Hi,
Based on the behaviour I've seen using parquet, the number of partitions in
the DataFrame will determine the number of files in each parquet partition.
I.e. when you use "PARTITION BY" you're actually partitioning twice, once
via the partitions spark has created internally and then again with the
partitions you specify in the "PARTITION BY" clause.
So if you have 10 partitions in your DataFrame, and save that as a parquet
file or table partitioned on a column with 3 values, you'll get 30
partitions, 10 per parquet partition.
You can reduce the number of partitions in the DataFrame by using
coalesce() before saving the data.
Regards,
James
On 1 March 2016 at 21:01, SRK <swethakasire...@gmail.com> wrote:
> Hi,
>
> How can I control the number of parquet files getting created under a
> partition? I have my sqlContext queries to create a table and insert the
> records as follows. It seems to create around 250 parquet files under each
> partition though I was expecting that to create around 2 or 3 files. Due to
> the large number of files, it takes a lot of time to scan the records. Any
> suggestions as to how to control the number of parquet files under each
> partition would be of great help.
>
> sqlContext.sql(" CREATE EXTERNAL TABLE IF NOT EXISTS testUserDts
> (userId STRING, savedDate STRING) PARTITIONED BY (partitioner STRING)
> stored as PARQUET LOCATION '/user/testId/testUserDts' ")
>
> sqlContext.sql(
> """from testUserDtsTemp ps insert overwrite table testUserDts
> partition(partitioner) select ps.userId, ps.savedDate , ps.partitioner
> """.stripMargin)
>
>
>
> Thanks!
>
>
>
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