[SPARK-36830][SQL] Support reading and writing ANSI intervals from/to…

… JSON datasources

### What changes were proposed in this pull request?

This PR aims to support reading and writing ANSI intervals from/to JSON datasources.
Aith this change, a interval data is written as a literal form like `{"col":"INTERVAL '1-2' YEAR TO MONTH"}`.
For the reading part, we need to specify the schema explicitly like:
```
val readDF = spark.read.schema("col INTERVAL YEAR TO MONTH").json(...)
```

### Why are the changes needed?

For better usability. There should be no reason to prohibit from reading/writing ANSI intervals from/to JSON datasources.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

New test. It covers both V1 and V2 sources.

Closes #34155 from sarutak/ansi-interval-json-source.

Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala

@@ -581,7 +581,7 @@ case class DataSource(
 
   // TODO: Remove the Set below once all the built-in datasources support ANSI interval types
   private val writeAllowedSources: Set[Class[_]] =
-    Set(classOf[ParquetFileFormat], classOf[CSVFileFormat])
+    Set(classOf[ParquetFileFormat], classOf[CSVFileFormat], classOf[JsonFileFormat])
 
   private def disallowWritingIntervals(
       dataTypes: Seq[DataType],

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonFileFormat.scala

@@ -134,8 +134,6 @@ class JsonFileFormat extends TextBasedFileFormat with DataSourceRegister {
   override def equals(other: Any): Boolean = other.isInstanceOf[JsonFileFormat]
 
   override def supportDataType(dataType: DataType): Boolean = dataType match {
-    case _: AnsiIntervalType => false
-
     case _: AtomicType => true
 
     case st: StructType => st.forall { f => supportDataType(f.dataType) }

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/json/JsonTable.scala

@@ -55,8 +55,6 @@ case class JsonTable(
     }
 
   override def supportsDataType(dataType: DataType): Boolean = dataType match {
-    case _: AnsiIntervalType => false
-
     case _: AtomicType => true
 
     case st: StructType => st.forall { f => supportsDataType(f.dataType) }

sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala

@@ -1445,32 +1445,13 @@ class SQLQuerySuite extends QueryTest with SharedSparkSession with AdaptiveSpark
 
     val ymDF = sql("select interval 3 years -3 month")
     checkAnswer(ymDF, Row(Period.of(2, 9, 0)))
-    withTempPath(f => {
-      val e = intercept[AnalysisException] {
-        ymDF.write.json(f.getCanonicalPath)
-      }
-      e.message.contains("Cannot save interval data type into external storage")
-    })
 
     val dtDF = sql("select interval 5 days 8 hours 12 minutes 50 seconds")
     checkAnswer(dtDF, Row(Duration.ofDays(5).plusHours(8).plusMinutes(12).plusSeconds(50)))
-    withTempPath(f => {
-      val e = intercept[AnalysisException] {
-        dtDF.write.json(f.getCanonicalPath)
-      }
-      e.message.contains("Cannot save interval data type into external storage")
-    })
 
     withSQLConf(SQLConf.LEGACY_INTERVAL_ENABLED.key -> "true") {
       val df = sql("select interval 3 years -3 month 7 week 123 microseconds")
       checkAnswer(df, Row(new CalendarInterval(12 * 3 - 3, 7 * 7, 123)))
-      withTempPath(f => {
-        // Currently we don't yet support saving out values of interval data type.
-        val e = intercept[AnalysisException] {
-          df.write.json(f.getCanonicalPath)
-        }
-        e.message.contains("Cannot save interval data type into external storage")
-      })
     }
   }
 

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/CommonFileDataSourceSuite.scala

@@ -36,7 +36,7 @@ trait CommonFileDataSourceSuite extends SQLHelper { self: AnyFunSuite =>
   protected def inputDataset: Dataset[_] = spark.createDataset(Seq("abc"))(Encoders.STRING)
 
   test(s"SPARK-36349: disallow saving of ANSI intervals to $dataSourceFormat") {
-    if (!Set("csv", "parquet").contains(dataSourceFormat.toLowerCase(Locale.ROOT))) {
+    if (!Set("parquet", "csv", "json").contains(dataSourceFormat.toLowerCase(Locale.ROOT))) {
       Seq("INTERVAL '1' DAY", "INTERVAL '1' YEAR").foreach { i =>
         withTempPath { dir =>
           val errMsg = intercept[AnalysisException] {

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala

@@ -21,7 +21,7 @@ import java.io._
 import java.nio.charset.{Charset, StandardCharsets, UnsupportedCharsetException}
 import java.nio.file.Files
 import java.sql.{Date, Timestamp}
-import java.time.{Instant, LocalDate, LocalDateTime, ZoneId}
+import java.time.{Duration, Instant, LocalDate, LocalDateTime, Period, ZoneId}
 import java.util.Locale
 
 import com.fasterxml.jackson.core.JsonFactory
@@ -2991,6 +2991,25 @@ abstract class JsonSuite
       }
     }
   }
+
+  test("SPARK-36830: Support reading and writing ANSI intervals") {
+    Seq(
+      YearMonthIntervalType() -> ((i: Int) => Period.of(i, i, 0)),
+      DayTimeIntervalType() -> ((i: Int) => Duration.ofDays(i).plusSeconds(i))
+    ).foreach { case (it, f) =>
+      val data = (1 to 10).map(i => Row(i, f(i)))
+      val schema = StructType(Array(StructField("d", IntegerType, false),
+        StructField("i", it, false)))
+      withTempPath { file =>
+        val df = spark.createDataFrame(sparkContext.parallelize(data), schema)
+        df.write.json(file.getCanonicalPath)
+        val df2 = spark.read.json(file.getCanonicalPath)
+        checkAnswer(df2, df.select($"d".cast(LongType), $"i".cast(StringType)).collect().toSeq)
+        val df3 = spark.read.schema(schema).json(file.getCanonicalPath)
+        checkAnswer(df3, df.collect().toSeq)
+      }
+    }
+  }
 }
 
 class JsonV1Suite extends JsonSuite {