Graphx: Graph Processing Inward A Distributed Dataflow Framework
This newspaper appeared inwards OSDI'14, too is authored past times Joseph E. Gonzalez, University of California, Berkeley; Reynold S. Xin, University of California, Berkeley, too Databricks; Ankur Dave, Daniel Crankshaw, too Michael J. Franklin, University of California, Berkeley; Ion Stoica, University of California, Berkeley, too Databricks. This link includes video too slides which are useful to sympathize the paper.
This newspaper comes from the AMP lab at UC Berkeley. (Nice name! AMP stands for Algorithms, Machines, too People.) This lab brought to us Spark too GraphLab. And this newspaper is a logical successor. This newspaper is most marrying Spark (dataflow systems) amongst GraphLab (graph-processing systems).
The newspaper sees an chance to unify the 2 tools (using a narrow-waist data/graph representation inwards the shape of mrTriplets) too render a unmarried organisation to address the entire analytics pipeline.
GraphX is truly a sparse abstraction layer on exceed of Spark that provides a conversion from graph computation to dataflow operations (Join, Map, GroupBy). During this reduction from graph computation to dataflow patterns, GraphX applies optimizations based on lessons learned inwards before move on efficient graph-processing (e.g., GraphLab).
As the programming abstraction GraphX introduces a normalized representation of graphs logically every bit a pair of vertex too border holding collections. This is called the triplet view.
The GroupBy phase gathers messages destined to the same vertex, an intervening Map functioning applies the message amount to update the vertex property, too the Join phase scatters the novel vertex holding to all following vertices. This allows GraphX to embed graphs inwards a distributed dataflow framework. Flexible vertex-cut partitioning is used to encode graphs every bit horizontally partitioned collections too gibe the solid soil of the fine art inwards distributed graph partitioning.
Here vertex mirroring approach substantially reduces communication for 2 reasons. First, real-world graphs unremarkably accept orders of magnitude to a greater extent than edges than vertices. Second, a unmarried vertex may accept many edges inwards the same partition, enabling substantial reuse of the vertex property.
As approximately other optimization learned from graph-processing systems, GraphX performs active vertices tracking. In graph algorithms, every bit algorithm converges, the develop of active vertices shrink significantly, too this optimization avoids, wasteful work. GraphX tracks active vertices past times restricting the graph using the subgraph operator. The vertex predicate is pushed to the border partitions, where it tin endure used to filter the triplets.
I am novel to the dataflow systems domain, too don't know plenty to laissez passer on a to a greater extent than authoritative answer. The contributions inwards GraphX may endure generally inwards the thought too academic contributions form. I intend the thought of representing graph computations dorsum to dataflow systems is nice. Unfortunately the GraphX newspaper does non compare amongst Naiad inwards price of performance. And, afterwards the OSDI presentation, at that spot were brace questions/complaints most this point.
This newspaper comes from the AMP lab at UC Berkeley. (Nice name! AMP stands for Algorithms, Machines, too People.) This lab brought to us Spark too GraphLab. And this newspaper is a logical successor. This newspaper is most marrying Spark (dataflow systems) amongst GraphLab (graph-processing systems).
Motivation
Here is the motivation for this merger. In large-scale computation, nosotros involve both dataflow processing too graph processing systems. Graph-processing systems outperform dataflow processing systems past times an guild of magnitude for iterative computations on graphs (e.g., connected-component analysis, PageRank analysis). Unfortunately, it is really cumbersome to work 2 unlike tools too convert information dorsum too forth betwixt the two. The pipeline becomes really inefficient.The newspaper sees an chance to unify the 2 tools (using a narrow-waist data/graph representation inwards the shape of mrTriplets) too render a unmarried organisation to address the entire analytics pipeline.
GraphX is truly a sparse abstraction layer on exceed of Spark that provides a conversion from graph computation to dataflow operations (Join, Map, GroupBy). During this reduction from graph computation to dataflow patterns, GraphX applies optimizations based on lessons learned inwards before move on efficient graph-processing (e.g., GraphLab).
Optimizations
GraphX introduces a attain of optimizations.As the programming abstraction GraphX introduces a normalized representation of graphs logically every bit a pair of vertex too border holding collections. This is called the triplet view.
The GroupBy phase gathers messages destined to the same vertex, an intervening Map functioning applies the message amount to update the vertex property, too the Join phase scatters the novel vertex holding to all following vertices. This allows GraphX to embed graphs inwards a distributed dataflow framework. Flexible vertex-cut partitioning is used to encode graphs every bit horizontally partitioned collections too gibe the solid soil of the fine art inwards distributed graph partitioning.
Here vertex mirroring approach substantially reduces communication for 2 reasons. First, real-world graphs unremarkably accept orders of magnitude to a greater extent than edges than vertices. Second, a unmarried vertex may accept many edges inwards the same partition, enabling substantial reuse of the vertex property.
As approximately other optimization learned from graph-processing systems, GraphX performs active vertices tracking. In graph algorithms, every bit algorithm converges, the develop of active vertices shrink significantly, too this optimization avoids, wasteful work. GraphX tracks active vertices past times restricting the graph using the subgraph operator. The vertex predicate is pushed to the border partitions, where it tin endure used to filter the triplets.
GraphX programming
While graph-processing systems, too most famously Pregel, advocated a "think similar a vertex" approach to programming, the GraphX programming model is closer to thinking most transformations on data. This may require approximately getting used to for programmers non familiar amongst dataflow programming too database operations.Evaluation
Comparison to Naiad
If you lot are familiar amongst the Naiad project, you lot mightiness endure thinking: "Well, Naiad solves the unified full general work dataflow & graph processing job too throws inwards stream-processing too dynamic graphs for skillful measure". (GraphX does non back upward dynamic graphs.) So, what are the contributions differences inwards GraphX over Naiad?I am novel to the dataflow systems domain, too don't know plenty to laissez passer on a to a greater extent than authoritative answer. The contributions inwards GraphX may endure generally inwards the thought too academic contributions form. I intend the thought of representing graph computations dorsum to dataflow systems is nice. Unfortunately the GraphX newspaper does non compare amongst Naiad inwards price of performance. And, afterwards the OSDI presentation, at that spot were brace questions/complaints most this point.
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