How to read a nested collection in Spark

Question

I have a parquet table with one of the columns being

, array>

Can run queries against this table in

Answer 1

There is no magic in the case of nested collection. Spark will handle the same way a RDD[(String, String)] and a RDD[(String, Seq[String])].

Reading such nested collection from Parquet files can be tricky, though.

Let's take an example from the spark-shell (1.3.1):

scala> import sqlContext.implicits._
import sqlContext.implicits._

scala> case class Inner(a: String, b: String)
defined class Inner

scala> case class Outer(key: String, inners: Seq[Inner])
defined class Outer

Write the parquet file:

scala> val outers = sc.parallelize(List(Outer("k1", List(Inner("a", "b")))))
outers: org.apache.spark.rdd.RDD[Outer] = ParallelCollectionRDD[0] at parallelize at :25

scala> outers.toDF.saveAsParquetFile("outers.parquet")

Read the parquet file:

scala> import org.apache.spark.sql.catalyst.expressions.Row
import org.apache.spark.sql.catalyst.expressions.Row

scala> val dataFrame = sqlContext.parquetFile("outers.parquet")
dataFrame: org.apache.spark.sql.DataFrame = [key: string, inners: array>]   

scala> val outers = dataFrame.map { row =>
     |   val key = row.getString(0)
     |   val inners = row.getAs[Seq[Row]](1).map(r => Inner(r.getString(0), r.getString(1)))
     |   Outer(key, inners)
     | }
outers: org.apache.spark.rdd.RDD[Outer] = MapPartitionsRDD[8] at map at DataFrame.scala:848

The important part is row.getAs[Seq[Row]](1). The internal representation of a nested sequence of struct is ArrayBuffer[Row], you could use any super-type of it instead of Seq[Row]. The 1 is the column index in the outer row. I used the method getAs here but there are alternatives in the latest versions of Spark. See the source code of the Row trait.

Now that you have a RDD[Outer], you can apply any wanted transformation or action.

// Filter the outers
outers.filter(_.inners.nonEmpty)

// Filter the inners
outers.map(outer => outer.copy(inners = outer.inners.filter(_.a == "a")))

Note that we used the spark-SQL library only to read the parquet file. You could for example select only the wanted columns directly on the DataFrame, before mapping it to a RDD.

dataFrame.select('col1, 'col2).map { row => ... }