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Could anyone explain to me what it means "native" versus "non-native" graph processing in that slide show? One of the leading native graph processing engines i

by espeed 13y ago

Could anyone explain to me what it means "native" versus "non-native" graph processing in that slide show? One of the leading native graph processing engines is GraphLab (http://graphlab.org/); http://graphlab.org/); however, the creator of GraphLab, Dr. Joey Gonzalez, is now focused on GraphX, which is essentially GraphLab built on Spark (http://spark.incubator.apache.org http://spark.incubator.apache.org), which is a non-native analytics platform. Building a graph-processing engine on a general processing system like Spark makes pre-processing and post-processing much easier. See "Introduction to GraphX - Presented by Joseph Gonzalez, Reynold Xin - UC Berkeley AmpLab 2013" (http://www.youtube.com/watch?v=mKEn9C5bRck http://www.youtube.com/watch?v=mKEn9C5bRck) Also, a bunch of advancements in graph processing are coming down the pipe, which will be released in a few months (see https://news.ycombinator.com/item?id=6786563 https://news.ycombinator.com/item?id=6786563). Ditto for "native" versus "non-native" graph storage. See this post by Dr. Matthias Broecheler, the creator of Titan (https://github.com/thinkaurelius/titan/wiki https://github.com/thinkaurelius/titan/wiki)... "A Letter Regarding Native Graph Databases" (http://thinkaurelius.com/2013/11/01/a-letter-regarding-native-graph-databases/ http://thinkaurelius.com/2013/11/01/a-letter-regarding-nativ...)

gngeal 13y ago
So essentially, it's totally meaningless marketing bullshit? As much as I favor memory optimizations, I think that merely trying to linearize the access patterns is completely futile in the case of graph databases. On that level of brute-force approach to speeding things up, you'll most likely gain more performance by using lower-latency memory modules, or simply by using different data structures to accommodate for your specific cache line sizes and latencies, then by trying to linearize generic graphs.

by espeed 13y ago

gngeal 13y ago

So essentially, it's totally meaningless marketing bullshit? As much as I favor memory optimizations, I think that merely trying to linearize the access patterns is completely futile in the case of graph databases. On that level of brute-force approach to speeding things up, you'll most likely gain more performance by using lower-latency memory modules, or simply by using different data structures to accommodate for your specific cache line sizes and latencies, then by trying to linearize generic graphs.