hi,
I'm not familiar with the Kinetica spark driver, but it seems that your job
has a single task which might indicate that you have a single partition in
the df
i would suggest to try to create your df with more partitions, this can be
done by adding the following options when reading the source:
.option("numPartitions", 4)
.option("partitionColumn", "id")
.option("lowerBound", 0)
.option("upperBound", 1000)
take a look at the spark jdbc configuration
<https://spark.apache.org/docs/latest/sql-programming-guide.html#jdbc-to-other-databases>
for
more info
you can also do df.repartition(10) but that might be less efficient since
the reading from the source will not be in parallel
hope it will help
Eyal
On Tue, Jan 16, 2018 at 11:01 AM, Onur EKİNCİ <oeki...@innova.com.tr> wrote:
> Sorry it is not oracle. It is Mssql.
>
>
>
> Do you have any opinion for the solution. I really appreciate
>
>
>
>
>
> Onur EKİNCİ
> Bilgi Yönetimi Yöneticisi
> Knowledge Management Manager
>
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>
> *From:* Richard Qiao [mailto:richardqiao2...@gmail.com]
> *Sent:* Tuesday, January 16, 2018 11:59 AM
> *To:* Onur EKİNCİ <oeki...@innova.com.tr>
> *Cc:* user@spark.apache.org
> *Subject:* Re: Run jobs in parallel in standalone mode
>
>
>
> Curious you are using"jdbc:sqlserve" to connect oracle, why?
>
> Also kindly reminder scrubbing your user id password.
>
> Sent from my iPhone
>
>
> On Jan 16, 2018, at 03:00, Onur EKİNCİ <oeki...@innova.com.tr> wrote:
>
> Hi,
>
>
>
> We are trying to get data from an Oracle database into Kinetica database
> through Apache Spark.
>
>
>
> We installed Spark in standalone mode. We executed the following commands.
> However, we have tried everything but we couldnt manage to run jobs in
> parallel. We use 2 IBM servers each of which has 128cores and 1TB memory.
>
>
>
> We also added in the spark-defaults.conf :
>
> spark.executor.memory=64g
>
> spark.executor.cores=32
>
> spark.default.parallelism=32
>
> spark.cores.max=64
>
> spark.scheduler.mode=FAIR
>
> spark.sql.shuffle.partions=32
>
>
>
>
>
> *On the machine: 10.20.10.228*
>
> ./start-master.sh --webui-port 8585
>
>
>
> ./start-slave.sh --webui-port 8586 spark://10.20.10.228:7077
>
>
>
>
>
> *On the machine 10.20.10.229 <http://10.20.10.229>:*
>
> ./start-slave.sh --webui-port 8586 spark://10.20.10.228:7077
>
>
>
>
>
> *On the machine: 10.20.10.228 <http://10.20.10.228>:*
>
>
>
> We start the Spark shell:
>
>
>
> spark-shell --master spark://10.20.10.228:7077
>
>
>
> Then we make configurations:
>
>
>
> val df = spark.read.format("jdbc").option("url", "jdbc:sqlserver://
> 10.20.10.148:1433;databaseName=testdb").option("dbtable",
> "dbo.temp_muh_hareket").option("user", "gpudb").option("password",
> "Kinetica2017!").load()
>
> import com.kinetica.spark._
>
> val lp = new LoaderParams("http://10.20.10.228:9191";, "jdbc:simba://
> 10.20.10.228:9292;ParentSet=MASTER", "muh_hareket_20",
> false,"",100000,true,true,"admin","Kinetica2017!",4, true, true, 1)
>
> SparkKineticaLoader.KineticaWriter(df,lp);
>
>
>
>
>
> The above commands successfully work. The data transfer completes. However,
> jobs work serially not in parallel. Also executors work serially and take
> turns. They donw work in parallel.
>
>
>
> How can we make jobs work in parallel?
>
>
>
>
>
> <image002.jpg>
>
>
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> <image006.jpg>
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> <image008.jpg>
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> <image012.jpg>
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> <image013.jpg>
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> <image014.jpg>
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>
> I really appreciate your help. We have done everything that we could.
>
>
>
> *Onur EKİNCİ*
> Bilgi Yönetimi Yöneticisi
> Knowledge Management Manager
>
> m:+90 553 044 2341 <+90%20553%20044%2023%2041> d:+90 212 329 7000
> <(212)%20329-7000>
>
> İTÜ Ayazağa Kampüsü, Teknokent ARI4 Binası 34469 Maslak İstanbul - Google
> Maps <http://www.innova.com.tr/istanbul.asp>
>
> <imza_2d4cbd2e-9f86-452e-8fa7-5851198cb9af.png>
> <http://www.innova.com.tr/>
>
>
>
>
> Yasal Uyarı :
> Bu elektronik posta işbu linki kullanarak ulaşabileceğiniz Koşul ve
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