Many real-world systems, such as social networks, rely on mining efficiently large graphs, with hundreds of millions of vertices and edges. This volume of information requires partitioning the graph across multiple nodes in a distributed system. This has a deep effect on performance, as traversing edges cut between partitions incurs a significant performance penalty due to the cost of communication. Thus, several systems in the literature have attempted to improve computational performance by enhancing graph partitioning, but they do not support another characteristic of real-world graphs: graphs are inherently dynamic, their topology evolves continuously, and subsequently the optimum partitioning also changes over time. In this work, we present the first system that dynamically repartitions massive graphs to adapt to structural changes. The system optimises graph partitioning to prevent performance degradation without using data replication. The system adopts an iterative vertex migration algorithm that relies on local information only, making complex coordination unnecessary. We show how the improvement in graph partitioning reduces execution time by over 50 , while adapting the partitioning to a large number of changes to the graph in three real-world scenarios.
Initial partitioning strategies only optimise the starting graph, with several of these techniques requiring some extent of global information. This poses significant scalability problems @cite_5 , whereas our approach relies only on local information. Additionally, as shown in Figure , these approaches cannot cope with changes that alter the structure of the elements already loaded (e.g. node and vertex deletion) or keeping optimal partitioning as the graph changes.
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Partitioning graphs at scale is a key challenge for any application that involves distributing a graph across disks, machines, or data centers. Graph partitioning is a very well studied problem with a rich literature, but existing algorithms typically can not scale to billions of edges, or can not provide guarantees about partition sizes. In this work we introduce an efficient algorithm, balanced label propagation, for precisely partitioning massive graphs while greedily maximizing edge locality, the number of edges that are assigned to the same shard of a partition. By combining the computational efficiency of label propagation --- where nodes are iteratively relabeled to the same 'label' as the plurality of their graph neighbors --- with the guarantees of constrained optimization --- guiding the propagation by a linear program constraining the partition sizes --- our algorithm makes it practically possible to partition graphs with billions of edges. Our algorithm is motivated by the challenge of performing graph predictions in a distributed system. Because this requires assigning each node in a graph to a physical machine with memory limitations, it is critically necessary to ensure the resulting partition shards do not overload any single machine. We evaluate our algorithm for its partitioning performance on the Facebook social graph, and also study its performance when partitioning Facebook's 'People You May Know' service (PYMK), the distributed system responsible for the feature extraction and ranking of the friends-of-friends of all active Facebook users. In a live deployment, we observed average query times and average network traffic levels that were 50.5 and 37.1 (respectively) when compared to the previous naive random sharding.