Note that even the Facebook "four degrees of separation" paper went down to a single machine running WebGraph (http://webgraph.di.unimi.it/) for the final steps, after running jobs in there Hadoop cluster to build the dataset for that final operation.
"The computations were performed on a 24-core machine with 72 GiB of memory and 1 TiB of disk space.6 The first task was to import the Facebook graph(s) into a compressed form for WebGraph [4], so that the multiple scans required by HyperANF’s diffusive process could be carried out relatively quickly." Some toolkits/libraries are optimised for that single dedicated use —yet are downstream of the raw data; where memory reads $L1-$L3 cache locality becomes the main performance problem, and where synchronisation techniques like BSP aren't necessarily needed. On 29 Mar 2015, at 23:18, Eran Medan <ehrann.meh...@gmail.com<mailto:ehrann.meh...@gmail.com>> wrote: Hi Sean, I think your point about the ETL costs are the wining argument here. but I would like to see more research on the topic. What I would like to see researched - is ability to run a specialized set of common algorithms in "fast-local-mode" just like a compiler optimizer can decide to inline some methods, or rewrite a recursive function as a for loop if it's in tail position, I would say that the future of GraphX can be that if a certain algorithm is a well known one (e.g. shortest paths) and can be run locally faster than on a distributed set (taking into account bringing all the data locally) then it will do so. Thanks! On Sat, Mar 28, 2015 at 1:34 AM, Sean Owen <so...@cloudera.com<mailto:so...@cloudera.com>> wrote: (I bet the Spark implementation could be improved. I bet GraphX could be optimized.) Not sure about this one, but "in core" benchmarks often start by assuming that the data is local. In the real world, data is unlikely to be. The benchmark has to include the cost of bringing all the data to the local computation too, since the point of distributed computation is bringing work to the data. Specialist implementations for a special problem should always win over generalist, and Spark is a generalist. Likewise you can factor matrices way faster in a GPU than in Spark. These aren't entirely either/or propositions; you can use Rust or GPU in a larger distributed program. Typically a real-world problem involves more than core computation: ETL, security, monitoring. Generalists are more likely to have an answer to hand for these. Specialist implementations do just one thing, and they typically have to be custom built. Compare the cost of highly skilled developer time to generalist computing resources; $1m buys several dev years but also rents a small data center. Speed is an important issue but by no means everything in the real world, and these are rarely mutually exclusive options in the OSS world. This is a great piece of work, but I don't think it's some kind of argument against distributed computing. On Fri, Mar 27, 2015 at 6:32 PM, Eran Medan <ehrann.meh...@gmail.com<mailto:ehrann.meh...@gmail.com>> wrote: > 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?
