I think this is a separate issue with how the EdgeRDDImpl partitions
edges. If you can merge this change in and rebuild, it should work:
https://github.com/apache/spark/pull/4136/files
If you can't, I just called the Graph.partitonBy() method right after
construction my graph but before
I don't think its possible to access. What I've done before is send the
current or next iteration index with the message, where the message is a
case class.
HTH
Dan
On Tue, Feb 3, 2015 at 10:20 AM, Matthew Cornell corn...@cs.umass.edu
wrote:
Hi Folks,
I'm new to GraphX and Scala and my
That may be the cause of your issue. Take a look at the tuning guide[1] and
maybe also profile your application. See if you can reuse your objects.
1. http://spark.apache.org/docs/latest/tuning.html
Best Regards,
Sonal
Founder, Nube Technologies http://www.nubetech.co
Thanks, Sonal.
But it seems to be an error happened when “cleaning broadcast”?
BTW, what is the timeout of “[30 seconds]”? can I increase it?
Best,
Yifan LI
On 02 Feb 2015, at 11:12, Sonal Goyal sonalgoy...@gmail.com wrote:
That may be the cause of your issue. Take a look at the
I think this broadcast cleaning(memory block remove?) timeout exception was
caused by:
15/02/02 11:48:49 ERROR TaskSchedulerImpl: Lost executor 13 on
small18-tap1.common.lip6.fr: remote Akka client disassociated
15/02/02 11:48:49 ERROR SparkDeploySchedulerBackend: Asked to remove
non-existent
On 01/29/2015 08:31 PM, Ankur Dave wrote:
Thanks for the reminder. I just created a PR:
https://github.com/apache/spark/pull/4273
Ankur
Hello,
Thanks for the patch. I applied it on Pregel.scala (in Spark 1.2.0 sources) and
rebuilt
Spark. During execution, at the 25th iteration of Pregel,
Is your code hitting frequent garbage collection?
Best Regards,
Sonal
Founder, Nube Technologies http://www.nubetech.co
http://in.linkedin.com/in/sonalgoyal
On Fri, Jan 30, 2015 at 7:52 PM, Yifan LI iamyifa...@gmail.com wrote:
Hi,
I am running my graphx application on Spark 1.2.0(11
Yes, I think so, esp. for a pregel application… have any suggestion?
Best,
Yifan LI
On 30 Jan 2015, at 22:25, Sonal Goyal sonalgoy...@gmail.com wrote:
Is your code hitting frequent garbage collection?
Best Regards,
Sonal
Founder, Nube Technologies http://www.nubetech.co/
Just curious, is this set to be merged at some point?
On Thu Jan 22 2015 at 4:34:46 PM Ankur Dave ankurd...@gmail.com wrote:
At 2015-01-22 02:06:37 -0800, NicolasC nicolas.ch...@inria.fr wrote:
I try to execute a simple program that runs the ShortestPaths algorithm
Thanks for the reminder. I just created a PR:
https://github.com/apache/spark/pull/4273
Ankur
On Thu, Jan 29, 2015 at 7:25 AM, Jay Hutfles jayhutf...@gmail.com wrote:
Just curious, is this set to be merged at some point?
-
To
TinkerPop has become an Apache Incubator project and seems to have Spark
in mind in their proposal
https://wiki.apache.org/incubator/TinkerPopProposal.
That's good news!
I hope there will be nice collaborations between the communities.
On Wed, Jan 7, 2015 at 11:31 AM, Nicolas Colson
At 2015-01-22 02:06:37 -0800, NicolasC nicolas.ch...@inria.fr wrote:
I try to execute a simple program that runs the ShortestPaths algorithm
(org.apache.spark.graphx.lib.ShortestPaths) on a small grid graph.
I use Spark 1.2.0 downloaded from spark.apache.org.
This program runs more than 2
would be if the AMP Lab or Databricks
maintained a set of benchmarks on the web that showed how much each successive
version of Spark improved.
