Top Outliers
Benchmarks
Date Lang Batch Benchmark Mean Z-Score Error
2023-01-25 03:39 Python csv-read gzip, arrow_table, file, fanniemae_2016Q4 5.775 s 0.665
2023-01-25 03:42 Python csv-read gzip, arrow_table, streaming, nyctaxi_2010-01 11.419 s -1.634
2023-01-25 03:45 Python dataframe-to-table type_nested 3.018 s -4.274
2023-01-25 03:45 Python dataset-filter nyctaxi_2010-01 1.032 s -0.009
2023-01-25 04:05 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.278 s -0.207
2023-01-25 03:39 Python csv-read uncompressed, arrow_table, file, fanniemae_2016Q4 1.239 s 0.238
2023-01-25 04:07 Python dataset-serialize parquet, 10pc, nyctaxi_multi_parquet_s3 2.905 s 1.453
2023-01-25 04:07 Python dataset-serialize feather, 10pc, nyctaxi_multi_parquet_s3 0.200 s -1.881
2023-01-25 03:41 Python csv-read uncompressed, arrow_table, file, nyctaxi_2010-01 1.129 s 0.111
2023-01-25 03:44 Python dataframe-to-table type_floats 0.010 s 0.091
2023-01-25 04:06 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.017 s 0.261
2023-01-25 04:06 Python dataset-serialize parquet, 1pc, nyctaxi_multi_parquet_s3 0.303 s 1.562
2023-01-25 04:11 Python dataset-serialize parquet, 100pc, nyctaxi_multi_parquet_s3 30.046 s 1.363
2023-01-25 03:40 Python csv-read gzip, arrow_table, streaming, fanniemae_2016Q4 13.539 s 1.319
2023-01-25 03:49 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 88.038 s -0.792
2023-01-25 04:06 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.664 s 0.228
2023-01-25 04:06 Python dataset-selectivity 10%, chi_traffic_2020_Q1 1.216 s -0.111
2023-01-25 04:08 Python dataset-serialize csv, 10pc, nyctaxi_multi_parquet_s3 7.260 s 1.209
2023-01-25 03:41 Python csv-read gzip, arrow_table, file, nyctaxi_2010-01 8.439 s -0.511
2023-01-25 03:44 Python dataframe-to-table type_integers 0.010 s -0.974
2023-01-25 04:06 Python dataset-serialize csv, 1pc, nyctaxi_multi_parquet_s3 0.730 s 1.224
2023-01-25 03:41 Python csv-read uncompressed, arrow_table, streaming, fanniemae_2016Q4 13.542 s 1.170
2023-01-25 03:42 Python csv-read uncompressed, arrow_table, streaming, nyctaxi_2010-01 11.385 s -1.700
2023-01-25 03:44 Python dataframe-to-table type_dict 0.011 s 2.581
2023-01-25 03:53 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.539 s 0.313
2023-01-25 04:05 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.244 s -0.168
2023-01-25 04:06 Python dataset-selectivity 100%, chi_traffic_2020_Q1 1.134 s 0.449
2023-01-25 03:44 Python dataframe-to-table chi_traffic_2020_Q1 20.667 s 2.118
2023-01-25 04:05 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.382 s -0.194
2023-01-25 04:05 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.372 s -0.414
2023-01-25 03:44 Python dataframe-to-table type_strings 0.427 s 0.264
2023-01-25 04:11 Python dataset-serialize arrow, 100pc, nyctaxi_multi_parquet_s3 2.153 s -1.335
2023-01-25 04:05 Python dataset-read async=True, nyctaxi_multi_ipc_s3 234.072 s -0.997
2023-01-25 04:06 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.036 s 0.213
2023-01-25 04:06 Python dataset-selectivity 1%, chi_traffic_2020_Q1 1.177 s -0.039
2023-01-25 04:06 Python dataset-serialize arrow, 1pc, nyctaxi_multi_parquet_s3 0.023 s -0.012
2023-01-25 04:06 Python dataset-serialize feather, 1pc, nyctaxi_multi_parquet_s3 0.023 s -1.758
2023-01-25 04:07 Python dataset-serialize arrow, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.133
2023-01-25 04:11 Python dataset-serialize feather, 100pc, nyctaxi_multi_parquet_s3 2.153 s -0.573
2023-01-25 04:19 Python dataset-serialize feather, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.321
2023-01-25 04:19 Python dataset-serialize parquet, 1pc, nyctaxi_multi_ipc_s3 0.283 s 1.231
2023-01-25 04:19 Python dataset-serialize arrow, 1pc, nyctaxi_multi_ipc_s3 0.026 s 0.687
2023-01-25 04:19 Python dataset-serialize parquet, 10pc, nyctaxi_multi_ipc_s3 3.000 s 1.217
2023-01-25 04:19 Python dataset-serialize csv, 100pc, nyctaxi_multi_parquet_s3 73.353 s 1.188
2023-01-25 04:19 Python dataset-serialize csv, 1pc, nyctaxi_multi_ipc_s3 0.836 s 1.128
2023-01-25 04:19 Python dataset-serialize feather, 10pc, nyctaxi_multi_ipc_s3 0.226 s -0.765
2023-01-25 04:19 Python dataset-serialize arrow, 10pc, nyctaxi_multi_ipc_s3 0.226 s -1.183
2023-01-25 04:20 Python dataset-serialize csv, 10pc, nyctaxi_multi_ipc_s3 8.395 s 1.136
