Top Outliers
Benchmarks
Date Lang Batch Benchmark Mean Z-Score Error
2023-01-24 01:41 Python csv-read uncompressed, arrow_table, file, fanniemae_2016Q4 1.239 s 0.232
2023-01-24 01:42 Python csv-read uncompressed, arrow_table, streaming, fanniemae_2016Q4 13.499 s 1.219
2023-01-24 01:43 Python csv-read gzip, arrow_table, file, nyctaxi_2010-01 8.444 s -1.301
2023-01-24 01:46 Python dataframe-to-table type_floats 0.010 s 0.156
2023-01-24 01:41 Python csv-read gzip, arrow_table, file, fanniemae_2016Q4 5.777 s 0.511
2023-01-24 01:43 Python csv-read uncompressed, arrow_table, file, nyctaxi_2010-01 1.126 s 0.144
2023-01-24 01:41 Python csv-read gzip, arrow_table, streaming, fanniemae_2016Q4 13.537 s 1.238
2023-01-24 01:44 Python csv-read uncompressed, arrow_table, streaming, nyctaxi_2010-01 11.417 s -1.787
2023-01-24 01:43 Python csv-read gzip, arrow_table, streaming, nyctaxi_2010-01 11.367 s -1.515
2023-01-24 01:46 Python dataframe-to-table type_dict 0.011 s 4.147
2023-01-24 01:46 Python dataframe-to-table type_integers 0.010 s 0.762
2023-01-24 01:46 Python dataframe-to-table type_nested 2.957 s 0.395
2023-01-24 01:46 Python dataframe-to-table chi_traffic_2020_Q1 20.726 s 1.877
2023-01-24 01:46 Python dataframe-to-table type_strings 0.427 s 0.037
2023-01-24 01:46 Python dataset-filter nyctaxi_2010-01 1.033 s -0.122
2023-01-24 01:50 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 83.345 s -0.130
2023-01-24 02:06 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.039 s 0.206
2023-01-24 01:55 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.206 s 0.441
2023-01-24 02:06 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.155 s 1.222
2023-01-24 02:06 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.216 s -0.021
2023-01-24 02:06 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.241 s 0.021
2023-01-24 02:06 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.340 s -0.183
2023-01-24 02:06 Python dataset-read async=True, nyctaxi_multi_ipc_s3 214.968 s 0.943
2023-01-24 02:06 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.042 s 0.186
2023-01-24 02:06 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.648 s 0.266
2023-01-24 02:06 Python dataset-selectivity 1%, chi_traffic_2020_Q1 1.181 s -0.426
2023-01-24 02:06 Python dataset-selectivity 10%, chi_traffic_2020_Q1 1.226 s -0.622
2023-01-24 02:07 Python dataset-serialize arrow, 1pc, nyctaxi_multi_parquet_s3 0.023 s -0.206
2023-01-24 02:07 Python dataset-selectivity 100%, chi_traffic_2020_Q1 1.145 s -0.027
2023-01-24 02:07 Python dataset-serialize csv, 1pc, nyctaxi_multi_parquet_s3 0.731 s 1.489
2023-01-24 02:07 Python dataset-serialize parquet, 1pc, nyctaxi_multi_parquet_s3 0.303 s 1.979
2023-01-24 02:07 Python dataset-serialize feather, 1pc, nyctaxi_multi_parquet_s3 0.023 s -0.233
2023-01-24 02:07 Python dataset-serialize parquet, 10pc, nyctaxi_multi_parquet_s3 2.903 s 1.736
2023-01-24 02:07 Python dataset-serialize arrow, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.292
2023-01-24 02:11 Python dataset-serialize arrow, 100pc, nyctaxi_multi_parquet_s3 2.147 s 0.929
2023-01-24 02:08 Python dataset-serialize csv, 10pc, nyctaxi_multi_parquet_s3 7.260 s 1.520
2023-01-24 02:07 Python dataset-serialize feather, 10pc, nyctaxi_multi_parquet_s3 0.199 s 1.433
2023-01-24 02:11 Python dataset-serialize parquet, 100pc, nyctaxi_multi_parquet_s3 30.026 s 1.604
2023-01-24 02:12 Python dataset-serialize feather, 100pc, nyctaxi_multi_parquet_s3 2.150 s 0.615
2023-01-24 02:19 Python dataset-serialize arrow, 1pc, nyctaxi_multi_ipc_s3 0.025 s 1.111
2023-01-24 02:19 Python dataset-serialize csv, 100pc, nyctaxi_multi_parquet_s3 73.350 s 1.491
2023-01-24 02:19 Python dataset-serialize feather, 1pc, nyctaxi_multi_ipc_s3 0.026 s 0.550
2023-01-24 02:19 Python dataset-serialize csv, 1pc, nyctaxi_multi_ipc_s3 0.836 s 1.414
2023-01-24 02:19 Python dataset-serialize parquet, 1pc, nyctaxi_multi_ipc_s3 0.283 s 0.925
2023-01-24 02:20 Python dataset-serialize parquet, 10pc, nyctaxi_multi_ipc_s3 3.002 s 1.015
2023-01-24 02:20 Python dataset-serialize feather, 10pc, nyctaxi_multi_ipc_s3 0.226 s -0.810
2023-01-24 02:20 Python dataset-serialize arrow, 10pc, nyctaxi_multi_ipc_s3 0.226 s -0.760
2023-01-24 02:21 Python dataset-serialize csv, 10pc, nyctaxi_multi_ipc_s3 8.393 s 1.442
