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Memory Management Fixes - Quick Summary

What Was Done

Analyzed and fixed 6 critical memory management issues in AIOps that could cause memory leaks and OOM errors in production.

Issues Fixed

# Issue File Severity Fix
1 Unbounded stampede locks dictionary cache.py 🔴 Critical LRU eviction, max 1000 locks
2 Unbounded RateLimiter calls list cache.py 🟡 High Bounded to 2x max_calls, thread-safe
3 Unbounded workflows storage orchestrator.py 🔴 Critical LRU eviction, max 100 workflows
4 Uncleaned DAG execution tasks orchestrator.py 🟡 High Proper cleanup in finally block
5 Unbounded agent instance cache registry.py 🔴 Critical LRU eviction, max 50 instances
6 Missing context managers Multiple 🟡 High Added __enter__/__exit__ support

Files Modified

  • ✅ aiops/core/cache.py - Fixed 3 issues
  • ✅ aiops/core/semantic_cache.py - Added context managers
  • ✅ aiops/agents/orchestrator.py - Fixed 2 issues
  • ✅ aiops/agents/registry.py - Fixed 1 issue

Testing

All fixes verified with comprehensive test suite:

  • ✅ Stampede locks bounded
  • ✅ RateLimiter bounded and thread-safe
  • ✅ SemanticCache bounded with LRU
  • ✅ Workflow history bounded
  • ✅ Agent registry bounded
  • ✅ Context managers working
  • ✅ All files compile successfully

Key Improvements

Before

# Could grow to millions of entries
_stampede_locks = {}  # Unbounded!
workflows = {}        # Unbounded!
_instances = {}       # Unbounded!

After

# Bounded with LRU eviction
_stampede_locks = {}  # Max 1000 with LRU
workflows = OrderedDict()  # Max 100 with LRU
_instances = OrderedDict()  # Max 50 with LRU

Usage Examples

Using Context Managers (Recommended)

# Automatic cleanup
async with Cache() as cache:
    cache.set("key", "value")
    # Cleanup happens automatically

async with SemanticCache() as scache:
    await scache.aset("prompt", "result")
    # Cleanup happens automatically

Configuring Limits

# Tune for your workload
orchestrator = AgentOrchestrator(max_workflow_history=200)
registry = AgentRegistry(max_cached_instances=100)
semantic_cache = SemanticCache(max_entries=500)

Manual Cleanup

# When needed
limiter.clear()  # Clear rate limit history
orchestrator.clear_workflows()  # Clear workflow history
registry.clear_cache()  # Clear agent instances

Production Recommendations

  1. Monitor cache sizes in production
  2. Configure limits based on your workload
  3. Use context managers for automatic cleanup
  4. Profile memory usage periodically

Impact

🎯 No more memory leaks from unbounded caches 🎯 Configurable limits for production tuning 🎯 Proper cleanup with context managers 🎯 Thread-safe operations throughout 🎯 Zero breaking changes to existing code

See MEMORY_MANAGEMENT_REPORT.md for detailed analysis and code changes.