So I am fairly new to Spark and Scala and from my understanding you should be able to pass a closure into a map function and have it modify the values however I am getting the Task not serializable error when attempting this.
My code is as follows:
// Spark Context
val sparkContext = spark.sparkContext
val random = scala.util.Random
// RDD Initialization
val array = Seq.fill(500)(random.nextInt(51))
val RDD = sc.parallelize(array)
// Spark Operations for Count, Sum, and Mean
var count = RDD.count()
var sum = RDD.reduce(_+_)
val mean = sum / count;
//Output Count, Sum, and Mean
println("Count: " + count)
println("Sum: " + sum)
println("Mean: " + mean)
val difference = (x:Int) => {x - mean}
var differences = RDD.map(difference)
Any help would be greatly appreciated
Instead of defining a function, try using a val fun
val difference = (x:Int) => {x-mean}
When you use def to define a function, Spark would try to serialize your object that has this function. This usually results into TaskNotSerializable as there might be something (val or var) in that Object which is not serializable.
Related
I have an array of DataFrames that I obtain by using randomSplit() in this manner:
val folds = df.randomSplit(Array.fill(5)(1.0/5)) //Array[Dataset[Row]]
I'll be iterating over folds using a for loop, where I will be dropping the ith entry inside folds and store it separately. Then I will be using all the others as another DataFrame as in my code below:
val df = spark.read.format("csv").load("xyz")
val folds = df.randomSplit(Array.fill(5)(1.0/5))
for (i <- folds.indices) {
var ts = folds
val testSet = ts(i)
ts = ts.drop(i)
var trainSet = spark.createDataFrame(spark.sparkContext.emptyRDD[Row], testSet.schema)
for (j <- ts.indices) {
trainSet = trainSet.union(ts(j))
}
}
While this does serve my purpose, I was also trying another approach where I would still separate folds into ts and testSet, and then use the flatten function for the remaining inside ts to create another DataFrame using something like this:
val df = spark.read.format("csv").load("xyz")
val folds = df.randomSplit(Array.fill(5)(1.0/5))
for (i <- folds.indices) {
var ts = folds
val testSet = ts(i)
ts = ts.drop(i)
var trainSet = ts.flatten
}
But at the initialization of the trainSet line, I get an error that: No Implicits Found for parameter asTrav: Dataset[Row] => Traversable[U_]. I have also done import spark.implicits._ after initializing the SparkSession.
My end goal with the creation of trainSet after flatten is to retrieve a DataFrame created after joining (union) the other Dataset[Row]s inside ts. I'm not sure where I'm going wrong.
I'm using Spark 2.4.5 with Scala 2.11.12
EDIT 1: Added how I read the Dataframe
I'm not sure what's your intention here but instead of using mutable variables and flattening you can do recursive iteration like this:
val folds = df.randomSplit(Array.fill(5)(1.0/5)) //Array[Dataset[Row]]
val testSet = spark.createDataFrame(Seq.empty)
val trainSet = spark.createDataFrame(spark.sparkContext.emptyRDD[Row], testSet.schema)
go(folds, Array.empty)
def go(items: Array[Dataset[Row]], result: Array[Dataset[Row]]): Array[Dataset[Row]] = items match {
case arr # Array(_, _*) =>
val res = arr.map { t =>
trainSet.union(t)
}
go(arr.tail, result ++ res)
case Array() => result
}
As I have seen the use case of testSet, there is no usage of it in the method body
I have replaced that for loop with a simple reduce:
val trainSet = ts.reduce((a,b) => a.union(b))
I am playing around with spark code to know more about shuffling. I wrote the following code to see how are stages formed if there is a if-else statement. I have declared val result so that the result could be assigned to it later in the if statement. But I am not sure about the return type to assign to it.
