Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-28 20:58 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.832 s -4.087092
2021-09-28 21:00 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.983 s 0.226796
2021-09-28 20:21 Python csv-read uncompressed, file, nyctaxi_2010-01 0.991 s 0.483757
2021-09-28 20:57 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.034 s -1.172855
2021-09-28 20:59 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.922 s -5.694115
2021-09-28 21:02 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.094 s 1.152246
2021-09-28 20:24 Python dataframe-to-table type_dict 0.011 s 1.623883
2021-09-28 20:24 Python dataframe-to-table type_floats 0.011 s 0.192310
2021-09-28 20:57 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.274 s -1.055602
2021-09-28 20:58 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.863 s -3.855051
2021-09-28 20:24 Python dataframe-to-table type_strings 0.368 s 0.392741
2021-09-28 20:45 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.033 s 0.026798
2021-09-28 20:20 Python csv-read gzip, streaming, fanniemae_2016Q4 15.006 s -0.826847
2021-09-28 20:56 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.007 s 0.033933
2021-09-28 20:58 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.299 s -1.209743
2021-09-28 20:21 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.709 s -0.633362
2021-09-28 20:27 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 56.503 s 0.612926
2021-09-28 20:41 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.316 s -0.157278
2021-09-28 20:57 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.713 s 0.374881
2021-09-28 20:58 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.864 s -1.282018
2021-09-28 20:41 Python dataset-read async=True, nyctaxi_multi_ipc_s3 177.161 s 1.273721
2021-09-28 20:45 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.014 s 0.097951
2021-09-28 21:01 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.635 s 0.165332
2021-09-28 20:58 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.168 s -1.574463
2021-09-28 20:59 Python file-read lz4, feather, table, fanniemae_2016Q4 0.609 s -1.445125
2021-09-28 21:00 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.030 s 0.421734
2021-09-28 20:21 Python csv-read gzip, streaming, nyctaxi_2010-01 10.672 s -0.529179
2021-09-28 20:24 Python dataframe-to-table chi_traffic_2020_Q1 19.736 s 0.300628
2021-09-28 20:25 Python dataset-filter nyctaxi_2010-01 4.392 s -0.941922
2021-09-28 20:58 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.316 s -1.815223
2021-09-28 20:45 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.039 s -0.066299
2021-09-28 20:19 Python csv-read uncompressed, file, fanniemae_2016Q4 1.180 s -0.155444
2021-09-28 20:56 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.816 s 0.468378
2021-09-28 20:19 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.072 s -0.824036
2021-09-28 20:32 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.182 s 2.219331
2021-09-28 20:24 Python dataframe-to-table type_integers 0.011 s -2.017416
2021-09-28 20:24 Python dataframe-to-table type_nested 2.889 s 2.828395
2021-09-28 21:00 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.182 s -1.116565
2021-09-28 21:01 Python file-read lz4, feather, table, nyctaxi_2010-01 0.675 s -1.187635
2021-09-28 20:22 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.733533
2021-09-28 20:24 Python dataframe-to-table type_simple_features 0.929 s -2.594428
2021-09-28 20:59 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.217 s -4.689673
2021-09-28 21:00 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.994 s 0.220470
2021-09-28 20:20 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s -0.197301
2021-09-28 20:59 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.025 s 0.658163
2021-09-28 21:01 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.129 s 0.171062
2021-09-28 21:03 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.445 s 1.185835
2021-09-28 21:04 Python file-write lz4, feather, table, fanniemae_2016Q4 1.154 s 0.613621
2021-09-28 21:08 Python wide-dataframe use_legacy_dataset=false 0.615 s 0.492868
2021-09-28 21:04 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.534 s 0.998177
2021-09-28 21:04 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.637 s 0.559655
2021-09-28 21:47 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.990 s -0.738284
2021-09-28 21:04 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.336 s 0.051847
2021-09-28 21:22 R dataframe-to-table type_integers, R 0.085 s -0.279140
2021-09-28 21:46 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.244 s 0.085154
2021-09-28 21:47 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.701883
2021-09-28 21:48 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.395 s -0.937853
2021-09-28 21:05 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.147 s 0.565554
2021-09-28 21:08 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.166958
2021-09-28 21:46 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.950 s -0.301224
2021-09-28 21:46 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.244 s 0.092939
2021-09-28 21:05 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.806 s 1.403599
2021-09-28 21:48 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.567 s -0.862655
2021-09-28 21:06 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.787 s 0.794868
2021-09-28 21:22 R dataframe-to-table chi_traffic_2020_Q1, R 5.379 s 0.493676
2021-09-28 21:46 R dataframe-to-table type_simple_features, R 275.465 s -1.301886
2021-09-28 21:06 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.865 s 1.525665
2021-09-28 21:22 R dataframe-to-table type_nested, R 0.538 s -0.342410
2021-09-28 21:49 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.062 s -1.069961
2021-09-28 21:07 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.353 s -0.138599
2021-09-28 21:07 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.354 s 0.055761
2021-09-28 21:08 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.794 s 0.372060
2021-09-28 21:22 R dataframe-to-table type_dict, R 0.029 s 2.590568
