And even more simple case:
>>> df = sc.parallelize([1] for x in xrange(760857)).toDF()
>>> for x in range(50, 70): print x, timeit.timeit(df.groupBy().sum(*(['_1'] *
>>> x)).collect, number=1)
50 1.91226291656
51 1.50933384895
52 1.582903862
53 1.90537405014
54 1.84442877769
55 1.91788887978
56 1.50977802277
57 1.5907189846
// after 57 rows it's 2x slower
58 3.22199988365
59 2.96345090866
60 2.8297970295
61 2.87895679474
62 2.92077898979
63 2.95195293427
64 4.10550689697
65 4.14798402786
66 3.13437199593
67 3.11248207092
68 3.18963003159
69 3.18774986267
>Суббота, 3 сентября 2016, 15:50 +03:00 от Сергей Романов
><romano...@inbox.ru.INVALID>:
>
>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
>>
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>Sergei Romanov
>
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