Basically what I want to do it'd be something like..
val errorLines = lines.filter(_.contains("h"))
val mapErrorLines = errorLines.map(line => ("key", line))
val grouping = errorLinesValue.groupByKeyAndWindow(Seconds(8), Seconds(4))
if (errorLinesValue.getValue().size() > X){
//iterate values and do something for each element.
}
I think that it must be pretty basic,, argg.
2014-12-17 18:43 GMT+01:00 Guillermo Ortiz <konstt2...@gmail.com>:
> What I would like to do it's to count the number of elements and if
> it's greater than a number, I have to iterate all them and store them
> in mysql or another system. So, I need to count them and preserve the
> values because saving in other system.
>
> I know about this map(line => ("key", line)), it was just a test, I
> want to change "key" for a value which comes from a RE.
>
> 2014-12-17 17:28 GMT+01:00 Gerard Maas <gerard.m...@gmail.com>:
>>
>> You can create a DStream that contains the count, transforming the grouped
>> windowed RDD, like this:
>> val errorCount = grouping.map{case (k,v) => v.size }
>>
>> If you need to preserve the key:
>> val errorCount = grouping.map{case (k,v) => (k,v.size) }
>>
>> or you if you don't care about the content of the values, you could count
>> directly, instead of grouping first:
>>
>> val errorCount = mapErrorLines.countByWindow(Seconds(8), Seconds(4))
>>
>> Not sure why you're using map(line => ("key", line)) as there only seem to
>> be one key. If that's not required, we can simplify one more step:
>>
>> val errorCount = errorLines.countByWindow(Seconds(8), Seconds(4))
>>
>>
>> The question is: what do you want to do with that count afterwards?
>>
>> -kr, Gerard.
>>
>>
>> On Wed, Dec 17, 2014 at 5:11 PM, Guillermo Ortiz <konstt2...@gmail.com>
>> wrote:
>>>
>>> I'm a newbie with Spark,,, a simple question
>>>
>>> val errorLines = lines.filter(_.contains("h"))
>>> val mapErrorLines = errorLines.map(line => ("key", line))
>>> val grouping = errorLinesValue.groupByKeyAndWindow(Seconds(8), Seconds(4))
>>>
>>> I get something like:
>>>
>>> 604: -------------------------------------------
>>> 605: Time: 1418832180000 ms
>>> 606: -------------------------------------------
>>> 607: (key,ArrayBuffer(h2, h3, h4))
>>>
>>> Now, I would like to get that ArrayBuffer and count the number of
>>> elements,,
>>> How could I get that arrayBuffer??? something like:
>>> val values = grouping.getValue()... How could I do this in Spark with
>>> Scala?
>>>
>>> ---------------------------------------------------------------------
>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
>>> For additional commands, e-mail: user-h...@spark.apache.org
>>>
>>
---------------------------------------------------------------------
To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
For additional commands, e-mail: user-h...@spark.apache.org
