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From Ofir Manor <ofir.ma...@equalum.io>
Subject Re: tuning - Spark data serialization for cache() ?
Date Mon, 07 Aug 2017 18:11:18 GMT
Thanks a lot for the quick pointer!
So, is the advice I linked to in official Spark 2.2 documentation
misleading? You are saying that Spark 2.2 does not use by Java
serialization? And the tip to switch to Kyro is also outdated?

Ofir Manor

Co-Founder & CTO | Equalum

Mobile: +972-54-7801286 | Email: ofir.manor@equalum.io

On Mon, Aug 7, 2017 at 8:47 PM, Kazuaki Ishizaki <ISHIZAKI@jp.ibm.com>
wrote:

> For Dataframe (and Dataset), cache() already uses fast
> serialization/deserialization with data compression schemes.
>
> We already identified some performance issues regarding cache(). We are
> working for alleviating these issues in https://issues.apache.org/
> jira/browse/SPARK-14098.
> We expect that these PRs will be integrated into Spark 2.3.
>
> Kazuaki Ishizaki
>
>
>
> From:        Ofir Manor <ofir.manor@equalum.io>
> To:        user <user@spark.apache.org>
> Date:        2017/08/08 02:04
> Subject:        tuning - Spark data serialization for cache() ?
> ------------------------------
>
>
>
> Hi,
> I'm using Spark 2.2, and have a big batch job, using dataframes (with
> built-in, basic types). It references the same intermediate dataframe
> multiple times, so I wanted to try to cache() that and see if it helps,
> both in memory footprint and performance.
>
> Now, the Spark 2.2 tuning page (
> *http://spark.apache.org/docs/latest/tuning.html*
> <http://spark.apache.org/docs/latest/tuning.html>) clearly says:
> 1. The default Spark serialization is Java serialization.
> 2. It is recommended to switch to Kyro serialization.
> 3. "Since Spark 2.0.0, we internally use Kryo serializer when shuffling
> RDDs with simple types, arrays of simple types, or string type".
>
> Now, I remember that in 2.0 launch, there were discussion of a third
> serialization format that is much more performant and compact. (Encoder?),
> but it is not referenced in the tuning guide and its Scala doc is not very
> clear to me. Specifically, Databricks shared some graphs etc of how much it
> is better than Kyro and Java serialization - see Encoders here:
>
> *https://databricks.com/blog/2016/01/04/introducing-apache-spark-datasets.html*
> <https://databricks.com/blog/2016/01/04/introducing-apache-spark-datasets.html>
>
> So, is that relevant to cache()? If so, how can I enable it - and is it
> for MEMORY_AND_DISK_ONLY or MEMORY_AND_DISK_SER?
>
> I tried to play with some other variations, like enabling Kyro by the
> tuning guide instructions, but didn't see any impact on the cached
> dataframe size (same tens of GBs in the UI). So any tips around that?
>
> Thanks.
>
> Ofir Manor
>
> Co-Founder & CTO | Equalum
>
> Mobile: *+972-54-7801286* <%2B972-54-7801286> | Email:
> *ofir.manor@equalum.io* <ofir.manor@equalum.io>
>
>
>

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