Outliers: 7


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-07 11:04 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.971 s -0.388092
2021-10-07 11:05 Python csv-read uncompressed, file, fanniemae_2016Q4 1.195 s -1.224248
2021-10-07 11:06 Python csv-read gzip, streaming, fanniemae_2016Q4 14.902 s -0.367978
2021-10-07 11:07 Python csv-read uncompressed, file, nyctaxi_2010-01 1.020 s -0.680264
2021-10-07 11:11 Python dataframe-to-table type_integers 0.011 s 0.431292
2021-10-07 11:08 Python csv-read gzip, streaming, nyctaxi_2010-01 10.487 s 1.196750
2021-10-07 11:06 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.504798
2021-10-07 11:12 Python dataframe-to-table type_nested 2.869 s 0.814992
2021-10-07 11:07 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.499 s 1.161868
2021-10-07 11:08 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s -0.167117
2021-10-07 11:12 Python dataset-filter nyctaxi_2010-01 4.349 s 0.836542
2021-10-07 11:10 Python dataframe-to-table chi_traffic_2020_Q1 19.696 s -0.247366
2021-10-07 11:11 Python dataframe-to-table type_strings 0.372 s -0.350815
2021-10-07 11:11 Python dataframe-to-table type_dict 0.012 s 0.782787
2021-10-07 11:11 Python dataframe-to-table type_floats 0.011 s -0.319059
2021-10-07 11:12 Python dataframe-to-table type_simple_features 0.912 s 0.152413
2021-10-07 11:15 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.227 s 0.440357
2021-10-07 11:20 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.792 s 0.568083
2021-10-07 11:29 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.887 s 0.163599
2021-10-07 11:32 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.061 s -0.474624
2021-10-07 11:41 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.941 s 0.508195
2021-10-07 11:41 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.755 s -0.084930
2021-10-07 11:44 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.029 s 0.433096
2021-10-07 11:45 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s 0.167020
2021-10-07 11:46 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.992 s -1.025818
2021-10-07 11:49 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.778 s -0.496636
2021-10-07 11:50 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.332 s -1.334402
2021-10-07 11:52 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.813 s 0.716455
2021-10-07 11:53 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.818 s -0.492824
2021-10-07 12:22 R dataframe-to-table type_strings, R 17.299 s -7.056953
2021-10-07 12:34 R dataframe-to-table type_simple_features, R 3.256 s 3.216865
2021-10-07 12:34 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.472 s 7.006269
2021-10-07 12:43 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.308 s 0.558414
2021-10-07 12:46 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.825 s 1.349527
2021-10-07 12:47 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.163963
2021-10-07 12:49 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.822 s 0.932321
2021-10-07 12:51 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.455 s 0.990427
2021-10-07 11:42 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.237 s 0.186938
2021-10-07 11:44 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.307 s -0.917719
2021-10-07 11:51 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.794 s 0.440425
2021-10-07 11:53 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.347 s -0.350463
2021-10-07 12:35 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.244 s 0.091477
2021-10-07 12:43 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.290 s 0.542983
2021-10-07 12:53 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.515415
2021-10-07 11:42 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.837 s -0.235226
2021-10-07 11:44 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.243 s -0.767427
2021-10-07 11:46 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.084 s 0.632578
2021-10-07 11:54 Python wide-dataframe use_legacy_dataset=false 0.624 s -0.563980
2021-10-07 12:35 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.472 s 5.735028
2021-10-07 12:37 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.409 s -1.510723
2021-10-07 12:39 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.109567
2021-10-07 11:42 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.287 s 0.170621
2021-10-07 11:43 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.915 s -0.429269
2021-10-07 11:43 Python file-read lz4, feather, table, fanniemae_2016Q4 0.609 s -0.956167
2021-10-07 11:45 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.360 s -1.106493
2021-10-07 11:47 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.295 s 0.131828
2021-10-07 12:35 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.238668
2021-10-07 11:43 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.867 s -0.715972
2021-10-07 11:53 Python file-write lz4, feather, table, nyctaxi_2010-01 1.808 s 0.079108
2021-10-07 12:22 R dataframe-to-table type_integers, R 0.082 s -0.011519
2021-10-07 12:36 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.957 s -2.237076
2021-10-07 11:29 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.394 s -0.514464
2021-10-07 11:32 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.046 s -0.131947
2021-10-07 11:32 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.005 s 0.244473
2021-10-07 12:55 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.232 s 1.636558
2021-10-07 11:41 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.845 s 0.270116
2021-10-07 11:42 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.001 s -0.011131
2021-10-07 11:43 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.147 s -0.091372
2021-10-07 11:44 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.059 s -0.369882
2021-10-07 11:48 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.443 s 0.616632
2021-10-07 11:48 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.664 s 0.120706
2021-10-07 11:50 Python file-write lz4, feather, table, fanniemae_2016Q4 1.157 s 0.374595
2021-10-07 11:50 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.780 s 0.901289
2021-10-07 11:51 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.845 s 0.858484
2021-10-07 11:52 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.357 s -0.378117
2021-10-07 11:54 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.680712
2021-10-07 12:34 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.244 s 0.262481
