Files
ragflow/common/decorator.py
Jack f0cb7a544b Refactor: Task Executor (#15154)
### What problem does this PR solve?

1. Break huge function into smaller pieces
2. Add unit test for the smaller pieces function
3. Layer-ed design
a. infra layer - task_context.py, recording_context.py,
write_operation_interceptor.py, ...
    b. service layer - *_service.py
    c. business layer - task_handler.py
4. Default behavior: use "refactor-ed version" - can switch to original
version by change env variable

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
- [x] Performance Improvement

---------

Co-authored-by: Liu An <asiro@qq.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
2026-05-27 21:54:17 +08:00

86 lines
2.8 KiB
Python

#
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import functools
import inspect
import logging
import os
import time
def singleton(cls, *args, **kw):
instances = {}
def _singleton():
key = str(cls) + str(os.getpid())
if key not in instances:
instances[key] = cls(*args, **kw)
return instances[key]
return _singleton
def timing(func=None, *, name=None, context=None):
"""Decorator that records function execution time.
Usage:
@timing
async def my_func(): ...
@timing(name="custom_name")
def my_func(): ...
@timing(context=recording_ctx)
async def my_func(): ...
Args:
func: The function to decorate (auto-passed when used as @timing)
name: Custom name for the timing record, defaults to function name
context: A RecordingContext-like object to record timing data into.
If not provided, will try to use global recording_context from task_executor.
Timing data will be recorded as "{name}_time".
"""
if func is None:
return functools.partial(timing, name=name, context=context)
func_name = name or func.__name__
log = logging.getLogger(__name__)
if inspect.iscoroutinefunction(func):
@functools.wraps(func)
async def async_wrapper(*args, **kwargs):
start = time.perf_counter()
try:
result = await func(*args, **kwargs)
return result
finally:
elapsed = time.perf_counter() - start
log.debug(f"[TIMING] {func_name} took {elapsed:.3f}s")
if context is not None:
context.record(f"{func_name}_time", elapsed)
return async_wrapper
else:
@functools.wraps(func)
def sync_wrapper(*args, **kwargs):
start = time.perf_counter()
try:
result = func(*args, **kwargs)
return result
finally:
elapsed = time.perf_counter() - start
log.debug(f"[TIMING] {func_name} took {elapsed:.3f}s")
if context is not None:
context.record(f"{func_name}_time", elapsed)
return sync_wrapper