Optimizing Message Passing in Graph Processing
Explore valuable insights on optimizing message passing in graph processing, including leveraging combiners for efficiency, handling large hash tables, custom partitioning for locality, and separating graph structure from weight computations to enhance performance. Dive into strategies like Schimmy pattern for effective graph processing in Hadoop environments.
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Presentation Transcript
Some ideas Combiners are helpful Store outgoing incrementVBy messages and aggregate them This is great for high indegree pages Hadoop s combiners are suboptimal Messages get emitted before being combined Hadoop makes weak guarantees about combiner usage
Id think you want to spill the hash table to memory when it gets large
Some ideas Most hyperlinks are within a domain If we keep domains on the same machine this will mean more messages are local To do this, build a custom partitioner that knows about the domain of each nodeId and keeps nodes on the same domain together Assign node id s so that nodes in the same domain are together partition node ids by range Change Hadoop s Partitioner for this
Some ideas Repeatedly shuffling the graph is expensive We should separate the messages about the graph structure (fixed over time) from messages about pageRank weights (variable) compute and distribute the edges once read them in incrementally in the reducer not easy to do in Hadoop! call this the Schimmy pattern
Schimmy Relies on fact that keys are sorted, and sorts the graph input the same way ..
