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import pstats
import time
import psutil
from fastapi import Request
from starlette.middleware.base import BaseHTTPMiddleware
from sqlalchemy.orm import Session
from src.config.db import SessionLocal
from src.models.profiling import ProfilingLog
class ProfilingMiddleware(BaseHTTPMiddleware):
def __init__(self, app, enabled: bool = False):
super().__init__(app)
self.enabled = enabled
self.process = psutil.Process()
async def dispatch(self, request: Request, call_next):
if not self.enabled:
return await call_next(request)
cpu_before = self.process.cpu_percent()
mem_before = self.process.memory_info().rss / 1024 / 1024
profiler = cProfile.Profile()
profiler.enable()
start_time = time.perf_counter()
response = await call_next(request)
total_time = (time.perf_counter() - start_time) * 1000
profiler.disable()
cpu_after = self.process.cpu_percent()
mem_after = self.process.memory_info().rss / 1024 / 1024
stats = pstats.Stats(profiler)
top_functions = self._extract_top_functions(stats, limit=10)
timings = self._extract_specific_timings(stats)
ncalls_total, ncalls_pandas, ncalls_db = self._count_calls_by_category(stats)
self._save_to_database(
endpoint=request.url.path,
method=request.method,
total_time_ms=total_time,
top_functions=top_functions,
timings=timings,
ncalls_total=ncalls_total,
ncalls_pandas=ncalls_pandas,
ncalls_database=ncalls_db,
cpu_percent=(cpu_after - cpu_before),
memory_mb=(mem_after - mem_before),
)
return response
def _extract_top_functions(self, stats: pstats.Stats, limit: int = 10) -> list:
stats.sort_stats(pstats.SortKey.CUMULATIVE)
top_funcs = []
for func, data in list(stats.stats.items())[:limit]:
cc, nc, tt, ct, callers = data
filename, line, func_name = func
top_funcs.append({
"name": func_name,
"file": filename.split("/")[-1],
"line": line,
"time_ms": ct * 1000,
"calls": nc,
})
return top_funcs
def _extract_specific_timings(self, stats: pstats.Stats) -> dict:
timings = {
"preprocessing": 0.0,
"inference": 0.0,
"database": 0.0,
"serialization": 0.0,
}
for func, data in stats.stats.items():
cc, nc, tt, ct, callers = data
filename, line, func_name = func
time_ms = ct * 1000
func_name_lower = func_name.lower()
file_name_lower = filename.lower()
# Preprocessing
if "compute_features" in func_name_lower or "features.py" in file_name_lower:
timings["preprocessing"] += time_ms
# Inference
elif "predict_proba" in func_name_lower:
timings["inference"] += time_ms
# Database
elif "psycopg" in file_name_lower or "sqlalchemy" in file_name_lower:
if any(kw in func_name_lower for kw in ["wait", "execute", "flush", "commit"]):
timings["database"] += time_ms
# Serialization
elif any(kw in func_name_lower for kw in ["json", "dumps", "serialize"]):
timings["serialization"] += time_ms
return timings
def _count_calls_by_category(self, stats: pstats.Stats) -> tuple[int, int, int]:
ncalls_total = 0
ncalls_pandas = 0
ncalls_db = 0
for func, data in stats.stats.items():
cc, nc, tt, ct, callers = data
filename, line, func_name = func
ncalls_total += nc
if "pandas" in filename:
ncalls_pandas += nc
elif "sqlalchemy" in filename or "psycopg" in filename:
ncalls_db += nc
return ncalls_total, ncalls_pandas, ncalls_db
def _save_to_database(
self,
endpoint: str,
method: str,
total_time_ms: float,
top_functions: list,
timings: dict,
ncalls_total: int,
ncalls_pandas: int,
ncalls_database: int,
cpu_percent: float,
memory_mb: float,
):
db: Session = SessionLocal()
try:
time_preprocessing = timings.get("preprocessing") or None
time_inference = timings.get("inference") or None
time_database = timings.get("database") or None
time_serialization = timings.get("serialization") or None
log = ProfilingLog(
endpoint=endpoint,
method=method,
total_time_ms=total_time_ms,
time_preprocessing_ms=time_preprocessing,
time_inference_ms=time_inference,
time_database_ms=time_database,
time_serialization_ms=time_serialization,
top_functions=top_functions,
ncalls_total=ncalls_total,
ncalls_pandas=ncalls_pandas,
ncalls_database=ncalls_database,
cpu_percent=cpu_percent,
memory_mb=memory_mb,
)
db.add(log)
db.commit()
except Exception:
import traceback
traceback.print_exc()
db.rollback()
finally:
db.close()
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