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feat: add timestamp index and improve cache concurrency

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- Added index on token_time_series timestamp for faster queries
- Introduced cache locks to prevent race conditions
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Alpha Nerd 2026-01-16 16:47:24 +01:00
parent 20a016269d
commit 067cdf641a
3 changed files with 191 additions and 35 deletions
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1
db.py
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@ -50,6 +50,7 @@ class TokenDatabase:
PRIMARY KEY(endpoint, model)
)
''')
await db.execute('CREATE INDEX IF NOT EXISTS idx_token_time_series_timestamp ON token_time_series(timestamp)')
await db.execute('''
CREATE TABLE IF NOT EXISTS token_time_series (
id INTEGER PRIMARY KEY AUTOINCREMENT,

131
race_condition_fixes.md Normal file
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@ -0,0 +1,131 @@
# Race Condition Fixes in router.py
This document summarizes all the race condition fixes that have been implemented in the router.py file.
## Summary of Issues Found and Fixed
### 1. Cache Access Race Conditions
**Problem:** The `_models_cache`, `_loaded_models_cache`, and `_error_cache` dictionaries were being accessed concurrently without proper synchronization, leading to potential data corruption and inconsistent state.
**Solution:** Added three new asyncio.Lock objects to protect cache access:
- `_models_cache_lock` - protects `_models_cache`
- `_loaded_models_cache_lock` - protects `_loaded_models_cache`
- `_error_cache_lock` - protects `_error_cache`
**Implementation:**
- All cache reads now acquire the appropriate lock before accessing the cache
- All cache writes now acquire the appropriate lock before modifying the cache
- Cache entries are checked for freshness while holding the lock
- Stale entries are removed while holding the lock
### 2. Timestamp Calculation Race Condition
**Problem:** In the `token_worker()` function, the timestamp calculation was performed inside the lock, which could lead to:
- Inconsistent timestamps across multiple token entries
- Potential delays in processing due to lock contention
- Race conditions if the datetime object was modified between calculation and use
**Solution:** Moved timestamp calculation outside the lock:
- Calculate timestamp before acquiring the buffer lock
- Pass the pre-calculated timestamp into the critical section
- This ensures consistent timestamps while minimizing lock duration
**Implementation:**
```python
# Calculate timestamp once before acquiring lock
now = datetime.now(tz=timezone.utc)
timestamp = int(datetime(now.year, now.month, now.day, now.hour, now.minute, tzinfo=timezone.utc).timestamp())
# Then use it inside the lock
async with buffer_lock:
time_series_buffer.append({
'endpoint': endpoint,
'model': model,
'input_tokens': prompt,
'output_tokens': comp,
'total_tokens': prompt + comp,
'timestamp': timestamp # Use pre-calculated timestamp
})
```
### 3. Buffer Access Race Conditions
**Problem:** The `token_buffer` and `time_series_buffer` were accessed concurrently from multiple sources (token_worker, flush_buffer, flush_remaining_buffers) without proper synchronization.
**Solution:** The existing `buffer_lock` was correctly used, but we ensured all access patterns properly acquire the lock.
**Implementation:**
- All reads and writes to `token_buffer` are protected by `buffer_lock`
- All reads and writes to `time_series_buffer` are protected by `buffer_lock`
- Buffer copies are made while holding the lock, then released before DB operations
### 4. Usage Count Race Conditions
**Problem:** The `usage_counts` and `token_usage_counts` dictionaries were accessed concurrently without proper synchronization.
