Advanced Strategies for Precise and Transparent Debugging of Performance Issues in In-Memory Data Store-Based Microservices

TL;DR

This paper presents a novel framework for transparent, fine-grained performance debugging in in-memory data store-based microservices, addressing the complexity and transparency challenges of existing monitoring methods.

Contribution

It introduces a new transparent tracing framework with a two-level analysis model for precise performance debugging in microservices.

Findings

Enables fine-grained performance analysis without code instrumentation

Provides visualization tools for performance issue identification

Improves debugging efficiency in complex microservice environments

Abstract

The rise of microservice architectures has revolutionized application design, fostering adaptability and resilience. These architectures facilitate scaling and encourage collaborative efforts among specialized teams, streamlining deployment and maintenance. Critical to this ecosystem is the demand for low latency, prompting the adoption of cloud-based structures and in-memory data storage. This shift optimizes data access times, supplanting direct disk access and driving the adoption of non-relational databases. Despite their benefits, microservice architectures present challenges in system performance and debugging, particularly as complexity grows. Performance issues can readily cascade through components, jeopardizing user satisfaction and service quality. Existing monitoring approaches often require code instrumentation, demanding extensive developer involvement. Recent strategies like proxies and service meshes aim to enhance tracing transparency, but introduce added configuration complexities. Our innovative solution introduces a new framework that transparently integrates heterogeneous microservices, enabling the creation of tailored tools for fine-grained performance debugging, especially for in-memory data store-based microservices. This approach leverages transparent user-level tracing, employing a two-level abstraction analysis model to pinpoint key performance influencers. It harnesses system tracing and advanced analysis to provide visualization tools for identifying intricate performance issues. In a performance-centric landscape, this approach offers a promising solution to ensure peak efficiency and reliability for in-memory data store-based cloud applications.