Abstract
Graph Analytics has been gaining an increasing amount of attention in recent years. This has given rise to the development of numerous graph processing and storage engines, each featuring different models in computation, storage and execution as well as performance. Multi-Engine Analytics present a solution towards adaptive, cost-based complex workflow scheduling to the best available underlying technology. To achieve this in the Graph Analytics case, detailed and accurate cost models for the various runtimes and operators must be defined and exported, such that intelligent planning can take place. In this work, we take a first step towards defining a cost model for graph-based operators based on an algebra and its primitives. We evaluate its accuracy over a state of the art graph database and discuss its advantages and shortcomings.
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Notes
- 1.
We use the notation \(\circ \) to denote composition of functions.
- 2.
Using default arguments: alpha=0.41, beta=0.54, gamma=0.05, delta_in=0.2, delta_out=0.
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AÂ Appendix
AÂ Appendix
1.1 A.1Â Random 4-Walk Benchmarks
For the random 4-walks, we first note that \(SpaceCost(RandRow(R)) = 1\), so final space costs collapse to 1. For the time costs, we present Fig. 11. We note that, in this case, both default and modified models fare well in their predictions – this can be attributed to the simplicity of the query and the fact that it uses only expandOut operations.
Space and time costs (actual and projected via default and modified models) of 4-walk queries on graphs with various connectivity probabilities.
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Singh, A., Tsoumakos, D. (2018). Towards an Algebraic Cost Model for Graph Operators. In: Alistarh, D., Delis, A., Pallis, G. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2017. Lecture Notes in Computer Science(), vol 10739. Springer, Cham. https://doi.org/10.1007/978-3-319-74875-7_6
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