feat(clp-package): Add Python profiling decorator for internal query performance measurement.

components/clp-py-utils/clp_py_utils/profiling_utils.py

@@ -0,0 +1,258 @@
+"""
+Profiling utilities for CLP query execution performance analysis.
+
+This module provides lightweight profiling decorators using pyinstrument.
+
+Profile outputs include:
+- HTML files with interactive flame graphs and call trees.
+- Text summaries showing call hierarchy and timing.
+"""
+
+import datetime
+import functools
+import inspect
+import os
+from collections.abc import Callable
+from pathlib import Path
+from typing import Any, TypeVar
+
+from pyinstrument import Profiler
+
+from clp_py_utils.clp_logging import get_logger
+
+logger = get_logger("profiler")
+
+F = TypeVar("F", bound=Callable[..., Any])
+
+PROFILING_INTERVAL_SECONDS = 0.001
+
+
+def profile(
+    section_name: str | None = None,
+    job_id_param: str = "job_id",
+    task_id_param: str = "task_id",
+) -> Callable[[F], F]:
+    """
+    Profiles function execution as decorator with automatic context extraction.
+
+    Output files are written to $CLP_LOGS_DIR/profiles/ (e.g., clp-package/var/log/query_worker/
+    profiles/).
+
+    :param section_name: Override for profile section name. If None, uses function name.
+    :param job_id_param: Parameter name to extract job_id from (default: "job_id").
+        Can use dot notation for attributes, e.g., "job.id".
+    :param task_id_param: Parameter name to extract task_id from (default: "task_id").
+        Can use dot notation for attributes, e.g., "task.id".
+    :return: Decorated function with profiling capabilities.
+    """
+
+    def decorator(func: F) -> F:
+        name = section_name or func.__name__
+        is_async = inspect.iscoroutinefunction(func)
+
+        if is_async:
+
+            @functools.wraps(func)
+            async def async_wrapper(*args: Any, **kwargs: Any) -> Any:
+                if not _is_profiling_enabled():
+                    return await func(*args, **kwargs)
+
+                # Profiling enabled: extract context and profile execution
+                job_id, task_id = _extract_context_from_args(
+                    func, args, kwargs, job_id_param, task_id_param
+                )
+
+                profiler = Profiler(interval=PROFILING_INTERVAL_SECONDS)
+                try:
+                    profiler.start()
+                except RuntimeError as e:
+                    # Skip profiling this function to avoid conflicts
+                    if "already a profiler running" in str(e):
+                        logger.debug(
+                            f"Skipping nested profiling for {name} "
+                            f"(parent profiler already active)"
+                        )
+                        return await func(*args, **kwargs)
+                    raise
+
+                try:
+                    result = await func(*args, **kwargs)
+                    return result
+                finally:
+                    profiler.stop()
+                    _save_profile(profiler, name, job_id, task_id)
+
+            return async_wrapper  # type: ignore
+
+        @functools.wraps(func)
+        def sync_wrapper(*args: Any, **kwargs: Any) -> Any:
+            if not _is_profiling_enabled():
+                return func(*args, **kwargs)
+
+            # Profiling enabled: extract context and profile execution
+            job_id, task_id = _extract_context_from_args(
+                func, args, kwargs, job_id_param, task_id_param
+            )
+
+            profiler = Profiler(interval=PROFILING_INTERVAL_SECONDS)
+            try:
+                profiler.start()
+            except RuntimeError as e:
+                # Skip profiling this function to avoid conflicts
+                if "already a profiler running" in str(e):
+                    logger.debug(
+                        f"Skipping nested profiling for {name} (parent profiler already active)"
+                    )
+                    return func(*args, **kwargs)
+                raise
+
+            try:
+                result = func(*args, **kwargs)
+                return result
+            finally:
+                profiler.stop()
+                _save_profile(profiler, name, job_id, task_id)
+
+        return sync_wrapper  # type: ignore
+
+    return decorator
+
+
+def _extract_context_from_args(
+    func: Callable,
+    args: tuple,
+    kwargs: dict,
+    job_id_param: str = "job_id",
+    task_id_param: str = "task_id",
+) -> tuple[str, str]:
+    """
+    Extracts job_id and task_id from function arguments.
+
+    :param func: The function being profiled.
+    :param args: Positional arguments passed to the function.
