如何在Spark Scala中合并连接多个数据帧高效的完全外部连接
如何高效地合并/连接多个Spark数据帧(Scala)?我想加入一个对所有表都通用的列,下面的“Date”,并得到(某种程度上)一个稀疏数组如何在Spark Scala中合并连接多个数据帧高效的完全外部连接,scala,join,apache-spark,sparse-matrix,apache-spark-sql,Scala,Join,Apache Spark,Sparse Matrix,Apache Spark Sql,如何高效地合并/连接多个Spark数据帧(Scala)?我想加入一个对所有表都通用的列,下面的“Date”,并得到(某种程度上)一个稀疏数组 Data Set A: Date Col A1 Col A2 ----------------------- 1/1/16 A11 A21 1/2/16 A12 A22 1/3/16 A13 A23 1/4/16 A14 A24 1/5/16 A15 A25 Data Set B: D
Data Set A:
Date Col A1 Col A2
-----------------------
1/1/16 A11 A21
1/2/16 A12 A22
1/3/16 A13 A23
1/4/16 A14 A24
1/5/16 A15 A25
Data Set B:
Date Col B1 Col B2
-----------------------
1/1/16 B11 B21
1/3/16 B13 B23
1/5/16 B15 B25
Data Set C:
Date Col C1 Col C2
-----------------------
1/2/16 C12 C22
1/3/16 C13 C23
1/4/16 C14 C24
1/5/16 C15 C25
Expected Result Set:
Date Col A1 Col A2 Col B1 Col B2 Col C1 Col C2
---------------------------------------------------------
1/1/16 A11 A21 B11 B12
1/2/16 A12 A22 C12 C22
1/3/16 A13 A23 B13 B23 C13 C23
1/4/16 A14 A24 C14 C24
1/5/16 A15 A25 B15 B25 C15 C25
这感觉像是多个表上的完全外部连接,但我不确定。
是否有更简单/更有效的方法来获得这个稀疏数组,而不使用数据帧上的Join方法?这是一篇老文章,因此我不确定OP是否仍在调整中。无论如何,实现所需结果的简单方法是通过
cogroup()
。将每个RDD
转换为日期为键的[K,V]RDD
,然后使用cogroup。下面是一个例子:
def mergeFrames(sc: SparkContext, sqlContext: SQLContext) = {
import sqlContext.implicits._
// Create three dataframes. All string types assumed.
val dfa = sc.parallelize(Seq(A("1/1/16", "A11", "A21"),
A("1/2/16", "A12", "A22"),
A("1/3/16", "A13", "A23"),
A("1/4/16", "A14", "A24"),
A("1/5/16", "A15", "A25"))).toDF()
val dfb = sc.parallelize(Seq(
B("1/1/16", "B11", "B21"),
B("1/3/16", "B13", "B23"),
B("1/5/16", "B15", "B25"))).toDF()
val dfc = sc.parallelize(Seq(
C("1/2/16", "C12", "C22"),
C("1/3/16", "C13", "C23"),
C("1/4/16", "C14", "C24"),
C("1/5/16", "C15", "C25"))).toDF()
val rdda = dfa.rdd.map(row => row(0) -> row.toSeq.drop(1))
val rddb = dfb.rdd.map(row => row(0) -> row.toSeq.drop(1))
val rddc = dfc.rdd.map(row => row(0) -> row.toSeq.drop(1))
val schema = StructType("date a1 a2 b1 b2 c1 c2".split(" ").map(fieldName => StructField(fieldName, StringType)))
// Form cogroups. `date` is assumed to be a key so there's at most one row for each date in an rdd/df
val cg: RDD[Row] = rdda.cogroup(rddb, rddc).map { case (k, (v1, v2, v3)) =>
val cols = Seq(k) ++
(if (v1.nonEmpty) v1.head else Seq(null, null)) ++
(if (v2.nonEmpty) v2.head else Seq(null, null)) ++
(if (v3.nonEmpty) v3.head else Seq(null, null))
Row.fromSeq(cols)
}
// Turn RDD back to DataFrame
val cgdf = sqlContext.createDataFrame(cg, schema).sort("date")
cgdf.show }
我已经编辑了我的答案并添加了一些示例代码。希望有帮助。