Optimizing Declarative Graph Queries at Large Scale

Zhang, Qizhen; Acharya, Akash; Chen, Hongzhi; Arora, Simran; Chen, Ang; Liu, Vincent; Loo, Boon Thau

doi:10.1145/3299869.3300064

Cited by 12 publications

(4 citation statements)

References 37 publications

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“…GraphRex [40] is a recent distributed graph processing system with a Datalog-like interface. It focuses on making full use of the characteristics of modern data center networks, and thus achieves very high performance in such an environment.…”

Section: Datalog Evaluation In Distributed Systemsmentioning

confidence: 99%

Fast datalog evaluation for batch and stream graph processing

et al. 2022

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Implementing complex algorithms for big data, artificial intelligence, and graph processing requires enormous effort. Succinct, declarative programs to solve complex problems that can be efficiently executed for batching and streaming data are in demand. This paper presents Nexus, a distributed Datalog evaluation system. It evaluates Datalog programs using the semi-naive algorithm for batch and streaming data using incremental and asynchronous iteration. Furthermore, we evaluate Datalog programs with aggregates to determine the advantages of implementing the semi-naive algorithm using incremental iteration on its performance. Our experimental results show that Nexus significantly outperforms acyclic dataflow-based systems.

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Section: Datalog Evaluation In Distributed Systemsmentioning

confidence: 99%

Fast datalog evaluation for batch and stream graph processing

et al. 2022

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“…There are also other studies that are optimized for communication networks. GraphRex [50] provides specific optimizations for high-performance network and under cross-rack cluster environment.…”

Section: Hardware-specific Optimizationsmentioning

confidence: 99%

Distributed Graph Processing: Techniques and Systems

Zhang

Wang

Gong

2021

Communications in Computer and Information Science

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During the past 10 years, there has been a surging interest in developing distributed graph processing systems. This tutorial provides a comprehensive review of existing distributed graph processing systems. We firstly review the programming models for distributed graph processing and then summarize the common optimization techniques for improving graph execution performance, including graph partitioning methods, communication mechanisms, parallel processing models, hardware-specific optimizations, and incremental graph processing. We also present an emerging hot topic, distributed Graph Neural Networks (GNN) frameworks, and review recent progress on this topic.

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“…Graph Processing Systems. Many graph processing systems have been proposed [8,19,34,35,50,58,61,63,67,68] based on Pregel model [43] or other computation models [55,62,66]. But they focus on offline graph workloads such as PageRank, Connected Component and SSSP.…”

Section: Related Workmentioning

confidence: 99%

G-Tran: Making Distributed Graph Transactions Fast

Chen,

Li,

Zheng

et al. 2021

Preprint

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Graph transaction processing raises many unique challenges such as random data access due to the irregularity of graph structures, low throughput and high abort rate due to the relatively large read/write sets in graph transactions. To address these challenges, we present G-Tran -an RDMA-enabled distributed in-memory graph database with serializable and snapshot isolation support. First, we propose a graph-native data store to achieve good data locality and fast data access for transactional updates and queries. Second, G-Tran adopts a fully decentralized architecture that leverages RDMA to process distributed transactions with the MPP model, which can achieve high performance by utilizing all computing resources. In addition, we propose a new MV-OCC implementation with two optimizations to address the issue of large read/write sets in graph transactions. Extensive experiments show that G-Tran achieves competitive performance compared with other popular graph databases on benchmark workloads.

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Optimizing Declarative Graph Queries at Large Scale

Cited by 12 publications

References 37 publications

Fast datalog evaluation for batch and stream graph processing

Fast datalog evaluation for batch and stream graph processing

Distributed Graph Processing: Techniques and Systems

G-Tran: Making Distributed Graph Transactions Fast

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