Apache Spark 中的 join 和 cogroup 有什么区别

Question

What's the difference between join and cogroup in Apache Spark? What's the use case for each method?

Answer 1

Let me help you to clarify them, both are common to use and important!

def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))]

This is prototype of join, please carefully look at it . For example,

val rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
val rdd2 = sc.makeRDD(Array(("A","a"),("C","c"),("D","d")),2)
 
scala> rdd1.join(rdd2).collect
res0: Array[(String, (String, String))] = Array((A,(1,a)), (C,(3,c)))

All keys that will appear in the final result is common to rdd1 and rdd2. This is similar to relation database operation INNER JOIN .

But cogroup is different ,

def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))]

as one key at least appear in either of the two rdds, it will appear in the final result, let me clarify it:

val rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
val rdd2 = sc.makeRDD(Array(("A","a"),("C","c"),("D","d")),2)

scala> var rdd3 = rdd1.cogroup(rdd2).collect
res0: Array[(String, (Iterable[String], Iterable[String]))] = Array(
(B,(CompactBuffer(2),CompactBuffer())), 
(D,(CompactBuffer(),CompactBuffer(d))), 
(A,(CompactBuffer(1),CompactBuffer(a))), 
(C,(CompactBuffer(3),CompactBuffer(c)))
)

This is very similar to relation database operation FULL OUTER JOIN , but instead of flattening the result per line per record, it will give you the iterable interface to you , the following operation is up to you as convenient !

Good Luck!

Spark docs is: http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions