Yes, and in general any mutable data structure. You have to immutable data
structures whose hashcode and equals is consistent enough for being put in
a set.
On Jun 6, 2017 4:50 PM, "swetha kasireddy" <swethakasire...@gmail.com>
wrote:
> Are you suggesting against the usage of HashSet?
>
> On Tue, Jun 6, 2017 at 3:36 PM, Tathagata Das <tathagata.das1...@gmail.com
> > wrote:
>
>> This may be because of HashSet is a mutable data structure, and it seems
>> you are actually mutating it in "set1 ++set2". I suggest creating a new
>> HashMap in the function (and add both maps into it), rather than mutating
>> one of them.
>>
>> On Tue, Jun 6, 2017 at 11:30 AM, SRK <swethakasire...@gmail.com> wrote:
>>
>>> Hi,
>>>
>>> I see the following error when I use ReduceByKeyAndWindow in my Spark
>>> Streaming app. I use reduce, invReduce and filterFunction as shown below.
>>> Any idea as to why I get the error?
>>>
>>> java.lang.Exception: Neither previous window has value for key, nor new
>>> values found. Are you sure your key class hashes consistently?
>>>
>>>
>>> def reduceWithHashSet: ((Long, HashSet[String]), (Long,
>>> HashSet[String]))
>>> => (Long, HashSet[String])= {
>>> case ((timeStamp1: Long, set1: HashSet[String]), (timeStamp2: Long,
>>> set2: HashSet[String])) => (Math.max(timeStamp1, timeStamp2), set1
>>> ++set2 )
>>>
>>> }
>>>
>>> def invReduceWithHashSet: ((Long, HashSet[String]), (Long,
>>> HashSet[String])) => (Long, HashSet[String])= {
>>> case ((timeStamp1: Long, set1: HashSet[String]), (timeStamp2: Long,
>>> set2: HashSet[String])) => (Math.max(timeStamp1, timeStamp2),
>>> set1.diff(set2))
>>> }
>>>
>>> def filterFuncForInvReduce: ((String, (Long, HashSet[String]))) =>
>>> (Boolean)= {
>>> case ((metricName:String, (timeStamp: Long, set: HashSet[String])))
>>> =>
>>> set.size>0
>>> }
>>>
>>>
>>>
>>>
>>>
>>>
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>>> ndow-in-Spark-Streaming-tp28748.html
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>>>
>>
>
