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.
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Streaming-Problem-with-DataFrame-UDFs-tp26024.html
>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>
>> ---------------------------------------------------------------------
>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
>> For additional commands, e-mail: user-h...@spark.apache.org
>>
>>
>
