I dint mean that. When you try the above approach only one client will have
access to the cached data.
But when you expose your data through a thrift server the case is quite
different.
In the case of thrift server all the request goes to the thrift server and
spark will be able to take the advan
Hi Ashish,
In order to answer your question , I assume that you are planning to
process data and cache them in the memory.If you are using a thrift server
that comes with Spark then you can query on top of it. And multiple
applications can use the cached data as internally all the requests go to
t
I am implementing this approach currently.
A
1.Create data tables in spark-sql and cache them.
2. Configure the hive metastore to read the cached tables and share the
same metastore as spark-sql (You get the spark caching advantage)
3.Run spark code to fetch form the cached tables. In the spark co
Hi,
I am planning to use Spark for a Web-based adhoc reporting tool on massive
date-sets on S3. Real-time queries with filters, aggregations and joins
could be constructed from UI selections.
Online documentation seems to suggest that SharkQL is deprecated and users
should move away from it. I u
