High-Performance Apache AGE Integration for Gap Analysis (#488)

[Khushi281300]

Overview

This PR introduces Apache AGE as a high-performance alternative to Neo4j for OpenCRE's gap analysis. It directly addresses the performance bottlenecks identified in Issue #488, where complex graph traversals were taking excessive time on standard hardware.

Following a deep dive into the previous implementation attempts, I have resolved critical stability and connectivity issues that previously led to "timeout" or "hanging" reports, making Apache AGE a production-ready alternative backend.

Key Improvements & Bug Fixes

1. Synchronous Connection Lifecycle (Stability)
   Problem: Previous attempts suffered from a race condition where the application would attempt to populate the database before the background connection thread was established, leading to "No AGE connection" errors. Fix: Refactored AGEDB.populate_DB to use
   instance_blocking(timeout=30)
   , ensuring the database is fully ready before execution.

2. Multi-Backend Safe Property Access (Correctness)
   Problem: Graph result mapping in
   gap_analysis
   used Neo4j-specific attribute access (.id), causing AttributeError when switching to AGE. Fix: Implemented a backend-agnostic property getter getattr(node, "id", None) or node.get("id") to ensure seamless switching between databases.

3. AGE Execution Environment Fix (Query logic)
   Problem: Verification and benchmarking scripts were failing because the age extension wasn't being explicitly loaded in the database session. Fix: Updated
   check_age_stats.py
   and internal query handlers to auto-load the AGE extension and set the correct ag_catalog search path.

4. Code Quality & CI
   Fix: Reformatted the entire modified codebase with black to ensure compliance with project styling and CI (Super-Linter) requirements.

Performance & Verification Evidence

1. Database Population
   The migration flow is now robust and handles over 2,000 nodes and 5,000 links into Apache AGE in seconds.Population Success

2. Data Parity & Metrics
   Verification scripts show identical path counts and node structures between backends, ensuring no data loss during the transition.Graph Metrics

3. End-to-End API Resilience
   Automated endpoint testing confirms the /rest/v1/map_analysis API is fully operational with the AGE backend.Resilience Test

result:

1.

#### 2. #### 3. #### 4.

How to Use

Ensure your Docker stack is running: docker start cre-age cre-redis-stack.
Update your
.env
file: GRAPH_DB_TYPE=age.
Populate the graph: python cre.py --populate_graph_db.
Verify stats: python check_age_stats.py.

Conclusion

By resolving the underlying connection and mapping bugs, Apache AGE now provides a stable and significantly more responsive gap analysis experience for OpenCRE users.