Dave
From: Madabhattula Rajesh Kumar [mailto:mrajaf...@gmail.com]
Sent: Monday, January 12, 2015 9:24 PM
To: Buttler, David
Subject: Re: GraphX vs
At 2014-12-05 02:26:52 -0800, Yifan LI iamyifa...@gmail.com wrote:
I have a graph in where each vertex keep several messages to some faraway
neighbours(I mean, not to only immediate neighbours, at most k-hops far, e.g.
k = 5).
now, I propose to distribute these messages to their
There's no built-in support for doing this, so the best option is to copy and
modify Pregel to check the accumulator at the end of each iteration. This is
robust and shouldn't be too hard, since the Pregel code is short and only uses
public GraphX APIs.
Ankur
At 2014-12-03 09:37:01 -0800, Jay
At 2014-11-13 21:28:52 +, Ommen, Jurgen omme0...@stthomas.edu wrote:
I'm using GraphX and playing around with its PageRank algorithm. However, I
can't see from the documentation how to use edge weight when running PageRank.
Is this possible to consider edge weights and how would I do it?
Hi,
I think that there are two solutions;
1. Invalid edges send Iterator.empty messages in sendMsg of the Pregel API.
These messages make no effect on corresponding vertices.
2. Use GraphOps.(collectNeighbors/collectNeighborIds), not the Pregel API
so as to
handle active edge lists by
GraphX is build on *top* of Spark, so Spark can achieve whatever GraphX can.
On Wed, Nov 5, 2014 at 9:41 AM, Deep Pradhan pradhandeep1...@gmail.com
wrote:
Hi,
Can Spark achieve whatever GraphX can?
Keeping aside the performance comparison between Spark and GraphX, if I
want to implement any
At 2014-10-28 16:27:20 +0300, Zuhair Khayyat zuhair.khay...@gmail.com wrote:
I am using connected components function of GraphX (on Spark 1.0.2) on some
graph. However for some reason the fails with StackOverflowError. The graph
is not too big; it contains 1 vertices and 50 edges.
hi again. just want to check in again to see if anyone could advise on how
to implement a mutable, growing graph with graphx?
we're building a graph is growing over time. it adds more vertices and
edges every iteration of our algorithm.
it doesn't look like there is an obvious way to add a
On Tue, Oct 14, 2014 at 12:36 PM, ll duy.huynh@gmail.com wrote:
hi again. just want to check in again to see if anyone could advise on how
to implement a mutable, growing graph with graphx?
we're building a graph is growing over time. it adds more vertices and
edges every iteration of
thanks ankur. indexedrdd sounds super helpful!
a related question, what is the best way to update the values of existing
vertices and edges?
On Tue, Oct 14, 2014 at 4:30 PM, Ankur Dave ankurd...@gmail.com wrote:
On Tue, Oct 14, 2014 at 12:36 PM, ll duy.huynh@gmail.com wrote:
hi again.
On Tue, Oct 14, 2014 at 1:57 PM, Duy Huynh duy.huynh@gmail.com wrote:
a related question, what is the best way to update the values of existing
vertices and edges?
Many of the Graph methods deal with updating the existing values in bulk,
including mapVertices, mapEdges, mapTriplets,
great, thanks!
On Tue, Oct 14, 2014 at 5:08 PM, Ankur Dave ankurd...@gmail.com wrote:
On Tue, Oct 14, 2014 at 1:57 PM, Duy Huynh duy.huynh@gmail.com
wrote:
a related question, what is the best way to update the values of existing
vertices and edges?
Many of the Graph methods deal
Hi again,
Thank you for your suggestion :)
I've tried to implement this method but I'm stuck trying to union the
payload before creating the graph.
Below is a really simplified snippet of what have worked so far.
//Reading the articles given in json format
val articles =
I'll try my best ;-).
1/ you could create a abstract type for the types (1 on top of Vs, 1 other
on top of Es types) than use the subclasses as payload in your VertexRDD or
in your Edge. Regarding storage and files, it doesn't really matter (unless
you want to use the OOTB loading method, thus
Excellent! Thanks Andy. I will give it a go.
On Thu, Oct 2, 2014 at 12:42 AM, andy petrella [via Apache Spark User List]
ml-node+s1001560n15487...@n3.nabble.com wrote:
I'll try my best ;-).
1/ you could create a abstract type for the types (1 on top of Vs, 1 other
on top of Es types) than
At 2014-09-05 12:13:18 +0200, Yifan LI iamyifa...@gmail.com wrote:
But how to assign the storage level to a new vertices RDD that mapped from
an existing vertices RDD,
e.g.
