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A Workload-Adaptive Streaming Partitioner for Distributed Graph Stores
- Source :
- Data Science and Engineering. 6:163-179
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Streaming graph partitioning methods have recently gained attention due to their ability to scale to very large graphs with limited resources. However, many such methods do not consider workload and graph characteristics. This may degrade the performance of queries by increasing inter-node communication and computational load imbalance. Moreover, existing workload-aware methods cannot consistently provide good performance as they do not consider dynamic workloads that keep emerging in graph applications. We address these issues by proposing a novel workload-adaptive streaming partitioner named WASP, that aims to achieve low-latency and high-throughput online graph queries. As each workload typically contains frequent query patterns, WASP exploits the existing workload to capture active vertices and edges which are frequently visited and traversed, respectively. This information is used to heuristically improve the quality of partitions either by avoiding the concentration of active vertices in a few partitions proportional to their visit frequencies or by reducing the probability of the cut of active edges proportional to their traversal frequencies. In order to assess the impact of WASP on a graph store and to show how easily the approach can be plugged on top of the system, we exploit it in a distributed graph-based RDF store. Our experiments over three synthetic and real-world graph datasets and the corresponding static and dynamic query workloads show that WASP achieves a better query performance against state-of-the-art graph partitioners, especially in dynamic query workloads.
- Subjects :
- Graph database
Exploit
Computer science
Distributed computing
Computational Mechanics
Graph partition
Workload
Scale (descriptive set theory)
02 engineering and technology
computer.file_format
computer.software_genre
Graph
Computer Science Applications
Tree traversal
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
RDF
computer
Subjects
Details
- ISSN :
- 23641541 and 23641185
- Volume :
- 6
- Database :
- OpenAIRE
- Journal :
- Data Science and Engineering
- Accession number :
- edsair.doi...........db2f133856dc6f018470f71427c66907
- Full Text :
- https://doi.org/10.1007/s41019-021-00156-2