Abstract
The last decade brought considerable improvements in distributed storage and query technologies, known as NoSQL systems. These systems provide quick evaluation of simple retrieval operations and are able to answer certain complex queries in a scalable way, albeit not instantly. Providing scalability and quick response times at the same time for querying large data sets is still a challenging task. Evaluating complex graph queries is particularly difficult, as it requires lots of join, antijoin and filtering operations. This paper presents optimization techniques used in relational database systems and applies them on graph queries. We evaluate various query plans on multiple datasets and discuss the effect of different optimization techniques.
Highlights
The key components of Big Data are often defined as variety, velocity and volume [28] of data
The results show that using basic optimization techniques avoiding Cartesian products already results in efficient query plans that scale for models with 9M+ elements, while applying further optimizations did not have a significant impact
Variant C, which requires the computation of a Cartesian product, shows much worse scalability characteristics, only scaling for small models
Summary
The key components of Big Data are often defined as variety, velocity and volume [28] of data. Applications operating on continuously changing graphs are a prime example: the semi-structured graph-like nature introduces a high variety, changes happen at high velocity, and datasets are often high-volume. Such applications include fraud detection in financial transactions [27], validation of engineering models [3], and static analysis of source code repositories [35]. Incremental query evaluation caches interim results, it only requires reevaluation on a small fragment of the dataset impacted by the change This leads to significant speedup for large and continuously changing data. Several approaches exist for incremental query evaluation [9, 20] in the context of expert systems, incremental query evaluation is not in widespread use in graph databases
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