Query Processing In Graph Databases
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Graph Databases
Author: Ian Robinson
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2013-06-10
Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems. Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution. Model data with the Cypher query language and property graph model Learn best practices and common pitfalls when modeling with graphs Plan and implement a graph database solution in test-driven fashion Explore real-world examples to learn how and why organizations use a graph database Understand common patterns and components of graph database architecture Use analytical techniques and algorithms to mine graph database information
Graph Databases
Author: Ian Robinson
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2015-06-10
Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems. This second edition includes new code samples and diagrams, using the latest Neo4j syntax, as well as information on new functionality. Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution. Model data with the Cypher query language and property graph model Learn best practices and common pitfalls when modeling with graphs Plan and implement a graph database solution in test-driven fashion Explore real-world examples to learn how and why organizations use a graph database Understand common patterns and components of graph database architecture Use analytical techniques and algorithms to mine graph database information
Query Processing in Graph Databases
Graph data are extensively associated with state-of-the-art applications in a variety of domains which include Linked Data and Social Media. This drives the need to have graph databases that can effectively store and manage graph data. Relational query processing has become efficient due to many decades of research in the field of data management and processing, among which translating SQL into relational algebra operations plays a key role in query processing. Based on relational algebra, many graph algebras have been defined that can be used for query processing and optimization in graph databases. We propose a graph algebra which operates on graph databases, for processing queries. We have implemented a graph algebra as a part of ScalaTion and compared it with Neo4j and MySQL with respect to query processing times. Various queries are tested on datasets with a few vertices to a large number of vertices. Graph databases perform well when the database gets larger compared to relational databases. Increase in the number of joins in queries, decreases the performance of relational databases, whereas equivalent queries in graph databases comparatively exhibit good performance. Among graph databases compared in the study, ScalaTion shows better performance.