I will try to ask the problem in the general way.
I have a function like this
myFunction (Object first, Object second)
And i have an rdd of Object RDD [Object].
I need to perform myFunction on all rdd's elements, in the end of process I have to be sure that all the couples of my object are performed with the myfunction (.., ..)
One way, maybe, is create a broadcast variable (as a copy of my RDD), and than
val broadcastVar = sc.broadcast(rdd.collect())
rdd_line.mapPartitions(p=> {
var brd = broadcastVar.value
var result = new ListBuffer[Double]()
brd.foreach(b => {
p.foreach(e => result+= myfunction(b ,e))
})
result.toList.toIterator
})
There is another way to do this with better performance?
Use RDD's .cartesian method to get an RDD containing all pairs of elements from the two. In this case, you want the RDD's cartesian with itself:
rdd.cartesian(rdd).map({ case (x, y) => myFunction(x, y) })
Note that this will include pairs of an element with itself, and pairs in both orders, i.e. (a, b) as well as (b, a). And (a, a).
Related
I have an RDD with strings like this (ordered in a specific way):
["A","B","C","D"]
And another RDD with lists like this:
["C","B","F","K"],
["B","A","Z","M"],
["X","T","D","C"]
I would like to order the elements in each list in the second RDD based on the order in which they appear in the first RDD. The order of the elements that do not appear in the first list is not of concern.
From the above example, I would like to get an RDD like this:
["B","C","F","K"],
["A","B","Z","M"],
["C","D","X","T"]
I know I am supposed to use a broadcast variable to broadcast the first RDD as I process each list in the second RDD. But I am very new to Spark/Scala (and functional programming in general) so I am not sure how to do this.
I am assuming that the first RDD is small since you talk about broadcasting it. In that case you are right, broadcasting the ordering is a good way to solve your problem.
// generating data
val ordering_rdd = sc.parallelize(Seq("A","B","C","D"))
val other_rdd = sc.parallelize(Seq(
Seq("C","B","F","K"),
Seq("B","A","Z","M"),
Seq("X","T","D","C")
))
// let's start by collecting the ordering onto the driver
val ordering = ordering_rdd.collect()
// Let's broadcast the list:
val ordering_br = sc.broadcast(ordering)
// Finally, let's use the ordering to sort your records:
val result = other_rdd
.map( _.sortBy(x => {
val index = ordering_br.value.indexOf(x)
if(index == -1) Int.MaxValue else index
}))
Note that indexOf returns -1 if the element is not found in the list. If we leave it as is, all non-found elements would end up at the beginning. I understand that you want them at the end so I relpace -1 by some big number.
Printing the result:
scala> result.collect().foreach(println)
List(B, C, F, K)
List(A, B, Z, M)
List(C, D, X, T)
I am new with spark and scala. I want to sum up all the values present in the RDD. below is the example.
RDD is key value pair and suppose after doing some join and transformation the output of RDD have 3 record as below, where A is key:
(A, List(1,1,1,1,1,1,1))
(A, List(1,1,1,1,1,1,1))
(A, List(1,1,1,1,1,1,1))
Now i want to sum up all values of each record with corresponding value in other records, so the output should come like
(A, List(3,3,3,3,3,3,3))
Can anyone please help me out on this. Is there any possible way to achieve this using scala?
Big Thanks in Advance
A naive approach is to reduceByKey:
rdd.reduceByKey(
(xs, ys) => xs.zip(ys).map { case (x, y) => x + y }
)
but it is rather inefficient because it creates a new List on each merge.
You can improve on that by using for example aggregateByKey with mutable buffer:
rdd.aggregateByKey(Array.fill(7)(0)) // Mutable buffer
// For seqOp we'll mutate accumulator
(acc, xs) => {
for {
(x, i) <- xs.zipWithIndex
} acc(i) += x
acc
},
// For performance you could modify acc1 as above
(acc1, acc2) => acc1.zip(acc2).map { case(x, y) => x + y }
).mapValues(_.toList)
It should be also possible to use DataFrames but by default recent versions schedule aggregations separately so without adjusting configuration it is probably not worth the effort.
I am working with Apache Spark in Scala.
I have a problem when trying to manipulate one RDD with data from a second RDD. I am trying to pass the 2nd RDD as an argument to a function being 'mapped' against the first RDD, but seemingly the closure created on that function binds an uninitialized version of that value.
Following is a simpler piece of code that shows the type of problem I'm seeing. (My real example where I first had trouble is larger and less understandable).
I don't really understand the argument binding rules for Spark closures.
