Hi Qian,
The way that I have gotten around this type of problem in the past is to do
a groupBy on the dimensions that you want to build a model for and then
initialize, and train a model using a package like scikit learn for each
group in something like a group map pandas udf. If you need these
That's fine. The other points that I mentioned still apply.
On Thu, Apr 11, 2019 at 4:52 PM V0lleyBallJunki3
wrote:
> I am not using pyspark. The job is written in Scala
>
>
>
> --
> Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/
>
>
You will probably need to do a couple of things. One, you will need to
probably increase the "spark.sql.broadcastTimeout" setting. As well, when
you broadcast a variable it gets replicated once per executor not once per
machine so you will need to increase your executor size and allow more
cores
tic, smaller and larger in
>> number executors are preferred over larger and fewer executors.
>> Changing GC algorithm
>>
>> http://orastack.com/spark-scaling-to-large-datasets.html
>>
>>
>> Here are a few tips
>>
>>
>>
>>
>> On Wed,
Hi William,
Just to get started, can you describe the spark version you are using and
the language? It doesn't sound like you are using pyspark, however,
problems arising from that can be different so I just want to be sure. As
well, can you talk through the scenario under which you are dealing
What is your function in mapToPair doing?
-Dillon
On Mon, Nov 5, 2018 at 1:41 PM Taylor Cox
wrote:
> At first glance, I wonder if your tables are partitioned? There may not be
> enough parallelism happening. You can also pass in the number of partitions
> and/or a custom partitioner to help
g shows up in the Storage tab of either Zeppelin or spark-shell.
> > However, I have several running applications in production that does
> > show the data in cache. I am using Scala and Spark 2.2.1 in EMR. Any
> > workarounds to see the data in cache.
> > On Mon,
In your program persist the smaller table and use count to force it to
materialize. Then in the Spark UI go to the Storage tab. The size of your
table as spark sees it should be displayed there. Out of curiosity what
version / language of Spark are you using?
On Mon, Oct 15, 2018 at 11:53 AM
VE, and it just saves processing time in terms of allocating containers.
> That said I am still trying to understand how do we determine one YARN
> container = one executor in SPARK.
>
> Regards,
> Gourav
>
> On Tue, Oct 9, 2018 at 9:04 PM Dillon Dukek
> wrote:
>
>>
n my head on this one. But how do we know
> that 1 yarn container = 1 executor?
>
> Regards,
> Gourav Sengupta
>
> On Tue, Oct 9, 2018 at 8:53 PM Dillon Dukek
> wrote:
>
>> Can you send how you are launching your streaming process? Also what
>> environment is this cl
Can you send how you are launching your streaming process? Also what
environment is this cluster running in (EMR, GCP, self managed, etc)?
On Tue, Oct 9, 2018 at 10:21 AM kant kodali wrote:
> Hi All,
>
> I am using Spark 2.3.1 and using YARN as a cluster manager.
>
> I currently got
>
> 1) 6
Actually, it appears walking through it in a debug terminal that the
deserializer can properly transform the data on read to an ArrayType, but
the serializer doesn't know what to do when we try to go back out from the
internal spark representation.
tags, if
I believe you need to set mapreduce.fileoutputcommitter.algorithm.version
to 2.
On Wed, Sep 19, 2018 at 10:45 AM Priya Ch
wrote:
> Hello Team,
>
> I am trying to write a DataSet as parquet file in Append mode partitioned
> by few columns. However since the job is time consuming, I would like to
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