2021-09-28 21:22 R dataframe-to-table type_floats, R 0.108 s 0.256088
2021-09-28 21:07 Python file-write lz4, feather, table, nyctaxi_2010-01 1.806 s 0.268540
2021-09-28 21:22 R dataframe-to-table type_strings, R 0.491 s 0.050499
2021-09-28 21:49 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.198 s -1.548819
2021-09-28 21:49 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.134 s -0.323593
2021-09-28 21:02 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.167 s 1.023415
2021-09-28 21:50 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.675174
2021-09-28 21:07 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.826 s 0.785632
2021-09-28 21:48 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.928 s -0.653566
2021-09-28 21:50 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.232 s 0.411283
2021-09-28 21:51 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.670 s 1.335187
2021-09-28 21:51 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.992 s -1.307570
2021-09-28 21:52 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.519 s -0.085316
2021-09-28 21:53 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.866 s 1.113125
2021-09-28 22:11 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.473 s -0.087581
2021-09-28 22:19 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.023074
2021-09-28 22:19 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.179826
2021-09-28 22:19 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.999859
2021-09-28 22:02 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.809 s 1.867334
2021-09-28 22:08 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.581 s 1.865209
2021-09-28 22:09 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 1.696132
2021-09-28 21:55 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.305 s 1.197446
2021-09-28 22:08 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 2.200273
2021-09-28 22:19 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.088048
2021-09-28 22:19 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.594461
2021-09-28 22:19 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.147102
2021-09-28 22:19 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.345893
2021-09-28 22:04 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.683 s 1.570622
2021-09-28 21:57 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.739 s 1.197460
2021-09-28 22:06 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.261 s 0.468291
2021-09-28 22:09 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.367677
2021-09-28 22:19 JavaScript Parse serialize, tracks 0.005 s 0.457435
2021-09-28 22:19 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.624 s -0.244966
2021-09-28 22:19 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.436788
2021-09-28 22:19 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.506 s 0.099711
2021-09-28 22:12 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.504 s 0.115249
2021-09-28 22:19 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.897 s 0.104963
2021-09-28 22:19 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.529902
2021-09-28 22:19 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.606336
2021-09-28 21:54 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.292 s 1.140995
2021-09-28 22:19 JavaScript Parse Table.from, tracks 0.000 s 0.880298
2021-09-28 22:19 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.042563
2021-09-28 22:19 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.212433
2021-09-28 22:19 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.040445
2021-09-28 21:58 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.591 s 0.611312
2021-09-28 22:07 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.197 s -0.209754
2021-09-28 22:10 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.348 s 1.660382
2021-09-28 22:11 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 0.996807
2021-09-28 21:59 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.403 s -0.209092
2021-09-28 22:19 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.447152
2021-09-28 21:57 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.834 s -0.780047
2021-09-28 22:00 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.216 s 0.856880
2021-09-28 22:08 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.887 s 1.915576
2021-09-28 22:09 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.523 s -1.018468
2021-09-28 22:19 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.566617
2021-09-28 22:03 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.467 s 1.898965
2021-09-28 22:19 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.362663
2021-09-28 22:19 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.042669
2021-09-28 22:19 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578334
2021-09-28 22:19 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.181303
2021-09-28 22:10 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.960 s 1.667989
2021-09-28 22:19 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.513673
2021-09-28 22:05 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -1.265582
2021-09-28 22:08 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.365125
2021-09-28 22:10 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.101575
2021-09-28 22:01 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.825 s 2.000036
2021-09-28 22:07 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.489 s 0.216197
2021-09-28 22:08 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.282431
2021-09-28 22:19 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.893 s -0.179441
2021-09-28 22:19 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.086066
2021-09-28 22:11 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.167 s 1.641826
2021-09-28 22:19 JavaScript Parse readBatches, tracks 0.000 s 0.376925
2021-09-28 22:19 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.599 s -0.092208
2021-09-28 22:19 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.662 s 0.380352
2021-09-28 22:10 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.701 s -1.394376
2021-09-28 22:19 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.683 s 0.378734