CODEWITHSUNDEEP
jsonscalaapache-sparkapache-spark-sql
kernelman
kernelman

Reputation: 41

Spark JSON reading fields that are completional in JSON into case classes

Consider the following case class schema,

case class Y (a: String, b: String)
case class X (dummy: String, b: Y)

The field b is optional, some of my data sets don't have field b. When I try to read a JSON string that doesn't contain I receive a field missing exception.

spark.read.json(Seq("{'dummy': '1', 'b': {'a': '1'}}").toDS).as[X]
org.apache.spark.sql.AnalysisException: No such struct field b in a;
  at org.apache.spark.sql.catalyst.expressions.ExtractValue$.findField(complexTypeExtractors.scala:85)
  at org.apache.spark.sql.catalyst.expressions.ExtractValue$.apply(complexTypeExtractors.scala:53)
  at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$resolveExpression$1.applyOrElse(Analyzer.scala:1074)
  at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$resolveExpression$1.applyOrElse(Analyzer.scala:1065)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:282)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$2.apply(TreeNode.scala:282)
  at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)

How do I automatically deserialize fields that aren't present in the JSON to be as null ?

Upvotes: 3

Views: 606

Answers (1)

notNull
notNull

Reputation: 31540

Define b field as Option type and use encoders to create struct type schema.

  • Pass the defined schema using .schema option with the case class X to create dataset!

Example:

case class Y (a: String, b: Option[String] = None)
case class X (dummy: String, b: Y)

import org.apache.spark.sql.Encoders

val schema = Encoders.product[X].schema

spark.read.schema(schema).json(Seq("{'dummy': '1', 'b': {'a': '1'}}").toDS).as[X].show()

//+-----+----+
//|dummy|   b|
//+-----+----+
//|    1|[1,]|
//+-----+----+

Select b column from struct type:

spark.read.schema(schema).json(Seq("{'dummy': '1', 'b': {'a': '1'}}").toDS).as[X].
select("b.b").show()

//+----+
//|   b|
//+----+
//|null|
//+----+

PrintSchema:

spark.read.schema(schema).json(Seq("{'dummy': '1', 'b': {'a': '1'}}").toDS).as[X].printSchema

//root
 //|-- dummy: string (nullable = true)
 //|-- b: struct (nullable = true)
 //|    |-- a: string (nullable = true)
 //|    |-- b: string (nullable = true)

Upvotes: 4

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