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Posted to commits@spark.apache.org by va...@apache.org on 2016/09/29 17:19:02 UTC
spark git commit: [DOCS] Reorganize explanation of Accumulators and
Broadcast Variables
Repository: spark
Updated Branches:
refs/heads/master b2e9731ca -> 958200497
[DOCS] Reorganize explanation of Accumulators and Broadcast Variables
## What changes were proposed in this pull request?
The discussion of the interaction of Accumulators and Broadcast Variables should logically follow the discussion on Checkpointing. As currently written, this section discusses Checkpointing before it is formally introduced. To remedy this:
- Rename this section to "Accumulators, Broadcast Variables, and Checkpoints", and
- Move this section after "Checkpointing".
## How was this patch tested?
Testing: ran
$ SKIP_API=1 jekyll build
, and verified changes in a Web browser pointed at docs/_site/index.html.
Author: Jos� Hiram Soltren <jo...@cloudera.com>
Closes #15281 from jsoltren/doc-changes.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/95820049
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/95820049
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/95820049
Branch: refs/heads/master
Commit: 958200497affb40f05e321c2b0e252d365ae02f4
Parents: b2e9731
Author: Jos� Hiram Soltren <jo...@cloudera.com>
Authored: Thu Sep 29 10:18:56 2016 -0700
Committer: Marcelo Vanzin <va...@cloudera.com>
Committed: Thu Sep 29 10:18:56 2016 -0700
----------------------------------------------------------------------
docs/streaming-programming-guide.md | 328 +++++++++++++++----------------
1 file changed, 164 insertions(+), 164 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/95820049/docs/streaming-programming-guide.md
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diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 43f1cf3..0b0315b 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -1368,170 +1368,6 @@ Note that the connections in the pool should be lazily created on demand and tim
***
-## Accumulators and Broadcast Variables
-
-[Accumulators](programming-guide.html#accumulators) and [Broadcast variables](programming-guide.html#broadcast-variables) cannot be recovered from checkpoint in Spark Streaming. If you enable checkpointing and use [Accumulators](programming-guide.html#accumulators) or [Broadcast variables](programming-guide.html#broadcast-variables) as well, you'll have to create lazily instantiated singleton instances for [Accumulators](programming-guide.html#accumulators) and [Broadcast variables](programming-guide.html#broadcast-variables) so that they can be re-instantiated after the driver restarts on failure. This is shown in the following example.
-
-<div class="codetabs">
-<div data-lang="scala" markdown="1">
-{% highlight scala %}
-
-object WordBlacklist {
-
- @volatile private var instance: Broadcast[Seq[String]] = null
-
- def getInstance(sc: SparkContext): Broadcast[Seq[String]] = {
- if (instance == null) {
- synchronized {
- if (instance == null) {
- val wordBlacklist = Seq("a", "b", "c")
- instance = sc.broadcast(wordBlacklist)
- }
- }
- }
- instance
- }
-}
-
-object DroppedWordsCounter {
-
- @volatile private var instance: LongAccumulator = null
-
- def getInstance(sc: SparkContext): LongAccumulator = {
- if (instance == null) {
- synchronized {
- if (instance == null) {
- instance = sc.longAccumulator("WordsInBlacklistCounter")
- }
- }
- }
- instance
- }
-}
-
-wordCounts.foreachRDD { (rdd: RDD[(String, Int)], time: Time) =>
- // Get or register the blacklist Broadcast
- val blacklist = WordBlacklist.getInstance(rdd.sparkContext)
- // Get or register the droppedWordsCounter Accumulator
- val droppedWordsCounter = DroppedWordsCounter.getInstance(rdd.sparkContext)
- // Use blacklist to drop words and use droppedWordsCounter to count them
- val counts = rdd.filter { case (word, count) =>
- if (blacklist.value.contains(word)) {
- droppedWordsCounter.add(count)
- false
- } else {
- true
- }
- }.collect().mkString("[", ", ", "]")
- val output = "Counts at time " + time + " " + counts
-})
-
-{% endhighlight %}
-
-See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala).
