[
https://issues.apache.org/jira/browse/CAMEL-9385?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Henryk Konsek resolved CAMEL-9385.
----------------------------------
Resolution: Fixed
Done.
> Create Apache Spark component
> -----------------------------
>
> Key: CAMEL-9385
> URL: https://issues.apache.org/jira/browse/CAMEL-9385
> Project: Camel
> Issue Type: New Feature
> Reporter: Henryk Konsek
> Assignee: Henryk Konsek
> Fix For: 2.17.0
>
>
> As a part of the the IoT project I'm working on, I have created a Spark
> component (1) to make it easier to handle analytics requests from devices. I
> would like to donate this code to the ASF Camel and extend it here, as I
> guess that there would be many people interested in using Spark from Camel.
> The URI looks like {{spark:rdd/rddName/rddCallback}} or
> {{spark:dataframe/frameName/frameCallback}} depending if you would like to
> work with RDDs or DataFrames.
> The idea here is that Camel route acts as a driver application. You specify
> RDD/DataFrames definitions (and callbacks to act against those) in a registry
> (for example as Spring beans or OSGi services). Then you send a parameters
> for the computations as a body of a message.
> For example in Spring Boot you specify RDD+callback as:
> {code}
> @Bean
> JavaRDD myRdd(SparkContext sparkContext) {
> return sparkContext.textFile("foo.txt");
> }
> @Bean
> class MyAnalytics {
> @RddCallback
> long countLines(JavaRDD<String> textFile, long argument) {
> return rdd.count() * argument;
> }
> }
> {code}
> Then you ask for the results of computations:
> {code}
> long results = producerTemplate.requestBody("spark:rdd/myRdd/MyAnalytics",
> 10, long.class);
> {code}
> Such setup is extremely useful for bridging Spark computations via different
> transports.
> (1) https://github.com/rhiot/rhiot/tree/master/components/camel-spark
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
