Q:

spark does not read all orc files from different folder using merge schema

I have three different orc files in three different folder, I want to read them all in to one data frame in one shot.
user1.orc at /data/user1/
+-------------------+--------------------+
| userid            |     name           |
+-------------------+--------------------+
|         1         |            aa      |
|         6         |            vv      |
+-------------------+--------------------+

user2.orc at /data/user2/
+-------------------+--------------------+
| userid            |     info           |
+-------------------+--------------------+
|         11        |            i1      |
|         66        |            i6      |
+-------------------+--------------------+

user3.orc at /data/user3/
+-------------------+--------------------+
| userid            |     con            |
+-------------------+--------------------+
|         12        |            888     |
|         17        |            123     |
+-------------------+--------------------+

I want to read all these at once and have the dataframe like below
+-------------------+--------------------+--------------------+----------+
| userid            |         name       |       info         |    con   |
+-------------------+--------------------+--------------------+----------+
|             1     |         aa         |       null         |  null    |
|             6     |         vv         |       null         |  null    |
|            11     |        null        |         i1         |  null    |
|            66     |        null        |         i6         |  null    |
|            12     |        null        |       null         |  888     |
|            17     |        null        |       null         |  123     |

so I used like this 
val df =spark.read.option("mergeSchema","true").orc("file:///home/hadoop/data/")

but its giving the common column across all files 
+-------------------+
| userid            |
+-------------------+
|             1     |
|             6     |
|            11     |
|            66     |
|            12     |
|            17     |

So how to read all these three files in one shot ?

A:

I have a very stupid workaround for you, just in case if you don't find any solution.
Read all those files into different data frames and then perform a union operation, something like below:
val user1 = sparkSession.read.orc("/home/prasadkhode/data/user1/").toJSON
val user2 = sparkSession.read.orc("/home/prasadkhode/data/user2/").toJSON
val user3 = sparkSession.read.orc("/home/prasadkhode/data/user3/").toJSON

val result = sparkSession.read.json(user1.union(user2).union(user3).rdd)

result.printSchema()
result.show(false)

and the output will be:
root
 |-- con: long (nullable = true)
 |-- info: string (nullable = true)
 |-- name: string (nullable = true)
 |-- userId: long (nullable = true)

+----+----+----+------+
|con |info|name|userId|
+----+----+----+------+
|null|null|vv  |6     |
|null|null|aa  |1     |
|null|i6  |null|66    |
|null|i1  |null|11    |
|888 |null|null|12    |
|123 |null|null|17    |
+----+----+----+------+

Update:
Looks like there is no support for mergeSchema for orc data, there is an open ticket in Spark Jira

