I think your requirement is that of OLTP system. Spark & Cassandra are more suitable for batch kind of jobs (They can be used for OLTP but there would be a performance hit)
Deepak "The greatness of a nation can be judged by the way its animals are treated. Please consider stopping the cruelty by becoming a Vegan" +91 73500 12833 deic...@gmail.com Facebook: https://www.facebook.com/deicool LinkedIn: www.linkedin.com/in/deicool "Plant a Tree, Go Green" Make In India : http://www.makeinindia.com/home On Sun, Jun 10, 2018 at 10:42 AM, onmstester onmstester <onmstes...@zoho.com > wrote: > Hi, > I'm using spark on top of cassandra as backend CRUD of a Restfull > Application. > Most of Rest API's retrieve huge amount of data from cassandra and doing a > lot of aggregation on them in spark which take some seconds. > > Problem: sometimes the output result would be a big list which make client > browser throw stop script, so we should paginate the result at the > server-side, > but it would be so annoying for user to wait some seconds on each page to > cassandra-spark processings, > > Current Dummy Solution: For now i was thinking about assigning a UUID to > each request which would be sent back and forth between server-side and > client-side, > the first time a rest API invoked, the result would be saved in a > temptable and in subsequent similar requests (request for next pages) the > result would be fetch from > temptable (instead of common flow of retrieve from cassandra + aggregation > in spark which would take some time). On memory limit, the old results > would be deleted. > > Is there any built-in clean caching strategy in spark to handle such > scenarios? > > Sent using Zoho Mail <https://www.zoho.com/mail/> > > >
