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Posted to issues@spark.apache.org by "Davies Liu (JIRA)" <ji...@apache.org> on 2016/04/27 23:54:12 UTC
[jira] [Commented] (SPARK-10069) Python's ReduceByKeyAndWindow
DStream Keeps Growing
[ https://issues.apache.org/jira/browse/SPARK-10069?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15261011#comment-15261011 ]
Davies Liu commented on SPARK-10069:
------------------------------------
cc [~zsxwing]
> Python's ReduceByKeyAndWindow DStream Keeps Growing
> ---------------------------------------------------
>
> Key: SPARK-10069
> URL: https://issues.apache.org/jira/browse/SPARK-10069
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.4.1
> Reporter: Asim Jalis
>
> When I use reduceByKeyAndWindow with func and invFunc (in PySpark) the size of the window keeps growing. I am appending the code that reproduces this issue. This prints out the count() of the dstream which goes up every batch by 10 elements.
> Is this a bug in the Python version of Scala or is this expected behavior?
> Here is the code that reproduces this issue.
> {code}
> from pyspark import SparkContext
> from pyspark.streaming import StreamingContext
> from pprint import pprint
> print 'Initializing ssc'
> ssc = StreamingContext(SparkContext(), batchDuration=1)
> ssc.checkpoint('ckpt')
> ds = ssc.textFileStream('input') \
> .map(lambda event: (event,1)) \
> .reduceByKeyAndWindow(
> func=lambda count1,count2: count1+count2,
> invFunc=lambda count1,count2: count1-count2,
> windowDuration=10,
> slideDuration=2)
> ds.pprint()
> ds.count().pprint()
> print 'Starting ssc'
> ssc.start()
> import itertools
> import time
> import random
> from distutils import dir_util
> def batch_write(batch_data, batch_file_path):
> with open(batch_file_path,'w') as batch_file:
> for element in batch_data:
> line = str(element) + "\n"
> batch_file.write(line)
> def xrange_write(
> batch_size = 5,
> batch_dir = 'input',
> batch_duration = 1):
> '''Every batch_duration write a file with batch_size numbers,
> forever. Start at 0 and keep incrementing. Intended for testing
> Spark Streaming code.'''
> dir_util.mkpath('./input')
> for i in itertools.count():
> min = batch_size * i
> max = batch_size * (i + 1)
> batch_data = xrange(min,max)
> file_path = batch_dir + '/' + str(i)
> batch_write(batch_data, file_path)
> time.sleep(batch_duration)
> print 'Feeding data to app'
> xrange_write()
>
> ssc.awaitTermination()
> {code}
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