Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/18498#discussion_r126284016
--- Diff: python/pyspark/sql/functions.py ---
@@ -1856,7 +1860,9 @@ def from_json(col, schema, options={}):
"""
sc = SparkContext._active_spark_context
- jc = sc._jvm.functions.from_json(_to_java_column(col), schema.json(),
options)
+ if isinstance(schema, DataType):
--- End diff --
Hm.. I am not very sure on this actually. For the current state, yes. It
should work only for `StructType` and `ArrayType` but `from_json` in
`functions.scala` takes both `StructType` and `DataType`.
This will give a proper message from JVM side. This gets parsed correctly
as below:
```python
from pyspark.sql.types import *
from pyspark.sql.functions import *
df = spark.createDataFrame([(1,)], ("value",))
df.select(from_json(df.value, IntegerType()).alias("json")).collect()
```
throws an `pyspark.sql.utils.AnalysisException`:
```
...
cannot resolve 'jsontostructs(`value`)' due to data type mismatch: Input
schema int must be a struct or an array of structs.
...
```
For the context, we started to support `StructType` only from 2.1 but we
decided to support `ArrayType` later. The method signature was decided as
`DataType` just in case for more types in the future and we had to keep
`StructType` one for binary compatibility.
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