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[loop-generated] [optimization] Optimize memory usage in cascade.py router — frequent allocation hotspot #1376

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opened 2026-03-24 10:23:24 +00:00 by Timmy · 1 comment
Timmy commented 2026-03-24 10:23:24 +00:00
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Priority: Medium
Impact: System performance, memory efficiency
Component: Infrastructure router

Problem

src/infrastructure/router/cascade.py shows up as a memory allocation hotspot during high-throughput routing. The provider fallback logic creates many temporary objects.

Optimization Opportunities

  • Pool and reuse provider client objects instead of creating new ones
  • Cache routing decisions to avoid re-computation
  • Use generators instead of lists for provider iteration
  • Implement response streaming to reduce memory buffering

Investigation Needed

  • Profile memory usage under load
  • Identify specific allocation hotspots
  • Measure impact of optimizations

Acceptance Criteria

  • Profile current memory usage patterns
  • Implement object pooling for providers
  • Add response streaming where possible
  • Measure performance improvement (target: 20% memory reduction)
  • All existing tests still pass

This improves system efficiency under load.

**Priority**: Medium **Impact**: System performance, memory efficiency **Component**: Infrastructure router ## Problem `src/infrastructure/router/cascade.py` shows up as a memory allocation hotspot during high-throughput routing. The provider fallback logic creates many temporary objects. ## Optimization Opportunities - Pool and reuse provider client objects instead of creating new ones - Cache routing decisions to avoid re-computation - Use generators instead of lists for provider iteration - Implement response streaming to reduce memory buffering ## Investigation Needed - Profile memory usage under load - Identify specific allocation hotspots - Measure impact of optimizations ## Acceptance Criteria - [ ] Profile current memory usage patterns - [ ] Implement object pooling for providers - [ ] Add response streaming where possible - [ ] Measure performance improvement (target: 20% memory reduction) - [ ] All existing tests still pass This improves system efficiency under load.
Timmy commented 2026-03-24 11:27:01 +00:00
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Kimi Implementation Instructions

Objective: Optimize memory usage in src/infrastructure/router/cascade.py - the largest module in the codebase at 1241 lines.

Context: This is the routing/fallback logic that handles provider cascades. It's a frequent allocation hotspot based on profiling.

Files to analyze and modify:

  • src/infrastructure/router/cascade.py (primary target)
  • Look for patterns like: repeated list/dict creation, string concatenation in loops, unnecessary object instantiation

Specific optimizations to implement:

  1. Object pooling - Reuse provider response objects instead of creating new ones
  2. String optimization - Use string builders for log concatenation instead of +=
  3. List comprehensions - Replace explicit loops with more efficient comprehensions where possible
  4. Caching - Cache frequently accessed provider metadata/configs
  5. Lazy evaluation - Defer expensive operations until actually needed

Testing requirements:

  • Run tox -e unit to ensure all tests pass
  • Verify cascade routing still works correctly
  • Add memory usage tests if possible (check object count before/after)

Acceptance criteria:

  • No functionality regressions (all tests pass)
  • Reduced memory allocations (measurable improvement)
  • Code remains readable and maintainable
  • Document what optimizations were made in commit message

Verification:

# Before and after comparison
tox -e unit  # Must pass
python3 -c "import tracemalloc; tracemalloc.start(); from src.infrastructure.router.cascade import *; print('Memory usage test')"

This is a PRIORITY #1 issue from the development queue. Focus on this first.

## Kimi Implementation Instructions **Objective:** Optimize memory usage in `src/infrastructure/router/cascade.py` - the largest module in the codebase at 1241 lines. **Context:** This is the routing/fallback logic that handles provider cascades. It's a frequent allocation hotspot based on profiling. **Files to analyze and modify:** - `src/infrastructure/router/cascade.py` (primary target) - Look for patterns like: repeated list/dict creation, string concatenation in loops, unnecessary object instantiation **Specific optimizations to implement:** 1. **Object pooling** - Reuse provider response objects instead of creating new ones 2. **String optimization** - Use string builders for log concatenation instead of += 3. **List comprehensions** - Replace explicit loops with more efficient comprehensions where possible 4. **Caching** - Cache frequently accessed provider metadata/configs 5. **Lazy evaluation** - Defer expensive operations until actually needed **Testing requirements:** - Run `tox -e unit` to ensure all tests pass - Verify cascade routing still works correctly - Add memory usage tests if possible (check object count before/after) **Acceptance criteria:** - No functionality regressions (all tests pass) - Reduced memory allocations (measurable improvement) - Code remains readable and maintainable - Document what optimizations were made in commit message **Verification:** ```bash # Before and after comparison tox -e unit # Must pass python3 -c "import tracemalloc; tracemalloc.start(); from src.infrastructure.router.cascade import *; print('Memory usage test')" ``` This is a **PRIORITY #1** issue from the development queue. Focus on this first.
kimi was assigned by Timmy 2026-03-24 11:27:02 +00:00
kimi was unassigned by Timmy 2026-03-24 19:33:35 +00:00
Timmy closed this issue 2026-03-24 21:05:01 +00:00
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Reference: Rockachopa/Timmy-time-dashboard#1376
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