Merge branch 'master' of https://github.com/mesos/spark into WithThing

2 files changed, 6 insertions, 6 deletions

diff --git a/core/src/main/scala/spark/storage/BlockManagerMessages.scala b/core/src/main/scala/spark/storage/BlockManagerMessages.scala
index 1494f90103..cff48d9909 100644
--- a/core/src/main/scala/spark/storage/BlockManagerMessages.scala
+++ b/core/src/main/scala/spark/storage/BlockManagerMessages.scala
@@ -49,16 +49,16 @@ class UpdateBlockInfo(
     blockManagerId.writeExternal(out)
     out.writeUTF(blockId)
     storageLevel.writeExternal(out)
-    out.writeInt(memSize.toInt)
-    out.writeInt(diskSize.toInt)
+    out.writeLong(memSize)
+    out.writeLong(diskSize)
   }
 
   override def readExternal(in: ObjectInput) {
     blockManagerId = BlockManagerId(in)
     blockId = in.readUTF()
     storageLevel = StorageLevel(in)
-    memSize = in.readInt()
-    diskSize = in.readInt()
+    memSize = in.readLong()
+    diskSize = in.readLong()
   }
 }
 
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 36d024f13a..216f7c9cc5 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -189,7 +189,7 @@ public class SimpleJob {
 }
 {% endhighlight %}
 
-This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Note that like in the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide]("java-programming-guide") describes these differences in more detail.
+This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Note that like in the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide](java-programming-guide.html) describes these differences in more detail.
 
 To build the job, we also write a Maven `pom.xml` file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version.
 
@@ -265,7 +265,7 @@ print "Lines with a: %i, lines with b: %i" % (numAs, numBs)
 This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file.
 Like in the Scala and Java examples, we use a SparkContext to create RDDs.
 We can pass Python functions to Spark, which are automatically serialized along with any variables that they reference.
-For jobs that use custom classes or third-party libraries, we can add those code dependencies to SparkContext to ensure that they will be available on remote machines; this is described in more detail in the [Python programming guide](python-programming-guide).
+For jobs that use custom classes or third-party libraries, we can add those code dependencies to SparkContext to ensure that they will be available on remote machines; this is described in more detail in the [Python programming guide](python-programming-guide.html).
 `SimpleJob` is simple enough that we do not need to specify any code dependencies.
 
 We can run this job using the `pyspark` script: