[
https://issues.apache.org/jira/browse/SPARK-23678?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-23678:
------------------------------------
Assignee: Apache Spark
> a more efficient partition strategy
> -----------------------------------
>
> Key: SPARK-23678
> URL: https://issues.apache.org/jira/browse/SPARK-23678
> Project: Spark
> Issue Type: New Feature
> Components: GraphX
> Affects Versions: 2.4.0
> Reporter: wenbinwei
> Assignee: Apache Spark
> Priority: Minor
>
> Recently, I found a new partition strategy (call EdgePartitionTriangle),
> which is a combination of the partition strategy EdgePartition2D and the the
> partition strategy CanonicalRandomVertexCut. This partition strategy has
> three advantages：
> 1. nicer bound on vertex replication, sqrt(2 * numParts).
> 2. colocate all edges between two vertices regardless of direction.
> 3. same work balance compared with EdgePartition2D
> See
> [https://github.com/weiwee/edgePartitionTri/blob/master/EdgePartitionTriangle.ipynb]
> The main idea is to virtually partitioned by EdgePartition2D, gets partitions
> {(i,j)|i=1,2,..,k, j=1,2,..,k}
> . Then relocate partitions by folding the virtual partitions, such as:
> (1,0) and (0,1) -> (1, 0)
> (2,1) and (1,2) -> (2, 1)
> ...
> (k, k-1) and (k-1, k) -> (k, k -1)
>
> Finally, maps \{(1,0), (2,0), (2,1), (3,0),(3,1),(3,2),...,(k, k-1)} to
> \{0,1,...,k*(k-1) / 2}
> The complete method needs to handle more details：
> 1. when numParts is not a triangle number, partitions are divided into two
> types: triangleParts and rests. The later one is partitioned by a different
> strategy.
> 2. when edges are virtually located to partition (a, a), Then they should be
> relocated to partition
> {(a, 0), (a, 1),..., (a, a-1), (a+1, a),...,(k, a)}
> to achieve better work balance.
> codes:
> {code:java}
> object EdgePartitionTriangle extends PartitionStrategy {
> override def getPartition(src: VertexId, dst: VertexId, numParts:
> PartitionID): PartitionID = {
> val mixingPrime: VertexId = 1125899906842597L
> val numRowTriParts = ((math.sqrt(1 + 8 * numParts) - 1) / 2).toInt
> val numTriParts = numRowTriParts * (numRowTriParts + 1) / 2
> val segmentFactor = 100 // positive even numbers
> val numSegments = (segmentFactor * math.sqrt(4 * numParts *
> numTriParts)).toInt
> val segRow = (math.abs(src * mixingPrime) % numSegments).toInt
> val segCol = (math.abs(dst * mixingPrime) % numSegments).toInt
> var row = segRow / (segmentFactor * numRowTriParts)
> var col = segCol / (segmentFactor * numRowTriParts)
> if (math.max(segRow, segCol) >= 2 * segmentFactor * numTriParts) {
> row = numRowTriParts + 1
> col = math.min(segRow, segCol) % (numParts - numTriParts)
> }
> else if (row == col) {
> val ind = math.min(segRow % numRowTriParts, segCol % numRowTriParts)
> col = (math.min(2 * numRowTriParts - ind - 1, ind) + row + 1) %
> (numRowTriParts + 1)
> }
> if (row > col) row * (row - 1) / 2 + col else col * (col - 1) / 2 + row
> }
> {code}
>
>
>
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
