Take a look at this SOF:
https://stackoverflow.com/questions/24804619/how-does-spark-aggregate-function-aggregatebykey-work
On Fri, Apr 5, 2019 at 12:25 PM Madabhattula Rajesh Kumar <
mrajaf...@gmail.com> wrote:
> Hi,
>
> Thank you for the details. It is a typo error while composing the mail.
>
Hi,
Thank you for the details. It is a typo error while composing the mail.
Below is the actual flow.
Any idea, why the combineByKey is not working. aggregateByKey is working.
//Defining createCombiner, mergeValue and mergeCombiner functions
def createCombiner = (Id: String, value: String) => (
I broke some of your code down into the following lines:
import spark.implicits._
val a: RDD[Messages]= sc.parallelize(messages)
val b: Dataset[Messages] = a.toDF.as[Messages]
val c: Dataset[(String, (String, String))] = b.map{x => (x.timeStamp +
"-" + x.Id, (x.Id, x.value))}
You
This problem was caused by the fact that I used a package jar with a Spark
version (0.9.1) different from that of the cluster (0.9.0). When I used the
correct package jar
(spark-assembly_2.10-0.9.0-cdh5.0.1-hadoop2.3.0-cdh5.0.1.jar) instead the
application can run as expected.
2014-09-15 14:57 G
How about this.
scala> val rdd2 = rdd.combineByKey(
| (v: Int) => v.toLong,
| (c: Long, v: Int) => c + v,
| (c1: Long, c2: Long) => c1 + c2)
rdd2: org.apache.spark.rdd.RDD[(String, Long)] = MapPartitionsRDD[9] at
combineB
yKey at :14
xj @ Tokyo
On Mon, Sep 15, 2014 at 3:06 PM, Tao
The streaming program contains the following main stages:
1. receive data from Kafka
2. preprocessing of the data. These are all map and filtering stages.
3. Group by a field
4. Process the groupBy results using map. Inside this processing, I use
collect, count.
Thanks!
Bill
On Tue, Jul 22, 20
Can you give an idea of the streaming program? Rest of the transformation
you are doing on the input streams?
On Tue, Jul 22, 2014 at 11:05 AM, Bill Jay
wrote:
> Hi all,
>
> I am currently running a Spark Streaming program, which consumes data from
> Kakfa and does the group by operation on the
This issue is turned out cased by version mismatch between driver(0.9.1) and
server(0.9.0-cdh5.0.1) just now. Other function works fine but combinebykey
before.
Thank you very much for your reply.
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/combinebyke
You asked off-list, and provided a more detailed example there:
val random = new Random()
val testdata = (1 to 1).map(_=>(random.nextInt(),random.nextInt()))
sc.parallelize(testdata).combineByKey[ArrayBuffer[Int]](
(instant:Int)=>{new ArrayBuffer[Int]()},
(bucket:ArrayB
