Performance introspection of graph databases

Abstract

The explosion of graph data in social and biological networks, recommendation systems, provenance databases, etc. makes graph storage and processing of paramount importance. We present a performance introspection framework for graph databases, PIG, which provides both a toolset and methodology for understanding graph database performance. PIG consists of a hierarchical collection of benchmarks that compose to produce performance models; the models provide a way to illuminate the strengths and weaknesses of a particular implementation. The suite has three layers of benchmarks: primitive operations, composite access patterns, and graph algorithms. While the framework could be used to compare different graph database systems, its primary goal is to help explain the observed performance of a particular system. Such introspection allows one to evaluate the degree to which systems exploit their knowledge of graph access patterns. We present both the PIG methodology and infrastructure and then demonstrate its efficacy by analyzing the popular Neo4j and DEX graph databases.

Using Naming Patterns for Identifying Architectural Technical Debt

“…For evaluating the proposed analysis technique, an application was implemented to getting the terms used in class name following the CamelCase coding style (predominant style due to its ease of writing and adoption [7,13]), storing terms in a Neo4j database (standard graph database in the industry [26]). The application was executed on twenty projects of the organizations Apache and Eclipse (see table 1).…”

Querying a graph database – language selection and performance considerations

“…A different benchmark comparison between Neo4j and DEX is reported in [28], but they mainly use micro operations like "get vertex" or "get edge" instead of more complex queries. They found that Neo4j scales very well for in-memory graphs, which is the case in our benchmark, but significantly loses performance when reading from disk and especially writing due to guaranteed ACID transactions.…”

Evaluation of Graph Databases Performance through Indexing Techniques

“…In contrast, the nonnative graph storage, rely on a mature non-graph backend whose production characteristics are well comprehended by operations teams. Native graph processing (indexfree adjacency) benefits traversal [Marek et al 2012, Macko et al 2013 performance, however at the expense of making some non-traversal queries difficult or memory intensive. …”