SparkR collect () and head () error for Spark DataFrame: arguments assume different number of rows

I read the parquet file from HDFS system:

path<-"hdfs://part_2015"
AppDF <- parquetFile(sqlContext, path)
printSchema(AppDF)

root
 |-- app: binary (nullable = true)
 |-- category: binary (nullable = true)
 |-- date: binary (nullable = true)
 |-- user: binary (nullable = true)

class(AppDF)

[1] "DataFrame"
attr(,"package")
[1] "SparkR"

collect(AppDF)
.....error:
arguments imply differing number of rows: 46021, 39175, 62744, 27137

head(AppDF)
.....error:
arguments imply differing number of rows: 36, 30, 48

      

        
        
        
      

    

I have read several articles about this issue. But that's none of my business. Actually, I just read the table from the parquet file and head()

or collect()

. My parquet table looks like this:

app   category  date        user
aaa   test      20150101    123
aaa   test      20150102    345
aaa   test      20150103    678
aaaa  testA     20150104    123
aaaa  testA     20150105    234
aaaa  testA     20150106    4345
bbbb  testB     20150101    5435

      

        
        
        
      

    

I am using spark-1.4.0-bin-hadoop2.6 And I am running this on a cluster using

./sparkR --master yarn--client

      

        
        
        
      

    

I also tried it in local mode, there is the same problem.

showDF(AppDF)

+-----------+-----------+-----------+-----------+
|        app|   category|       date|       user|
+-----------+-----------+-----------+-----------+
|[B@217fa749|[B@43bfbacd|[B@60810b7a|[B@3818a815|
|[B@5ac31778|[B@3e39f5d5|[B@4f3a92dd| [B@e8013ce|
|[B@7a9440d1|[B@1b2b9836|[B@4b160f29|[B@153d7342|
|[B@7559fcf2|[B@66edb00e|[B@7ec19bec|[B@58e3e3f7|
|[B@598b9ab8|[B@5c5ad3f5|[B@4f11a931|[B@107af885|
|[B@7951ec36|[B@716b0b73|[B@2abce531|[B@576b09e2|
|[B@34560144|[B@7a6d3233|[B@16faf110|[B@34e85d39|
| [B@3406452|[B@787a4528|[B@235282e3|[B@7e0f1732|
|[B@10bc1446|[B@2bd7083f|[B@325e7695|[B@57bb4a08|
|[B@48f98037|[B@7450c04e|[B@61817c8a|[B@7c177a08|
|[B@694ce2dd|[B@36c2512d| [B@f5f7d71|[B@46248d99|
|[B@479dee25|[B@517de3de|[B@1ffb2d9e|[B@236ff079|
|[B@52ac196f|[B@20b9f0d0| [B@f70f879|[B@41c8d7da|
|[B@68d34af3| [B@7ddcd49|[B@72d077a7|[B@545fafd4|
|[B@5610b292|[B@623bbb62|[B@3f8b5150|[B@53877bc7|
|[B@63cf70a8|[B@47ed58c9|[B@2f601903|[B@4e0a2c41|
|[B@7ddf876d|[B@5e3445aa|[B@39c9cc37|[B@6f7e4c84|
|[B@4cd1a74b|[B@583e5453|[B@64124267|[B@6ac5ab84|
|[B@577f9ddf|[B@7b55c859|[B@3cd48a51|[B@25c4eb0a|
|[B@2322f0e5|[B@4af55c68|[B@3285d64a|[B@70b7ae2f|
+-----------+-----------+-----------+-----------+

      

        
        
        
      

    

I already tried to read this parquet file in Scala. And do the collect () operation. Everything seems to work well. So this should be a SparkR specific issue