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// Depends on AllPairsShortestPaths: https://github.com/apache/spark/pull/3619 | |
import org.apache.spark.graphx._ | |
import org.apache.spark.graphx.lib._ | |
val edges = sc.parallelize((0 until 10).map(x => Edge(x, x + 1, 1))) | |
val graph = Graph.fromEdges(edges, 1) | |
val dists = AllPairsShortestPaths.run(graph).cache() | |
val maxDists = dists.mapValues(_._2).reduceByKey((a, b) => if (a > b) a else b) |
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import org.apache.spark.rdd.RDD | |
import org.apache.spark.graphx._ | |
import scala.reflect.ClassTag | |
/** Returns the subgraph of `graph` containing only `vertices` and their neighbors. */ | |
def subgraphWithNeighbors[VD, ED: ClassTag, A: ClassTag]( | |
graph: Graph[VD, ED], vertices: RDD[(VertexId, A)]): Graph[VD, ED] = { | |
// Label each vertex in graph with true if it is a member of `vertices` and false if not | |
val labeledGraph = graph.outerJoinVertices(vertices) { | |
(id, oldAttr, isSampled) => isSampled.nonEmpty |
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name := "my-project" | |
version := "0.1-SNAPSHOT" | |
organization := "com.example" | |
scalaVersion := "2.10.4" | |
libraryDependencies += "org.apache.spark" %% "spark-core" % "1.2.1" |
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import org.apache.spark.SparkConf | |
import org.apache.spark.SparkContext | |
import org.apache.spark.sql.SQLContext | |
import org.apache.spark.sql.DataFrame | |
val conf = new SparkConf() | |
val sc = new SparkContext("local", "test") | |
val sqlContext = new SQLContext(sc) | |
val v = sqlContext.createDataFrame(List( |
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public class A<T> { | |
public A(T t) { | |
this.t = t; | |
} | |
public T t; | |
public void print() { | |
System.out.println(t.getClass().getSimpleName() + " " + t); | |
} | |
} |
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import org.apache.spark.graphx._ | |
/** | |
* Returns the shortest directed-edge path from src to dst in the graph. If no path exists, returns | |
* the empty list. | |
*/ | |
def bfs[VD, ED](graph: Graph[VD, ED], src: VertexId, dst: VertexId): Seq[VertexId] = { | |
if (src == dst) return List(src) | |
// The attribute of each vertex is (dist from src, id of vertex with dist-1) |
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val rdd = sc.parallelize((0 until 1000).map(x => (1, x)) ++ List((2,1), (2,2))) | |
// rdd: org.apache.spark.rdd.RDD[(Int, Int)] | |
rdd.collect | |
// res1: Array[(Int, Int)] = Array((1,0), (1,1), (1,2), (1,3), (1,4), (1,5), (1,6), (1,7), (1,8), (1,9), (1,10), (1,11), (1,12), (1,13), (1,14), (1,15), (1,16), (1,17), (1,18), (1,19), (1,20), (1,21), (1,22), (1,23), (1,24), (1,25), (1,26), (1,27), (1,28), (1,29), (1,30), (1,31), (1,32), (1,33), (1,34), (1,35), (1,36), (1,37), (1,38), (1,39), (1,40), (1,41), (1,42), (1,43), (1,44), (1,45), (1,46), (1,47), (1,48), (1,49), (1,50), (1,51), (1,52), (1,53), (1,54), (1,55), (1,56), (1,57), (1,58), (1,59), (1,60), (1,61), (1,62), (1,63), (1,64), (1,65), (1,66), (1,67), (1,68), (1,69), (1,70), (1,71), (1,72), (1,73), (1,74), (1,75), (1,76), (1,77), (1,78), (1,79), (1,80), (1,81), (1,82), (1,83), (1,84), (1,85), (1,86), (1,87), (1,88), (1,89), (1,90), (1,91), (1,92), (1,93), (1,94), (1,95), (1,96),... | |
val nestedGroups = rdd.groupBy(kv => (kv._1, kv._2 % 10)).groupBy(_._1._1).map(_._2 |
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brew install juju | |
juju generate-config | |
export AWS_ACCESS_KEY_ID=... | |
export AWS_SECRET_ACCESS_KEY=... | |
juju bootstrap | |
juju status | |
juju deploy juju-gui --to 0 | |
juju expose juju-gui | |
# Wait until `juju status` shows it has started |
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<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN"> | |
<!-- Created by htmlize-1.51 in inline-css mode. --> | |
<html> | |
<head> | |
<title>test.py</title> | |
</head> | |
<body style="color: #DCDCCC; background-color: #3F3F3F;"> | |
<pre> | |
<span style="color: #F0DFAF; font-weight: bold;">def</span> <span style="color: #93E0E3;">test1</span>(<span style="color: #fff099;">a</span>, <span style="color: #c2b6e1;">b</span>): | |
<span style="color: #F0DFAF; font-weight: bold;">return</span> <span style="color: #fff099;">a</span> + <span style="color: #c2b6e1;">b</span> |
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