Exactly!
The sharing part is used in the Spark Notebook (this one
https://github.com/andypetrella/spark-notebook/blob/master/notebooks/Tachyon%20Test.snb)
so we can share stuffs between notebooks which are different SparkContext
(in diff JVM).
OTOH, we have a project that creates micro services
Spark is an in-memory engine and attempts to do computation in-memory.
Tachyon is memory-centeric distributed storage, OK, but how would that help
ran Spark faster?
Hi,
Tachyon http://tachyon-project.org manages memory off heap which can help
prevent long GC pauses. Also, using Tachyon will allow the data to be
shared between Spark jobs if they use the same dataset.
Here's http://www.meetup.com/Tachyon/events/222485713/ a production use
case where Baidu