2023-01-24 02:34 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 0.983 s -0.046
2023-01-24 02:24 Python dataset-serialize feather, 100pc, nyctaxi_multi_ipc_s3 2.407 s 0.839
2023-01-24 02:37 Python file-write snappy, parquet, table, fanniemae_2016Q4 10.680 s 1.024
2023-01-24 02:38 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.627 s 3.131
2023-01-24 02:55 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 0.567 s 0.175
2023-01-24 02:58 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 9.703 s 0.620
2023-01-24 02:39 Python file-write lz4, feather, table, fanniemae_2016Q4 1.558 s 2.750
2023-01-24 02:24 Python dataset-serialize arrow, 100pc, nyctaxi_multi_ipc_s3 2.408 s -0.427
2023-01-24 02:34 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 5.744 s -0.206
2023-01-24 02:41 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 8.957 s 2.415
2023-01-24 02:54 R dataframe-to-table type_floats, R 0.013 s -1.402
2023-01-24 02:55 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.314 s 0.201
2023-01-24 02:56 R file-read snappy, parquet, table, nyctaxi_2010-01, R 0.577 s -0.440
2023-01-24 02:57 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.902 s 0.183
2023-01-24 03:03 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 17.132 s 0.520
2023-01-24 02:33 Python dataset-serialize csv, 100pc, nyctaxi_multi_ipc_s3 83.302 s 1.430
2023-01-24 02:35 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 0.938 s 0.022
2023-01-24 02:38 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 20.235 s 0.673
2023-01-24 02:41 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 7.024 s 2.607
2023-01-24 02:42 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 3.168 s 0.097
2023-01-24 02:57 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s 0.065
2023-01-24 02:24 Python dataset-serialize parquet, 100pc, nyctaxi_multi_ipc_s3 30.442 s 1.025
2023-01-24 02:33 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.643 s 0.075
2023-01-24 02:34 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.992 s -0.186
2023-01-24 02:35 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 10.366 s 1.706
2023-01-24 02:40 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.527 s 2.678
2023-01-24 02:53 R dataframe-to-table chi_traffic_2020_Q1, R 4.378 s -0.303
2023-01-24 02:54 R dataframe-to-table type_nested, R 0.573 s 0.440
2023-01-24 02:56 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.847 s 0.243
2023-01-24 02:57 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.817 s 0.148
2023-01-24 02:57 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.577 s 0.187
2023-01-24 03:10 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.658 s 0.706
2023-01-24 02:33 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 1.521 s -0.782
2023-01-24 02:33 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 1.634 s 0.027
2023-01-24 02:35 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 1.325 s 0.330
2023-01-24 02:42 Python wide-dataframe use_legacy_dataset=true 0.375 s 0.406
2023-01-24 02:55 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.595 s 0.158
2023-01-24 02:56 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 0.574 s -0.379
2023-01-24 03:00 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 16.690 s 0.454
2023-01-24 03:05 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.495 s -1.328
2023-01-24 03:15 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.089 s -0.538
2023-01-24 02:34 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.336 s 0.136
2023-01-24 02:34 Python file-read lz4, feather, table, fanniemae_2016Q4 0.833 s -1.042
2023-01-24 02:34 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.948 s -0.222
2023-01-24 02:35 Python file-read lz4, feather, table, nyctaxi_2010-01 0.688 s 0.015
2023-01-24 02:42 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.565 s 0.570
2023-01-24 02:55 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.604 s -1.304
2023-01-24 02:56 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 0.921 s -0.405
2023-01-24 03:01 R file-write snappy, parquet, table, fanniemae_2016Q4, R 10.126 s 0.585
2023-01-24 03:07 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 7.500 s -0.766
2023-01-24 03:15 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.258 s -1.086
2023-01-24 02:33 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.534 s -1.081
2023-01-24 02:35 Python file-read uncompressed, feather, table, nyctaxi_2010-01 0.941 s 0.155
2023-01-24 02:42 Python wide-dataframe use_legacy_dataset=false 0.513 s 0.303
2023-01-24 02:54 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.350 s -1.460
2023-01-24 03:03 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.957 s 0.639
2023-01-24 03:09 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.775 s 0.451
2023-01-24 02:34 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 4.322 s -2.489
2023-01-24 02:35 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 1.583 s 0.243
2023-01-24 02:42 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 4.096 s -0.030
2023-01-24 03:13 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.182 s -0.205
2023-01-24 03:15 R partitioned-dataset-filter dims, dataset-taxi-parquet, R 0.704 s -6.759
2023-01-24 02:37 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 19.895 s 1.573
2023-01-24 02:39 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 13.669 s 3.089
2023-01-24 02:40 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 10.628 s 2.325
2023-01-24 02:53 R dataframe-to-table type_strings, R 0.536 s -0.200
2023-01-24 02:54 R dataframe-to-table type_integers, R 0.010 s -0.231
2023-01-24 02:55 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.346 s -1.197
2023-01-24 03:05 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.004 s 0.580
2023-01-24 03:14 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.473 s -0.905
2023-01-24 02:41 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.450 s 2.542
2023-01-24 02:42 Python file-write lz4, feather, table, nyctaxi_2010-01 1.755 s 0.049
2023-01-24 02:53 R dataframe-to-table type_dict, R 0.061 s -0.911
2023-01-24 02:55 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.603 s -1.266
2023-01-24 02:56 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 0.917 s -0.137
2023-01-24 03:08 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.932 s 0.695
2023-01-24 03:15 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.558 s 0.435
2023-01-24 03:11 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.730 s 0.456
2023-01-24 03:12 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.313 s -0.905
2023-01-24 03:15 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.620 s -1.196
2023-01-24 03:26 JavaScript Spread Vector int8Array 0.006 s 0.237
2023-01-24 03:26 JavaScript Iterate Vector numbers 0.002 s -1.286
2023-01-24 03:26 JavaScript Iterate Vector dictionary 0.004 s 0.832
2023-01-24 03:26 JavaScript get Vector string 0.124 s 0.633
2023-01-24 03:26 JavaScript Spread Vector uint16Array 0.007 s -0.553
2023-01-24 03:26 JavaScript Spread Vector int64Array 0.012 s 0.377
2023-01-24 03:26 JavaScript Iterate Vector uint8Array 0.002 s -1.015
2023-01-24 03:26 JavaScript Iterate Vector int8Array 0.002 s -0.477
2023-01-24 03:26 JavaScript Iterate Vector int32Array 0.002 s -0.425
2023-01-24 03:26 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.474
2023-01-24 03:26 JavaScript Spread Vector float64Array 0.008 s 0.442
2023-01-24 03:26 JavaScript Spread Vector booleans 0.010 s -0.121
2023-01-24 03:26 JavaScript toArray Vector uint64Array
2023-01-24 03:26 JavaScript toArray Vector int16Array
2023-01-24 03:26 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.030 s 0.634
2023-01-24 03:26 JavaScript vectorFromArray dictionary 0.016 s 1.423
2023-01-24 03:26 JavaScript Iterate Vector uint16Array 0.002 s 0.044
2023-01-24 03:26 JavaScript Iterate Vector uint64Array 0.004 s 0.819
2023-01-24 03:26 JavaScript Iterate Vector int16Array 0.002 s 0.131
2023-01-24 03:26 JavaScript Spread Vector int16Array 0.006 s 0.227
2023-01-24 03:26 JavaScript Spread Vector string 0.146 s -0.215
2023-01-24 03:26 JavaScript toArray Vector uint16Array
2023-01-24 03:26 JavaScript vectorFromArray booleans 0.018 s -0.128
2023-01-24 03:26 JavaScript Spread Vector int32Array 0.006 s 0.356
2023-01-24 03:26 JavaScript toArray Vector uint32Array
2023-01-24 03:26 JavaScript vectorFromArray numbers 0.017 s -0.203
2023-01-24 03:26 JavaScript Iterate Vector float64Array 0.002 s -1.294
2023-01-24 03:26 JavaScript toArray Vector int64Array