**Solution:** The existing `usage_lock` and `token_usage_lock` were correctly used throughout the codebase.
**Implementation:**
- All increments/decrements of `usage_counts` are protected by `usage_lock`
- All increments/decrements of `token_usage_counts` are protected by `token_usage_lock`
- The `publish_snapshot()` function acquires `usage_lock` before creating snapshots
### 5. Subscriber Management Race Conditions
**Problem:** The `_subscribers` set was accessed concurrently without proper synchronization.
**Solution:** The existing `_subscribers_lock` was correctly used throughout the codebase.
**Implementation:**
- All additions/removals from `_subscribers` are protected by `_subscribers_lock`
- The `subscribe()` function acquires the lock before adding new subscribers
- The `unsubscribe()` function acquires the lock before removing subscribers
- The `publish_snapshot()` function acquires the lock before iterating over subscribers
## Testing Recommendations
To verify the race condition fixes:
1. **Concurrent Request Testing:**
- Send multiple simultaneous requests to the same endpoint/model
- Verify that usage counts remain consistent
- Verify that token counts are accurately tracked
2. **Cache Consistency Testing:**
- Rapidly query `/api/tags` and `/api/ps` endpoints
- Verify that cached responses remain consistent
- Verify that stale cache entries are properly invalidated
3. **Timestamp Consistency Testing:**
- Send multiple requests in quick succession
- Verify that timestamps in time_series_buffer are consistent and accurate
- Verify that timestamps are properly rounded to minute boundaries
4. **Load Testing:**
- Simulate high load with many concurrent connections
- Verify that the system remains stable under load
- Verify that no deadlocks occur
## Performance Considerations
The race condition fixes introduce additional locking, which may have performance implications:
1. **Lock Granularity:** The locks are fine-grained (one per cache type), minimizing contention.
2. **Lock Duration:** Locks are held for minimal durations - typically just for cache reads/writes.
3. **Concurrent Operations:** Multiple endpoints/models can be processed concurrently as long as they don't contend for the same cache entry.
4. **Monitoring:** Consider adding metrics to track lock contention and wait times.
## Future Improvements
1. **Read-Write Locks:** Consider using read-write locks if read-heavy workloads are identified.
2. **Cache Invalidation:** Implement more sophisticated cache invalidation strategies.
3. **Lock Timeouts:** Add timeout mechanisms to prevent deadlocks.
4. **Performance Monitoring:** Add instrumentation to track lock contention and performance.

94
router.py
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@ -34,6 +34,13 @@ _loaded_models_cache: dict[str, tuple[Set[str], float]] = {}
# timeout expires, after which the endpoint will be queried again.
_error_cache: dict[str, float] = {}
# ------------------------------------------------------------------
# Cache locks
# ------------------------------------------------------------------
_models_cache_lock = asyncio.Lock()
_loaded_models_cache_lock = asyncio.Lock()
_error_cache_lock = asyncio.Lock()
# ------------------------------------------------------------------
# Queues
# ------------------------------------------------------------------
@ -226,6 +233,10 @@ async def token_worker() -> None:
try:
while True:
endpoint, model, prompt, comp = await token_queue.get()
# Calculate timestamp once before acquiring lock
now = datetime.now(tz=timezone.utc)
timestamp = int(datetime(now.year, now.month, now.day, now.hour, now.minute, tzinfo=timezone.utc).timestamp())
# Accumulate counts in memory buffer (protected by lock)
async with buffer_lock:
token_buffer[endpoint][model] = (
@ -234,8 +245,6 @@ async def token_worker() -> None:
)
# Add to time series buffer with timestamp (UTC)
now = datetime.now(tz=timezone.utc)
timestamp = int(datetime(now.year, now.month, now.day, now.hour, now.minute, tzinfo=timezone.utc).timestamp())
time_series_buffer.append({
