Top Outliers
Benchmarks
Date Lang Batch Benchmark Mean Z-Score Error
2023-01-24 17:25 Python dataframe-to-table type_dict 0.011 s 3.181
2023-01-24 17:44 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.028 s 0.238
2023-01-24 18:03 Python dataset-serialize feather, 100pc, nyctaxi_multi_ipc_s3 2.412 s -0.808
2023-01-24 17:21 Python csv-read gzip, arrow_table, streaming, fanniemae_2016Q4 13.548 s 1.233
2023-01-24 17:25 Python dataframe-to-table type_strings 0.426 s 0.612
2023-01-24 17:44 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.363 s -0.111
2023-01-24 17:44 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.359 s -0.326
2023-01-24 17:45 Python dataset-selectivity 1%, chi_traffic_2020_Q1 1.178 s -0.164
2023-01-24 17:45 Python dataset-selectivity 100%, chi_traffic_2020_Q1 1.298 s -5.936
2023-01-24 17:45 Python dataset-serialize arrow, 1pc, nyctaxi_multi_parquet_s3 0.024 s -8.552
2023-01-24 17:45 Python dataset-serialize csv, 1pc, nyctaxi_multi_parquet_s3 0.731 s 1.308
2023-01-24 17:58 Python dataset-serialize csv, 1pc, nyctaxi_multi_ipc_s3 0.837 s 1.223
2023-01-24 17:58 Python dataset-serialize arrow, 10pc, nyctaxi_multi_ipc_s3 0.225 s 0.828
2023-01-24 17:33 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.641 s 0.657
2023-01-24 18:12 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 1.638 s -0.005
2023-01-24 17:20 Python csv-read uncompressed, arrow_table, file, fanniemae_2016Q4 1.248 s 0.142
2023-01-24 17:23 Python csv-read gzip, arrow_table, streaming, nyctaxi_2010-01 11.397 s -1.577
2023-01-24 17:25 Python dataframe-to-table type_floats 0.010 s 0.194
2023-01-24 17:45 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.657 s 0.244
2023-01-24 17:45 Python dataset-serialize parquet, 1pc, nyctaxi_multi_parquet_s3 0.304 s 0.991
2023-01-24 17:46 Python dataset-serialize parquet, 10pc, nyctaxi_multi_parquet_s3 2.906 s 1.451
2023-01-24 18:02 Python dataset-serialize parquet, 100pc, nyctaxi_multi_ipc_s3 30.429 s 1.154
2023-01-24 18:12 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.523 s -0.694
2023-01-24 17:20 Python csv-read gzip, arrow_table, file, fanniemae_2016Q4 5.789 s -0.367
2023-01-24 17:25 Python dataframe-to-table type_nested 2.954 s 0.546
2023-01-24 17:46 Python dataset-serialize arrow, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.688
2023-01-24 17:57 Python dataset-serialize csv, 100pc, nyctaxi_multi_parquet_s3 73.354 s 1.328
2023-01-24 17:58 Python dataset-serialize arrow, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.274
2023-01-24 17:59 Python dataset-serialize csv, 10pc, nyctaxi_multi_ipc_s3 8.397 s 1.256
2023-01-24 18:03 Python dataset-serialize arrow, 100pc, nyctaxi_multi_ipc_s3 2.405 s 0.660
2023-01-24 17:22 Python csv-read gzip, arrow_table, file, nyctaxi_2010-01 8.434 s 0.351
2023-01-24 17:23 Python csv-read uncompressed, arrow_table, streaming, nyctaxi_2010-01 11.354 s -1.635
2023-01-24 17:44 Python dataset-read async=True, nyctaxi_multi_ipc_s3 211.857 s 1.252
2023-01-24 17:44 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.248 s -0.233
2023-01-24 17:45 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.014 s 0.270
2023-01-24 17:46 Python dataset-serialize csv, 10pc, nyctaxi_multi_parquet_s3 7.263 s 1.329
2023-01-24 17:21 Python csv-read uncompressed, arrow_table, streaming, fanniemae_2016Q4 13.419 s 1.526
2023-01-24 17:22 Python csv-read uncompressed, arrow_table, file, nyctaxi_2010-01 1.125 s 0.151
2023-01-24 17:25 Python dataframe-to-table chi_traffic_2020_Q1 21.012 s 0.118
2023-01-24 17:45 Python dataset-serialize feather, 1pc, nyctaxi_multi_parquet_s3 0.023 s -1.315
2023-01-24 17:50 Python dataset-serialize feather, 100pc, nyctaxi_multi_parquet_s3 2.152 s -0.166
2023-01-24 17:58 Python dataset-serialize parquet, 1pc, nyctaxi_multi_ipc_s3 0.283 s 1.130
2023-01-24 17:58 Python dataset-serialize feather, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.283
2023-01-24 17:58 Python dataset-serialize parquet, 10pc, nyctaxi_multi_ipc_s3 3.002 s 1.093
2023-01-24 17:58 Python dataset-serialize feather, 10pc, nyctaxi_multi_ipc_s3 0.226 s -0.610
2023-01-24 18:11 Python dataset-serialize csv, 100pc, nyctaxi_multi_ipc_s3 83.306 s 1.270
2023-01-24 17:25 Python dataframe-to-table type_integers 0.010 s 1.294
