Quick correction in the code snippet I sent in my previous email:
Line: val enrichedDF = inputDF.withColumn("semantic", udf(col("url")))
Should be replaced by: val enrichedDF = inputDF.withColumn("semantic",
enrichUDF(col("url")))
On Thu, Jan 21, 2016 at 11:07 AM, Jean-Pierre OCALAN <jpoca...@gmail.com>
wrote:
> Hi Cody,
>
> First of all thanks a lot for your quick reply, although I have removed
> this post couple of hours after posting it because I ended up finding it
> was due to the way I was using DataFrame UDFs.
>
> Essentially I didn't know that UDFs were purely lazy and in case of the
> example below the UDF gets executed 3 times on the entire data set.
>
> // let's imagine we have an input DataFrame inputDF with "url" column
>
> // semanticClassifier.classify(url) returns Map[Int, Int]
> val enrichUDF = udf { (url: String) => semanticClassifier.classify(url) }
>
> // enrichedDF will have all columns contained in inputDF + a semantic
> column which will contain result of execution of the classification on the
> url column value.
> val enrichedDF = inputDF.withColumn("semantic", udf(col("url")))
>
> val outputDF = enrichedDF.select(col("*"),
> col("semantic")(0).as("semantic1"), col("semantic")(1).as("semantic2"),
> col("semantic")(2).as("semantic3")).drop("semantic")
>
> // The udf will be executed 3 times on the entire dataset
> outputDF.count()
>
> By adding enrichedDF.persist() and unpersist() later on I was able to
> quickly solve the issue but don't really like it, so I will probably work
> directly with the RDD[Row] and schema maintenance in order to recreate
> DataFrame.
>
> Thanks again,
> JP.
>
>
>
> On Thu, Jan 21, 2016 at 10:45 AM, Cody Koeninger <c...@koeninger.org>
> wrote:
>
>> If you can share an isolated example I'll take a look. Not something
>> I've run into before.
>>
>> On Wed, Jan 20, 2016 at 3:53 PM, jpocalan <jpoca...@gmail.com> wrote:
>>
>>> Hi,
>>>
>>> I have an application which creates a Kafka Direct Stream from 1 topic
>>> having 5 partitions.
>>> As a result each batch is composed of an RDD having 5 partitions.
>>> In order to apply transformation to my batch I have decided to convert
>>> the
>>> RDD to DataFrame (DF) so that I can easily add column to the initial DF
>>> by
>>> using custom UDFs.
>>>
>>> Although, when I am applying any udf to the DF I am noticing that the udf
>>> will get execute multiple times and this factor is driven by the number
>>> of
>>> partitions.
>>> For example, imagine I have a RDD with 10 records and 5 partitions
>>> ideally
>>> my UDF should get called 10 times, although it gets consistently called
>>> 50
>>> times, but the resulting DF is correct and when executing a count()
>>> properly
>>> return 10, as expected.
>>>
>>> I have changed my code to work directly with RDDs using mapPartitions and
>>> the transformation gets called proper amount of time.
>>>
>>> As additional information, I have set spark.speculation to false and no
>>> tasks failed.
>>>
>>> I am working on a smaller example that would isolate this potential
>>> issue,
>>> but in the meantime I would like to know if somebody encountered this
>>> issue.
>>>
>>> Thank you.
>>>
>>>
>>>
>>> --
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>>
>
--
jean-pierre ocalan
jpoca...@gmail.com
