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From JF Chen <darou...@gmail.com>
Subject Re: spark df.write.partitionBy run very slow
Date Thu, 07 Mar 2019 08:21:28 GMT
Yes, I agree.

>From the spark UI I can ensure data is not skewed. There is only about
100MB for each task, where most of tasks takes several seconds to write the
data to hdfs, and some tasks takes minutes of time.

Regard,
Junfeng Chen


On Wed, Mar 6, 2019 at 2:39 PM Shyam P <shyamabigdata@gmail.com> wrote:

> Hi JF,
> Yes first we should know actual number of partitions dataframe has and its
> counts of records. Accordingly we should try to have data evenly in all
> partitions.
> It always better to have Num of paritions = N * Num of executors.
>
>
>   "But the sequence of columns in  partitionBy  decides the
> directory  hierarchy structure. I hope the sequence of columns not change"
> , this is correct.
> Hence sometimes we should go with bigger number first then lesser .... try
> this ..i.e. more parent directories and less child directories. Tweet
> around it and try.
>
> "some tasks in write hdfs stage cost much more time than others" may be
> data is skewed, need to  distrube them evenly for all partitions.
>
> ~Shyam
>
> On Wed, Mar 6, 2019 at 8:33 AM JF Chen <darouwan@gmail.com> wrote:
>
>> Hi Shyam
>> Thanks for your reply.
>> You mean after knowing the partition number of column_a, column_b,
>> column_c, the sequence of column in partitionBy should be same to the order
>> of partitions number of column a, b and c?
>> But the sequence of columns in  partitionBy  decides the
>> directory  hierarchy structure. I hope the sequence of columns not change.
>>
>> And I found one more strange things, some tasks in write hdfs stage cost
>> much more time than others, where the amount of writing data is similar.
>> How to solve it?
>>
>> Regard,
>> Junfeng Chen
>>
>>
>> On Tue, Mar 5, 2019 at 3:05 PM Shyam P <shyamabigdata@gmail.com> wrote:
>>
>>> Hi JF ,
>>>  Try to execute it before df.write....
>>>
>>> //count by partition_id
>>>         import org.apache.spark.sql.functions.spark_partition_id
>>>         df.groupBy(spark_partition_id).count.show()
>>>
>>> You will come to know how data has been partitioned inside df.
>>>
>>> Small trick we can apply here while partitionBy(column_a, column_b,
>>> column_c)
>>> Makes sure  you should have ( column_a  partitions) > ( column_b
>>> partitions) >  ( column_c  partitions) .
>>>
>>> Try this.
>>>
>>> Regards,
>>> Shyam
>>>
>>> On Mon, Mar 4, 2019 at 4:09 PM JF Chen <darouwan@gmail.com> wrote:
>>>
>>>> I am trying to write data in dataset to hdfs via df.write.partitionBy(column_a,
>>>> column_b, column_c).parquet(output_path)
>>>> However, it costs several minutes to write only hundreds of MB data to
>>>> hdfs.
>>>> From this article
>>>> <https://stackoverflow.com/questions/45269658/spark-df-write-partitionby-run-very-slow>,
>>>> adding repartition method before write should work. But if there is
>>>> data skew, some tasks may cost much longer time than average, which still
>>>> cost much time.
>>>> How to solve this problem? Thanks in advance !
>>>>
>>>>
>>>> Regard,
>>>> Junfeng Chen
>>>>
>>>

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