584083
2021-09-30 19:17 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.341 s -0.013013
2021-09-30 19:18 Python file-write lz4, feather, table, fanniemae_2016Q4 1.155 s 0.608607
2021-09-30 19:19 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.791 s 1.783656
2021-09-30 18:32 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.002651
2021-09-30 19:11 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.046 s -1.587537
2021-09-30 19:14 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.016578
2021-09-30 19:15 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.096 s 1.184496
2021-09-30 19:17 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.593 s 0.757688
2021-09-30 19:12 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.801 s -4.049703
2021-09-30 18:36 Python dataframe-to-table type_simple_features 0.934 s -3.747525
2021-09-30 19:13 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.922 s 0.526157
2021-09-30 19:16 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.265 s 0.605463
2021-09-30 19:35 R dataframe-to-table chi_traffic_2020_Q1, R 5.391 s 0.257975
2021-09-30 18:33 Python csv-read gzip, streaming, nyctaxi_2010-01 10.809 s -1.083718
2021-09-30 19:12 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.912 s -5.229385
2021-09-30 19:13 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.221 s -6.117582
2021-09-30 19:15 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.631 s 0.196267
2021-09-30 19:21 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.882 s -0.340652
2021-09-30 18:36 Python dataset-filter nyctaxi_2010-01 4.401 s -1.253797
2021-09-30 19:13 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.039 s 0.107801
2021-09-30 19:13 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.049 s -0.757536
2021-09-30 18:44 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.621 s 2.391607
2021-09-30 18:33 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.823 s -1.093786
2021-09-30 19:20 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.861 s 1.675611
2021-09-30 19:21 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.349 s 0.048268
2021-09-30 19:12 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.291 s -0.007176
2021-09-30 19:18 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.750 s -0.321511
2021-09-30 19:18 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.229 s -0.129925
2021-09-30 18:53 Python dataset-read async=True, nyctaxi_multi_ipc_s3 182.936 s 0.578108
2021-09-30 19:14 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.161 s 0.040107
2021-09-30 19:35 R dataframe-to-table type_dict, R 0.041 s 1.099300
2021-09-30 19:10 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.009 s 0.021179
2021-09-30 19:21 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.394 s -0.247943
2021-09-30 19:11 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.880 s -1.801078
2021-09-30 19:12 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.169 s -1.728295
2021-09-30 19:21 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.406653
2021-09-30 19:12 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.866 s -4.645003
2021-09-30 19:16 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.432 s 1.318775
2021-09-30 19:36 R dataframe-to-table type_nested, R 0.539 s -0.697575
2021-09-30 20:08 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.304 s 1.046086
2021-09-30 20:21 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.582 s 1.954231
2021-09-30 20:24 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 1.050699
2021-09-30 20:03 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.252 s -0.560639
2021-09-30 20:16 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.473 s 1.828324
2021-09-30 20:19 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.267 s -0.000529
2021-09-30 20:23 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.029536
2021-09-30 20:32 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.456958
2021-09-30 18:35 Python dataframe-to-table chi_traffic_2020_Q1 19.642 s 0.827857
2021-09-30 18:36 Python dataframe-to-table type_floats 0.012 s -2.089994
2021-09-30 18:53 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.311 s -0.135137
2021-09-30 18:58 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.025 s 0.131612
2021-09-30 19:35 R dataframe-to-table type_floats, R 0.107 s 0.877007
2021-09-30 20:21 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.570 s 2.364638
2021-09-30 20:22 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.603 s 1.793927
2021-09-30 20:23 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.515 s 1.361645
2021-09-30 20:23 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.348 s 1.782186
2021-09-30 20:15 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.808 s 1.886682
2021-09-30 20:18 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.424044
2021-09-30 20:32 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.475304
2021-09-30 20:32 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.502859
2021-09-30 20:32 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.929672
2021-09-30 20:00 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.662944
2021-09-30 20:06 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.847 s 1.235874
2021-09-30 20:12 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.595 s 0.490673
2021-09-30 20:25 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.484 s 0.157360
2021-09-30 20:32 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.924739
2021-09-30 20:32 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.082561
2021-09-30 20:32 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.195168
2021-09-30 20:32 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.501510
2021-09-30 20:32 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.599865
2021-09-30 20:32 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.547 s -0.578711
2021-09-30 20:10 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.423432
2021-09-30 20:14 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.818 s 2.330387
2021-09-30 20:21 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.878 s 2.021381
2021-09-30 20:04 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.677 s -0.014522
2021-09-30 20:00 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.941 s -0.192357
2021-09-30 20:10 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.748 s 1.115966
2021-09-30 20:20 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.493 s -0.454557
2021-09-30 20:32 JavaScript Parse serialize, tracks 0.005 s -0.645012
2021-09-30 20:22 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.509 s 0.926119
2021-09-30 20:32 JavaScript Parse readBatches, tracks 0.000 s 0.984895
2021-09-30 20:32 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.667 s -0.265552
2021-09-30 20:32 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.672 s 0.118158
2021-09-30 20:32 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.930 s -1.062255
2021-09-30 20:00 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.237 s 0.163374
2021-09-30 19:59 R dataframe-to-table type_simple_features, R 275.240 s -0.793749
2021-09-30 20:02 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.405 s -1.289481
2021-09-30 20:08 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.303 s 1.194604
2021-09-30 20:12 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.543956
2021-09-30 20:21 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.275125
2021-09-30 20:24 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.475 s -0.750037
2021-09-30 20:13 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.230 s 0.514935
2021-09-30 20:32 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.643 s -0.292114
2021-09-30 20:32 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.767 s -0.192772
2021-09-30 20:32 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.971 s -1.568244
2021-09-30 20:01 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.934 s -0.863500
2021-09-30 20:04 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.443545
2021-09-30 20:22 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.606 s 0.185423
2021-09-30 20:32 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.615046
2021-09-30 20:32 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.105574
2021-09-30 20:01 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.773087
2021-09-30 20:04 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.976 s -0.404305
2021-09-30 20:17 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.667 s 1.949072
2021-09-30 20:21 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.197 s -0.240849
2021-09-30 20:24 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.182 s 1.736305
2021-09-30 20:32 JavaScript Parse Table.from, tracks 0.000 s 0.819264
2021-09-30 20:32 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.636452
2021-09-30 20:03 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.184 s -0.889397
2021-09-30 20:23 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.965 s 1.809581
2021-09-30 19:59 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.248 s 0.055540
2021-09-30 20:32 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.618543
2021-09-30 20:32 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.118251
2021-09-30 20:32 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.418337
2021-09-30 20:32 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.065702
2021-09-30 20:00 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.938 s -0.160871
2021-09-30 20:02 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.052 s 0.684754
2021-09-30 20:03 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.135 s -0.388872
2021-09-30 20:21 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.440821
2021-09-30 20:05 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.527 s -0.434300
2021-09-30 20:32 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.063955
2021-09-30 20:32 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.394202
2021-09-30 20:32 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.267085
2021-09-30 20:32 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.791253
2021-09-30 20:32 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.387815