D4M 2.0 Schema: A General Purpose High Performance Schema for the Accumulo Database

TL;DR

This paper introduces the D4M 2.0 Schema, a high-performance, general-purpose schema for the Accumulo database that enables rapid querying and indexing of diverse datasets with minimal customization.

Contribution

The paper presents a novel, simple schema based on associative arrays that significantly improves Accumulo's ingest rates and applicability across various data types without extensive customization.

Findings

Achieves the highest published Accumulo ingest rates

Applicable to diverse data types with minimal parsing

Independent of the D4M interface

Abstract

Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source relaxed consistency database that is widely used for government applications. Obtaining the full benefits of Accumulo requires using novel schemas. The Dynamic Distributed Dimensional Data Model (D4M)[http://d4m.mit.edu] provides a uniform mathematical framework based on associative arrays that encompasses both traditional (i.e., SQL) and non-traditional databases. For non-traditional databases D4M naturally leads to a general purpose schema that can be used to fully index and rapidly query every unique string in a dataset. The D4M 2.0 Schema has been applied with little or no customization to cyber, bioinformatics, scientific citation, free text, and social media data. The D4M 2.0 Schema is simple, requires minimal parsing, and achieves the highest published Accumulo ingest rates. The benefits of the D4M 2.0 Schema are independent of the D4M interface. Any interface to Accumulo can achieve these benefits by using the D4M 2.0 Schema