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Posted to commits@tinkerpop.apache.org by ok...@apache.org on 2016/03/31 21:10:53 UTC
[34/40] incubator-tinkerpop git commit: Merge branch 'tp31'
Merge branch 'tp31'
Project: http://git-wip-us.apache.org/repos/asf/incubator-tinkerpop/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-tinkerpop/commit/bc1c89c8
Tree: http://git-wip-us.apache.org/repos/asf/incubator-tinkerpop/tree/bc1c89c8
Diff: http://git-wip-us.apache.org/repos/asf/incubator-tinkerpop/diff/bc1c89c8
Branch: refs/heads/TINKERPOP-1225
Commit: bc1c89c8584013357a3e6f657d21d111348f8fca
Parents: 87e9c6f 3dfe3dd
Author: Daniel Kuppitz <da...@hotmail.com>
Authored: Thu Mar 31 18:16:42 2016 +0200
Committer: Daniel Kuppitz <da...@hotmail.com>
Committed: Thu Mar 31 18:16:42 2016 +0200
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docs/src/reference/implementations-hadoop.asciidoc | 8 ++++++++
1 file changed, 8 insertions(+)
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http://git-wip-us.apache.org/repos/asf/incubator-tinkerpop/blob/bc1c89c8/docs/src/reference/implementations-hadoop.asciidoc
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diff --cc docs/src/reference/implementations-hadoop.asciidoc
index 68c9a6d,4a4d4d8..e8671c7
--- a/docs/src/reference/implementations-hadoop.asciidoc
+++ b/docs/src/reference/implementations-hadoop.asciidoc
@@@ -317,10 -328,19 +317,18 @@@ and `OutputRDD` is used for writing a g
If the graph system provider uses an `InputRDD`, the RDD should maintain an associated `org.apache.spark.Partitioner`. By doing so,
`SparkGraphComputer` will not partition the loaded graph across the cluster as it has already been partitioned by the graph system provider.
-This can save a significant amount of time and space resources.
-If the `InputRDD` does not have a registered partitioner, `SparkGraphComputer` will partition the graph using
-a `org.apache.spark.HashPartitioner` with the number of partitions being either the number of existing partitions in the input (e.g. input splits)
-or the user specified number of `GraphComputer.workers()`.
+This can save a significant amount of time and space resources. If the `InputRDD` does not have a registered partitioner,
+`SparkGraphComputer` will partition the graph using a `org.apache.spark.HashPartitioner` with the number of partitions
+being either the number of existing partitions in the input (i.e. input splits) or the user specified number of `GraphComputer.workers()`.
+ Storage Levels
+ ++++++++++++++
+
+ The `SparkGraphComputer` uses `MEMORY_ONLY` to cache the input graph and the output graph by default. Users should be aware of the impact of
+ different storage levels, since the default settings can quickly lead to memory issues on larger graphs. An overview of Spark's persistence
+ settings is provided in link:http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence[Spark's programming guide].
+
+
Using a Persisted Context
+++++++++++++++++++++++++