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From Silvio Fiorito <>
Subject RE: How to increase parallelism of a Spark cluster?
Date Sun, 02 Aug 2015 21:29:49 GMT
Can you share the transformations up to the foreachPartition?
From: Sujit Pal<>
Sent: ‎8/‎2/‎2015 4:42 PM
To: Igor Berman<>
Cc: user<>
Subject: Re: How to increase parallelism of a Spark cluster?

Hi Igor,

The cluster is a Databricks Spark cluster. It consists of 1 master + 4 workers, each worker
has 60GB RAM and 4 CPUs. The original mail has some more details (also the reference to the
HttpSolrClient in there should be HttpSolrServer, sorry about that, mistake while writing
the email).

There is no additional configuration on the external Solr host from my code, I am using the
default HttpClient provided by HttpSolrServer. According to the Javadocs, you can pass in
a HttpClient object as well. Is there some specific configuration you would suggest to get
past any limits?

On another project, I faced a similar problem but I had more leeway (was using a Spark cluster
from EC2) and less time, my workaround was to use python multiprocessing to create a program
that started up 30 python JSON/HTTP clients and wrote output into 30 output files, which were
then processed by Spark. Reason I mention this is that I was using default configurations
there as well, just needed to increase the number of connections against Solr to a higher

This time round, I would like to do this through Spark because it makes the pipeline less


On Sun, Aug 2, 2015 at 10:52 AM, Igor Berman <<>>

What kind of cluster? How many cores on each worker? Is there config for http solr client?
I remember standard httpclient has limit per route/host.

On Aug 2, 2015 8:17 PM, "Sujit Pal" <<>>
No one has any ideas?

Is there some more information I should provide?

I am looking for ways to increase the parallelism among workers. Currently I just see number
of simultaneous connections to Solr equal to the number of workers. My number of partitions
is (2.5x) larger than number of workers, and the workers seem to be large enough to handle
more than one task at a time.

I am creating a single client per partition in my mapPartition call. Not sure if that is creating
the gating situation? Perhaps I should use a Pool of clients instead?

Would really appreciate some pointers.

Thanks in advance for any help you can provide.


On Fri, Jul 31, 2015 at 1:03 PM, Sujit Pal <<>>

I am trying to run a Spark job that hits an external webservice to get back some information.
The cluster is 1 master + 4 workers, each worker has 60GB RAM and 4 CPUs. The external webservice
is a standalone Solr server, and is accessed using code similar to that shown below.

def getResults(keyValues: Iterator[(String, Array[String])]):
        Iterator[(String, String)] = {
    val solr = new HttpSolrClient()
    initializeSolrParameters(solr) => (keyValue._1, process(solr, keyValue)))
             .mapPartitions(keyValues => getResults(keyValues))

The mapPartitions does some initialization to the SolrJ client per partition and then hits
it for each record in the partition via the getResults() call.

I repartitioned in the hope that this will result in 10 clients hitting Solr simultaneously
(I would like to go upto maybe 30-40 simultaneous clients if I can). However, I counted the
number of open connections using "netstat -anp | grep ":8983.*ESTABLISHED" in a loop on the
Solr box and observed that Solr has a constant 4 clients (ie, equal to the number of workers)
over the lifetime of the run.

My observation leads me to believe that each worker processes a single stream of work sequentially.
However, from what I understand about how Spark works, each worker should be able to process
number of tasks parallelly, and that repartition() is a hint for it to do so.

Is there some SparkConf environment variable I should set to increase parallelism in these
workers, or should I just configure a cluster with multiple workers per machine? Or is there
something I am doing wrong?

Thank you in advance for any pointers you can provide.


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