Did you do
yourRdd.coalesce(6).saveAsTextFile()
or
yourRdd.coalesce(6)
yourRdd.saveAsTextFile()
?
Srikanth
On Tue, Jul 7, 2015 at 12:59 PM, Umesh Kacha <umesh.ka...@gmail.com> wrote:
> Hi I tried both approach using df. repartition(6) and df.coalesce(6) it
> doesn't reduce part-xxxxx files. Even after calling above method I still
> see around 200 small part files of size 20 mb each which is again orc files.
>
>
> On Tue, Jul 7, 2015 at 12:52 AM, Sathish Kumaran Vairavelu <
> vsathishkuma...@gmail.com> wrote:
>
>> Try coalesce function to limit no of part files
>> On Mon, Jul 6, 2015 at 1:23 PM kachau <umesh.ka...@gmail.com> wrote:
>>
>>> Hi I am having couple of Spark jobs which processes thousands of files
>>> every
>>> day. File size may very from MBs to GBs. After finishing job I usually
>>> save
>>> using the following code
>>>
>>> finalJavaRDD.saveAsParquetFile("/path/in/hdfs"); OR
>>> dataFrame.write.format("orc").save("/path/in/hdfs") //storing as ORC
>>> file as
>>> of Spark 1.4
>>>
>>> Spark job creates plenty of small part files in final output directory.
>>> As
>>> far as I understand Spark creates part file for each partition/task
>>> please
>>> correct me if I am wrong. How do we control amount of part files Spark
>>> creates? Finally I would like to create Hive table using these
>>> parquet/orc
>>> directory and I heard Hive is slow when we have large no of small files.
>>> Please guide I am new to Spark. Thanks in advance.
>>>
>>>
>>>
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>
