You should look at .transform – it’s a powerful transformation (sic) that allows you to dynamically load resources and it gets executed in every micro batch.
Re-broadcasting something should be possible from inside transform as that code is executed on the driver but it’s still a controversial topic, as you probably need to create a NEW broadcast variable instead of updating the existing one. http://search-hadoop.com/?q=transform+update+broadcast&page=2&fc_project=Spark An alternative is to load the filters from mysql and apply them implicitly inside the .transform via rdd.filter instead of broadcast them to the executors. See this thread: http://search-hadoop.com/m/q3RTt2UD6KyBO5M1&subj=Re+Streaming+checkpoints+and+logic+change Hope this helps, -adrian From: Kay-Uwe Moosheimer Date: Thursday, November 5, 2015 at 3:33 PM To: "user@spark.apache.org<mailto:user@spark.apache.org>" Subject: How to use data from Database and reload every hour I have the following problem. We have MySQL and an Spark cluster. We need to load 5 different validation-instructions (several thousand of entries each) and use this information on the executors to decide if content from Kafka-Streaming is for process a or b. The streaming data from kafka are json messages and the validation-info from MySQL says „if field a is that and field b ist that then process a“ and so on. The tables on MySQL are changing over time and we have to reload the data every hour. I tried to use broadcasting where I load the data and store it on HashSets and HashMaps (java code), but It’s not possible to redistribute the data. What would be the best way to resolve my problem? Se native jdbc in executor task an load the data – can the executor store this data on HashSets etc. for next call so that I only load the data every hour? Use other possibilities?
