Spark Transformation - Why is it lazy and what is the advantage?

Question

Spark Transformations are lazily evaluated - when we call the action it executes all the transformations based on lineage graph.

What is the advantage of having the Transformations Lazily evaluated?

Will it improve the performance and less amount of memory consumption compare to eagerly evaluated?

Is there any disadvantage of having the Transformation lazily evaluated?

Answer 1

For transformations, Spark adds them to a DAG of computation and only when driver requests some data, does this DAG actually gets executed.

One advantage of this is that Spark can make many optimization decisions after it had a chance to look at the DAG in entirety. This would not be possible if it executed everything as soon as it got it.

For example -- if you executed every transformation eagerly, what does that mean? Well, it means you will have to materialize that many intermediate datasets in memory. This is evidently not efficient -- for one, it will increase your GC costs. (Because you're really not interested in those intermediate results as such. Those are just convnient abstractions for you while writing the program.) So, what you do instead is -- you tell Spark what is the eventual answer you're interested and it figures out best way to get there.