Are you writing to an existing hive orc table?
On Wed, Jun 17, 2015 at 3:25 PM, Cheng Lian <lian.cs....@gmail.com> wrote:
> Thanks for reporting this. Would you mind to help creating a JIRA for this?
>
>
> On 6/16/15 2:25 AM, patcharee wrote:
>
>> I found if I move the partitioned columns in schemaString and in Row to
>> the end of the sequence, then it works correctly...
>>
>> On 16. juni 2015 11:14, patcharee wrote:
>>
>>> Hi,
>>>
>>> I am using spark 1.4 and HiveContext to append data into a partitioned
>>> hive table. I found that the data insert into the table is correct, but the
>>> partition(folder) created is totally wrong.
>>> Below is my code snippet>>
>>>
>>> -------------------------------------------------------------------------------------------------------------------------------------------------------------------
>>>
>>> val schemaString = "zone z year month date hh x y height u v w ph phb t
>>> p pb qvapor qgraup qnice qnrain tke_pbl el_pbl"
>>> val schema =
>>> StructType(
>>> schemaString.split(" ").map(fieldName =>
>>> if (fieldName.equals("zone") || fieldName.equals("z") ||
>>> fieldName.equals("year") || fieldName.equals("month") ||
>>> fieldName.equals("date") || fieldName.equals("hh") ||
>>> fieldName.equals("x") || fieldName.equals("y"))
>>> StructField(fieldName, IntegerType, true)
>>> else
>>> StructField(fieldName, FloatType, true)
>>> ))
>>>
>>> val pairVarRDD =
>>> sc.parallelize(Seq((Row(2,42,2009,3,1,0,218,365,9989.497.floatValue(),29.627113.floatValue(),19.071793.floatValue(),0.11982734.floatValue(),3174.6812.floatValue(),
>>>
>>> 97735.2.floatValue(),16.389032.floatValue(),-96.62891.floatValue(),25135.365.floatValue(),2.6476808E-5.floatValue(),0.0.floatValue(),13195.351.floatValue(),
>>>
>>> 0.0.floatValue(),0.1.floatValue(),0.0.floatValue()))
>>> ))
>>>
>>> val partitionedTestDF2 = sqlContext.createDataFrame(pairVarRDD, schema)
>>>
>>> partitionedTestDF2.write.format("org.apache.spark.sql.hive.orc.DefaultSource")
>>>
>>> .mode(org.apache.spark.sql.SaveMode.Append).partitionBy("zone","z","year","month").saveAsTable("test4DimBySpark")
>>>
>>>
>>> -------------------------------------------------------------------------------------------------------------------------------------------------------------------
>>>
>>>
>>> The table contains 23 columns (longer than Tuple maximum length), so I
>>> use Row Object to store raw data, not Tuple.
>>> Here is some message from spark when it saved data>>
>>>
>>> 15/06/16 10:39:22 INFO metadata.Hive: Renaming
>>> src:hdfs://service-10-0.local:8020/tmp/hive-patcharee/hive_2015-06-16_10-39-21_205_8768669104487548472-1/-ext-10000/zone=13195/z=0/year=0/month=0/part-00001;dest:
>>> hdfs://service-10-0.local:8020/apps/hive/warehouse/test4dimBySpark/zone=13195/z=0/year=0/month=0/part-00001;Status:true
>>>
>>> 15/06/16 10:39:22 INFO metadata.Hive: New loading path =
>>> hdfs://service-10-0.local:8020/tmp/hive-patcharee/hive_2015-06-16_10-39-21_205_8768669104487548472-1/-ext-10000/zone=13195/z=0/year=0/month=0
>>> with partSpec {zone=13195, z=0, year=0, month=0}
>>>
>>> From the raw data (pairVarRDD) zone = 2, z = 42, year = 2009, month = 3.
>>> But spark created a partition {zone=13195, z=0, year=0, month=0}.
>>>
>>> When I queried from hive>>
>>>
>>> hive> select * from test4dimBySpark;
>>> OK
>>> 2 42 2009 3 1.0 0.0 218.0 365.0 9989.497
>>> 29.627113 19.071793 0.11982734 -3174.6812 97735.2 16.389032
>>> -96.62891 25135.365 2.6476808E-5 0.0 13195 0 0 0
>>> hive> select zone, z, year, month from test4dimBySpark;
>>> OK
>>> 13195 0 0 0
>>> hive> dfs -ls /apps/hive/warehouse/test4dimBySpark/*/*/*/*;
>>> Found 2 items
>>> -rw-r--r-- 3 patcharee hdfs 1411 2015-06-16 10:39
>>> /apps/hive/warehouse/test4dimBySpark/zone=13195/z=0/year=0/month=0/part-00001
>>>
>>> The data stored in the table is correct zone = 2, z = 42, year = 2009,
>>> month = 3, but the partition created was wrong
>>> "zone=13195/z=0/year=0/month=0"
>>>
>>> Is this a bug or what could be wrong? Any suggestion is appreciated.
>>>
>>> BR,
>>> Patcharee
>>>
>>>
>>>
>>>
>>>
>>>
>>>
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