I suspect that Bookings and Customerdetails both have a PolicyType field, one is string and the other is an int.

Cheng

On 6/8/15 9:15 PM, Bipin Nag wrote:
Hi Jeetendra, Cheng

I am using following code for joining

val Bookings = sqlContext.load("/home/administrator/stageddata/Bookings")
val Customerdetails = sqlContext.load("/home/administrator/stageddata/Customerdetails")

val CD = Customerdetails.
    where($"CreatedOn" > "2015-04-01 00:00:00.0").
    where($"CreatedOn" < "2015-05-01 00:00:00.0")

//Bookings by CD
val r1 = Bookings.
    withColumnRenamed("ID","ID2")
val r2 = CD.
    join(r1,CD.col("CustomerID") === r1.col("ID2"),"left")

r2.saveAsParquetFile("/home/administrator/stageddata/BOOKING_FULL");

@Cheng I am not appending the joined table to an existing parquet file, it is a new file. @Jitender I have a rather large parquet file and it also contains some confidential data. Can you tell me what you need to check in it.

Thanks


On 8 June 2015 at 16:47, Jeetendra Gangele <gangele...@gmail.com <mailto:gangele...@gmail.com>> wrote:

    Parquet file when are you loading these file?
    can you please share the code where you are passing parquet file
    to spark?.

    On 8 June 2015 at 16:39, Cheng Lian <lian.cs....@gmail.com
    <mailto:lian.cs....@gmail.com>> wrote:

        Are you appending the joined DataFrame whose PolicyType is
        string to an existing Parquet file whose PolicyType is int?
        The exception indicates that Parquet found a column with
        conflicting data types.

        Cheng


        On 6/8/15 5:29 PM, bipin wrote:

            Hi I get this error message when saving a table:

            parquet.io <http://parquet.io>.ParquetDecodingException:
            The requested schema is not compatible
            with the file schema. incompatible types: optional binary
            PolicyType (UTF8)
            != optional int32 PolicyType
                    at
            
parquet.io.ColumnIOFactory$ColumnIOCreatorVisitor.incompatibleSchema(ColumnIOFactory.java:105)
                    at
            
parquet.io.ColumnIOFactory$ColumnIOCreatorVisitor.visit(ColumnIOFactory.java:97)
                    at
            parquet.schema.PrimitiveType.accept(PrimitiveType.java:386)
                    at
            
parquet.io.ColumnIOFactory$ColumnIOCreatorVisitor.visitChildren(ColumnIOFactory.java:87)
                    at
            
parquet.io.ColumnIOFactory$ColumnIOCreatorVisitor.visit(ColumnIOFactory.java:61)
                    at
            parquet.schema.MessageType.accept(MessageType.java:55)
                    at
            parquet.io.ColumnIOFactory.getColumnIO(ColumnIOFactory.java:148)
                    at
            parquet.io.ColumnIOFactory.getColumnIO(ColumnIOFactory.java:137)
                    at
            parquet.io.ColumnIOFactory.getColumnIO(ColumnIOFactory.java:157)
                    at
            
parquet.hadoop.InternalParquetRecordWriter.initStore(InternalParquetRecordWriter.java:107)
                    at
            
parquet.hadoop.InternalParquetRecordWriter.<init>(InternalParquetRecordWriter.java:94)
                    at
            
parquet.hadoop.ParquetRecordWriter.<init>(ParquetRecordWriter.java:64)
                    at
            
parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:282)
                    at
            
parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:252)
                    at
            org.apache.spark.sql.parquet.ParquetRelation2.org
            
<http://org.apache.spark.sql.parquet.ParquetRelation2.org>$apache$spark$sql$parquet$ParquetRelation2$$writeShard$1(newParquet.scala:667)
                    at
            
org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$insert$2.apply(newParquet.scala:689)
                    at
            
org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$insert$2.apply(newParquet.scala:689)
                    at
            org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
                    at org.apache.spark.scheduler.Task.run(Task.scala:64)
                    at
            
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
                    at
            
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
                    at
            
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
                    at java.lang.Thread.run(Thread.java:745)

            I joined two tables both loaded from parquet file, the
            joined table when
            saved throws this error. I could not find anything about
            this error. Could
            this be a bug ?



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-- Hi,

    Find my attached resume. I have total around 7 years of work
    experience.
    I worked for Amazon and Expedia in my previous assignments and
    currently I am working with start- up technology company called
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