NoSQL Graph Databases: an overview

Robert Pavliš

20 Mei 2024

As the global volume of data continues to rise at an unprecedented rate, the challenges of storing and analyzing data are becoming more and more highlighted. This is especially apparent when the data are heavily interconnected. The traditional methods of storing and analyzing data such as relational databases often encounter difficulties when dealing with large amounts of data and this is even more pronounced when the data exhibits intricate interconnections. This paper examines graph databases as an alternative to relational databases in an interconnected Big Data environment. It will also show the theoretical basis behind graph databases and how they outperform relational databases in such an environment, but also why they are better suited for this kind of environment than other NoSQL alternatives. A state of the art in graph databases and how they compare to relational databases in various scenarios will also be presented in this paper.

A. A. Frozza R. Mello Salomão Rodrigues Jacinto

1 Agustus 2020

Currently, a large volume of heterogeneous data is generated and consumed by several classes of applications, which raise a new family of database models called NoSQL. NoSQL graph databases is a member of this family. They provide high scalability and are schemaless, i.e., they do not require an implicit schema such as relational databases. However, the knowledge of how data is structured may be of great importance for data integration or data analysis processes. There are some works in the literature that extract the schema from graph structures or graph-based data sources. Different from them, this work proposes a comprehensive approach that consider all the common NoSQL database graph data model concepts, and generates a schema in the recent JSON Schema recommendation. Experimental evaluations show that our solution generates a suitable schema representation with a linear complexity.

Matus Stovcik Barbora Buhnova M. Mačák

2020

: Digitalization of our society brings various new digital ecosystems (e.g., Smart Cities, Smart Buildings, Smart Mobility), which rely on the collection, storage, and processing of Big Data. One of the recently popular advancements in Big Data storage and processing are the graph databases. A graph database is specialized to handle highly connected data, which can be, for instance, found in the cross-domain setting where various levels of data interconnection take place. Existing works suggest that for data with many relationships, the graph databases perform better than non-graph databases. However, it is not clear where are the borders for specific query types, for which it is still efficient to use a graph database. In this paper, we design and perform tests that examine these borders. We perform the tests in a cluster of three machines so that we explore the database behavior in Big Data scenarios concerning the query. We specifically work with Neo4j as a representative of graph databases and PostgreSQL as a representative of non-graph databases.

Yuanyuan Tian

23 November 2022

Rapidly growing social networks and other graph data have created a high demand for graph technologies in the market. A plethora of graph databases, systems, and solutions have emerged, as a result. On the other hand, graph has long been a well studied area in the database research community. Despite the numerous surveys on various graph research topics, there is a lack of survey on graph technologies from an industry perspective. The purpose of this paper is to provide the research community with an industrial perspective on the graph database landscape, so that graph researcher can better understand the industry trend and the challenges that the industry is facing, and work on solutions to help address these problems.

James Clarkson Georgios Theodorakis Jim Webber

2024

Modern graph database management systems (DBMSs) can process highly dynamic labeled property graphs (LPGs) with many billions of relationships comfortably, but those systems often ignore the temporal dimension of data, how a graph evolved over time. Temporal analytics allow users to query and compute over the graph throughout its history so that valuable line-of-business data is always accessible and never lost. However, existing approaches tend to be ad-hoc and vary in performance depending on the size of the effective graph workload, such as local pattern matching or global graph algorithms. In this work, we describe Aion, a transactional temporal graph DBMS that generalizes previous approaches for LPGs. Aion extends Neo4j, a modern graph DBMS, incurring minimal performance overhead by decoupling the graph’s history from the latest graph version. To support efficient temporal analytics independently of workload characteristics, Aion adopts a hybrid temporal storage approach: (i) for fast full graph restoration at arbitrary time points, it uses TimeStore that indexes updates by time; (ii) for fine-grained graph history accesses, it uses LineageStore that indexes updates by entity identifiers. To enable incremental graph computations for improved latency, Aion introduces a compute-efficient in-memory LPG representation. Our experiments show that Aion achieves comparable or better performance versus existing non-transactional temporal systems and provides up to an order of magnitude speedup over classic Neo4j.