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Row is a generic row object with an ordered collection of fields that can be accessed by an <> (aka generic access by ordinal), a name (aka native primitive access) or using <>.

NOTE: Row is also called Catalyst Row.

Row may have an optional <>.

The traits of Row:

  • length or size - Row knows the number of elements (columns).
  • schema - Row knows the schema

Row belongs to org.apache.spark.sql.Row package.

[source, scala]

import org.apache.spark.sql.Row

=== [[apply]] Creating Row -- apply Factory Method


=== [[field-access]][[get]][[apply-index]] Field Access by Index -- apply and get methods

Fields of a Row instance can be accessed by index (starting from 0) using apply or get.

[source, scala]

scala> val row = Row(1, "hello") row: org.apache.spark.sql.Row = [1,hello]

scala> row(1) res0: Any = hello

scala> row.get(1) res1: Any = hello

NOTE: Generic access by ordinal (using apply or get) returns a value of type Any.

=== [[getAs]] Get Field As Type -- getAs method

You can query for fields with their proper types using getAs with an index

[source, scala]

val row = Row(1, "hello")

scala> row.getAsInt res1: Int = 1

scala> row.getAsString res2: String = hello


FIXME [source, scala]



=== [[schema]] Schema

A Row instance can have a schema defined.

NOTE: Unless you are instantiating Row yourself (using <>), a Row has always a schema.


It is RowEncoder to take care of assigning a schema to a Row when toDF on a Dataset or when instantiating DataFrame through DataFrameReader.

=== [[row-object]] Row Object

Row companion object offers factory methods to create Row instances from a collection of elements (apply), a sequence of elements (fromSeq) and tuples (fromTuple).

[source, scala]

scala> Row(1, "hello") res0: org.apache.spark.sql.Row = [1,hello]

scala> Row.fromSeq(Seq(1, "hello")) res1: org.apache.spark.sql.Row = [1,hello]

scala> Row.fromTuple((0, "hello")) res2: org.apache.spark.sql.Row = [0,hello]

Row object can merge Row instances.

[source, scala]

scala> Row.merge(Row(1), Row("hello")) res3: org.apache.spark.sql.Row = [1,hello]

It can also return an empty Row instance.

[source, scala]

scala> Row.empty == Row() res4: Boolean = true

=== [[pattern-matching-on-row]] Pattern Matching on Row

Row can be used in pattern matching (since <> comes with unapplySeq).

[source, scala]

scala> Row.unapplySeq(Row(1, "hello")) res5: Some[Seq[Any]] = Some(WrappedArray(1, hello))

Row(1, "hello") match { case Row(key: Int, value: String) => key -> value }