Re: Get the value of DStream[(String, Iterable[String])]
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: 141883218 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?
-
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Re: Get the value of DStream[(String, Iterable[String])]
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: 141883218 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
Re: Get the value of DStream[(String, Iterable[String])]
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: 141883218 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