2021-10-07 12:48 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.209 s 0.222831
2021-10-07 11:43 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.811 s -0.976004
2021-10-07 12:36 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.568006
2021-10-07 12:56 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.491 s -0.250238
2021-10-07 13:00 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.513 s 0.524682
2021-10-07 11:43 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.284 s 1.015991
2021-10-07 11:45 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.497 s -1.044271
2021-10-07 11:49 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.310 s 0.166555
2021-10-07 12:24 R dataframe-to-table type_nested, R 17.634 s -7.126356
2021-10-07 12:37 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.069 s -2.081781
2021-10-07 12:47 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.561 s 0.526785
2021-10-07 11:45 Python file-read lz4, feather, table, nyctaxi_2010-01 0.672 s -0.646921
2021-10-07 12:37 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.180 s 7.031074
2021-10-07 12:37 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.131 s -0.421789
2021-10-07 12:53 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.629 s 1.409324
2021-10-07 12:38 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.231 s 7.046292
2021-10-07 12:38 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.451427
2021-10-07 12:50 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.772 s 1.295491
2021-10-07 12:21 R dataframe-to-table chi_traffic_2020_Q1, R 300.474 s -5.750446
2021-10-07 12:22 R dataframe-to-table type_dict, R 0.053 s -0.227077
2021-10-07 12:39 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.974 s 0.754474
2021-10-07 12:23 R dataframe-to-table type_floats, R 0.107 s 0.001501
2021-10-07 12:40 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.523 s 0.581002
2021-10-07 12:45 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.742 s 0.594064
2021-10-07 12:41 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.858 s 0.543665
2021-10-07 12:58 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.857 s 0.655596
2021-10-07 12:57 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.174 s 0.581932
2021-10-07 12:58 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.664 s -0.925174
2021-10-07 12:58 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.570 s 0.624560
2021-10-07 12:59 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.564599
2021-10-07 13:00 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 0.624719
2021-10-07 12:59 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.386454
2021-10-07 13:00 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.784715
2021-10-07 13:01 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.908 s 0.589856
2021-10-07 13:01 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.521 s 1.095572
2021-10-07 13:02 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -2.004468
2021-10-07 13:02 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s 0.219698
2021-10-07 13:03 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.481 s -0.760024
2021-10-07 13:03 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.210 s -2.158201
2021-10-07 13:04 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.220 s 0.452437
2021-10-07 13:04 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.500 s -0.465587
2021-10-07 13:12 JavaScript Parse readBatches, tracks 0.000 s 0.750436
2021-10-07 13:16 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.905 s -0.006324
2021-10-07 13:12 JavaScript Parse readBatches, tracks 0.000 s 0.750436
2021-10-07 13:12 JavaScript Parse Table.from, tracks 0.000 s 0.564056
2021-10-07 13:12 JavaScript Parse Table.from, tracks 0.000 s 0.564056
2021-10-07 13:13 JavaScript Parse serialize, tracks 0.004 s 0.476108
2021-10-07 13:18 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.135598
2021-10-07 13:14 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -2.436437
2021-10-07 13:15 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.536061
2021-10-07 13:16 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.725 s 0.126564
2021-10-07 13:17 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.563815
2021-10-07 13:15 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.254 s 2.622113
2021-10-07 13:13 JavaScript Parse serialize, tracks 0.004 s 0.476108
2021-10-07 13:17 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.480942
2021-10-07 13:15 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.305 s 2.596624
2021-10-07 13:16 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.571812
2021-10-07 13:15 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.697 s -0.175829
2021-10-07 13:16 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.841658
2021-10-07 13:14 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.467168
2021-10-07 13:19 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.400157
2021-10-07 13:15 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.717780
2021-10-07 13:17 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.480942
2021-10-07 13:16 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.872 s 0.149927
2021-10-07 13:16 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.608590
2021-10-07 13:18 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.199918
2021-10-07 13:16 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.730983
2021-10-07 13:18 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.135598
2021-10-07 13:21 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.406358
2021-10-07 13:22 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.618 s -2.067873
2021-10-07 13:18 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.199918
2021-10-07 13:22 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.158710
2021-10-07 13:20 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.451582
2021-10-07 13:19 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.400157
2021-10-07 13:20 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.169355
2021-10-07 13:21 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.115729
2021-10-07 13:20 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.375205
2021-10-07 13:21 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.449744
2021-10-07 13:22 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.158710
2021-10-07 13:22 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.618 s -2.067873