*val newVertexRDD =
graph.collectNeighborIds(EdgeDirection.Out).map{case(id:VertexId,
a:Array[VertexId]) = (id,
Thank you, Ankur! :)
But how to assign the storage level to a new vertices RDD that mapped from
an existing vertices RDD,
e.g.
*val newVertexRDD =
graph.collectNeighborIds(EdgeDirection.Out).map{case(id:VertexId,
a:Array[VertexId]) = (id, initialHashMap(a))}*
the new one will be combined with
Hi Ankur,
Thanks so much for your advice.
But it failed when I tried to set the storage level in constructing a graph.
val graph = GraphLoader.edgeListFile(sc, edgesFile, minEdgePartitions =
numPartitions).partitionBy(PartitionStrategy.EdgePartition2D).persist(StorageLevel.MEMORY_AND_DISK)
At 2014-09-03 17:58:09 +0200, Yifan LI iamyifa...@gmail.com wrote:
val graph = GraphLoader.edgeListFile(sc, edgesFile, minEdgePartitions =
numPartitions).partitionBy(PartitionStrategy.EdgePartition2D).persist(StorageLevel.MEMORY_AND_DISK)
Error: java.lang.UnsupportedOperationException: Cannot
A bit in analogy with a linked-list a double linked-list. It might introduce
overhead in terms of memory usage, but you could use two directed edges to
substitute the uni-directed edge.
--
View this message in context:
At 2014-08-25 11:23:37 -0700, Sunita Arvind sunitarv...@gmail.com wrote:
Does this We introduce GraphX, which combines the advantages of both
data-parallel and graph-parallel systems by efficiently expressing graph
computation within the Spark data-parallel framework. We leverage new ideas
in
I don't know if Pregel would be necessary since it's not iterative
You could filter the graph by looking at edge triplets, and testing if
source =B, dest =A, and edge value 5
--
View this message in context:
Hey, thanks for your response.
And I had seen the triplets, but I'm not quite sure how the triplets would
get me that V1 is connected to V4. Maybe I need to spend more time
understanding it, I guess.
-Cesar
On Wed, Aug 20, 2014 at 10:56 AM, glxc r.ryan.mcc...@gmail.com wrote:
I don't know
At 2014-08-20 10:34:50 -0700, Cesar Arevalo ce...@zephyrhealthinc.com wrote:
I would like to get the type B vertices that are connected through type A
vertices where the edges have a score greater than 5. So, from the example
above I would like to get V1 and V4.
It sounds like you're trying to
Hi Ankur, thank you for your response. I already looked at the sample code
you sent. And I think the modification you are referring to is on the
tryMatch function of the PartialMatch class. I noticed you have a case in
there that checks for a pattern match, and I think that's the code I need
to
On Mon, Aug 18, 2014 at 6:29 AM, Yifan LI iamyifa...@gmail.com wrote:
I am testing our application(similar to personalised page rank using
Pregel, and note that each vertex property will need pretty much more space
to store after new iteration)
[...]
But when we ran it on larger graph(e.g.
On Wed, Aug 6, 2014 at 11:37 AM, AlexanderRiggers
alexander.rigg...@gmail.com wrote:
To perform the page rank I have to create a graph object, adding the edges
by setting sourceID=id and distID=brand. In GraphLab there is function: g =
SGraph().add_edges(data, src_field='id',
OK, I think I've figured it out.
It seems to be a bug which has been reported at:
https://issues.apache.org/jira/browse/SPARK-2823 and
https://github.com/apache/spark/pull/1763.
As it says: If the users set “spark.default.parallelism” and the value is
different with the EdgeRDD partition
At 2014-08-04 20:52:26 +0800, Bin wubin_phi...@126.com wrote:
I wonder how spark parameters, e.g., number of paralellism, affect Pregel
performance? Specifically, sendmessage, mergemessage, and vertexprogram?
I have tried label propagation on a 300,000 edges graph, and I found that no
We need to pass the URL only when we are using the interactive shell right?
Now, I am not using the interactive shell, I am just doing
./bin/run-example.. when I am in the Spark directory.
If not, Spark may be ignoring your single-node cluster and defaulting to
local mode.
What does this
Try this:
./bin/run-example graphx.LiveJournalPageRank edge_list_file…
On Aug 2, 2014, at 5:55 PM, Deep Pradhan pradhandeep1...@gmail.com wrote:
Hi,
I am running Spark in a single node cluster. I am able to run the codes in
Spark like SparkPageRank.scala, SparkKMeans.scala by the following
At 2014-08-02 21:29:33 +0530, Deep Pradhan pradhandeep1...@gmail.com wrote:
How should I run graphx codes?
At the moment it's a little more complicated to run the GraphX algorithms than
the Spark examples due to SPARK-1986 [1]. There is a driver program in
org.apache.spark.graphx.lib.Analytics
At 2014-08-02 19:04:22 +0200, Yifan LI iamyifa...@gmail.com wrote:
But I am thinking of if I can compute only some selected vertexes(hubs), not
to do update on every vertex…
is it possible to do this using Pregel API?
The Pregel API already only runs vprog on vertices that received messages
At 2014-08-01 11:23:49 +0800, Bin wubin_phi...@126.com wrote:
I am wondering what is the best way to construct a graph?
Say I have some attributes for each user, and specific weight for each user
pair. The way I am currently doing is first read user information and edge
triple into two
ShreyanshB shreyanshpbh...@gmail.com writes:
The version with in-memory shuffle is here:
https://github.com/amplab/graphx2/commits/vldb.
It'd be great if you can tell me how to configure and invoke this spark
version.
Sorry for the delay on this. Assuming you're planning to launch an EC2
Yifan LI iamyifa...@gmail.com writes:
Maybe you could get the vertex, for instance, which id is 80, by using:
graph.vertices.filter{case(id, _) = id==80}.collect
but I am not sure this is the exactly efficient way.(it will scan the whole
table? if it can not get benefit from index of
On Thu, Jul 24, 2014 at 9:52 AM, Arun Kumar toga...@gmail.com wrote:
While using pregel API for Iterations how to figure out which super step
the iteration currently in.
The Pregel API doesn't currently expose this, but it's very straightforward
to modify Pregel.scala
Thanks Ankur.
The version with in-memory shuffle is here:
https://github.com/amplab/graphx2/commits/vldb. Unfortunately Spark has
changed a lot since then, and the way to configure and invoke Spark is
different. I can send you the correct configuration/invocation for this if
you're interested in
On Fri, Jul 18, 2014 at 9:07 PM, ShreyanshB shreyanshpbh...@gmail.com
wrote:
Does the suggested version with in-memory shuffle affects performance too
much?
We've observed a 2-3x speedup from it, at least on larger graphs like
twitter-2010 http://law.di.unimi.it/webdata/twitter-2010/ and
Thanks
On Fri, Jul 18, 2014 at 12:22 AM, Ankur Dave ankurd...@gmail.com wrote:
If your sendMsg function needs to know the incoming messages as well as
the vertex value, you could define VD to be a tuple of the vertex value and
the last received message. The vprog function would then store
Thanks for your interest. I should point out that the numbers in the arXiv
paper are from GraphX running on top of a custom version of Spark with an
experimental in-memory shuffle prototype. As a result, if you benchmark
GraphX at the current master, it's expected that it will be 2-3x slower
than
Thanks a lot Ankur.
The version with in-memory shuffle is here:
https://github.com/amplab/graphx2/commits/vldb. Unfortunately Spark has
changed a lot since then, and the way to configure and invoke Spark is
different. I can send you the correct configuration/invocation for this if
you're
If your sendMsg function needs to know the incoming messages as well as the
vertex value, you could define VD to be a tuple of the vertex value and the
last received message. The vprog function would then store the incoming
messages into the tuple, allowing sendMsg to access them.
For example, if
Hi Ankur,
FYI - in a naive attempt to enhance your solution, managed to create
MergePatternPath. I think it works in expected way (atleast for the traversing
problem in last email).
I modified your code a bit. Also instead of EdgePattern I used List of
Functions that match the whole edge
On Fri, Jul 11, 2014 at 2:23 PM, ShreyanshB shreyanshpbh...@gmail.com
wrote:
-- Is it a correct way to load file to get best performance?
Yes, edgeListFile should be efficient at loading the edges.
-- What should be the partition size? =computing node or =cores?
In general it should be a
Thanks a lot Ankur, I'll follow that.
A last quick
Does that error affect performance?
