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| -rw-r--r-- | chapter/8/big-data.md | 4 |
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diff --git a/chapter/8/big-data.md b/chapter/8/big-data.md index 38b0691..fc4e8f2 100644 --- a/chapter/8/big-data.md +++ b/chapter/8/big-data.md @@ -284,14 +284,14 @@ Graph-parallel computation requires every vertex or edge to be processed in the <figure class="main-container"> <img src="./edge-cuts.png" alt="edge cuts" /> </figure> - +*Figure from {%cite xin2013graphx --file big-data%}* ***Why Edge-cuts are expensive ?*** Edge-cuts for partitioning requires random assignment of vertices and edges across all the machines. hus the communication and storage overhead is proportional to the number of edges cut, and this makes balancing the number of cuts a priority. For most real-world graphs, constructing an optimal edge-cut is cost prohibitive, and most systems use random edge-cuts which achieve appropriate work balance, but nearly worst-case communication overhead. <figure class="main-container"> <img src="./vertex-cuts.png" alt="Vertex cuts" /> </figure> - +*Figure from {%cite xin2013graphx --file big-data%}* ***Vertex-cuts - GraphX’s solution to effective partitioning*** : An alternative approach which does the opposite of edge-cut — evenly assign edges to machines, but allow vertices to span multiple machines. The communication and storage overhead of a vertex-cut is directly proportional to the sum of the number of machines spanned by each vertex. Therefore, we can reduce communication overhead and ensure balanced computation by evenly assigning edges to machines in way that minimizes the number of machines spanned by each vertex. The GraphX RDG structure implements a vertex-cut representation of a graph using three unordered horizontally partitioned RDD tables. These three tables are as follows: |