2023-01-24 03:26 JavaScript Iterate Vector uint32Array 0.002 s 0.355
2023-01-24 03:26 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.030 s 0.797
2023-01-24 03:26 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.366
2023-01-24 03:26 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.040 s 0.080
2023-01-24 03:26 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s -0.006
2023-01-24 03:26 JavaScript Iterate Vector int64Array 0.004 s 0.251
2023-01-24 03:26 JavaScript Iterate Vector booleans 0.004 s 1.043
2023-01-24 03:26 JavaScript Iterate Vector string 0.125 s 0.657
2023-01-24 03:26 JavaScript Spread Vector uint64Array 0.012 s -0.279
2023-01-24 03:26 JavaScript get Vector uint16Array 0.003 s 0.849
2023-01-24 03:26 JavaScript Iterate Vector float32Array 0.002 s -0.381
2023-01-24 03:26 JavaScript toArray Vector uint8Array
2023-01-24 03:26 JavaScript toArray Vector int32Array
2023-01-24 03:26 JavaScript get Vector int32Array 0.003 s 1.443
2023-01-24 03:26 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s
2023-01-24 03:26 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-24 03:26 JavaScript Spread vectors lat, 1,000,000, Float32, tracks 0.187 s 0.380
2023-01-24 03:26 JavaScript Spread Vector uint8Array 0.006 s 0.292
2023-01-24 03:26 JavaScript Spread Vector uint32Array 0.007 s 0.150
2023-01-24 03:26 JavaScript Spread Vector numbers 0.008 s 0.404
2023-01-24 03:26 JavaScript toArray Vector int8Array
2023-01-24 03:26 JavaScript toArray Vector float32Array
2023-01-24 03:26 JavaScript Spread Vector float32Array 0.008 s 0.938
2023-01-24 03:26 JavaScript Spread Vector dictionary 0.010 s 1.124
2023-01-24 03:26 JavaScript get Vector uint8Array 0.003 s 1.569
2023-01-24 03:26 JavaScript Spread vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.243
2023-01-24 03:26 JavaScript Table tracks, 1,000,000 0.050 s -0.071
2023-01-24 03:26 JavaScript toArray Vector float64Array
2023-01-24 03:26 JavaScript toArray Vector booleans 0.010 s 0.088
2023-01-24 03:26 JavaScript toArray Vector string 0.145 s 0.068
2023-01-24 03:26 JavaScript get Vector uint64Array 0.003 s 1.243
2023-01-24 03:26 JavaScript get Vector booleans 0.002 s 0.464
2023-01-24 03:26 JavaScript Parse write recordBatches, tracks 0.002 s -1.610
2023-01-24 03:26 JavaScript toArray Vector numbers
2023-01-24 03:26 JavaScript toArray Vector dictionary 0.010 s 0.388
2023-01-24 03:26 JavaScript get Vector int8Array 0.003 s 1.172
2023-01-24 03:26 JavaScript get Vector numbers 0.002 s 0.009
2023-01-24 03:26 JavaScript Parse read recordBatches, tracks 0.000 s -1.757
2023-01-24 03:26 JavaScript get Vector uint32Array 0.003 s 0.729
2023-01-24 03:26 JavaScript get Vector float32Array 0.002 s 0.337
2023-01-24 03:26 JavaScript get Vector dictionary 0.002 s 0.400
2023-01-24 03:26 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.023 s -0.002
2023-01-24 03:26 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.107 s 0.591
2023-01-24 03:26 JavaScript get Vector int16Array 0.003 s 1.610
2023-01-24 03:26 JavaScript get Vector int64Array 0.003 s 1.126
2023-01-24 03:26 JavaScript get Vector float64Array 0.002 s 0.308
2023-01-24 03:26 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s -0.613
2023-01-24 03:26 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.023 s 0.063
2023-01-24 03:26 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-24 03:26 JavaScript Spread vectors lng, 1,000,000, Float32, tracks 0.192 s -1.576
2023-01-24 03:26 JavaScript Table tracks, 1,000,000 0.261 s 0.537
2023-01-24 03:26 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.040 s -0.091
2023-01-24 03:26 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s
2023-01-24 03:26 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s -0.131
2023-01-24 03:26 JavaScript Table Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.032 s 0.052
2023-01-24 03:26 JavaScript Spread vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.107 s 1.073
2023-01-24 03:26 JavaScript Table 1,000,000, tracks 0.265 s 0.374
2023-01-24 03:26 JavaScript Table Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.032 s -0.334
2023-01-24 03:26 JavaScript Table tracks, 1,000,000 0.094 s 1.129
2023-01-24 03:26 JavaScript Table Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.419