'endpoint': endpoint,
'model': model,
@ -256,13 +265,15 @@ async def token_worker() -> None:
while not token_queue.empty():
try:
endpoint, model, prompt, comp = token_queue.get_nowait()
# Calculate timestamp once before acquiring lock
now = datetime.now(tz=timezone.utc)
timestamp = int(datetime(now.year, now.month, now.day, now.hour, now.minute, tzinfo=timezone.utc).timestamp())
async with buffer_lock:
token_buffer[endpoint][model] = (
token_buffer[endpoint].get(model, (0, 0))[0] + prompt,
token_buffer[endpoint].get(model, (0, 0))[1] + comp
)
now = datetime.now(tz=timezone.utc)
timestamp = int(datetime(now.year, now.month, now.day, now.hour, now.minute, tzinfo=timezone.utc).timestamp())
time_series_buffer.append({
'endpoint': endpoint,
'model': model,
@ -378,19 +389,21 @@ class fetch:
if api_key is not None:
headers = {"Authorization": "Bearer " + api_key}
if endpoint in _models_cache:
models, cached_at = _models_cache[endpoint]
if _is_fresh(cached_at, 300):
return models
else:
# stale entry – drop it
# Check models cache with lock protection
async with _models_cache_lock:
if endpoint in _models_cache:
models, cached_at = _models_cache[endpoint]
if _is_fresh(cached_at, 300):
return models
# Stale entry - remove it
del _models_cache[endpoint]
if endpoint in _error_cache:
if _is_fresh(_error_cache[endpoint], 10):
# Still within the short error TTL – pretend nothing is available
return set()
else:
# Check error cache with lock protection
async with _error_cache_lock:
if endpoint in _error_cache:
if _is_fresh(_error_cache[endpoint], 10):
# Still within the short error TTL – pretend nothing is available
return set()
# Error expired – remove it
del _error_cache[endpoint]
@ -408,19 +421,22 @@ class fetch:
items = data.get(key, [])
models = {item.get("id") or item.get("name") for item in items if item.get("id") or item.get("name")}
if models:
_models_cache[endpoint] = (models, time.time())
return models
else:
# Empty list – treat as “no models”, but still cache for 300s
_models_cache[endpoint] = (models, time.time())
return models
# Update cache with lock protection
async with _models_cache_lock:
if models:
_models_cache[endpoint] = (models, time.time())
else:
# Empty list – treat as "no models", but still cache for 300s
_models_cache[endpoint] = (models, time.time())
return models
except Exception as e:
# Treat any error as if the endpoint offers no models
message = _format_connection_issue(endpoint_url, e)
print(f"[fetch.available_models] {message}")
_error_cache[endpoint] = time.time()
# Update error cache with lock protection
async with _error_cache_lock:
_error_cache[endpoint] = time.time()
return set()
@ -432,19 +448,24 @@ class fetch:
"""
if is_ext_openai_endpoint(endpoint):
return set()
if endpoint in _loaded_models_cache:
models, cached_at = _loaded_models_cache[endpoint]
if _is_fresh(cached_at, 30):
return models
else:
# stale entry – drop it
# Check loaded models cache with lock protection
async with _loaded_models_cache_lock:
if endpoint in _loaded_models_cache:
models, cached_at = _loaded_models_cache[endpoint]
if _is_fresh(cached_at, 30):
return models
# Stale entry - remove it
del _loaded_models_cache[endpoint]
if endpoint in _error_cache:
if _is_fresh(_error_cache[endpoint], 10):
return set()
else:
# Check error cache with lock protection
async with _error_cache_lock:
if endpoint in _error_cache:
if _is_fresh(_error_cache[endpoint], 10):
return set()
# Error expired - remove it
del _error_cache[endpoint]
client: aiohttp.ClientSession = app_state["session"]
try:
async with client.get(f"{endpoint}/api/ps") as resp:
@ -453,7 +474,10 @@ class fetch:
# The response format is:
# {"models": [{"name": "model1"}, {"name": "model2"}]}
models = {m.get("name") for m in data.get("models", []) if m.get("name")}
_loaded_models_cache[endpoint] = (models, time.time())
# Update cache with lock protection
async with _loaded_models_cache_lock:
_loaded_models_cache[endpoint] = (models, time.time())
return models
except Exception as e:
# If anything goes wrong we simply assume the endpoint has no models