2023-01-24 17:50 Python dataset-serialize arrow, 100pc, nyctaxi_multi_parquet_s3 2.151 s -0.855
2023-01-24 17:26 Python dataset-filter nyctaxi_2010-01 1.034 s -0.089
2023-01-24 17:46 Python dataset-serialize feather, 10pc, nyctaxi_multi_parquet_s3 0.200 s -2.547
2023-01-24 17:49 Python dataset-serialize parquet, 100pc, nyctaxi_multi_parquet_s3 30.074 s 1.199
2023-01-24 17:29 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 68.841 s 1.443
2023-01-24 17:44 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.259 s -0.093
2023-01-24 17:45 Python dataset-selectivity 10%, chi_traffic_2020_Q1 1.210 s 0.073
2023-01-24 18:12 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 1.527 s -0.954
2023-01-24 18:12 Python file-read lz4, feather, table, fanniemae_2016Q4 0.814 s 0.165
2023-01-24 18:12 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.659 s -0.145
2023-01-24 18:12 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.319 s 0.198
2023-01-24 18:12 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 5.711 s -0.056
2023-01-24 18:13 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.893 s 0.986
2023-01-24 18:13 Python file-read uncompressed, feather, table, nyctaxi_2010-01 0.942 s 0.149
2023-01-24 18:13 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.994 s -0.160
2023-01-24 18:13 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 4.292 s -1.385
2023-01-24 18:13 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 0.947 s -0.084
2023-01-24 18:13 Python file-read lz4, feather, table, nyctaxi_2010-01 0.675 s 0.214
2023-01-24 18:13 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 0.982 s 0.017
2023-01-24 18:13 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 1.577 s 0.285
2023-01-24 18:13 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 1.321 s 0.392
2023-01-24 18:14 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 10.510 s 0.858
2023-01-24 18:15 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 20.077 s 0.722
2023-01-24 18:16 Python file-write snappy, parquet, table, fanniemae_2016Q4 10.748 s 0.757
2023-01-24 18:17 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 20.237 s 0.819
2023-01-24 18:17 Python file-write uncompressed, feather, table, fanniemae_2016Q4 6.438 s -0.466
2023-01-24 18:18 Python file-write lz4, feather, table, fanniemae_2016Q4 1.872 s -0.303
2023-01-24 18:18 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 15.521 s -0.709
2023-01-24 18:19 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 10.911 s -0.240
2023-01-24 18:19 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.855 s 0.152
2023-01-24 18:19 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 7.348 s 0.130
2023-01-24 18:20 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.732 s 0.260
2023-01-24 18:20 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 9.193 s 0.502
2023-01-24 18:21 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 4.214 s -0.568
2023-01-24 18:20 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.798 s -0.461
2023-01-24 18:21 Python file-write lz4, feather, table, nyctaxi_2010-01 1.761 s -0.002
2023-01-24 18:21 Python wide-dataframe use_legacy_dataset=true 0.376 s 0.220
2023-01-24 18:21 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 3.222 s -0.328
2023-01-24 18:21 Python wide-dataframe use_legacy_dataset=false 0.508 s 0.884
2023-01-24 18:31 R dataframe-to-table chi_traffic_2020_Q1, R 4.370 s -0.128
2023-01-24 18:32 R dataframe-to-table type_integers, R 0.010 s -1.689
2023-01-24 18:31 R dataframe-to-table type_dict, R 0.056 s -0.343
2023-01-24 18:32 R dataframe-to-table type_floats, R 0.013 s 0.515
2023-01-24 18:31 R dataframe-to-table type_strings, R 0.535 s -0.020
2023-01-24 18:32 R dataframe-to-table type_nested, R 0.576 s -0.403
2023-01-24 18:32 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.339 s -0.760
2023-01-24 18:32 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.597 s -0.837
2023-01-24 18:33 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.344 s -0.948
2023-01-24 18:33 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.603 s -1.143
2023-01-24 18:33 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.316 s 0.180
2023-01-24 18:33 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 0.570 s 0.146
2023-01-24 18:34 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 0.574 s -0.302
2023-01-24 18:33 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.597 s 0.135
2023-01-24 18:34 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.855 s 0.194
2023-01-24 18:34 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 0.923 s -0.421
2023-01-24 18:34 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 0.920 s -0.169
2023-01-24 18:34 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s 0.157
2023-01-24 18:34 R file-read snappy, parquet, table, nyctaxi_2010-01, R 0.579 s -0.453
2023-01-24 18:35 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.811 s 0.191
2023-01-24 18:35 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.579 s 0.171
2023-01-24 18:35 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.905 s 0.160
2023-01-24 18:36 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 9.706 s 0.751
2023-01-24 18:38 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 16.685 s 0.608
2023-01-24 18:39 R file-write snappy, parquet, table, fanniemae_2016Q4, R 10.144 s 0.618
2023-01-24 18:41 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 17.116 s 0.749
2023-01-24 18:41 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.965 s -0.540
2023-01-24 18:43 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.020 s 0.066
2023-01-24 18:43 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.499 s -2.886
2023-01-24 18:45 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 7.510 s -1.237
2023-01-24 18:46 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.925 s 0.917
2023-01-24 18:47 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.774 s 0.583
2023-01-24 18:48 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.655 s 0.868
2023-01-24 18:49 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.727 s 0.604
2023-01-24 18:50 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.311 s 0.112
2023-01-24 18:51 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.179 s -0.026
2023-01-24 18:52 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.472 s -0.086
2023-01-24 18:53 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.083 s -0.056
2023-01-24 18:53 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.596 s -0.035
2023-01-24 19:04 JavaScript Spread Vector booleans 0.010 s 1.072
2023-01-24 19:04 JavaScript Spread Vector string 0.142 s 1.638
2023-01-24 19:04 JavaScript Spread Vector uint32Array 0.007 s 0.221
2023-01-24 18:53 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.572 s -0.420
2023-01-24 18:53 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.257 s -0.782
2023-01-24 19:04 JavaScript toArray Vector uint8Array
2023-01-24 19:04 JavaScript toArray Vector int32Array
2023-01-24 19:04 JavaScript get Vector uint32Array 0.003 s -0.199
2023-01-24 18:53 R partitioned-dataset-filter dims, dataset-taxi-parquet, R 0.591 s 0.763
2023-01-24 19:04 JavaScript vectorFromArray numbers 0.016 s 0.505
2023-01-24 19:04 JavaScript vectorFromArray dictionary 0.017 s 0.005
2023-01-24 19:04 JavaScript get Vector uint16Array 0.003 s -0.023
2023-01-24 19:04 JavaScript toArray Vector uint32Array
2023-01-24 19:04 JavaScript toArray Vector float32Array
2023-01-24 19:04 JavaScript toArray Vector dictionary 0.010 s 0.675
2023-01-24 19:04 JavaScript get Vector dictionary 0.002 s 0.451
2023-01-24 19:04 JavaScript Parse read recordBatches, tracks 0.000 s -1.770
2023-01-24 19:04 JavaScript Iterate Vector uint16Array 0.002 s 0.136
2023-01-24 19:04 JavaScript Iterate Vector uint64Array 0.004 s 0.828
2023-01-24 19:04 JavaScript Spread Vector int16Array 0.007 s -0.196
2023-01-24 19:04 JavaScript Spread Vector int64Array 0.012 s 0.043
2023-01-24 19:04 JavaScript Iterate Vector uint32Array 0.002 s -0.064
2023-01-24 19:04 JavaScript Iterate Vector float32Array 0.002 s 0.736
2023-01-24 19:04 JavaScript Spread Vector float32Array 0.008 s 1.617
2023-01-24 19:04 JavaScript Spread Vector numbers 0.008 s 0.343
2023-01-24 19:04 JavaScript Spread Vector dictionary 0.010 s 0.274
2023-01-24 19:04 JavaScript vectorFromArray booleans 0.018 s 0.258
2023-01-24 19:04 JavaScript Iterate Vector uint8Array 0.002 s -0.390
2023-01-24 19:04 JavaScript Iterate Vector int8Array 0.002 s 0.344
2023-01-24 19:04 JavaScript Iterate Vector int32Array 0.002 s 0.238
