Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/4696#discussion_r26079822
--- Diff: docs/programming-guide.md ---
@@ -728,6 +728,69 @@ def doStuff(self, rdd):
</div>
+### Understanding closures <a name="ClosuresLink"></a>
+One of the harder things about Spark is understanding the scope and life
cycle of variables and methods when executing code across a cluster. RDD
operations that modify variables outside of their scope can be a frequent
source of confusion. In the example below we'll look at code that uses
`foreach()` to increment a counter, but similar issues can occur for other
operations as well.
+
+#### Example
+
+Consider the naive RDD element sum below, which behaves completely
differently depending on whether execution is happening within the same JVM. A
common example of this is when running spark in `local` mode (`--master =
local[n]`) versus deploying a Spark application to a cluster (e.g. via
spark-submit to YARN):
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+var counter = 0
+var rdd = sc.parallelize(data)
+
+// Wrong: Don't do this!!
+rdd.foreach(x => counter += x)
+
+println("Counter value: " + counter)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+int counter = 0;
+JavaRDD<Integer> rdd = sc.parallelize(data);
+
+// Wrong: Don't do this!!
+rdd.foreach(x -> counter += x;)
+
+println("Counter value: " + counter)
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+{% highlight python %}
+counter = 0
+rdd = sc.parallelize(data)
+
+# Wrong: Don't do this!!
+rdd.foreach(lambda x => counter += x)
+
+print("Counter value: " + counter)
+
+{% endhighlight %}
+</div>
+
+</div>
+
+#### Local vs. cluster modes
+
+The primary challenge is that the behavior of the above code is undefined.
In local mode with a single JVM, the above code will sum the values within the
RDD and store it in **counter**. This is because both the RDD and the variable
**counter** are in the same memory space on the driver node.
+
+However, in `cluster` mode, what happens is more complicated, and the
above may not work as intended. To execute jobs, Spark breaks up the processing
of RDD operations into tasks - each of which is operated on by an executor.
Prior to execution, Spark computes the **closure**. The closure is those
variables and methods which must be visible for the executor to perform its
computations on the RDD (in this case `foreach()`). This closure is serialized
and sent to each executor. In `local` mode, there is only the one executors so
everything shares the same closure. In `remote` mode however, this is not the
case and the executors running on seperate worker nodes each have their own
copy of the closure.
--- End diff --
This refers to `remote` mode but that's not a thing... I think it's safest
to refer to "other modes" here.
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