Columnar Storage and List-based Processing for Graph Database Management Systems

TL;DR

This paper explores columnar storage and list-based processing techniques tailored for graph database management systems, enhancing scalability and query performance through novel data structures and optimized algorithms.

Contribution

It introduces new columnar storage and query processing methods specifically designed for GDBMSs, including a list-based query processor and novel data structures, with integration into GraphflowDB.

Findings

Demonstrates improved scalability of GDBMS with proposed techniques.

Shows significant query performance gains in experiments.

Validates effectiveness of new data structures for graph data.

Abstract

We revisit column-oriented storage and query processing techniques in the context of contemporary graph database management systems (GDBMSs). Similar to column-oriented RDBMSs, GDBMSs support read-heavy analytical workloads that however have fundamentally different data access patterns than traditional analytical workloads. We first derive a set of desiderata for optimizing storage and query processors of GDBMS based on their access patterns. We then present the design of columnar storage, compression, and query processing techniques based on these desiderata. In addition to showing direct integration of existing techniques from columnar RDBMSs, we also propose novel ones that are optimized for GDBMSs. These include a novel list-based query processor, which avoids expensive data copies of traditional block-based processors under many-to-many joins, a new data structure we call single-indexed edge property pages and an accompanying edge ID scheme, and a new application of Jacobson's bit vector index for compressing NULL values and empty lists. We integrated our techniques into the GraphflowDB in-memory GDBMS. Through extensive experiments, we demonstrate the scalability and query performance benefits of our techniques.