如果数据适合单个机器，那么使用Spark是否有意义？

Question

I have 20GB of data that requires processing, all of this data fits on my local machine. I'm planning on using Spark or Scala parallel colleections to implement some algorithms and matrix multiplication against this data.

Since the data fits on a single machine should I use Scala parallel collections ?

Is this true : The main bottleneck in parallel tasks is getting the data to the CPU for processing, so since all of the data is as close as can be to the CPU Spark will not give any significant performance improvement ?

Spark will have the overhead setting up parallel tasks even though it will be just running on one machine, so this overhead is redundant in this case ?

Answer 1

It's hard to provide some non-obvious instructions, like if you had your data and doesn't goes up to the 80% of memory and ..., then use local mode . Having said this, there are couple of points, which, in general, may make you use spark even if your data fits one's machine memory:

1. really intensive CPU processing, from the top of my head, it might be complicated parsing of texts
2. stability -- say you have many processing stages and you don't want to lose results, once your single machine goes down. it's especially important in case you have recurrent calculations, not one-off queries (this way, time you spend on bringing spark to the table might pay-off)
3. streaming -- you get your data from somewhere in a stream manner, and, though it's snapshot fits single machine, you have to orchestrate it somehow

In your particular case

so since all of the data is as close as can be to the CPU Spark will not give any significant performance improvement

Of course it's not, Spark is not a voodoo magic that somehow might get your data closer to the CPU, but it can help you scale among machines, thus CPUs (point #1)

Spark will have the overhead setting up parallel tasks even though it will be just running on one machine, so this overhead is redundant in this case ?

I may sound captain obvious, but

1. Take #2 and #3 into consideration, do you need them? If yes, go spark or something else
2. If no, implement your processing in a dumb way (parallel collections)
3. Profile and take a look. Are your processing is CPU bound? Can you speed up it, without lot of tweaks? If no, go spark.

There is also [cheeky] point 4) in the list of Why should I use Spark? . It's the hype -- Spark is a very sexy technology which is easy to "sell" to both your devs (it's the cutting edge of big data) and the company (your boss, in case you're building your own product, your customer in case you're building product for somebody else).