mirror of
https://github.com/Routstr/routstr-core.git
synced 2026-08-12 12:13:21 +00:00
Implement caching in LogManager for improved performance and optimize log entry retrieval
This commit is contained in:
Evan Yang
+110
-106
@@ -303,128 +303,132 @@ class LogManager:
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return 0
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def get_usage_summary(self, hours: int = 24) -> dict:
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entries = list(
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self._yield_log_entries(
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hours_back=hours, window_center=datetime.now(timezone.utc)
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)
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return self._cache_call(
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("usage_summary", hours),
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lambda: self._calculate_summary_stats(self._get_cached_entries(hours)),
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)
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return self._calculate_summary_stats(entries)
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def get_usage_metrics(self, interval: int = 15, hours: int = 24) -> dict:
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entries = list(
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self._yield_log_entries(
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hours_back=hours, window_center=datetime.now(timezone.utc)
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)
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return self._cache_call(
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("usage_metrics", interval, hours),
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lambda: self._aggregate_metrics_by_time(
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self._get_cached_entries(hours), interval, hours
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),
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)
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return self._aggregate_metrics_by_time(entries, interval, hours)
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def get_error_details(self, hours: int = 24, limit: int = 100) -> dict:
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errors: list[dict[str, Any]] = []
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def compute() -> dict:
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errors: list[dict[str, Any]] = []
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for entry in self._yield_log_entries(hours_back=hours):
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if str(entry.get("levelname", "")).upper() != "ERROR":
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continue
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for entry in self._get_cached_entries(hours):
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if str(entry.get("levelname", "")).upper() != "ERROR":
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continue
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errors.append(
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{
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"timestamp": entry.get("asctime", ""),
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"message": entry.get("message", ""),
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"error_type": entry.get("error_type", "unknown"),
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"pathname": entry.get("pathname", ""),
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"lineno": entry.get("lineno", 0),
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"request_id": entry.get("request_id", ""),
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}
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)
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errors.append(
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{
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"timestamp": entry.get("asctime", ""),
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"message": entry.get("message", ""),
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"error_type": entry.get("error_type", "unknown"),
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"pathname": entry.get("pathname", ""),
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"lineno": entry.get("lineno", 0),
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"request_id": entry.get("request_id", ""),
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}
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)
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errors.sort(key=lambda x: str(x["timestamp"]), reverse=True)
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return {"errors": errors[:limit], "total_count": len(errors)}
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errors.sort(key=lambda x: str(x["timestamp"]), reverse=True)
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return {"errors": errors[:limit], "total_count": len(errors)}
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return self._cache_call(("error_details", hours, limit), compute)
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def get_revenue_by_model(self, hours: int = 24, limit: int = 20) -> dict:
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entries = list(
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self._yield_log_entries(
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hours_back=hours, window_center=datetime.now(timezone.utc)
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)
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)
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def compute() -> dict:
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entries = self._get_cached_entries(hours)
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model_stats: dict[str, dict[str, int | float]] = defaultdict(
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lambda: {
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"revenue_msats": 0,
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"refunds_msats": 0,
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"requests": 0,
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"successful": 0,
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"failed": 0,
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}
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)
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for entry in entries:
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try:
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model = entry.get("model", "unknown")
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if not isinstance(model, str):
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model = "unknown"
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message = str(entry.get("message", "")).lower()
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completed, revenue_msats, _, _ = self._extract_success_metrics(
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entry, message
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)
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if completed:
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model_stats[model]["requests"] += 1
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model_stats[model]["successful"] += 1
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if revenue_msats > 0:
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model_stats[model]["revenue_msats"] += revenue_msats
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failed = (
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"revert payment" in message or "upstream request failed" in message
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)
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if failed:
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model_stats[model]["requests"] += 1
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model_stats[model]["failed"] += 1
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if "revert payment" in message:
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max_cost = entry.get("max_cost_for_model", 0)
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if isinstance(max_cost, (int, float)) and max_cost > 0:
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model_stats[model]["refunds_msats"] += max_cost
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except Exception:
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continue
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models: list[dict[str, Any]] = []
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total_revenue = 0.0
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for model, stats in model_stats.items():
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revenue_msats = float(stats["revenue_msats"])
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refunds_msats = float(stats["refunds_msats"])
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revenue_sats = revenue_msats / 1000
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refunds_sats = refunds_msats / 1000
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net_revenue_sats = revenue_sats - refunds_sats
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total_revenue += net_revenue_sats
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requests = int(stats["requests"])
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successful = int(stats["successful"])
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models.append(
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{
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"model": model,
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"revenue_sats": revenue_sats,
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"refunds_sats": refunds_sats,
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"net_revenue_sats": net_revenue_sats,
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"requests": requests,
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"successful": successful,
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"failed": int(stats["failed"]),
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"avg_revenue_per_request": (
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revenue_sats / successful if successful > 0 else 0
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),
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model_stats: dict[str, dict[str, int | float]] = defaultdict(
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lambda: {
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"revenue_msats": 0,
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"refunds_msats": 0,
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"requests": 0,
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"successful": 0,
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"failed": 0,
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}
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)
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models.sort(key=lambda x: float(x["net_revenue_sats"]), reverse=True)
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for entry in entries:
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try:
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model = entry.get("model", "unknown")
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if not isinstance(model, str):
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model = "unknown"
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return {
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"models": models[:limit],
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"total_revenue_sats": total_revenue,
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"total_models": len(models),
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}
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message = str(entry.get("message", "")).lower()
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completed, revenue_msats, _, _ = self._extract_success_metrics(
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entry, message
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)
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if completed:
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model_stats[model]["requests"] += 1
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model_stats[model]["successful"] += 1
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if revenue_msats > 0:
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model_stats[model]["revenue_msats"] += revenue_msats
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failed = (
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"revert payment" in message
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or "upstream request failed" in message
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)
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if failed:
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model_stats[model]["requests"] += 1
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model_stats[model]["failed"] += 1
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if "revert payment" in message:
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max_cost = entry.get("max_cost_for_model", 0)
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if (
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isinstance(max_cost, (int, float))
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and max_cost > 0
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):
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model_stats[model]["refunds_msats"] += max_cost
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except Exception:
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continue
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models: list[dict[str, Any]] = []
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total_revenue = 0.0
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for model, stats in model_stats.items():
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revenue_msats = float(stats["revenue_msats"])
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refunds_msats = float(stats["refunds_msats"])
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revenue_sats = revenue_msats / 1000
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refunds_sats = refunds_msats / 1000
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net_revenue_sats = revenue_sats - refunds_sats
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total_revenue += net_revenue_sats
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requests = int(stats["requests"])
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successful = int(stats["successful"])
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models.append(
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{
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"model": model,
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"revenue_sats": revenue_sats,
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"refunds_sats": refunds_sats,
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"net_revenue_sats": net_revenue_sats,
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"requests": requests,
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"successful": successful,
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"failed": int(stats["failed"]),
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"avg_revenue_per_request": (
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revenue_sats / successful if successful > 0 else 0
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),
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}
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)
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models.sort(key=lambda x: float(x["net_revenue_sats"]), reverse=True)
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return {
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"models": models[:limit],
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"total_revenue_sats": total_revenue,
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"total_models": len(models),
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}
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return self._cache_call(("revenue_by_model", hours, limit), compute)
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def _build_summary_response(self, stats: dict[str, Any]) -> dict[str, Any]:
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revenue_sats = stats["revenue_msats"] / 1000
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