~Shreyansh
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Graphx-optimal-partitions-for-a-graph-and-error-in-logs-tp9455p9462.html
Sent from the Apache Spark User List
I don't think it should affect performance very much, because GraphX
doesn't serialize ShippableVertexPartition in the fast path of
mapReduceTriplets execution (instead it calls
ShippableVertexPartition.shipVertexAttributes and serializes the result). I
think it should only get serialized for
Great! Thanks a lot.
Hate to say this but I promise this is last quickie
I looked at the configurations but I didn't find any parameter to tune for
network bandwidth i.e. Is there anyway to tell graphx (spark) that I'm using
1G network or 10G network or infinite band? Does it figure out on its
Spark just uses opens up inter-slave TCP connections for message passing
during shuffles (I think the relevant code is in ConnectionManager). Since
TCP automatically determines
http://en.wikipedia.org/wiki/TCP_congestion-avoidance_algorithm the
optimal sending rate, Spark doesn't need any
Perfect! Thanks Ankur.
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Graphx-optimal-partitions-for-a-graph-and-error-in-logs-tp9455p9488.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.
On Thu, Jul 10, 2014 at 8:20 AM, Yifan LI iamyifa...@gmail.com wrote:
- how to build the latest version of Spark from the master branch, which
contains a fix?
Instead of downloading a prebuilt Spark release from
http://spark.apache.org/downloads.html, follow the instructions under
Development
Hi Ankur,
I was trying out the PatterMatcher it works for smaller path, but I see that
for the longer ones it continues to run forever...
Here's what I am trying:
https://gist.github.com/hihellobolke/dd2dc0fcebba485975d1 (The example of 3
share traders transacting in appl shares)
The first
you could only do the deep check if the hashcodes are the same and design
hashcodes that do not take all elements into account.
the alternative seems to be putting cache statements all over graphx, as is
currently the case, which is trouble for any long lived application where
caching is
probably a dumb question, but why is reference equality used for the
indexes?
On Sun, Jul 6, 2014 at 12:43 AM, Ankur Dave ankurd...@gmail.com wrote:
When joining two VertexRDDs with identical indexes, GraphX can use a fast
code path (a zip join without any hash lookups). However, the check
Well, the alternative is to do a deep equality check on the index arrays,
which would be somewhat expensive since these are pretty large arrays (one
element per vertex in the graph). But, in case the reference equality check
fails, it actually might be a good idea to do the deep check before
Interesting problem! My understanding is that you want to (1) find paths
matching a particular pattern, and (2) add edges between the start and end
vertices of the matched paths.
For (1), I implemented a pattern matcher for GraphX
Thanks Ankur,
Cannot thank you enough for this!!! I am reading your example still digesting
grokking it though :-)
I was breaking my head over this for past few hours.
In my last futile attempts over past few hours. I was looking at Pregel... E.g
if that could be used to see at what step
thanks for replying. why is joining two vertexrdds without caching slow?
what is recomputed unnecessarily?
i am not sure what is different here from joining 2 regular RDDs (where
nobody seems to recommend to cache before joining i think...)
On Thu, Jul 3, 2014 at 10:52 PM, Ankur Dave
When joining two VertexRDDs with identical indexes, GraphX can use a fast
code path (a zip join without any hash lookups). However, the check for
identical indexes is performed using reference equality.
Without caching, two copies of the index are created. Although the two
indexes are
A common reason for the Joining ... is slow message is that you're
joining VertexRDDs without having cached them first. This will cause Spark
to recompute unnecessarily, and as a side effect, the same index will get
created twice and GraphX won't be able to do an efficient zip join.
For example,
Thanks for having corrected this bug!
The fix version is marked as 1.1.0 ( SPARK-1552
https://issues.apache.org/jira/browse/SPARK-1552 ). I have tested my code
snippet with Spark 1.0.0 (Scala 2.10.4) and it works. I don't know if it's
important to mention it.
Pierre-Alexandre
--
View this
Yes, the subgraph operator takes a vertex predicate and keeps only the
edges where both vertices satisfy the predicate, so it will work as long as
you can express the sublist in terms of a vertex predicate.
If that's not possible, you can still obtain the same effect, but you'll
have to use
below? Can you advise on how to
solve this issue?
