Automating Microservice Failure Diagnosis: GitHub Project Unveils Causal Discovery Approach

Debugging failures in microservice architectures often feels like finding needles in distributed haystacks. When dozens of interdependent services interact, traditional monitoring struggles to isolate root causes efficiently. A new GitHub project called RCD tackles this pain point head-on by applying causal discovery algorithms—a branch of AI that identifies cause-and-effect relationships—to automate failure diagnosis.

The Causal Inference Advantage

Unlike correlation-based monitoring tools, RCD employs constraint-based causal discovery methods (specifically adapted versions of the PC and FCI algorithms) to build dependency graphs from system data. The key innovation is its localized analysis approach: Instead of mapping the entire service topology, RCD focuses on the neighborhood of observed failures, dramatically reducing computational overhead. As the README explains:

"--local option enables the localized RCD while --k estimates the top-k root causes"

This allows engineers to run targeted diagnostics using ./rcd.py with configurable depth parameters, making it practical for production environments.

Technical Implementation Insights

The project modifies established causal learning libraries (causal-learn and pyAgrum) to optimize for infrastructure observability:

# Critical customizations include:
ln -fs ~/rcd/causallearn/search/ConstraintBased/FCI.py ...
ln -fs ~/rcd/causallearn/utils/PCUtils/SkeletonDiscovery.py ...

These tweaks enable:

1. Tracking of conditional independence test counts during analysis
2. Implementation of the localized discovery algorithm
3. Fixes for edge-case bugs in underlying dependencies

Why This Matters for DevOps Teams

• Reduced MTTR: By automatically generating causal graphs from failure data (via ./gen_data.py), RCD sidesteps manual dependency mapping
• Scalable Troubleshooting: The ./compare.py benchmark utility tests performance across node counts—critical for growing microservice ecosystems
• Open Framework: As Python-based OSS, it integrates with existing observability stacks while avoiding vendor lock-in

The Road to Production Readiness

While promising, causal approaches face challenges like data quality requirements and algorithmic complexity. RCD's synthetic data generator helps validate models, but real-world deployment would require integration with tracing systems like OpenTelemetry. Still, this represents a significant leap toward autonomous incident management—where AI doesn't just alert engineers, but diagnoses problems.

As distributed systems grow more complex, solutions like RCD highlight a paradigm shift: The future of reliability engineering lies not in bigger dashboards, but in smarter causal inference.