What I'm really looking for is a basic approach or pattern for how to manipulate one RDD using the content of another (which was previously constructed elsewhere).
In the following code, calling Test1.process(sc) will fail with a null pointer access in findSquare (as the 2nd arg bound in the closure is not initialized)
object Test1 {
def process(sc: SparkContext) {
val squaresMap = (1 to 10).map(n => (n, n * n))
val squaresRDD = sc.parallelize(squaresMap)
val primes = sc.parallelize(List(2, 3, 5, 7))
for (p <- primes) {
println("%d: %d".format(p, findSquare(p, squaresRDD)))
}
}
def findSquare(n: Int, squaresRDD: RDD[(Int, Int)]): Int = {
squaresRDD.filter(kv => kv._1 == n).first._1
}
}
Problem you experience has nothing to do with closures or RDDs which, contrary to popular belief, are serializable.
It is simply breaks a fundamental Spark rule which states that you cannot trigger an action or transformation from another action or transformation* and different variants of this question have been asked on SO multiple times.
To understand why that's the case you have to think about the architecture:
SparkContext is managed on the driver
everything that happens inside transformations is executed on the workers. Each worker have access only to its own part of the data and don't communicate with other workers**.
If you want to use content of multiple RDDs you have to use one of the transformations which combine RDDs, like join, cartesian, zip or union.
Here you most likely (I am not sure why you pass tuple and use only first element of this tuple) want to either use a broadcast variable:
val squaresMapBD = sc.broadcast(squaresMap)
def findSquare(n: Int): Seq[(Int, Int)] = {
squaresMapBD.value
.filter{case (k, v) => k == n}
.map{case (k, v) => (n, k)}
.take(1)
}
primes.flatMap(findSquare)
or Cartesian:
primes
.cartesian(squaresRDD)
.filter{case (n, (k, _)) => n == k}.map{case (n, (k, _)) => (n, k)}
Converting primes to dummy pairs (Int, null) and join would be more efficient:
primes.map((_, null)).join(squaresRDD).map(...)
but based on your comments I assume you're interested in a scenario when there is natural join condition.
Depending on a context you can also consider using database or files to store common data.
On a side note RDDs are not iterable so you cannot simply use for loop. To be able to do something like this you have to collect or convert toLocalIterator first. You can also use foreach method.
* To be precise you cannot access SparkContext.
** Torrent broadcast and tree aggregates involve communication between executors so it is technically possible.
RDD are not serializable, so you can't use an rdd inside an rdd trasformation.
Then I've never seen enumerate an rdd with a for statement, usually I use foreach statement that is part of rdd api.
In order to combine data from two rdd, you can leverage join, union or broadcast ( in case your rdd is small)
I was wondering if somebody could help.
I'm trying to aggregate some data in a list based on id values, I have a listBuffer which is updated from a foreach function. My output means I have an id number and a value, because the foreach applies a function to each id often more than once, the list I end up with looks something like the following:
ListBuffer(3106;0, 3106;3, 3108;2, 3108;0, 3110;1, 3110;2, 3113;0, 3113;2, 3113;0)
What I want to do is apply a simple function to aggregate this data, so I am left with
List(3106;3 ,3108;2, 3110;3, 3113;2)
I thought this could be done with foldLeft or groupBy, however I'm not sure how to get it to recognise id values and normal values.
Any help or pointers would be much appreciated
First of all, you can't group key-value pairs this way. In scala you have tuples which are written as
val pair: (Int, Int) = (3106,3), where
pair._1 == 3106
pair._2 == 3
are true statements.
So you have:
val l = ListBuffer((3106,0), (3106,3), (3108,2), (3108,0), (3110,1), (3110,2), (3113,0), (3113,2), (3113,0))
val result = l.groupBy(x => x._1).map(x => (x._1, x._2.map(_._2))).map(x => (x._1, x._2.sum)).toList
println(result)
will give you
List((3106,3), (3108,2), (3110,3), (3113,2))
I am new to Spark and Scala. I was confused about the way reduceByKey function works in Spark. Suppose we have the following code:
val lines = sc.textFile("data.txt")
val pairs = lines.map(s => (s, 1))
val counts = pairs.reduceByKey((a, b) => a + b)
The map function is clear: s is the key and it points to the line from data.txt and 1 is the value.
However, I didn't get how the reduceByKey works internally? Does "a" points to the key? Alternatively, does "a" point to "s"? Then what does represent a + b? how are they filled?