Is there an abstract class that goes with all the RDDs?
val conf = new SparkConf().setMaster("local").setAppName("spark shuffle")
val sc = new SparkContext(conf)
val d = sc.parallelize(0 until 1000).map(i => (i%1000, i))
val x = d.reduceByKey(_+_)
val count = 1
val result: RDD // What is the correct return type here?
if(count == 1)
{
result= d.rightOuterJoin(x)
result.collect()
}
d is a RDD[(Int, Int)]
Then doing a reduce by key gives the same thing but reduced down
Doing a right outer join then gives you RDD of (Int, (Option[Int], Int)) - ie for each key the L and R value (with the L option being optional if not there)
So doing a collect gives you an array of the same thing
The API documentation is not easy to follow for all these functions, there is a lot of generic types, and a lot of implicit types. I would recommend that you either use an IDE which will hint the types for you, or else use a tool that gives you a console that you can try snippets in.
you can avoid assignment to var (it should be var, not val)
val conf = new SparkConf().setMaster("local").setAppName("spark shuffle")
val sc = new SparkContext(conf)
val d = sc.parallelize(0 until 1000).map(i => (i%1000, i))
val x = d.reduceByKey(_+_)
val count = 1
if (count == 1) {
d.rightOuterJoin(x).collect()
}
Consider I have a dataframe. How can I retrieve the contents of that dataframe and represent it as a string.
Consider I try to do that with the below example code.
val tvalues: Array[Double] = Array(1.866393526974307, 2.864048126935307, 4.032486069215076, 7.876169953355888, 4.875333799256043, 14.316322626848278)
val pvalues: Array[Double] = Array(0.064020056478447, 0.004808399479386827, 8.914865448939047E-5, 7.489564524121306E-13, 2.8363794106756046E-6, 0.0)
val conf = new SparkConf().setAppName("Simple Application").setMaster("local[2]");
val sc = new SparkContext(conf)
val df = sc.parallelize(tvalues zip pvalues)
val sb = StringBuilder.newBuilder
df.foreach(x => {
println("x = ", x)
sb.append(x)
})
println("sb = ", sb)
The output of the code shows the example dataframe has contents:
(x = ,(1.866393526974307,0.064020056478447))
(x = ,(7.876169953355888,7.489564524121306E-13))
(x = ,(2.864048126935307,0.004808399479386827))
(x = ,(4.032486069215076,8.914865448939047E-5))
(x = ,(4.875333799256043,2.8363794106756046E-6))
However, the final stringbuilder contains an empty string.
Any thoughts how to retrieve a String for a given dataframe in Scala?
Many thanks
UPD: as mentioned by #user8371915, solution below will work only in single JVM in development (local) mode. In fact we cant modify broadcast variables like globals. You can use accumulators, but it will be quite inefficient. Also you can read an answer about read/write global vars here. Hope it will help you.
I think you should read topic about shared variables in Spark. Link here
Normally, when a function passed to a Spark operation (such as map or reduce) is executed on a remote cluster node, it works on separate copies of all the variables used in the function. These variables are copied to each machine, and no updates to the variables on the remote machine are propagated back to the driver program. Supporting general, read-write shared variables across tasks would be inefficient. However, Spark does provide two limited types of shared variables for two common usage patterns: broadcast variables and accumulators.
Let's have a look at broadcast variables. I edited your code:
val tvalues: Array[Double] = Array(1.866393526974307, 2.864048126935307, 4.032486069215076, 7.876169953355888, 4.875333799256043, 14.316322626848278)
val pvalues: Array[Double] = Array(0.064020056478447, 0.004808399479386827, 8.914865448939047E-5, 7.489564524121306E-13, 2.8363794106756046E-6, 0.0)
val conf = new SparkConf().setAppName("Simple Application").setMaster("local[2]");
val sc = new SparkContext(conf)
val df = sc.parallelize(tvalues zip pvalues)
val sb = StringBuilder.newBuilder
val broadcastVar = sc.broadcast(sb)
df.foreach(x => {
println("x = ", x)
broadcastVar.value.append(x)
})
println("sb = ", broadcastVar.value)
Here I used broadcastVar as a container for a StringBuilder variable sb.
Here is output:
(x = ,(1.866393526974307,0.064020056478447))
(x = ,(2.864048126935307,0.004808399479386827))
(x = ,(4.032486069215076,8.914865448939047E-5))
(x = ,(7.876169953355888,7.489564524121306E-13))
(x = ,(4.875333799256043,2.8363794106756046E-6))
(x = ,(14.316322626848278,0.0))
(sb = ,(7.876169953355888,7.489564524121306E-13)(1.866393526974307,0.064020056478447)(4.875333799256043,2.8363794106756046E-6)(2.864048126935307,0.004808399479386827)(14.316322626848278,0.0)(4.032486069215076,8.914865448939047E-5))
Hope this helps.