2023-01-24 19:04 JavaScript Iterate Vector numbers 0.002 s 0.221
2023-01-24 19:04 JavaScript Iterate Vector int16Array 0.002 s 0.483
2023-01-24 19:04 JavaScript Iterate Vector int64Array 0.004 s -0.848
2023-01-24 19:04 JavaScript Iterate Vector float64Array 0.002 s 0.737
2023-01-24 19:04 JavaScript Iterate Vector booleans 0.004 s 0.561
2023-01-24 19:04 JavaScript Iterate Vector string 0.126 s 0.078
2023-01-24 19:04 JavaScript Iterate Vector dictionary 0.004 s 0.999
2023-01-24 19:04 JavaScript Spread Vector uint8Array 0.006 s 0.725
2023-01-24 19:04 JavaScript Spread Vector int8Array 0.006 s 0.427
2023-01-24 19:04 JavaScript Spread Vector int32Array 0.007 s 0.128
2023-01-24 19:04 JavaScript toArray Vector numbers
2023-01-24 19:04 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.030 s 0.407
2023-01-24 19:04 JavaScript Spread Vector uint16Array 0.007 s -0.017
2023-01-24 19:04 JavaScript toArray Vector int64Array
2023-01-24 19:04 JavaScript Table tracks, 1,000,000 0.254 s 1.033
2023-01-24 19:04 JavaScript Table Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.513
2023-01-24 19:04 JavaScript Spread Vector uint64Array 0.012 s 0.285
2023-01-24 19:04 JavaScript toArray Vector booleans 0.010 s 1.017
2023-01-24 19:04 JavaScript get Vector uint64Array 0.003 s -1.039
2023-01-24 19:04 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.030 s 0.477
2023-01-24 19:04 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.023 s -0.131
2023-01-24 19:04 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 1.382
2023-01-24 19:04 JavaScript Spread Vector float64Array 0.008 s -0.021
2023-01-24 19:04 JavaScript toArray Vector string 0.143 s 1.148
2023-01-24 19:04 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s
2023-01-24 19:04 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-24 19:04 JavaScript toArray Vector uint16Array
2023-01-24 19:04 JavaScript toArray Vector uint64Array
2023-01-24 19:04 JavaScript toArray Vector int16Array
2023-01-24 19:04 JavaScript toArray Vector float64Array
2023-01-24 19:04 JavaScript get Vector booleans 0.002 s 0.473
2023-01-24 19:04 JavaScript Parse write recordBatches, tracks 0.002 s -1.451
2023-01-24 19:04 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.345
2023-01-24 19:04 JavaScript toArray Vector int8Array
2023-01-24 19:04 JavaScript get Vector uint8Array 0.003 s -0.056
2023-01-24 19:04 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.647
2023-01-24 19:04 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.023 s -0.030
2023-01-24 19:04 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.040 s -1.345
2023-01-24 19:04 JavaScript Spread vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.235
2023-01-24 19:04 JavaScript Table Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.032 s 1.586
2023-01-24 19:04 JavaScript get Vector int8Array 0.003 s -0.096
2023-01-24 19:04 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 1.245
2023-01-24 19:04 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s
2023-01-24 19:04 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-24 19:04 JavaScript Spread vectors lat, 1,000,000, Float32, tracks 0.186 s 0.624
2023-01-24 19:04 JavaScript Table tracks, 1,000,000 0.050 s 0.198
2023-01-24 19:04 JavaScript Table 1,000,000, tracks 0.253 s 0.909
2023-01-24 19:04 JavaScript get Vector int16Array 0.003 s -0.152
2023-01-24 19:04 JavaScript get Vector int64Array 0.003 s -1.393
2023-01-24 19:04 JavaScript get Vector float64Array 0.002 s 0.501
2023-01-24 19:04 JavaScript get Vector string 0.123 s 0.974
2023-01-24 19:04 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.040 s -1.449
2023-01-24 19:04 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.037
2023-01-24 19:04 JavaScript Spread vectors lng, 1,000,000, Float32, tracks 0.187 s 0.628
2023-01-24 19:04 JavaScript get Vector int32Array 0.003 s -0.305
2023-01-24 19:04 JavaScript get Vector float32Array 0.002 s 0.562
2023-01-24 19:04 JavaScript get Vector numbers 0.002 s 0.418
2023-01-24 19:04 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.771
2023-01-24 19:04 JavaScript Spread vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s -0.068
2023-01-24 19:04 JavaScript Table tracks, 1,000,000 0.094 s 1.286
2023-01-24 19:04 JavaScript Table Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.924