Hi all,
Recently in our project, we need to update a RDD using data regularly
received from DStream, I plan to use "foreachRDD" API to achieve this:
var MyRDD = ...
dstream.foreachRDD { rdd =>
MyRDD = MyRDD.join(rdd).......
...
}
Is this usage correct? My concern is, as I am repeatedly and endlessly
reassigning MyRDD in order to update it, will it create a too long RDD
lineage to process when I want to query MyRDD later on (similar as
https://issues.apache.org/jira/browse/SPARK-4672) ?
Maybe I should:
1. cache or checkpoint latest MyRDD and unpersist old MyRDD every time a
dstream comes in.
2. use the unpublished IndexedRDD
(https://github.com/amplab/spark-indexedrdd) to conduct efficient RDD
update.
As I lack experience using Spark Streaming and indexedRDD, I am here to make
sure my thoughts are on the right track. Your wise suggestions will be
greatly appreciated.
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