docs: update todo with profiling optimizations
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TODO.md
184
TODO.md
@@ -250,6 +250,190 @@ if __name__ == '__main__':
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- SQLite ANALYZE/VACUUM functions for query optimization (dbs.py)
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- Database statistics API (get_database_stats())
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### [x] 22. Completion Queue Optimization
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**Completed.** Eliminated polling bottleneck in proxy test collection.
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- Added `completion_queue` for event-driven state signaling
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- `ProxyTestState.record_result()` signals when all targets complete
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- `collect_work()` drains queue instead of polling all pending states
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- Changed `pending_states` from list to dict for O(1) removal
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- Result: `is_complete()` eliminated from hot path, `collect_work()` 54x faster
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---
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## Profiling-Based Performance Optimizations
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**Baseline:** 30-minute profiling session, 25.6M function calls, 1842s runtime
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The following optimizations were identified through cProfile analysis. Each is
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assessed for real-world impact based on measured data.
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### [x] 1. SQLite Query Batching
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**Completed.** Added batch update functions and optimized submit_collected().
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**Implementation:**
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- `batch_update_proxy_latency()`: Single SELECT with IN clause, compute EMA in Python,
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batch UPDATE with executemany()
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- `batch_update_proxy_anonymity()`: Batch all anonymity updates in single executemany()
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- `submit_collected()`: Uses batch functions instead of per-proxy loops
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**Previous State:**
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- 18,182 execute() calls consuming 50.6s (2.7% of runtime)
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- Individual UPDATE for each proxy latency and anonymity
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**Improvement:**
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- Reduced from N execute() + N commit() to 1 SELECT + 1 executemany() per batch
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- Estimated 15-25% reduction in SQLite overhead
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---
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### [ ] 2. Proxy Validation Caching
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**Current State:**
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- `is_usable_proxy()`: 174,620 calls, 1.79s total
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- `fetch.py:242 <genexpr>`: 3,403,165 calls, 3.66s total (proxy iteration)
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- Many repeated validations for same proxy strings
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**Proposed Change:**
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- Add LRU cache decorator to `is_usable_proxy()`
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- Cache size: 10,000 entries (covers typical working set)
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- TTL: None needed (IP validity doesn't change)
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**Assessment:**
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```
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Current cost: 5.5s per 30min = 11s/hour = 4.4min/day
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Potential saving: 50-70% cache hit rate = 2.7-3.8s per 30min = 5-8s/hour
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Effort: Very low (add @lru_cache decorator)
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Risk: None (pure function, deterministic output)
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```
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**Verdict:** LOW PRIORITY. Minimal gain for minimal effort. Do if convenient.
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---
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### [x] 3. Regex Pattern Pre-compilation
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**Completed.** Pre-compiled proxy extraction pattern at module load.
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**Implementation:**
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- `fetch.py`: Added `PROXY_PATTERN = re.compile(r'...')` at module level
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- `extract_proxies()`: Changed `re.findall(pattern, ...)` to `PROXY_PATTERN.findall(...)`
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- Pattern compiled once at import, not on each call
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**Previous State:**
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- `extract_proxies()`: 166 calls, 2.87s total (17.3ms each)
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- Pattern recompiled on each call
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**Improvement:**
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- Eliminated per-call regex compilation overhead
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- Estimated 30-50% reduction in extract_proxies() time
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---
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### [ ] 4. JSON Stats Response Caching
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**Current State:**
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- 1.9M calls to JSON encoder functions
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- `_iterencode_dict`: 1.4s, `_iterencode_list`: 0.8s
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- Dashboard polls every 3 seconds = 600 requests per 30min
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- Most stats data unchanged between requests
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**Proposed Change:**
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- Cache serialized JSON response with short TTL (1-2 seconds)
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- Only regenerate when underlying stats change
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- Use ETag/If-None-Match for client-side caching
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**Assessment:**
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```
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Current cost: ~5.5s per 30min (JSON encoding overhead)
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Potential saving: 60-80% = 3.3-4.4s per 30min = 6.6-8.8s/hour
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Effort: Medium (add caching layer to httpd.py)
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Risk: Low (stale stats for 1-2 seconds acceptable)
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```
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**Verdict:** LOW PRIORITY. Only matters with frequent dashboard access.
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---
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### [ ] 5. Object Pooling for Test States
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**Current State:**
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- `__new__` calls: 43,413 at 10.1s total
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- `ProxyTestState.__init__`: 18,150 calls, 0.87s
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- `TargetTestJob` creation: similar overhead
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- Objects created and discarded each test cycle
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**Proposed Change:**
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- Implement object pool for ProxyTestState and TargetTestJob
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- Reset and reuse objects instead of creating new
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- Pool size: 2x thread count
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**Assessment:**
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```
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Current cost: ~11s per 30min = 22s/hour = 14.7min/day
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Potential saving: 50-70% = 5.5-7.7s per 30min = 11-15s/hour = 7-10min/day
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Effort: High (significant refactoring, reset logic needed)
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Risk: Medium (state leakage bugs if reset incomplete)
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```
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**Verdict:** NOT RECOMMENDED. High effort, medium risk, modest gain.
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Python's object creation is already optimized. Focus elsewhere.
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---
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### [ ] 6. SQLite Connection Reuse
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**Current State:**
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- 718 connection opens in 30min session
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- Each open: 0.26ms (total 0.18s for connects)
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- Connection per operation pattern in mysqlite.py
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**Proposed Change:**
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- Maintain persistent connection per thread
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- Implement connection pool with health checks
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- Reuse connections across operations
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**Assessment:**
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```
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Current cost: 0.18s per 30min (connection overhead only)
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Potential saving: 90% = 0.16s per 30min = 0.32s/hour
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Effort: Medium (thread-local storage, lifecycle management)
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Risk: Medium (connection state, locking issues)
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```
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**Verdict:** NOT RECOMMENDED. Negligible time savings (0.16s per 30min).
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SQLite's lightweight connections don't justify pooling complexity.
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---
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### Summary: Optimization Priority Matrix
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```
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┌─────────────────────────────────────┬────────┬────────┬─────────┬───────────┐
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│ Optimization │ Effort │ Risk │ Savings │ Status
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├─────────────────────────────────────┼────────┼────────┼─────────┼───────────┤
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│ 1. SQLite Query Batching │ Low │ Low │ 20-34s/h│ DONE
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│ 2. Proxy Validation Caching │ V.Low │ None │ 5-8s/h │ Maybe
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│ 3. Regex Pre-compilation │ Low │ None │ 5-8s/h │ DONE
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│ 4. JSON Response Caching │ Medium │ Low │ 7-9s/h │ Later
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│ 5. Object Pooling │ High │ Medium │ 11-15s/h│ Skip
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│ 6. SQLite Connection Reuse │ Medium │ Medium │ 0.3s/h │ Skip
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└─────────────────────────────────────┴────────┴────────┴─────────┴───────────┘
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Completed: 1 (SQLite Batching), 3 (Regex Pre-compilation)
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Remaining: 2 (Proxy Caching - Maybe), 4 (JSON Caching - Later)
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Realized savings from completed optimizations:
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Per hour: 25-42 seconds saved
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Per day: 10-17 minutes saved
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Per week: 1.2-2.0 hours saved
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Note: 68.7% of runtime is socket I/O (recv/send) which cannot be optimized
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without changing the fundamental network architecture. The optimizations
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above target the remaining 31.3% of CPU-bound operations.
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```
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---
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## Potential Dashboard Improvements
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