Graphflow

Kankanamge, Chathura; Sahu, Siddhartha; Mhedbhi, Amine; Chen, Jeremy; Salihoğlu, Semih

doi:10.1145/3035918.3056445

Cited by 90 publications

(7 citation statements)

References 4 publications

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“…Document stores, such as OrientDB [18] , are wellsuited for storing and indexing hierarchical data, such as property graph data in this case. OrientDB has vertex documents and edge documents to record graph data and supports abundant property operations.…”

Section: Related Workmentioning

confidence: 99%

Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores

Lin,

Wang,

et al. 2024

Big Data Min. Anal.

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Graph databases have gained widespread adoption in various industries and have been utilized in a range of applications, including financial risk assessment, commodity recommendation, and data lineage tracking. While the principles and design of these databases have been the subject of some investigation, there remains a lack of comprehensive examination of aspects such as storage layout, query language, and deployment. The present study focuses on the design and implementation of graph storage layout, with a particular emphasis on tree-structured key-value stores. We also examine different design choices in the graph storage layer and present our findings through the development of TuGraph, a highly efficient single-machine graph database that significantly outperforms well-known Graph DataBase Management System (GDBMS).Additionally, TuGraph demonstrates superior performance in the Linked Data Benchmark Council (LDBC) Social Network Benchmark (SNB) interactive benchmark.

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Section: Related Workmentioning

confidence: 99%

Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores

Lin,

Wang,

et al. 2024

Big Data Min. Anal.

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“…Graph ow [15] is an active graph database for incremental openCypher queries. However, it does not support nested data structures.…”

Section: Related Workmentioning

confidence: 99%

“…(4) Create an incremental view for the FRA expression. Incremental view maintenance algorithms for FRA are well studied both from a theoretical perspective [2,4,10,11] and implementation-wise, with many practical tools [12,33] and research prototypes [15,26,31]. While they are not expressible in rst-order logic, it is possible to evaluate transitive operations incrementally [3,23].…”

Section: Approach and Contributionsmentioning

confidence: 99%

Incremental View Maintenance for Property Graph Queries

Szárnyas

2018

Proceedings of the 2018 International Conference on Management of Data

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Graph processing challenges are common in modern database systems, with the property graph data model gaining widespread adoption [29]. Due to the novelty of the eld, graph databases and frameworks typically provide their own query language, such as Cypher for Neo4j [27], Gremlin for TinkerPop [28] and GraphScript for SAP HANA [24]. These languages often lack a formal background for their data model and semantics [1]. To address this, the openCypher initiative [21] aims to standardise a subset of the Cypher language, for which it currently provides grammar speci cation and a set of acceptance tests to allow vendors to implement their openCypher compatible engine.Incremental view maintenance has been used for decades in relational database systems [4]. In the graph domain, numerous use cases rely on complex queries and require low latency, including nancial fraud detection, source code analysis [32] and checking integrity (or well-formedness) constraints in databases [30]. While these could bene t from incremental evaluation, currently no property graph system provides incremental views. Our research investigates the incremental view maintenance for openCypher queries. A key challenge is that the property graph data model includes lists and maps, and queries can return arbitrarily nested data structures.We propose three desirable properties for an incremental property graph query engine: (IVM) incremental view maintenance, (FGN) ne granularity update operations on nested data structures, (ORD) ordering. Previous research showed that IVM and FGN is possible [19]. However, as stated in [8], "incremental view maintenance [IVM] strategies for data models that preserve order [ORD] remain an open problem to date". While removing support for ordering might seem a plausible workaround, it would pose serious limitations: (1) queries that require top-k results are common [17] and (2) even more importantly, Cypher handles paths as an alternating list of vertices and edges, which must be kept ordered.Therefore, we investigate the following research question: Which practical fragment of the openCypher language is incrementally maintainable?

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“…Graph processing is an invaluable tool for data analytics, as illustrated by the plethora of relatively recent work aiming at achieving high performance for graph algorithms [1][2][3][4][5][6], such as PageRank [7] (PR), or graph querying/mining [8][9][10][11][12][13][14], e.g., using PGQL [15]. Furthermore, there has been a lot of recent interest in streaming data processing (e.g., Twitter, financial transactions, etc.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations

Firmli,

Chiadmi

2023

Data

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The graph model enables a broad range of analyses; thus, graph processing (GP) is an invaluable tool in data analytics. At the heart of every GP system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, GP systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and fast, low-memory graph mutations. Existing graph structures offer a hard tradeoff among read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics, and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists (ALs) to achieve the best of both worlds. We compare CSR++ to CSR, ALs from the Boost Graph Library (BGL), and the following state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular GP algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average) while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed those of several state-of-the-art graph structures while maintaining low memory consumption when the workload includes updates.

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Graphflow

Cited by 90 publications

References 4 publications

Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores

Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores

Incremental View Maintenance for Property Graph Queries

A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations

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