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Merge pull request apache#64 from markhamstra/csd-1.4
SKIPME Spark 1.4.1
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assembly/pom.xml

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@@ -21,7 +21,7 @@
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<parent>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-parent_2.10</artifactId>
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<version>1.4.0-csd-5-SNAPSHOT</version>
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<version>1.4.1-csd-1-SNAPSHOT</version>
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<relativePath>../pom.xml</relativePath>
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</parent>
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bagel/pom.xml

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@@ -21,7 +21,7 @@
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<parent>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-parent_2.10</artifactId>
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<version>1.4.0-csd-5-SNAPSHOT</version>
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<version>1.4.1-csd-1-SNAPSHOT</version>
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<relativePath>../pom.xml</relativePath>
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</parent>
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core/pom.xml

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@@ -21,7 +21,7 @@
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<parent>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-parent_2.10</artifactId>
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<version>1.4.0-csd-5-SNAPSHOT</version>
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<version>1.4.1-csd-1-SNAPSHOT</version>
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<relativePath>../pom.xml</relativePath>
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</parent>
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core/src/main/scala/org/apache/spark/ui/JettyUtils.scala

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@@ -210,10 +210,16 @@ private[spark] object JettyUtils extends Logging {
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conf: SparkConf,
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serverName: String = ""): ServerInfo = {
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val collection = new ContextHandlerCollection
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collection.setHandlers(handlers.toArray)
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addFilters(handlers, conf)
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val collection = new ContextHandlerCollection
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val gzipHandlers = handlers.map { h =>
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val gzipHandler = new GzipHandler
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gzipHandler.setHandler(h)
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gzipHandler
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}
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collection.setHandlers(gzipHandlers.toArray)
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// Bind to the given port, or throw a java.net.BindException if the port is occupied
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def connect(currentPort: Int): (Server, Int) = {
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val server = new Server(new InetSocketAddress(hostName, currentPort))

dev/create-release/create-release.sh

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@@ -118,13 +118,13 @@ if [[ ! "$@" =~ --skip-publish ]]; then
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rm -rf $SPARK_REPO
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build/mvn -DskipTests -Pyarn -Phive \
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build/mvn -DskipTests -Pyarn -Phive -Prelease\
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-Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
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clean install
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./dev/change-version-to-2.11.sh
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build/mvn -DskipTests -Pyarn -Phive \
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build/mvn -DskipTests -Pyarn -Phive -Prelease\
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-Dscala-2.11 -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
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clean install
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docs/configuration.md

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@@ -1007,9 +1007,9 @@ Apart from these, the following properties are also available, and may be useful
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<tr>
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<td><code>spark.rpc.numRetries</code></td>
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<td>3</td>
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<td>
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Number of times to retry before an RPC task gives up.
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An RPC task will run at most times of this number.
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<td>
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</td>
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</tr>
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<tr>
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<tr>
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<td><code>spark.rpc.lookupTimeout</code></td>
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<td>120s</td>
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Duration for an RPC remote endpoint lookup operation to wait before timing out.
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<td>
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Duration for an RPC remote endpoint lookup operation to wait before timing out.
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</td>
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</tr>
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</table>

docs/sparkr.md

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@@ -68,7 +68,7 @@ you can specify the packages with the `packages` argument.
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<div data-lang="r" markdown="1">
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{% highlight r %}
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sc <- sparkR.init(packages="com.databricks:spark-csv_2.11:1.0.3")
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sc <- sparkR.init(sparkPackages="com.databricks:spark-csv_2.11:1.0.3")
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sqlContext <- sparkRSQL.init(sc)
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{% endhighlight %}
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</div>
@@ -116,7 +116,7 @@ sql(hiveContext, "CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
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sql(hiveContext, "LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
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# Queries can be expressed in HiveQL.
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results <- hiveContext.sql("FROM src SELECT key, value")
119+
results <- sql(hiveContext, "FROM src SELECT key, value")
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# results is now a DataFrame
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head(results)

docs/sql-programming-guide.md

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@@ -828,7 +828,7 @@ using this syntax.
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{% highlight scala %}
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val df = sqlContext.read.format("json").load("examples/src/main/resources/people.json")
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df.select("name", "age").write.format("json").save("namesAndAges.json")
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df.select("name", "age").write.format("parquet").save("namesAndAges.parquet")
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{% endhighlight %}
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</div>
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sql(sqlContext, "LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
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# Queries can be expressed in HiveQL.
1521-
results = sqlContext.sql("FROM src SELECT key, value").collect()
1521+
results <- collect(sql(sqlContext, "FROM src SELECT key, value"))
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{% endhighlight %}
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docs/streaming-programming-guide.md

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@@ -854,6 +854,8 @@ it with new information. To use this, you will have to do two steps.
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1. Define the state update function - Specify with a function how to update the state using the
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previous state and the new values from an input stream.
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In every batch, Spark will apply the state update function for all existing keys, regardless of whether they have new data in a batch or not. If the update function returns `None` then the key-value pair will be eliminated.
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Let's illustrate this with an example. Say you want to maintain a running count of each word
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seen in a text data stream. Here, the running count is the state and it is an integer. We
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define the update function as:

ec2/spark_ec2.py

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@@ -127,7 +127,7 @@ def setup_external_libs(libs):
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)
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with open(tgz_file_path, "wb") as tgz_file:
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tgz_file.write(download_stream.read())
130-
with open(tgz_file_path) as tar:
130+
with open(tgz_file_path, "rb") as tar:
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if hashlib.md5(tar.read()).hexdigest() != lib["md5"]:
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print("ERROR: Got wrong md5sum for {lib}.".format(lib=lib["name"]), file=stderr)
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sys.exit(1)
@@ -1111,8 +1111,8 @@ def ssh(host, opts, command):
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# If this was an ssh failure, provide the user with hints.
11121112
if e.returncode == 255:
11131113
raise UsageError(
1114-
"Failed to SSH to remote host {0}.\n" +
1115-
"Please check that you have provided the correct --identity-file and " +
1114+
"Failed to SSH to remote host {0}.\n"
1115+
"Please check that you have provided the correct --identity-file and "
11161116
"--key-pair parameters and try again.".format(host))
11171117
else:
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raise e

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