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From Pedro Tuero <>
Subject Re: Spark 2.4 partitions and tasks
Date Wed, 13 Feb 2019 15:56:05 GMT
I changed the explicit repartitions to parameters  of parallelism to
pairRddFunctions, and it works better but I still have to get a magic
Clusters will not be fully utilized unless you set the level of parallelism
for each operation high enough. Spark automatically sets the number of
“map” tasks to run on each file according to its size (though you can
control it through optional parameters to SparkContext.textFile, etc), and
for distributed “reduce” operations, such as groupByKey and reduceByKey, it
uses the largest parent RDD’s number of partitions. You can pass the level
of parallelism as a second argument (see the spark.PairRDDFunctions
 documentation), or set the config property spark.default.parallelism to
change the default. In general, we recommend 2-3 tasks per CPU core in your

I wonder how is it possible that my jobs work fine with tens of task per
cores and work so badly (or even do not) with 2-3 task per core, as it is
recommended in Spark documentation.
As the number of tasks in previous versions depended on the input
partitions, I can assume that jobs were working by chance, because the
input partitions could be more or less, left by another Spark job or Hadoop
job or just text.
It is possible that the jobs could run in much less time if I choose better
parallelism. I have a dozen jobs, running with different input size and
partitions... so it is very hard to find a calculation to fit all scenarios.

El mar., 12 de feb. de 2019 a la(s) 15:25, Pedro Tuero (

> * It is not getPartitions() but getNumPartitions().
> El mar., 12 de feb. de 2019 a la(s) 13:08, Pedro Tuero (
> escribió:
>> And this is happening in every job I run. It is not just one case. If I
>> add a forced repartitions it works fine, even better than before. But I run
>> the same code for different inputs so the number to make repartitions must
>> be related to the input.
>> El mar., 12 de feb. de 2019 a la(s) 11:22, Pedro Tuero (
>> escribió:
>>> Hi Jacek.
>>> I 'm not using SparkSql, I'm using RDD API directly.
>>> I can confirm that the jobs and stages are the same on both executions.
>>> In the environment tab of the web UI, when using spark 2.4
>>> spark.default.parallelism=128 is shown while in 2.3.1 is not.
>>> But in 2.3.1 should be the same, because 128 is the number of cores of
>>> cluster * 2 and it didn't change in the latest version.
>>> In the example I gave, 5580 is the number of parts left by a previous
>>> job in S3, in Hadoop sequence files. So the initial RDD has 5580
>>> partitions.
>>> While in 2.3.1, RDDs that are created with transformations from the
>>> initial RDD conserve the same number of partitions, in 2.4 the number of
>>> partitions reset to default.
>>> So RDD1, the product of the first mapToPair, prints 5580 when
>>> getPartitions() is called in 2.3.1, while prints 128 in 2.4.
>>> Regards,
>>> Pedro
>>> El mar., 12 de feb. de 2019 a la(s) 09:13, Jacek Laskowski (
>>> escribió:
>>>> Hi,
>>>> Can you show the plans with explain(extended=true) for both versions?
>>>> That's where I'd start to pinpoint the issue. Perhaps the underlying
>>>> execution engine change to affect keyBy? Dunno and guessing...
>>>> Pozdrawiam,
>>>> Jacek Laskowski
>>>> ----
>>>> Mastering Spark SQL
>>>> Spark Structured Streaming
>>>> Mastering Kafka Streams
>>>> Follow me at
>>>> On Fri, Feb 8, 2019 at 5:09 PM Pedro Tuero <>
>>>> wrote:
>>>>> I did a repartition to 10000 (hardcoded) before the keyBy and it ends
>>>>> in 1.2 minutes.
>>>>> The questions remain open, because I don't want to harcode paralellism.
>>>>> El vie., 8 de feb. de 2019 a la(s) 12:50, Pedro Tuero (
>>>>> escribió:
>>>>>> 128 is the default parallelism defined for the cluster.
>>>>>> The question now is why keyBy operation is using default parallelism
>>>>>> instead of the number of partition of the RDD created by the previous
>>>>>> (5580).
>>>>>> Any clues?
>>>>>> El jue., 7 de feb. de 2019 a la(s) 15:30, Pedro Tuero (
>>>>>> escribió:
>>>>>>> Hi,
>>>>>>> I am running a job in spark (using aws emr) and some stages are
>>>>>>> taking a lot more using spark  2.4 instead of Spark 2.3.1:
>>>>>>> Spark 2.4:
>>>>>>> [image: image.png]
>>>>>>> Spark 2.3.1:
>>>>>>> [image: image.png]
>>>>>>> With Spark 2.4, the keyBy operation take more than 10X what it
>>>>>>> with Spark 2.3.1
>>>>>>> It seems to be related to the number of tasks / partitions.
>>>>>>> Questions:
>>>>>>> - Is it not supposed that the number of task of a job is related
>>>>>>> number of parts of the RDD left by the previous job? Did that
change in
>>>>>>> version 2.4??
>>>>>>> - Which tools/ configuration may I try, to reduce this aberrant
>>>>>>> downgrade of performance??
>>>>>>> Thanks.
>>>>>>> Pedro.

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