Hadoop 火花-寻找重叠值或寻找共同朋友的变体

我有一个问题，我正试图用Spark解决。我是Spark的新手，所以我不确定设计它的最佳方式是什么

输入： 我想找出每对用户之间的相互组数。因此，对于上述输入，我期望的输出是：

输出： 我认为有几种方法可以解决这个问题，其中一种可能是：

• 创建一个键、值对，其中键是用户，值是组
• 按键分组，然后我们将有一个用户所属组的列表
• 然后找到两个组之间的交点/并集

例如：

(1st stage): Map
group1=user1,user2 ==>
          user1, group1
          user2, group1
group2=user1,user2,user3 ==>
          user1, group2
          user2, group2
          user3, group2
....
....
....


(2nd stage): Reduce by key
user1 -> group1, group2, group4, group8
user2 -> group1, group2, group3, group7, group9

但我的问题是，在按键减少计数后，用我想要的方式表示计数的最佳方式是什么

有没有更好的办法来处理这个问题？用户的最大数量是恒定的，不会超过5000，因此这是它将创建的最大密钥数。但输入可能包含接近1B行的几行。我不认为那会是个问题，如果我错了，请纠正我

更新： 这是我用对Spark的一点了解来解决这个问题的一段代码（上个月刚开始学习Spark）：

我很想得到一些关于我的代码和我缺少的东西的反馈。请随意批评我的代码，因为我刚刚开始学习Spark。再次感谢@axiom的回答，这是一个比我预想的更小更好的解决方案。

总结：

获取对计数，然后使用以下事实

并集（a，b）=计数（a）+计数（b）-交点（a，b）

---

详细信息：

总共有5000个用户，2500万个密钥（每对1个）应该不会太多。我们可以使用

reduceByKey

计算交叉口计数

在地图中可以轻松地

广播单个计数

现在大家都知道：

联合（user1，user2）=计数（user1）+计数（user2）-交叉点（user1，user2）

前两个计数从广播映射中读取，而我们映射成对计数的rdd

代码：

//为对计数生成（（user1，user2），1）
def对（str:String）={
val users=str.split（“=”）1.split（“，”）
val n=users.length
为了
1st user || 2nd user || mutual/intersection count || union count
------------------------------------------------------------
user1        user2           2                       7
user1        user3           1                       6
user1        user4           1                       9
user2        user4           3                       8

(1st stage): Map
group1=user1,user2 ==>
          user1, group1
          user2, group1
group2=user1,user2,user3 ==>
          user1, group2
          user2, group2
          user3, group2
....
....
....


(2nd stage): Reduce by key
user1 -> group1, group2, group4, group8
user2 -> group1, group2, group3, group7, group9

def createPair(line: String): Array[(String, String)] = {
    val splits = line.split("=")
    val kuid = splits(0)
    splits(1).split(",").map { segment => (segment, kuid) }
}


val input = sc.textFile("input/test.log")
val pair = input.flatMap { line => createPair(line) }

val pairListDF = pair
  .aggregateByKey(scala.collection.mutable.ListBuffer.empty[String])(
    (kuidList, kuid) => { kuidList += kuid; kuidList },
    (kuidList1, kuidList2) => { kuidList1.appendAll(kuidList2); kuidList1 })
  .mapValues(_.toList).toDF().select($"_1".alias("user"), $"_2".alias("groups"))

pairListDF.registerTempTable("table")

sqlContext.udf.register("intersectCount", (list1: WrappedArray[String], list2: WrappedArray[String]) => list1.intersect(list2).size)
sqlContext.udf.register("unionCount", (list1: WrappedArray[String], list2: WrappedArray[String]) => list1.union(list2).distinct.size)

val populationDF = sqlContext.sql("SELECT t1.user AS user_first,"
  + "t2.user AS user_second,"
  + "intersectCount(t1.groups, t2.groups) AS intersect_count,"
  + "unionCount(t1.groups, t2.groups) AS union_count"
  + " FROM table t1 INNER JOIN table t2"
  + " ON t1.user < t2.user"
  + " ORDER BY user_first,user_second")

+----------+-----------+---------------+-----------+
|user_first|user_second|intersect_count|union_count|
+----------+-----------+---------------+-----------+
|     user1|      user2|              2|          7|
|     user1|      user3|              1|          6|
|     user1|      user4|              1|          9|
|     user1|      user5|              1|          8|
|     user2|      user3|              1|          7|
|     user2|      user4|              3|          8|
|     user2|      user5|              1|          9|
|     user3|      user4|              1|          8|
|     user3|      user5|              2|          6|
|     user4|      user5|              3|          8|
+----------+-----------+---------------+-----------+

val data = sc.textFile("test")
//optionally data.cache(), depending on size of data.
val pairCounts  = data.flatMap(pairs).reduceByKey(_ + _)
val singleCounts = data.flatMap(singles).reduceByKey(_ + _)
val singleCountMap = sc.broadcast(singleCounts.collectAsMap())
val result = pairCounts.map{case ((user1, user2), intersectionCount) =>(user1, user2, intersectionCount, singleCountMap.value(user1) + singleCountMap.value(user2) - intersectionCount)}

//generate ((user1, user2), 1) for pair counts
def pairs(str: String) = {
 val users = str.split("=")(1).split(",")
 val n = users.length
 for(i <- 0 until n; j <- i + 1 until n) yield {
  val pair = if(users(i) < users(j)) {
    (users(i), users(j))
  } else {
   (users(j), users(i))
  } //order of the user in a list shouldn't matter
  (pair, 1)
 } 
}

//generate (user, 1), to obtain single counts
def singles(str: String) = {
  for(user <- str.split("=")(1).split(",")) yield (user, 1)
}


//read the rdd
scala> val data = sc.textFile("test")
scala> data.collect.map(println)
group1=user1,user2
group2=user1,user2,user3
group3=user2,user4
group4=user1,user4
group5=user3,user5
group6=user3,user4,user5
group7=user2,user4
group8=user1,user5
group9=user2,user4,user5
group10=user4,user5

//get the pair counts
scala> val pairCounts  = data.flatMap(pairs).reduceByKey(_ + _)
pairCounts: org.apache.spark.rdd.RDD[((String, String), Int)] = ShuffledRDD[16] at reduceByKey at <console>:25



//just checking
scala> pairCounts.collect.map(println)
((user2,user3),1)
((user1,user3),1)
((user3,user4),1)
((user2,user5),1)
((user1,user5),1)
((user2,user4),3)
((user4,user5),3)
((user1,user4),1)
((user3,user5),2)
((user1,user2),2)

//single counts
scala> val singleCounts = data.flatMap(singles).reduceByKey(_ + _)
singleCounts: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[20] at reduceByKey at <console>:25

scala> singleCounts.collect.map(println)

(user5,5)
(user3,3)
(user1,4)
(user2,5)
(user4,6)


//broadcast single counts
scala> val singleCountMap = sc.broadcast(singleCounts.collectAsMap())

//calculate the results:

scala> val res = pairCounts.map{case ((user1, user2), intersectionCount) => (user1, user2, intersectionCount, singleCountMap.value(user1) + singleCountMap.value(user2) - intersectionCount)}
res: org.apache.spark.rdd.RDD[(String, String, Int, Int)] = MapPartitionsRDD[23] at map at <console>:33

scala> res.collect.map(println)
(user2,user3,1,7)
(user1,user3,1,6)
(user3,user4,1,8)
(user2,user5,1,9)
(user1,user5,1,8)
(user2,user4,3,8)
(user4,user5,3,8)
(user1,user4,1,9)
(user3,user5,2,6)
(user1,user2,2,7)