MQTT Adapter for Spark Streaming
MQTT is a machine-to-machine (M2M)/Internet of Things connectivity protocol.
It was designed as an extremely lightweight publish/subscribe messaging transport. You may read more about it here http://mqtt.org/
Message Queue Telemetry Transport (MQTT) is an open message protocol for M2M communications. It enables the transfer of telemetry-style data in the form of messages from devices like sensors and actuators, to mobile phones, embedded systems on vehicles, or laptops and full scale computers.
The protocol was invented by Andy Stanford-Clark of IBM, and Arlen Nipper of Cirrus Link Solutions
This protocol enables a publish/subscribe messaging model in an extremely lightweight way. It is useful for connections with remote locations where line of code and network bandwidth is a constraint.
MQTT is one of the widely used protocol for 'Internet of Things'. This protocol is getting much attraction as anything and everything is getting connected to internet and they all produce data. Researchers and companies predict some 25 billion devices will be connected to the internet by 2015.
Plugin/Support for MQTT is available in popular MQs like RabbitMQ, ActiveMQ etc.
Support for MQTT in Spark will help people with Internet of Things (IoT) projects to use Spark Streaming for their real time data processing needs (from sensors and other embedded devices etc).
- Examples assembly included a log4j.properties which clobbered Spark's
- Example had an error where some classes weren't serializable
- Did some other clean-up in this example
This includes the following changes:
- The "assembly" package now builds in Maven by default, and creates an
assembly containing both hadoop-client and Spark, unlike the old
BigTop distribution assembly that skipped hadoop-client
- There is now a bigtop-dist package to build the old BigTop assembly
- The repl-bin package is no longer built by default since the scripts
don't reply on it; instead it can be enabled with -Prepl-bin
- Py4J is now included in the assembly/lib folder as a local Maven repo,
so that the Maven package can link to it
- run-example now adds the original Spark classpath as well because the
Maven examples assembly lists spark-core and such as provided
- The various Maven projects add a spark-yarn dependency correctly
This commit makes Spark invocation saner by using an assembly JAR to
find all of Spark's dependencies instead of adding all the JARs in
lib_managed. It also packages the examples into an assembly and uses
that as SPARK_EXAMPLES_JAR. Finally, it replaces the old "run" script
with two better-named scripts: "run-examples" for examples, and
"spark-class" for Spark internal classes (e.g. REPL, master, etc). This
is also designed to minimize the confusion people have in trying to use
"run" to run their own classes; it's not meant to do that, but now at
least if they look at it, they can modify run-examples to do a decent
job for them.
As part of this, Bagel's examples are also now properly moved to the
examples package instead of bagel.
This change adds Java examples and unit tests for all GLM algorithms
to make sure the MLLib interface works from Java. Changes include
- Introduce LabeledPoint and avoid using Doubles in train arguments
- Rename train to run in class methods
- Make the optimizer a member variable of GLM to make sure the builder
pattern works
- Changes ALS to accept RDD[Rating] instead of (Int, Int, Double) making it
easier to call from Java
- Renames class methods from `train` to `run` to enable static methods to be
called from Java.
- Add unit tests which check if both static / class methods can be called.
- Also add examples which port the main() function in ALS, KMeans to the
examples project.
Couple of minor changes to existing code:
- Add a toJavaRDD method in RDD to convert scala RDD to java RDD easily
- Workaround a bug where using double[] from Java leads to class cast exception in
KMeans init
