Name
Abstract | ||
|---|---|---|
ABSTRACTVarious applications model problems as streaming graphs, which need to quickly apply a stream of updates and run algorithms on the updated graph. Furthermore, many dynamic real-world graphs, such as social networks, follow a skewed distribution of vertex degrees, where there are a few high-degree vertices and many low-degree vertices. Existing static graph-processing systems optimized for graph skewness achieve high performance and low space usage by preprocessing a cache-efficient graph partitioning based on vertex degree. In the streaming setting, the whole graph is not available upfront, however, so finding an optimal partitioning is not feasible in the presence of updates. As a result, existing streaming graph-processing systems take a "one-size-fits-all" approach, leaving performance on the table. We present Terrace, a system for streaming graphs that uses a hierarchical data structure design to store a vertex's neighbors in different data structures depending on the degree of the vertex. This multi-level structure enables Terrace to dynamically partition vertices based on their degrees and adapt to skewness in the underlying graph. Our experiments show that Terrace supports faster batch insertions for batch sizes up to 1M when compared to Aspen, a state-of-the-art graph streaming system. On graph query algorithms, Terrace is between 1.7X--2.6X faster than Aspen and between 0.5X--1.3X as fast as Ligra, a state-of-the-art static graph-processing system. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3448016.3457313 | International Conference on Management of Data |
Keywords | DocType | ISSN |
Graph data structures, Streaming, Indexing | Conference | 0730-8078 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prashant Pandey | 1 | 18 | 3.01 |
| Brian Wheatman | 2 | 1 | 1.02 |
| Helen Xu | 3 | 2 | 1.03 |
| Aydin Buluc | 4 | 1057 | 67.49 |