duber duber - 1 year ago 101
SQL Question

Why would I want .union over .unionAll in Spark for SchemaRDDs?

I'm trying to wrap my head around these two functions in the Spark SQL documentation

  • def union(other: RDD[Row]): RDD[Row]

    Return the union of this RDD and another one.

  • def unionAll(otherPlan: SchemaRDD): SchemaRDD

    Combines the tuples of two RDDs with the same schema, keeping duplicates.

This is not the standard behavior of UNION vs UNION ALL, as documented in this SO question.

My code here, borrowing from the Spark SQL documentation, has the two functions returning the same results.

scala> case class Person(name: String, age: Int)
scala> import org.apache.spark.sql._
scala> val one = sc.parallelize(Array(Person("Alpha",1), Person("Beta",2)))
scala> val two = sc.parallelize(Array(Person("Alpha",1), Person("Beta",2), Person("Gamma", 3)))
scala> val schemaString = "name age"
scala> val schema = StructType(schemaString.split(" ").map(fieldName => StructField(fieldName, StringType, true)))
scala> val peopleSchemaRDD1 = sqlContext.applySchema(one, schema)
scala> val peopleSchemaRDD2 = sqlContext.applySchema(two, schema)
scala> peopleSchemaRDD1.union(peopleSchemaRDD2).collect
res34: Array[org.apache.spark.sql.Row] = Array([Alpha,1], [Beta,2], [Alpha,1], [Beta,2], [Gamma,3])
scala> peopleSchemaRDD1.unionAll(peopleSchemaRDD2).collect
res35: Array[org.apache.spark.sql.Row] = Array([Alpha,1], [Beta,2], [Alpha,1], [Beta,2], [Gamma,3])

Why would I prefer one over the other?

Answer Source

Judging from its type signature and (questionable) semantics I believe union() was vestigial.

The more modern DataFrame API offers only unionAll().

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