Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/16981#discussion_r101948378
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala ---
@@ -174,4 +174,44 @@ class JsonFunctionsSuite extends QueryTest with
SharedSQLContext {
.select(to_json($"struct").as("json"))
checkAnswer(dfTwo, readBackTwo)
}
+
+ test("SPARK-19637 Support to_json/from_json in SQL") {
+ // to_json
+ val df1 = Seq(Tuple1(Tuple1(1))).toDF("a")
+ checkAnswer(
+ df1.selectExpr("to_json(a)"),
+ Row("""{"_1":1}""") :: Nil)
+
+ val df2 = Seq(Tuple1(Tuple1(java.sql.Timestamp.valueOf("2015-08-26
18:00:00.0")))).toDF("a")
+ checkAnswer(
+ df2.selectExpr("""to_json(a, '{"timestampFormat": "dd/MM/yyyy
HH:mm"}')"""),
+ Row("""{"_1":"26/08/2015 18:00"}""") :: Nil)
+
+ val errMsg1 = intercept[AnalysisException] {
+ df2.selectExpr("""to_json(a, '{"k": [{"k": "v"}]}')""").collect
+ }
+ assert(errMsg1.getMessage.startsWith(
+ """The format must be '{"key": "value", ...}', but {"k": [{"k":
"v"}]}"""))
+
+ // from_json
+ val df3 = Seq("""{"a": 1}""").toDS()
+ val schema1 = new StructType().add("a", IntegerType)
+ checkAnswer(
+ df3.selectExpr(s"from_json(value, '${schema1.json}')"),
+ Row(Row(1)) :: Nil)
+
+ val df4 = Seq("""{"time": "26/08/2015 18:00"}""").toDS()
+ val schema2 = new StructType().add("time", TimestampType)
+ checkAnswer(
+ df4.selectExpr(
+ s"""from_json(value, '${schema2.json}', """ +
+ """'{"timestampFormat": "dd/MM/yyyy HH:mm"}')"""),
--- End diff --
Regarding the format of options, another way is to use the MapType.
For example, ```Scala
from_json(value, '${schema2.json}', map("timestampFormat", "dd/MM/yyyy
HH:mm"))
```
I am not sure whether using JSON to represent options is a good way.
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