2023-01-25 04:23 Python dataset-serialize parquet, 100pc, nyctaxi_multi_ipc_s3 30.418 s 1.240
2023-01-25 04:24 Python dataset-serialize arrow, 100pc, nyctaxi_multi_ipc_s3 2.409 s -0.646
2023-01-25 04:24 Python dataset-serialize feather, 100pc, nyctaxi_multi_ipc_s3 2.407 s 1.052
2023-01-25 04:34 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 4.301 s -1.400
2023-01-25 04:34 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 0.963 s 0.484
2023-01-25 04:53 R dataframe-to-table type_strings, R 0.535 s -0.125
2023-01-25 04:54 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.601 s -0.982
2023-01-25 04:33 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 1.527 s -0.904
2023-01-25 04:39 Python file-write lz4, feather, table, fanniemae_2016Q4 1.839 s -0.006
2023-01-25 04:33 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 1.661 s -0.293
2023-01-25 04:34 Python file-read lz4, feather, table, fanniemae_2016Q4 0.816 s 0.047
2023-01-25 04:34 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.978 s 0.275
2023-01-25 04:42 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 4.122 s -0.143
2023-01-25 04:52 R dataframe-to-table chi_traffic_2020_Q1, R 4.353 s 0.343
2023-01-25 04:54 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.351 s -1.320
2023-01-25 04:55 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.589 s 0.235
2023-01-25 04:55 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 0.575 s -0.274
2023-01-25 04:56 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.214 s 0.228
2023-01-25 05:03 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.969 s -1.194
2023-01-25 05:04 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.022 s 0.063
2023-01-25 05:11 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.321 s -3.728
2023-01-25 04:33 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.319 s 0.200
2023-01-25 04:34 Python file-read lz4, feather, table, nyctaxi_2010-01 0.676 s 0.207
2023-01-25 04:35 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 10.654 s -0.128
2023-01-25 04:38 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 20.260 s 0.789
2023-01-25 04:40 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.833 s 0.332
2023-01-25 04:42 Python file-write lz4, feather, table, nyctaxi_2010-01 1.792 s -0.261
2023-01-25 04:55 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 0.571 s 0.132
2023-01-25 05:05 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.493 s -0.122
2023-01-25 05:06 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 7.472 s 0.154
2023-01-25 04:33 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 5.705 s -0.012
2023-01-25 04:34 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.954 s -0.269
2023-01-25 04:36 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 20.124 s 0.526
2023-01-25 04:41 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 9.216 s 0.340
2023-01-25 04:53 R dataframe-to-table type_dict, R 0.058 s -0.418
2023-01-25 04:53 R dataframe-to-table type_floats, R 0.017 s -25.931
2023-01-25 04:54 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.316 s 0.174
2023-01-25 04:33 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.523 s -0.610
2023-01-25 04:34 Python file-read uncompressed, feather, table, nyctaxi_2010-01 0.931 s 0.226
2023-01-25 04:40 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 10.872 s 0.128
2023-01-25 04:41 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.742 s 0.194
2023-01-25 04:41 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.729 s -0.160
2023-01-25 04:42 Python wide-dataframe use_legacy_dataset=true 0.377 s 0.178
2023-01-25 04:53 R dataframe-to-table type_integers, R 0.010 s -1.388
2023-01-25 04:32 Python dataset-serialize csv, 100pc, nyctaxi_multi_ipc_s3 83.288 s 1.147
2023-01-25 04:34 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 0.945 s -0.013
2023-01-25 04:34 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 1.343 s 0.053
2023-01-25 04:53 R dataframe-to-table type_nested, R 0.576 s -0.825
2023-01-25 04:56 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.576 s 0.192
2023-01-25 05:00 R file-write snappy, parquet, table, fanniemae_2016Q4, R 10.131 s 0.841
2023-01-25 05:12 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.169 s 0.678