-</div>
-<div data-lang="java" markdown="1">
-{% highlight java %}
-
-class JavaWordBlacklist {
-
- private static volatile Broadcast<List<String>> instance = null;
-
- public static Broadcast<List<String>> getInstance(JavaSparkContext jsc) {
- if (instance == null) {
- synchronized (JavaWordBlacklist.class) {
- if (instance == null) {
- List<String> wordBlacklist = Arrays.asList("a", "b", "c");
- instance = jsc.broadcast(wordBlacklist);
- }
- }
- }
- return instance;
- }
-}
-
-class JavaDroppedWordsCounter {
-
- private static volatile LongAccumulator instance = null;
-
- public static LongAccumulator getInstance(JavaSparkContext jsc) {
- if (instance == null) {
- synchronized (JavaDroppedWordsCounter.class) {
- if (instance == null) {
- instance = jsc.sc().longAccumulator("WordsInBlacklistCounter");
- }
- }
- }
- return instance;
- }
-}
-
-wordCounts.foreachRDD(new Function2<JavaPairRDD<String, Integer>, Time, Void>() {
- @Override
- public Void call(JavaPairRDD<String, Integer> rdd, Time time) throws IOException {
- // Get or register the blacklist Broadcast
- final Broadcast<List<String>> blacklist = JavaWordBlacklist.getInstance(new JavaSparkContext(rdd.context()));
- // Get or register the droppedWordsCounter Accumulator
- final LongAccumulator droppedWordsCounter = JavaDroppedWordsCounter.getInstance(new JavaSparkContext(rdd.context()));
- // Use blacklist to drop words and use droppedWordsCounter to count them
- String counts = rdd.filter(new Function<Tuple2<String, Integer>, Boolean>() {
- @Override
- public Boolean call(Tuple2<String, Integer> wordCount) throws Exception {
- if (blacklist.value().contains(wordCount._1())) {
- droppedWordsCounter.add(wordCount._2());
- return false;
- } else {
- return true;
- }
- }
- }).collect().toString();
- String output = "Counts at time " + time + " " + counts;
- }
-}
-
-{% endhighlight %}
-
-See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java).
-</div>
-<div data-lang="python" markdown="1">
-{% highlight python %}
-def getWordBlacklist(sparkContext):
- if ("wordBlacklist" not in globals()):
- globals()["wordBlacklist"] = sparkContext.broadcast(["a", "b", "c"])
- return globals()["wordBlacklist"]
-
-def getDroppedWordsCounter(sparkContext):
- if ("droppedWordsCounter" not in globals()):
- globals()["droppedWordsCounter"] = sparkContext.accumulator(0)
- return globals()["droppedWordsCounter"]
-
-def echo(time, rdd):
- # Get or register the blacklist Broadcast
- blacklist = getWordBlacklist(rdd.context)
- # Get or register the droppedWordsCounter Accumulator
- droppedWordsCounter = getDroppedWordsCounter(rdd.context)
-
- # Use blacklist to drop words and use droppedWordsCounter to count them
- def filterFunc(wordCount):
- if wordCount[0] in blacklist.value:
- droppedWordsCounter.add(wordCount[1])
- False
- else:
- True
-
- counts = "Counts at time %s %s" % (time, rdd.filter(filterFunc).collect())
-
-wordCounts.foreachRDD(echo)
-
-{% endhighlight %}
-
-See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/recoverable_network_wordcount.py).
-
-</div>
-</div>
-
-***
-
## DataFrame and SQL Operations
You can easily use [DataFrames and SQL](sql-programming-guide.html) operations on streaming data. You have to create a SparkSession using the SparkContext that the StreamingContext is using. Furthermore this has to done such that it can be restarted on driver failures. This is done by creating a lazily instantiated singleton instance of SparkSession. This is shown in the following example. It modifies the earlier [word count example](#a-quick-example) to generate word counts using DataFrames and SQL. Each RDD is converted to a DataFrame, registered as a temporary table and then queried using SQL.
@@ -1877,6 +1713,170 @@ batch interval that is at least 10 seconds. It can be set by using
***
+## Accumulators, Broadcast Variables, and Checkpoints
+
+[Accumulators](programming-guide.html#accumulators) and [Broadcast variables](programming-guide.html#broadcast-variables) cannot be recovered from checkpoint in Spark Streaming. If you enable checkpointing and use [Accumulators](programming-guide.html#accumulators) or [Broadcast variables](programming-guide.html#broadcast-variables) as well, you'll have to create lazily instantiated singleton instances for [Accumulators](programming-guide.html#accumulators) and [Broadcast variables](programming-guide.html#broadcast-variables) so that they can be re-instantiated after the driver restarts on failure. This is shown in the following example.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+
+object WordBlacklist {
+
+ @volatile private var instance: Broadcast[Seq[String]] = null
+
+ def getInstance(sc: SparkContext): Broadcast[Seq[String]] = {
+ if (instance == null) {
+ synchronized {
+ if (instance == null) {
+ val wordBlacklist = Seq("a", "b", "c")
+ instance = sc.broadcast(wordBlacklist)
+ }
+ }
+ }
+ instance
+ }
+}
+
+object DroppedWordsCounter {
+
+ @volatile private var instance: LongAccumulator = null
+
+ def getInstance(sc: SparkContext): LongAccumulator = {
+ if (instance == null) {
+ synchronized {
+ if (instance == null) {
+ instance = sc.longAccumulator("WordsInBlacklistCounter")
+ }
+ }
+ }
+ instance
+ }
+}
+
+wordCounts.foreachRDD { (rdd: RDD[(String, Int)], time: Time) =>
+ // Get or register the blacklist Broadcast
+ val blacklist = WordBlacklist.getInstance(rdd.sparkContext)
+ // Get or register the droppedWordsCounter Accumulator
+ val droppedWordsCounter = DroppedWordsCounter.getInstance(rdd.sparkContext)
+ // Use blacklist to drop words and use droppedWordsCounter to count them
+ val counts = rdd.filter { case (word, count) =>
+ if (blacklist.value.contains(word)) {
+ droppedWordsCounter.add(count)
+ false
+ } else {
+ true
+ }
+ }.collect().mkString("[", ", ", "]")
+ val output = "Counts at time " + time + " " + counts
+})
+
+{% endhighlight %}
+
+See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala).