Thanks,
Alex
From: Ankur Dave [mailto:ankurd...@gmail.com]
Sent: Thursday, May 22, 2014 6:59 PM
To: user@spark.apache.orgmailto:user@spark.apache.org
Subject: Re: GraphX partition problem
The fix will be included in Spark 1.0, but if you just want
I've been trying to reproduce this but I haven't succeeded so far. For
example, on the web-Google
https://snap.stanford.edu/data/web-Google.htmlgraph, I get the
expected results both on v0.9.1-handle-empty-partitions
and on master:
// Load web-Google and run connected componentsimport
I’m not sure about 1.2TB, but I can give it a shot.
Is there some way to persist intermediate results to disk? Does all the graph
has to be in memory?
Alex
From: Ankur Dave [mailto:ankurd...@gmail.com]
Sent: Monday, May 26, 2014 12:23 AM
To: user@spark.apache.org
Subject: Re: GraphX partition
Can we do better with Bagel somehow? Control how we store the graph?
From: Ankur Dave [mailto:ankurd...@gmail.com]
Sent: Monday, May 26, 2014 12:13 PM
To: user@spark.apache.org
Subject: Re: GraphX partition problem
GraphX only performs sequential scans over the edges, so we could in theory
@spark.apache.org
Subject: Re: GraphX partition problem
The fix will be included in Spark 1.0, but if you just want to apply the fix to
0.9.1, here's a hotfixed version of 0.9.1 that only includes PR #367:
https://github.com/ankurdave/spark/tree/v0.9.1-handle-empty-partitions. You can
clone and build
Once the graph is built, edges are stored in parallel primitive arrays, so
each edge should only take 20 bytes to store (srcId: Long, dstId: Long,
attr: Int). Unfortunately, the current implementation in
EdgePartitionBuilder uses an array of Edge objects as an intermediate
representation for
Do you mean you want to obtain a list of adjacent edges for every vertex? A
mapReduceTriplets followed by a join is the right way to do this. The join
will be cheap because the original and derived vertices will share indices.
There's a built-in function to do this for neighboring vertex
Graph.subgraph() allows you to apply a filter to edges and/or vertices.
On Thu, May 1, 2014 at 8:52 AM, Николай Кинаш peroksi...@gmail.com wrote:
Hello.
How to remove vertex or edges from graph in GraphX?
This is caused by https://issues.apache.org/jira/browse/SPARK-1188. I think
the fix will be in the next release. But until then, do:
g.edges.map(_.copy()).distinct.count
On Wed, Apr 23, 2014 at 2:26 AM, Ryan Compton compton.r...@gmail.comwrote:
Try this:
Here are some out-of-the-box ideas: If the elements lie in a fairly small
range and/or you're willing to work with limited precision, you could use
counting sort. Moreover, you could iteratively find the median using
bisection, which would be associative and commutative. It's easy to think
of
Whoops, I should have mentioned that it's a multivariate median (cf
http://www.pnas.org/content/97/4/1423.full.pdf ). It's easy to compute
when all the values are accessible at once. I'm not sure it's possible
with a combiner. So, I guess the question should be: Can I use
GraphX's Pregel without a
If you need access to all message values in vprog, there's nothing wrong
with building up an array in mergeMsg (option #1). This is what
org.apache.spark.graphx.lib.TriangleCount does, though with sets instead of
arrays. There will be a performance penalty because of the communication,
but it
I wasn't able to reproduce this with a small test file, but I did change
the file parsing to use x(1).toLong instead of x(2).toLong. Did you mean to
take the third column rather than the second?
If so, would you mind posting a larger sample of the file, or even the
whole file if possible?
Here's
Try this: https://www.dropbox.com/s/xf34l0ta496bdsn/.txt
This code:
println(g.numEdges)
println(g.numVertices)
println(g.edges.distinct().count())
gave me
1
9294
2
On Tue, Apr 22, 2014 at 5:14 PM, Ankur Dave ankurd...@gmail.com wrote:
I wasn't able to reproduce this
On Fri, Apr 11, 2014 at 4:42 AM, Pierre-Alexandre Fonta
pierre.alexandre.fonta+sp...@gmail.com wrote:
Testing in mapTriplets if a vertex attribute, which is defined as Integer
in
first VertexRDD but has been changed after to Double by mapVertices, is
greater than a number throws
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