Row (Spark 3.5.5 JavaDoc) (original) (raw)

Represents one row of output from a relational operator. Allows both generic access by ordinal, which will incur boxing overhead for primitives, as well as native primitive access.

It is invalid to use the native primitive interface to retrieve a value that is null, instead a user must check isNullAt before attempting to retrieve a value that might be null.

To create a new Row, use RowFactory.create() in Java or Row.apply() in Scala.

A Row object can be constructed by providing field values. Example:

 import org.apache.spark.sql._

 // Create a Row from values.
 Row(value1, value2, value3, ...)
 // Create a Row from a Seq of values.
 Row.fromSeq(Seq(value1, value2, ...))
 

A value of a row can be accessed through both generic access by ordinal, which will incur boxing overhead for primitives, as well as native primitive access. An example of generic access by ordinal:

 import org.apache.spark.sql._

 val row = Row(1, true, "a string", null)
 // row: Row = [1,true,a string,null]
 val firstValue = row(0)
 // firstValue: Any = 1
 val fourthValue = row(3)
 // fourthValue: Any = null
 

For native primitive access, it is invalid to use the native primitive interface to retrieve a value that is null, instead a user must check isNullAt before attempting to retrieve a value that might be null. An example of native primitive access:

 // using the row from the previous example.
 val firstValue = row.getInt(0)
 // firstValue: Int = 1
 val isNull = row.isNullAt(3)
 // isNull: Boolean = true
 

In Scala, fields in a Row object can be extracted in a pattern match. Example:

 import org.apache.spark.sql._

 val pairs = sql("SELECT key, value FROM src").rdd.map {
   case Row(key: Int, value: String) =>
     key -> value
 }