You should try using the appropriate memory settings (i.e.
-Dmapred.child.java.opts="-Xms30g -Xmx30g -Xss128k") for a 30 GB heap.
This depends on how much memory you can get.
Avery
On 7/9/12 5:57 AM, Amani Alonazi wrote:
Actually, I had the same problem of running out of memory with Giraph
when trying to implement strongly connected components algorithm on
Giraph. My input graph is 1 million nodes and 7 million edges.
I'm using cluster of 21 computers.
On Mon, Jul 9, 2012 at 3:44 PM, Benjamin Heitmann
<benjamin.heitm...@deri.org <mailto:benjamin.heitm...@deri.org>> wrote:
Hello Stephen,
sorry for the very late reply.
On 28 Jun 2012, at 02:50, Fleischman, Stephen (ISS SCI - Plano TX)
wrote:
Hello Avery and all:
I have a cluster of 10 two-processor/48 GB RAM servers, upon
which we are conducting Hadoop performance characterization
tests. I plan to use the Giraph pagerank and simple shortest
path example tests as part of this exercise and would appreciate
guidance on problem sizes for both tests. I’m looking at paring
down an obfuscated Twitter dataset and it would save a lot of
time if someone has some knowledge on roughly how the time and
memory scales with number of nodes in a graph.
I can provide some suggestions for the kind of algorithm and data
which does currently surpass the scalability of giraph.
While the limits to my knowledge of Giraph and Hadoop are probably
also to blame for this, please see the recent discussions on this
list,
and on JIRA for other indications that the scalability of Giraph
needs improvement:
* post by Yuanyuan Tian in the thread "wierd communication
errors" on user@giraph.apache.org <mailto:user@giraph.apache.org>
* GIRAPH-234 about GC overhead
https://issues.apache.org/jira/browse/GIRAPH-234?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
If you want to stretch the limits of Giraph, then you need to try
an algorithm which is conceptually different from PageRank, and
you need a big data set.
If you use an algorithm which has complex application logic (maybe
even domain specific logic), which needs to be embedded in the
algorithm,
then the nodes need to have a lot of state. In addition, such
algorithms probably send around a lot of messages, and each of the
messages might have a payload
which is more complex then one floating point number. In addition,
it helps to have a graph format, which requires strings on the
edges and vertices.
The strings are required for the domain specific business logic
which the graph algorithm needs to follow.
Finally, imagine a data set which has a big loading time, and
where one run of the algorithm only provides results for one user.
The standard Hadoop paradigm is to throw away the graph after
loading it.
So if you have 100s or 1000s of users, then you need a way to
execute the algorithm multiple times in parallel.
Again this will add a lot of state, as each of the vertices will
need to hold one state object for each user who has visited the
vertex.
In my specific case, I had the following data and algorithm:
Data:
* an RDF graph with 10 million vertices and 40 million edges
I used my own import code to map the RDF graph to a undirected
graph with a limit of one edge between any two nodes (so it was
not a multi-graph)
* each vertex and each edge uses a string as an identity to
represent a URI in the RDF graph (required for the business logic
in the algorithm)
Algorithm:
* spreading activation.
You can think of it as depth first search guided by domain
specific logic.
A short introduction here:
https://en.wikipedia.org/wiki/Spreading_activation
The wikipedia article only mentions using spreading activation on
weighted graphs, however I used it on graphs which have additional
types on the edges.
The whole area of using the semantics of the edges to guide the
algorithm is an active research topic, so thats why I can't point
you to a good article on that.
* parallel execution:
I need to run the algorithm once for every user in the system,
however loading the data set takes around 15 minutes alone.
So each node has an array of states, one for each user for which
the algorithm has visited a node.
I experimented with user numbers between 30 and 1000, anything
more did not work for concurrent execution of the algorithm.
Infrastructure:
* a single server with 24 Intel Xeon 2.4 GHz cpus and 96 GB of RAM
* Hadoop 1.0, pseudo-distributed setup
* between 10 and 20 Giraph workers
A few weeks ago I stopped work on my Giraph based implementation,
as Giraph ran out of memory almost immediately after loading and
initialising the data.
I made sure that the Giraph workers do not run out of memory, so
it was probably due to IPC and messaging.
The general discussion on the Giraph mailing list strongly
indicates that I did hit the current IPC scalability limits.
Currently I am working on a non-Hadoop version of the algorithm
which is not as scalable but which is fast for *one* user. ( less
then a second per user, but single threaded).
In addition, this new version allows me to better integrate with
an existing ecosystem of technologies (Semantic Web technologies)
to which Hadoop and Giraph is currently completely disconnected.
However, I will probably revisit Giraph at some time on the future.
If you want to look at the code or the data or any other asset
which I have, then I will gladly share that with you.
I would really like Giraph to reach the maturity required for this
kind of algorithm.
However, I have the feeling that the current development focus is
on clear-cut numerical algorithms such as pagerank.
--
Amani AlOnazi
MSc Computer Science
King Abdullah University of Science and Technology
Kingdom of Saudi Arabia
amani.alon...@kaust.edu.sa <mailto:amani.alon...@kaust.edu.sa> | +966
(0) 555 191 795
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