+</div>
+<div data-lang="java" markdown="1">
+{% highlight java %}
+
+class JavaWordBlacklist {
+
+ private static volatile Broadcast<List<String>> instance = null;
+
+ public static Broadcast<List<String>> getInstance(JavaSparkContext jsc) {
+ if (instance == null) {
+ synchronized (JavaWordBlacklist.class) {
+ if (instance == null) {
+ List<String> wordBlacklist = Arrays.asList("a", "b", "c");
+ instance = jsc.broadcast(wordBlacklist);
+ }
+ }
+ }
+ return instance;
+ }
+}
+
+class JavaDroppedWordsCounter {
+
+ private static volatile LongAccumulator instance = null;
+
+ public static LongAccumulator getInstance(JavaSparkContext jsc) {
+ if (instance == null) {
+ synchronized (JavaDroppedWordsCounter.class) {
+ if (instance == null) {
+ instance = jsc.sc().longAccumulator("WordsInBlacklistCounter");
+ }
+ }
+ }
+ return instance;
+ }
+}
+
+wordCounts.foreachRDD(new Function2<JavaPairRDD<String, Integer>, Time, Void>() {
+ @Override
+ public Void call(JavaPairRDD<String, Integer> rdd, Time time) throws IOException {
+ // Get or register the blacklist Broadcast
+ final Broadcast<List<String>> blacklist = JavaWordBlacklist.getInstance(new JavaSparkContext(rdd.context()));
+ // Get or register the droppedWordsCounter Accumulator
+ final LongAccumulator droppedWordsCounter = JavaDroppedWordsCounter.getInstance(new JavaSparkContext(rdd.context()));
+ // Use blacklist to drop words and use droppedWordsCounter to count them
+ String counts = rdd.filter(new Function<Tuple2<String, Integer>, Boolean>() {
+ @Override
+ public Boolean call(Tuple2<String, Integer> wordCount) throws Exception {
+ if (blacklist.value().contains(wordCount._1())) {
+ droppedWordsCounter.add(wordCount._2());
+ return false;
+ } else {
+ return true;
+ }
+ }
+ }).collect().toString();
+ String output = "Counts at time " + time + " " + counts;
+ }
+}
+
+{% endhighlight %}
+
+See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java).
+</div>
+<div data-lang="python" markdown="1">
+{% highlight python %}
+def getWordBlacklist(sparkContext):
+ if ("wordBlacklist" not in globals()):
+ globals()["wordBlacklist"] = sparkContext.broadcast(["a", "b", "c"])
+ return globals()["wordBlacklist"]
+
+def getDroppedWordsCounter(sparkContext):
+ if ("droppedWordsCounter" not in globals()):
+ globals()["droppedWordsCounter"] = sparkContext.accumulator(0)
+ return globals()["droppedWordsCounter"]
+
+def echo(time, rdd):
+ # Get or register the blacklist Broadcast
+ blacklist = getWordBlacklist(rdd.context)
+ # Get or register the droppedWordsCounter Accumulator
+ droppedWordsCounter = getDroppedWordsCounter(rdd.context)
+
+ # Use blacklist to drop words and use droppedWordsCounter to count them
+ def filterFunc(wordCount):
+ if wordCount[0] in blacklist.value:
+ droppedWordsCounter.add(wordCount[1])
+ False
+ else:
+ True
+
+ counts = "Counts at time %s %s" % (time, rdd.filter(filterFunc).collect())
+
+wordCounts.foreachRDD(echo)
+
+{% endhighlight %}
+
+See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/recoverable_network_wordcount.py).
+
+</div>
+</div>
+
+***
+
## Deploying Applications
This section discusses the steps to deploy a Spark Streaming application.
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