2023-01-25 04:33 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.642 s 0.139
2023-01-25 04:37 Python file-write snappy, parquet, table, fanniemae_2016Q4 10.742 s 0.913
2023-01-25 04:54 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.601 s -0.995
2023-01-25 04:55 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.841 s 0.286
2023-01-25 04:56 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 0.923 s -0.390
2023-01-25 04:56 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.896 s 0.222
2023-01-25 05:07 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.929 s 0.967
2023-01-25 04:34 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 1.574 s 0.302
2023-01-25 04:38 Python file-write uncompressed, feather, table, fanniemae_2016Q4 6.420 s -0.434
2023-01-25 04:40 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 7.337 s 0.236
2023-01-25 04:42 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 3.214 s -0.255
2023-01-25 04:42 Python wide-dataframe use_legacy_dataset=false 0.512 s 0.334
2023-01-25 04:54 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.340 s -0.718
2023-01-25 04:56 R file-read snappy, parquet, table, nyctaxi_2010-01, R 0.577 s -0.333
2023-01-25 04:57 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 9.729 s 0.733
2023-01-25 04:59 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 16.706 s 0.623
2023-01-25 05:09 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.659 s 0.910
2023-01-25 04:39 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 15.340 s -0.354
2023-01-25 04:56 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.814 s 0.167
2023-01-25 05:08 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.782 s 0.583
2023-01-25 05:10 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.725 s 0.764
2023-01-25 04:55 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 0.919 s -0.207
2023-01-25 05:02 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 17.119 s 0.888
2023-01-25 05:13 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.473 s -0.767
2023-01-25 05:14 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.570 s -0.220
2023-01-25 05:14 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.262 s -1.708
2023-01-25 05:14 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.075 s 0.704
2023-01-25 05:14 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.597 s 0.012
2023-01-25 05:15 R partitioned-dataset-filter dims, dataset-taxi-parquet, R 0.589 s 0.860
2023-01-25 05:25 JavaScript Iterate Vector uint8Array 0.002 s 1.855
2023-01-25 05:25 JavaScript Iterate Vector numbers 0.002 s 0.727
2023-01-25 05:25 JavaScript vectorFromArray dictionary 0.017 s 0.008
2023-01-25 05:25 JavaScript Iterate Vector int16Array 0.002 s 0.365
2023-01-25 05:25 JavaScript Iterate Vector int8Array 0.002 s 0.301
2023-01-25 05:25 JavaScript Iterate Vector int32Array 0.002 s 0.675
2023-01-25 05:25 JavaScript Iterate Vector uint64Array 0.004 s -0.077
2023-01-25 05:25 JavaScript Iterate Vector int64Array 0.004 s -0.544
2023-01-25 05:25 JavaScript Iterate Vector booleans 0.004 s 0.240
2023-01-25 05:25 JavaScript Iterate Vector float64Array 0.002 s 0.485
2023-01-25 05:25 JavaScript toArray Vector uint16Array
2023-01-25 05:25 JavaScript toArray Vector int16Array
2023-01-25 05:25 JavaScript toArray Vector int64Array
2023-01-25 05:25 JavaScript Iterate Vector string 0.126 s 0.024
2023-01-25 05:25 JavaScript Spread Vector uint16Array 0.006 s 0.079
2023-01-25 05:25 JavaScript Spread Vector int64Array 0.012 s 0.040
2023-01-25 05:25 JavaScript get Vector int64Array 0.003 s -0.252
2023-01-25 05:25 JavaScript Parse write recordBatches, tracks 0.002 s -1.985
2023-01-25 05:25 JavaScript vectorFromArray numbers 0.017 s -0.252
2023-01-25 05:25 JavaScript Iterate Vector uint16Array 0.002 s 0.535
2023-01-25 05:25 JavaScript get Vector string 0.124 s 0.602
2023-01-25 05:25 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.572
2023-01-25 05:25 JavaScript Iterate Vector uint32Array 0.002 s 0.553
2023-01-25 05:25 JavaScript Iterate Vector dictionary 0.004 s -1.254
2023-01-25 05:25 JavaScript toArray Vector int8Array
2023-01-25 05:25 JavaScript vectorFromArray booleans 0.018 s 0.047
2023-01-25 05:25 JavaScript Spread Vector numbers 0.008 s -0.192
2023-01-25 05:25 JavaScript toArray Vector uint32Array
2023-01-25 05:25 JavaScript toArray Vector dictionary 0.010 s 1.044
2023-01-25 05:25 JavaScript get Vector uint8Array 0.003 s -0.304
2023-01-25 05:25 JavaScript Spread Vector uint8Array 0.007 s -0.548
2023-01-25 05:25 JavaScript Spread Vector float32Array 0.008 s 0.899
2023-01-25 05:25 JavaScript toArray Vector int32Array
2023-01-25 05:25 JavaScript toArray Vector numbers
2023-01-25 05:25 JavaScript get Vector dictionary 0.002 s 0.039
2023-01-25 05:25 JavaScript Iterate Vector float32Array 0.002 s -0.054
2023-01-25 05:25 JavaScript Spread Vector uint32Array 0.007 s 0.155
2023-01-25 05:25 JavaScript Spread Vector int8Array 0.006 s 0.119
2023-01-25 05:25 JavaScript Spread Vector int32Array 0.007 s 0.195
2023-01-25 05:25 JavaScript Spread Vector dictionary 0.010 s 0.414
2023-01-25 05:25 JavaScript Spread vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.103
2023-01-25 05:25 JavaScript Spread Vector uint64Array 0.012 s -0.460
2023-01-25 05:25 JavaScript Spread Vector int16Array 0.007 s -0.519
2023-01-25 05:25 JavaScript Spread Vector float64Array 0.008 s -0.402
2023-01-25 05:25 JavaScript toArray Vector string 0.144 s 0.537
2023-01-25 05:25 JavaScript get Vector int16Array 0.003 s 0.184
2023-01-25 05:25 JavaScript Spread Vector booleans 0.010 s -0.102
2023-01-25 05:25 JavaScript Spread Vector string 0.144 s 0.719
2023-01-25 05:25 JavaScript toArray Vector uint64Array
2023-01-25 05:25 JavaScript toArray Vector float64Array
2023-01-25 05:25 JavaScript get Vector uint16Array 0.003 s 0.141
2023-01-25 05:25 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.656
2023-01-25 05:25 JavaScript toArray Vector uint8Array
2023-01-25 05:25 JavaScript toArray Vector float32Array
2023-01-25 05:25 JavaScript Table Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.032 s 1.064
2023-01-25 05:25 JavaScript toArray Vector booleans 0.010 s 0.270
2023-01-25 05:25 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.023 s 0.464
2023-01-25 05:25 JavaScript get Vector uint32Array 0.003 s -0.016
2023-01-25 05:25 JavaScript get Vector numbers 0.002 s 0.416
2023-01-25 05:25 JavaScript Parse read recordBatches, tracks 0.000 s -1.412
2023-01-25 05:25 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.502
2023-01-25 05:25 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.023 s 0.344
2023-01-25 05:25 JavaScript Table tracks, 1,000,000 0.319 s -3.254
2023-01-25 05:25 JavaScript get Vector uint64Array 0.003 s -0.228
2023-01-25 05:25 JavaScript get Vector booleans 0.002 s -0.194
2023-01-25 05:25 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.030 s 0.138
2023-01-25 05:25 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s
2023-01-25 05:25 JavaScript get Vector int8Array 0.003 s 0.648
2023-01-25 05:25 JavaScript get Vector int32Array 0.003 s -0.098
2023-01-25 05:25 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.030 s 0.043
2023-01-25 05:25 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.642
2023-01-25 05:25 JavaScript get Vector float32Array 0.002 s 0.328
2023-01-25 05:25 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s
2023-01-25 05:25 JavaScript get Vector float64Array 0.002 s -1.304
2023-01-25 05:25 JavaScript Spread vectors lng, 1,000,000, Float32, tracks 0.188 s 0.090
2023-01-25 05:25 JavaScript Spread vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.164
2023-01-25 05:25 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s -0.755
2023-01-25 05:25 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.107 s 0.641
2023-01-25 05:25 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-25 05:25 JavaScript Spread vectors lat, 1,000,000, Float32, tracks 0.190 s -0.864
2023-01-25 05:25 JavaScript Table tracks, 1,000,000 0.050 s -0.358
2023-01-25 05:25 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s -0.566
2023-01-25 05:25 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s -0.039
2023-01-25 05:25 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-25 05:25 JavaScript Table 1,000,000, tracks 0.315 s -1.724
2023-01-25 05:25 JavaScript Table Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.032 s -2.951
2023-01-25 05:25 JavaScript Table tracks, 1,000,000 0.094 s 1.246
2023-01-25 05:25 JavaScript Table Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.032 s -0.158