Let's break it down to discrete methods and types. That usually exposes the intricacies for new devs:
pairs.reduceByKey((a, b) => a + b)
becomes
pairs.reduceByKey((a: Int, b: Int) => a + b)
and renaming the variables makes it a little more explicit
pairs.reduceByKey((accumulatedValue: Int, currentValue: Int) => accumulatedValue + currentValue)
So, we can now see that we are simply taking an accumulated value for the given key and summing it with the next value of that key. NOW, let's break it further so we can understand the key part. So, let's visualize the method more like this:
pairs.reduce((accumulatedValue: List[(String, Int)], currentValue: (String, Int)) => {
//Turn the accumulated value into a true key->value mapping
val accumAsMap = accumulatedValue.toMap
//Try to get the key's current value if we've already encountered it
accumAsMap.get(currentValue._1) match {
//If we have encountered it, then add the new value to the existing value and overwrite the old
case Some(value : Int) => (accumAsMap + (currentValue._1 -> (value + currentValue._2))).toList
//If we have NOT encountered it, then simply add it to the list
case None => currentValue :: accumulatedValue
}
})
So, you can see that the reduceByKey takes the boilerplate of finding the key and tracking it so that you don't have to worry about managing that part.
Deeper, truer if you want
All that being said, that is a simplified version of what happens as there are some optimizations that are done here. This operation is associative, so the spark engine will perform these reductions locally first (often termed map-side reduce) and then once again at the driver. This saves network traffic; instead of sending all the data and performing the operation, it can reduce it as small as it can and then send that reduction over the wire.
One requirement for the reduceByKey function is that is must be associative. To build some intuition on how reduceByKey works, let's first see how an associative associative function helps us in a parallel computation:
As we can see, we can break an original collection in pieces and by applying the associative function, we can accumulate a total. The sequential case is trivial, we are used to it: 1+2+3+4+5+6+7+8+9+10.
Associativity lets us use that same function in sequence and in parallel. reduceByKey uses that property to compute a result out of an RDD, which is a distributed collection consisting of partitions.
Consider the following example:
// collection of the form ("key",1),("key,2),...,("key",20) split among 4 partitions
val rdd =sparkContext.parallelize(( (1 to 20).map(x=>("key",x))), 4)
rdd.reduceByKey(_ + _)
rdd.collect()
> Array[(String, Int)] = Array((key,210))
In spark, data is distributed into partitions. For the next illustration, (4) partitions are to the left, enclosed in thin lines. First, we apply the function locally to each partition, sequentially in the partition, but we run all 4 partitions in parallel. Then, the result of each local computation are aggregated by applying the same function again and finally come to a result.
reduceByKey is an specialization of aggregateByKey aggregateByKey takes 2 functions: one that is applied to each partition (sequentially) and one that is applied among the results of each partition (in parallel). reduceByKey uses the same associative function on both cases: to do a sequential computing on each partition and then combine those results in a final result as we have illustrated here.
In your example of
val counts = pairs.reduceByKey((a,b) => a+b)
a and b are both Int accumulators for _2 of the tuples in pairs. reduceKey will take two tuples with the same value s and use their _2 values as a and b, producing a new Tuple[String,Int]. This operation is repeated until there is only one tuple for each key s.
Unlike non-Spark (or, really, non-parallel) reduceByKey where the first element is always the accumulator and the second a value, reduceByKey operates in a distributed fashion, i.e. each node will reduce it's set of tuples into a collection of uniquely-keyed tuples and then reduce the tuples from multiple nodes until there is a final uniquely-keyed set of tuples. This means as the results from nodes are reduced, a and b represent already reduced accumulators.
Spark RDD reduceByKey function merges the values for each key using an associative reduce function.
The reduceByKey function works only on the RDDs and this is a transformation operation that means it is lazily evaluated. And an associative function is passed as a parameter, which is applied to source RDD and creates a new RDD as a result.
So in your example, rdd pairs has a set of multiple paired elements like (s1,1), (s2,1) etc. And reduceByKey accepts a function (accumulator, n) => (accumulator + n), which initialise the accumulator variable to default value 0 and adds up the element for each key and return the result rdd counts having the total counts paired with key.
Simple if your input RDD data look like this:
(aa,1)
(bb,1)
(aa,1)
(cc,1)
(bb,1)
and if you apply reduceByKey on above rdd data then few you have to remember,
reduceByKey always takes 2 input (x,y) and always works with two rows at a time.
As it is reduceByKey it will combine two rows of same key and combine the result of value.
val rdd2 = rdd.reduceByKey((x,y) => x+y)
rdd2.foreach(println)
output:
(aa,2)
(bb,2)
(cc,1)