Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/15951
Your concern is right. I just did another try. Using the string-type
columns as partition columns. See the following code:
```Scala
val rowRdd: RDD[Row] = sparkContext.parallelize(1 to 10).map(i => Row(i,
i.toString))
val inputSchema = StructType(Seq(
StructField("intCol", IntegerType),
StructField("stringCol", StringType)
))
spark.createDataFrame(rowRdd, inputSchema)
.write.partitionBy("stringCol").mode("overwrite").parquet(src.toString)
val schema = new StructType()
.add("intCol", IntegerType)
.add("stringCol", IntegerType)
spark.read
.schema(schema)
.format("parquet")
.load(src.toString).show()
```
Users have to use `IntegerType` for these partition columns even if the
original data type is `StringType`. This looks werid. Otherwise, they will hit
the following error:
```
Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most
recent failure: Lost task 0.0 in stage 1.0 (TID 2, localhost, executor driver):
java.lang.NullPointerException
at
org.apache.spark.sql.execution.vectorized.OnHeapColumnVector.getArrayLength(OnHeapColumnVector.java:375)
```
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