java.io.NotSerializableException in Spark Streaming with enabled checkpointing - apache-spark

Question

code below:
def main(args: Array[String]) {
val sc = new SparkContext
val sec = Seconds(3)
val ssc = new StreamingContext(sc, sec)
ssc.checkpoint("./checkpoint")
val rdd = ssc.sparkContext.parallelize(Seq("a","b","c"))
val inputDStream = new ConstantInputDStream(ssc, rdd)
inputDStream.transform(rdd => {
val buf = ListBuffer[String]()
buf += "1"
buf += "2"
buf += "3"
val other_rdd = ssc.sparkContext.parallelize(buf) // create a new rdd
rdd.union(other_rdd)
}).print()
ssc.start()
ssc.awaitTermination()
}
and throw exception:
java.io.NotSerializableException: DStream checkpointing has been enabled but the DStreams with their functions are not serializable
org.apache.spark.streaming.StreamingContext
Serialization stack:
- object not serializable (class: org.apache.spark.streaming.StreamingContext, value: org.apache.spark.streaming.StreamingContext#5626e185)
- field (class: com.mirrtalk.Test$$anonfun$main$1, name: ssc$1, type: class org.apache.spark.streaming.StreamingContext)
- object (class com.mirrtalk.Test$$anonfun$main$1, <function1>)
- field (class: org.apache.spark.streaming.dstream.DStream$$anonfun$transform$1$$anonfun$apply$21, name: cleanedF$2, type: interface scala.Function1)
- object (class org.apache.spark.streaming.dstream.DStream$$anonfun$transform$1$$anonfun$apply$21, <function2>)
- field (class: org.apache.spark.streaming.dstream.DStream$$anonfun$transform$2$$anonfun$5, name: cleanedF$3, type: interface scala.Function2)
- object (class org.apache.spark.streaming.dstream.DStream$$anonfun$transform$2$$anonfun$5, <function2>)
- field (class: org.apache.spark.streaming.dstream.TransformedDStream, name: transformFunc, type: interface scala.Function2)
when I remove code ssc.checkpoint("./checkpoint"), the application can work well, but I need enable checkpoint.
how to fix this issue when enable checkpoint?

Answer 1

You can move context initialization and configuration tasks outside main:
object App {
val sc = new SparkContext(new SparkConf().setAppName("foo").setMaster("local"))
val sec = Seconds(3)
val ssc = new StreamingContext(sc, sec)
ssc.checkpoint("./checkpoint") // enable checkpoint
def main(args: Array[String]) {
val rdd = ssc.sparkContext.parallelize(Seq("a", "b", "c"))
val inputDStream = new ConstantInputDStream(ssc, rdd)
inputDStream.transform(rdd => {
val buf = ListBuffer[String]()
buf += "1"
buf += "2"
buf += "3"
val other_rdd = ssc.sparkContext.parallelize(buf)
rdd.union(other_rdd) // I want to union other RDD
}).print()
ssc.start()
ssc.awaitTermination()
}
}