+    :param kwargs: Keyword arguments passed to the function.
+    :param job_id_param: Name/path of the parameter containing job_id (default: "job_id").
+    :param task_id_param: Name/path of the parameter containing task_id (default: "task_id").
+    :return: tuple of (job_id, task_id) as strings. Empty strings if not found.
+    """
+    job_id = ""
+    task_id = ""
+
+    try:
+        # Get function signature
+        sig = inspect.signature(func)
+        param_names = list(sig.parameters.keys())
+
+        def extract_value(param_spec: str) -> str:
+            """Extract value from parameter, supporting dot notation for attributes."""
+            if not param_spec:
+                return ""
+
+            # Split on '.' to handle attribute access
+            parts = param_spec.split(".")
+            param_name = parts[0]
+
+            # Find the parameter value
+            value = None
+            if param_name in kwargs:
+                value = kwargs[param_name]
+            elif param_name in param_names:
+                idx = param_names.index(param_name)
+                if idx < len(args):
+                    value = args[idx]
+
+            if value is None:
+                return ""
+
+            # Navigate through attributes if dot notation was used
+            for attr_name in parts[1:]:
+                if hasattr(value, attr_name):
+                    value = getattr(value, attr_name)
+                else:
+                    return ""
+
+            return str(value)
+
+        # Extract job_id and task_id
+        job_id = extract_value(job_id_param)
+        task_id = extract_value(task_id_param)
+
+    except Exception as e:
+        logger.debug(f"Failed to extract context from {func.__name__}: {e}")
+
+    return job_id, task_id
+
+
+def _is_profiling_enabled() -> bool:
+    """
+    Checks if profiling is enabled.
+    TODO: Add `CLPConfig` mechanism to enable/disable profiling for each component.
+
+    :return: Whether the profiler is enabled.
+    """
+    return False
+
+
+def _save_profile(
+    profiler: Profiler, section_name: str, job_id: str = "", task_id: str = ""
+) -> None:
+    """
+    Saves profiler output to HTML and text formats. Generates .html and .txt files.
+
+    :param profiler: The pyinstrument Profiler object containing profiling data.
+    :param section_name: Name identifying this profiling section.
+    :param job_id: Optional job identifier for filename.
+    :param task_id: Optional task identifier for filename.
+    """
+    try:
+        # Get the session for logging
+        session = profiler.last_session
+        if not session:
+            logger.debug(f"No profiling session for {section_name}")
+            return
+
+        duration = session.duration
+        sample_count = session.sample_count
+
+        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+        filename_parts = [section_name]
+
+        if job_id:
+            filename_parts.append(f"job{job_id}")
+        if task_id:
+            filename_parts.append(f"task{task_id}")
+
+        filename_parts.append(timestamp)
+        base_filename = "_".join(filename_parts)
+
+        output_dir = Path(os.getenv("CLP_LOGS_DIR")) / "profiles"
+        output_dir.mkdir(exist_ok=True, parents=True)
+
+        # Save HTML with interactive visualization
+        html_path = output_dir / f"{base_filename}.html"
+        with open(html_path, "w", encoding="utf-8") as f:
+            f.write(profiler.output_html())
+
+        # Save human-readable text summary with call hierarchy
+        txt_path = output_dir / f"{base_filename}.txt"
+        with open(txt_path, "w", encoding="utf-8") as f:
+            # Header
+            f.write("=" * 80 + "\n")
+            f.write(f"CLP Query Profiling Report (pyinstrument)\n")
+            f.write(f"Section: {section_name}\n")
+            if job_id:
+                f.write(f"Job ID: {job_id}\n")
+            if task_id:
+                f.write(f"Task ID: {task_id}\n")
+            f.write(f"Timestamp: {timestamp}\n")
+            f.write("=" * 80 + "\n\n")
+            f.write(profiler.output_text(unicode=True, color=False))
+
+        logger.info(
+            f"Profile saved: {section_name} "
+            f"(duration={duration:.6f}s, samples={sample_count}) "
+            f"HTML={html_path}, TXT={txt_path}"
+        )
+
+    except Exception as e:
+        logger.error(f"Failed to save profile for {section_name}: {e}", exc_info=True)

components/clp-py-utils/pyproject.toml

@@ -12,6 +12,7 @@ dependencies = [
     "mariadb>=1.0.11,<1.1.dev0",
     "mysql-connector-python>=9.4.0",
     "pydantic>=2.12.3",
+    "pyinstrument>=5.1.1",
     "python-Levenshtein>=0.27.1",
     "PyYAML>=6.0.3",
     "result>=0.17.0",