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From John Lilley <john.lil...@redpoint.net>
Subject RE: Question about GraphX connected-components
Date Mon, 12 Oct 2015 17:18:40 GMT
Thanks Igor,
We are definitely thinking along these lines, but I am hoping to shortcut our search of the
Spark/GraphX tuning parameter space to find a reasonable set of starting points.  There are
simultaneous questions of “what should we expect form GraphX?” and “what are the best
parameters to do that?”.

What I’m asking is fairly specific:
-- What is a good set of tuning parameters (partitions, memory?) for a large data set that
“should” fit into memory on an 8-node cluster with 8GB/node available to YARN?
-- Does anyone have or know of sample code that performs well on a real data set without adjusting
lots of tuning knobs first?
-- How much available YARN memory is required to hold a given number of vertices+edges, with
enough cushion to be comfortable?  You are giving some tantalizing hints (3x as much as I
expected…), but no clear indication of how much memory should be needed.  Arriving at the
answer through experimentation isn’t a good approach, because that assumes -- chicken-and-egg
problem -- that we have already arrived at an optimal configuration.
-- Does GraphX connected-components performance degrade slowly or catastrophically when that
memory limit is reached?  Are there tuning parameters that optimize for data all fitting in
memory vs. data that must spill?

Thanks,
John Lilley

From: Igor Berman [mailto:igor.berman@gmail.com]
Sent: Saturday, October 10, 2015 12:06 PM
To: John Lilley <john.lilley@redpoint.net>
Cc: user@spark.apache.org; Geoff Thompson <geoff.thompson@redpoint.net>
Subject: Re: Question about GraphX connected-components

let's start from some basics: might be u need to split your data into more partitions?
spilling depends on your configuration when you create graph(look for storage level param)
and your global configuration.
in addition, you assumption of 64GB/100M is probably wrong, since spark divides memory into
3 regions - for in memory caching, for shuffling and for "workspace" of serialization/deserialization
etc see fraction parameters.

so depending on number of your partitions might be worker will try to ingest too much data
at once(#cores * memory pressure of one task per one partition)

there is no such thing as "right" configuration. It depends on your application. You can post
your configuration and people will suggest some tunning, still best way is to try what is
best for ur case depending on what u see in spark ui metrics(as starting point)

On 10 October 2015 at 00:13, John Lilley <john.lilley@redpoint.net<mailto:john.lilley@redpoint.net>>
wrote:
Greetings,
We are looking into using the GraphX connected-components algorithm on Hadoop for grouping
operations.  Our typical data is on the order of 50-200M vertices with an edge:vertex ratio
between 2 and 30.  While there are pathological cases of very large groups, they tend to be
small.  I am trying to get a handle on the level of performance and scaling we should expect,
and how to best configure GraphX/Spark to get there.  After some trying, we cannot get to
100M vertices/edges without running out of memory on a small cluster (8 nodes with 4 cores
and 8GB available for YARN on each node).  This limit seems low, as 64GB/100M is 640 bytes
per vertex, which should be enough.  Is this within reason?  Does anyone have sample they
can share that has the right configurations for succeeding with this size of data and cluster?
 What level of performance should we expect?  What happens when the data set exceed memory,
does it spill to disk “nicely” or degrade catastrophically?

Thanks,
John Lilley


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