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fective window, if the memory is not enough,
>>>> BlockManager will discard the blocks at LRU policy, so something unexpected
>>>> will be occurred.
>>>>
>>>> Thanks
>>>> Jerry
>>>>
>>>> -Original Message-
&
gt;> Thanks
>>> Jerry
>>>
>>> -Original Message-
>>> From: avilevi3 [mailto:avile...@gmail.com]
>>> Sent: Monday, February 23, 2015 12:57 AM
>>> To: user@spark.apache.org
>>> Subject: spark streaming window operations on a large
t enough,
>> BlockManager will discard the blocks at LRU policy, so something unexpected
>> will be occurred.
>>
>> Thanks
>> Jerry
>>
>> -Original Message-
>> From: avilevi3 [mailto:avile...@gmail.com]
>> Sent: Monday, February 23, 2
2015 12:57 AM
> To: user@spark.apache.org
> Subject: spark streaming window operations on a large window size
>
> Hi guys,
>
> does spark streaming supports window operations on a sliding window that
> is data is larger than the available memory?
> we would like to
> curre
thing unexpected
will be occurred.
Thanks
Jerry
-Original Message-
From: avilevi3 [mailto:avile...@gmail.com]
Sent: Monday, February 23, 2015 12:57 AM
To: user@spark.apache.org
Subject: spark streaming window operations on a large window size
Hi guys,
does spark streaming supports window opera
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Hi,
The window size in a spark streaming is time based which means we have
different number of elements in each window. For example if you have two
streams (might be more) which are related to each other and you want to compare
them in a specific time interval. I am not clear how it will
work
