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author | Ankur Dave <ankurdave@gmail.com> | 2014-01-12 13:10:53 -0800 |
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committer | Ankur Dave <ankurdave@gmail.com> | 2014-01-12 13:10:53 -0800 |
commit | 5e35d39e0f26db3b669bc2318bd7b3f9f6c5fc50 (patch) | |
tree | 78095f94c96743bca07ce225f237c93628894620 /docs/graphx-programming-guide.md | |
parent | f096f4eaf1f8e936eafc2006ecd01faa2f208cf2 (diff) | |
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Add PageRank example and data
Diffstat (limited to 'docs/graphx-programming-guide.md')
-rw-r--r-- | docs/graphx-programming-guide.md | 32 |
1 files changed, 31 insertions, 1 deletions
diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md index 7f93754edb..52668b07c8 100644 --- a/docs/graphx-programming-guide.md +++ b/docs/graphx-programming-guide.md @@ -470,10 +470,40 @@ things to worry about.) # Graph Algorithms <a name="graph_algorithms"></a> -This section should describe the various algorithms and how they are used. +GraphX includes a set of graph algorithms in to simplify analytics. The algorithms are contained in the `org.apache.spark.graphx.lib` package and can be accessed directly as methods on `Graph` via an implicit conversion to [`Algorithms`][Algorithms]. This section describes the algorithms and how they are used. + +[Algorithms]: api/graphx/index.html#org.apache.spark.graphx.lib.Algorithms ## PageRank +PageRank measures the importance of each vertex in a graph, assuming an edge from *u* to *v* represents an endorsement of *v*'s importance by *u*. For example, if a Twitter user is followed by many others, the user will be ranked highly. + +Spark includes an example social network dataset that we can run PageRank on. A set of users is given in `graphx/data/users.txt`, and a set of relationships between users is given in `graphx/data/followers.txt`. We can compute the PageRank of each user as follows: + +{% highlight scala %} +// Load the implicit conversion to Algorithms +import org.apache.spark.graphx.lib._ +// Load the datasets into a graph +val users = sc.textFile("graphx/data/users.txt").map { line => + val fields = line.split("\\s+") + (fields(0).toLong, fields(1)) +} +val followers = sc.textFile("graphx/data/followers.txt").map { line => + val fields = line.split("\\s+") + Edge(fields(0).toLong, fields(1).toLong, 1) +} +val graph = Graph(users, followers) +// Run PageRank +val ranks = graph.pageRank(0.0001).vertices +// Join the ranks with the usernames +val ranksByUsername = users.leftOuterJoin(ranks).map { + case (id, (username, rankOpt)) => (username, rankOpt.getOrElse(0.0)) +} +// Print the result +println(ranksByUsername.collect().mkString("\n")) +{% endhighlight %} + + ## Connected Components ## Shortest Path |