Same problem happens with CSV data file, so it's not parquet-related either.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.0.0
/_/
Using Python version 2.7.6 (default, Jun 22 2015 17:58:13)
SparkSession available as 'spark'.
>>> import timeit
>>> from pyspark.sql.types import *
>>> schema = StructType([StructField('dd_convs', FloatType(), True)])
>>> for x in range(50, 70): print x,
>>> timeit.timeit(spark.read.csv('file:///data/dump/test_csv',
>>> schema=schema).groupBy().sum(*(['dd_convs'] * x) ).collect, number=1)
50 0.372850894928
51 0.376906871796
52 0.381325960159
53 0.385444164276
54 0.386877775192
55 0.388918161392
56 0.397624969482
57 0.391713142395
58 2.62714004517
59 2.68421196938
60 2.74627685547
61 2.81081581116
62 3.43532109261
63 3.07742786407
64 3.03904604912
65 3.01616096497
66 3.06293702126
67 3.09386610985
68 3.27610206604
69 3.2041969299 Суббота, 3 сентября 2016, 15:40 +03:00 от Сергей Романов
<romano...@inbox.ru.INVALID>:
>
>Hi,
>I had narrowed down my problem to a very simple case. I'm sending 27kb parquet
>in attachment. (file:///data/dump/test2 in example)
>Please, can you take a look at it? Why there is performance drop after 57 sum
>columns?
>Welcome to
> ____ __
> / __/__ ___ _____/ /__
> _\ \/ _ \/ _ `/ __/ '_/
> /__ / .__/\_,_/_/ /_/\_\ version 2.0.0
> /_/
>
>Using Python version 2.7.6 (default, Jun 22 2015 17:58:13)
>SparkSession available as 'spark'.
>>>> import timeit
>>>> for x in range(70): print x,
>>>> timeit.timeit(spark.read.parquet('file:///data/dump/test2').groupBy().sum(*(['dd_convs']
>>>> * x) ).collect, number=1)
>...
>SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
>SLF4J: Defaulting to no-operation (NOP) logger implementation
>SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further
>details.
>0 1.05591607094
>1 0.200426101685
>2 0.203800916672
>3 0.176458120346
>4 0.184863805771
>5 0.232321023941
>6 0.216032981873
>7 0.201778173447
>8 0.292424917221
>9 0.228524923325
>10 0.190534114838
>11 0.197028160095
>12 0.270443916321
>13 0.429781913757
>14 0.270851135254
>15 0.776989936829
>16 0.233337879181
>17 0.227638959885
>18 0.212944030762
>19 0.2144780159
>20 0.22200012207
>21 0.262261152267
>22 0.254227876663
>23 0.275084018707
>24 0.292124032974
>25 0.280488014221
>16/09/03 15:31:28 WARN Utils: Truncated the string representation of a plan
>since it was too large. This behavior can be adjusted by setting
>'spark.debug.maxToStringFields' in SparkEnv.conf.
>26 0.290093898773
>27 0.238478899002
>28 0.246420860291
>29 0.241401195526
>30 0.255286931992
>31 0.42702794075
>32 0.327946186066
>33 0.434395074844
>34 0.314198970795
>35 0.34576010704
>36 0.278323888779
>37 0.289474964142
>38 0.290827989578
>39 0.376291036606
>40 0.347742080688
>41 0.363158941269
>42 0.318687915802
>43 0.376327991486
>44 0.374994039536
>45 0.362971067429
>46 0.425967931747
>47 0.370860099792
>48 0.443903923035
>49 0.374128103256
>50 0.378985881805
>51 0.476850986481
>52 0.451028823853
>53 0.432540893555
>54 0.514838933945
>55 0.53990483284
>56 0.449142932892
>57 0.465240001678 // 5x slower after 57 columns
>58 2.40412116051
>59 2.41632795334
>60 2.41812801361
>61 2.55726218224
>62 2.55484509468
>63 2.56128406525
>64 2.54642391205
>65 2.56381797791
>66 2.56871509552
>67 2.66187620163
>68 2.63496208191
>69 2.81545996666
>
>Sergei Romanov
>
>---------------------------------------------------------------------
>To unsubscribe e-mail: user-unsubscr...@spark.apache.org
Sergei Romanov
bad.csv.tgz
Description: Binary data
--------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