Does the output of df.show(false) help? If yes, then this SO answer helps: Is there any way to get the output of Spark's Dataset.show() method as a string?
Thanks everybody for the feedback and for understanding this slightly better.
The combination of responses result in the below. The requirements have changed slightly in that I represent my df as a list of jsons. The code below does this, without the use of the broadcast.
class HandleDf(df: DataFrame, limit: Int) extends java.io.Serializable {
val jsons = df.limit(limit).collect.map(rowToJson(_))
def rowToJson(r: org.apache.spark.sql.Row) : JSONObject = {
try { JSONObject(r.getValuesMap(r.schema.fieldNames)) }
catch { case t: Throwable =>
JSONObject.apply(Map("Row with error" -> t.toString))
}
}
}
The class I use here...
val jsons = new HandleDf(df, 100).jsons
I have an RDD[String], wordRDD. I also have a function that creates an RDD[String] from a string/word. I would like to create a new RDD for each string in wordRDD. Here are my attempts:
1) Failed because Spark does not support nested RDDs:
var newRDD = wordRDD.map( word => {
// execute myFunction()
(new MyClass(word)).myFunction()
})
2) Failed (possibly due to scope issue?):
var newRDD = sc.parallelize(new Array[String](0))
val wordArray = wordRDD.collect
for (w <- wordArray){
newRDD = sc.union(newRDD,(new MyClass(w)).myFunction())
}
My ideal result would look like:
// input RDD (wordRDD)
wordRDD: org.apache.spark.rdd.RDD[String] = ('apple','banana','orange'...)
// myFunction behavior
new MyClass('apple').myFunction(): RDD[String] = ('pple','aple'...'appl')
// after executing myFunction() on each word in wordRDD:
newRDD: RDD[String] = ('pple','aple',...,'anana','bnana','baana',...)
I found a relevant question here: Spark when union a lot of RDD throws stack overflow error, but it didn't address my issue.
Use flatMap to get RDD[String] as you desire.
var allWords = wordRDD.flatMap { word =>
(new MyClass(word)).myFunction().collect()
}
You cannot create a RDD from within another RDD.
However, it is possible to rewrite your function myFunction: String => RDD[String], which generates all words from the input where one letter is removed, into another function modifiedFunction: String => Seq[String] such that it can be used from within an RDD. That way, it will also be executed in parallel on your cluster. Having the modifiedFunction you can obtain the final RDD with all words by simply calling wordRDD.flatMap(modifiedFunction).
The crucial point is to use flatMap (to map and flatten the transformations):
def main(args: Array[String]) {
val sparkConf = new SparkConf().setAppName("Test").setMaster("local[*]")
val sc = new SparkContext(sparkConf)
val input = sc.parallelize(Seq("apple", "ananas", "banana"))
// RDD("pple", "aple", ..., "nanas", ..., "anana", "bnana", ...)
val result = input.flatMap(modifiedFunction)
}
def modifiedFunction(word: String): Seq[String] = {
word.indices map {
index => word.substring(0, index) + word.substring(index+1)
}
}
I am trying to perform Scala operation on Shark. I am creating an RDD as follows:
val tmp: shark.api.TableRDD = sc.sql2rdd("select duration from test")
I need it to convert it to RDD[Array[Double]]. I tried toArray, but it doesn't seem to work.
I also tried converting it to Array[String] and then converting using map as follows:
val tmp_2 = tmp.map(row => row.getString(0))
val tmp_3 = tmp_2.map { row =>
val features = Array[Double] (row(0))
}
But this gives me a Spark's RDD[Unit] which cannot be used in the function. Is there any other way to proceed with this type conversion?
Edit I also tried using toDouble, but this gives me an RDD[Double] type, not RDD[Array[Double]]
val tmp_5 = tmp_2.map(_.toDouble)
Edit 2:
I managed to do this as follows:
A sample of the data:
296.98567000000003
230.84362999999999
212.89751000000001
914.02404000000001
305.55383
A Spark Table RDD was created first.
val tmp = sc.sql2rdd("select duration from test")
I made use of getString to translate it to a RDD[String] and then converted it to an RDD[Array[Double]].
val duration = tmp.map(row => Array[Double](row.getString(0).toDouble))