Hallo, Well all problems you want to solve with technology need to have good justification for a certain technology. So the first thing is that you ask which technology fits to my current and future problems. This is also what the article says. Unfortunately, it does only provide a vague answer why there is this performance gap. Is it a Spark architecture issue? Is it a configuration issue? Is it a design issue of the spark version of the algorithms? Is it an amazon issue? Why did he use a laptop and not a single Amazon machine to compare? Why did he not run multiple threads on a single machine (for some problems single thread might be the fastest solution anyway)?
Based on my experience a single machine can be already quiet useful for graph algorithms. There are also different graph systems all for different purposes. Spark Graphx is more general (can be used in combination with the whole Spark Plattform!) and probably less performant than highly specialed graph systems leveraging GPU etc. - These systems have the disadvantage that they are not generally suitable or integrated with other types of processing, such as streaming, mr, rdd, etc. I am always curios for any technology why and where do one looses performance. That's why one does proof-of-concepts and evaluates technology depending on the business case. Maybe the article is right, but it is unclear if it can be generalized or if it really has an impact of your business case for Spark/Graphx. His algorithms can only do graph processing for a very special case and are not suitable for a general all-purpose big data infrastructure. Best regards Le 27 mars 2015 19:33, "Eran Medan" <ehrann.meh...@gmail.com> a écrit : > Remember that article that went viral on HN? (Where a guy showed how > GraphX / Giraph / GraphLab / Spark have worse performance on a 128 cluster > than on a 1 thread machine? if not here is the article - > http://www.frankmcsherry.org/graph/scalability/cost/2015/01/15/COST.html) > > > Well as you may recall, this stirred up a lot of commotion in the big data > community (and Spark/GraphX in particular) > > People (justly I guess) blamed him for not really having “big data”, as > all of his data set fits in memory, so it doesn't really count. > > > So he took the challenge and came with a pretty hard to argue counter > benchmark, now with a huge data set (1TB of data, encoded using Hilbert > curves to 154GB, but still large). > see at - > http://www.frankmcsherry.org/graph/scalability/cost/2015/02/04/COST2.html > > He provided the source here https://github.com/frankmcsherry/COST as an > example > > His benchmark shows how on a 128 billion edges graph, he got X2 to X10 > faster results on a single threaded Rust based implementation > > So, what is the counter argument? it pretty much seems like a blow in the > face of Spark / GraphX etc, (which I like and use on a daily basis) > > Before I dive into re-validating his benchmarks with my own use cases. > What is your opinion on this? If this is the case, then what IS the use > case for using Spark/GraphX at all? >
