CrypQ: A Database Benchmark Based on Dynamic, Ever-Evolving Ethereum Data

TL;DR

CrypQ is a new database benchmark that uses real-time, evolving Ethereum blockchain data to evaluate database systems' performance on dynamic, complex, and realistic datasets.

Contribution

CrypQ introduces a novel benchmark leveraging real-world, ever-changing blockchain data to better evaluate database systems' handling of dynamic, complex datasets.

Findings

Demonstrates CrypQ's effectiveness in testing query optimizers with evolving data.

Provides a detailed schema and procedures for creating realistic, high-volume updates.

Shows CrypQ's ability to reflect real-world data skewness and dependencies.

Abstract

Modern database systems are expected to handle dynamic data whose characteristics may evolve over time. Many popular database benchmarks are limited in their ability to evaluate this dynamic aspect of the database systems. Those that use synthetic data generators often fail to capture the complexity and unpredictable nature of real data, while most real-world datasets are static and difficult to create high-volume, realistic updates for. This paper introduces CrypQ, a database benchmark leveraging dynamic, public Ethereum blockchain data. CrypQ offers a high-volume, ever-evolving dataset reflecting the unpredictable nature of a real and active cryptocurrency market. We detail CrypQ's schema, procedures for creating data snapshots and update sequences, and a suite of relevant SQL queries. As an example, we demonstrate CrypQ's utility in evaluating cost-based query optimizers on complex, evolving data distributions with real-world skewness and dependencies.