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Spark:对RDD中的高效质量查找

[英]Spark: Efficient mass lookup in pair RDD's

In Apache Spark I have two RDD's. 在Apache Spark中我有两个RDD。 The first data : RDD[(K,V)] containing data in key-value form. 第一个data : RDD[(K,V)]包含键值形式的数据。 The second pairs : RDD[(K,K)] contains a set of interesting key-pairs of this data. 第二pairs : RDD[(K,K)]包含一组有趣的数据密钥对。

How can I efficiently construct an RDD pairsWithData : RDD[((K,K)),(V,V))] , such that it contains all the elements from pairs as the key-tuple and their corresponding values (from data ) as the value-tuple? 如何有效地构造RDD对与pairsWithData : RDD[((K,K)),(V,V))] ,使得它包含来自pairs所有元素作为键元组及其对应的值(来自data )as价值元组?

Some properties of the data: 数据的一些属性:

  • The keys in data are unique data中的键是唯一的
  • All entries in pairs are unique pairs所有条目都是唯一的
  • For all pairs (k1,k2) in pairs it is guaranteed that k1 <= k2 对于所有对(k1,k2)pairs可以保证k1 <= k2
  • The size of 'pairs' is only a constant the size of data |pairs| = O(|data|) “对”的大小只是数据|pairs| = O(|data|)的大小的常量 |pairs| = O(|data|)
  • Current data sizes (expected to grow): |data| ~ 10^8, |pairs| ~ 10^10 当前数据大小(预计会增长): |data| ~ 10^8, |pairs| ~ 10^10 |data| ~ 10^8, |pairs| ~ 10^10

Current attempts 目前的尝试

Here is some example code in Scala: 以下是Scala中的一些示例代码:

import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._

// This kind of show the idea, but fails at runtime.
def massPairLookup1(keyPairs : RDD[(Int, Int)], data : RDD[(Int, String)]) = {
  keyPairs map {case (k1,k2) =>
    val v1 : String = data lookup k1 head;
    val v2 : String = data lookup k2 head;
    ((k1, k2), (v1,v2))
  }
}

// Works but is O(|data|^2)
def massPairLookup2(keyPairs : RDD[(Int, Int)], data : RDD[(Int, String)]) = {
  // Construct all possible pairs of values
  val cartesianData = data cartesian data map {case((k1,v1),(k2,v2)) => ((k1,k2),(v1,v2))}
  // Select only the values who's keys are in keyPairs
  keyPairs map {(_,0)} join cartesianData mapValues {_._2}
}

// Example function that find pairs of keys
// Runs in O(|data|) in real life, but cannot maintain the values
def relevantPairs(data : RDD[(Int, String)]) = {
  val keys = data map (_._1)
  keys cartesian keys filter {case (x,y) => x*y == 12 && x < y}
}

// Example run
val data = sc parallelize(1 to 12) map (x => (x, "Number " + x))
val pairs = relevantPairs(data)
val pairsWithData = massPairLookup2(pairs, data) 


// Print: 
// ((1,12),(Number1,Number12))
// ((2,6),(Number2,Number6))
// ((3,4),(Number3,Number4))
pairsWithData.foreach(println)

Attempt 1 尝试1

First I tried just using the lookup function on data , but that throws an runtime error when executed. 首先,我尝试在data上使用lookup函数,但在执行时会抛出运行时错误。 It seems like self is null in the PairRDDFunctions trait. 好像self是在空PairRDDFunctions特征。

In addition I am not sure about the performance of lookup . 另外我不确定lookup的性能。 The documentation says This operation is done efficiently if the RDD has a known partitioner by only searching the partition that the key maps to. 文档如果RDD通过仅搜索键映射到的分区而具有已知分区器,则此操作有效地完成。 This sounds like n lookups takes O(n*|partition|) time at best, which I suspect could be optimized. 这听起来像n查找最多需要O(n * |分区|)时间,我怀疑可以优化。

Attempt 2 尝试2

This attempt works, but I create |data|^2 pairs which will kill performance. 这种尝试有效,但我创建了|data|^2对会破坏性能。 I do not expect Spark to be able to optimize that away. 我不希望Spark能够优化它。

Your lookup 1 doesn't work because you cannot perform RDD transformations inside workers (inside another transformation). 您的查找1不起作用,因为您无法在工作者内部执行RDD转换(在另一个转换中)。

In the lookup 2, I don't think it's necessary to perform full cartesian... 在查找2中,我认为没有必要执行完整的笛卡尔...

You can do it like this: 你可以这样做:

val firstjoin = pairs.map({case (k1,k2) => (k1, (k1,k2))})
    .join(data)
    .map({case (_, ((k1, k2), v1)) => ((k1, k2), v1)})
val result = firstjoin.map({case ((k1,k2),v1) => (k2, ((k1,k2),v1))})
    .join(data)
    .map({case(_, (((k1,k2), v1), v2))=>((k1, k2), (v1, v2))})

Or in a more dense form: 或者以更密集的形式:

    val firstjoin = pairs.map(x => (x._1, x)).join(data).map(_._2)
    val result = firstjoin.map({case (x,y) => (x._2, (x,y))})
        .join(data).map({case(x, (y, z))=>(y._1, (y._2, z))})

I don't think you can do it more efficiently, but I might be wrong... 我认为你不能更有效地做到这一点,但我可能错了......

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