concurrent_log
支持多进程多线程环境使用的日志处理器
ConcurrentTimedRotatingFileHandler
支持的功能
- 按照时间进行切割日志
- 支持多进程多线程环境使用
怎么用
与标准库TimedRotatingFileHandler完全兼容。
如果项目已经使用了TimedRotatingFileHandler,来进行日志处理,因为引入了多进程机制需要一个支持多进程环境的日志处理器,只需要在
日志配置界面引入concurrent_log模块,然后将TimedRotatingFileHandler替换为ConcurrentTimedRotatingFileHandler即
可,其他代码不需要任何改动。
压测示例代码
import time
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
class ConcurrentTimedRotatingFileHandlerTest:
"""
ConcurrentTimedRotatingFileHandler 测试
"""
def __init__(self):
import logging
import logging.config
import concurrent_log
log_conf = {
'version': 1,
'formatters': {
'default': {
'format': '%(asctime)s - %(process)d-%(threadName)s - '
'%(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s',
'datefmt': "%Y-%m-%d %H:%M:%S"
},
},
'handlers': {
'file': {
'level': 'DEBUG',
'class': 'logging.handlers.ConcurrentTimedRotatingFileHandler',
'backupCount': 100,
'when': 's',
'delay': True,
'filename': 'log/test.log',
'encoding': 'utf-8',
'formatter': 'default',
}
},
'root': {
'handlers': ['file'],
'level': 'DEBUG',
},
}
logging.config.dictConfig(log_conf)
self.logger = logging.getLogger(__name__)
def write_log(self, index):
self.logger.debug('debug-%s' % index)
self.logger.info('info-%s' % index)
self.logger.warning('警告-%s' % index)
self.logger.error('报错-%s' % index)
self.logger.critical('严重-%s' % index)
def mutil_thread_write_log(self):
with ThreadPoolExecutor(100) as thread_pool:
for i in range(1000):
thread_pool.submit(self.write_log, i).add_done_callback(self._executor_callback)
def mutil_process_write_log(self):
with ProcessPoolExecutor() as process_pool:
for i in range(100):
process_pool.submit(self.mutil_thread_write_log).add_done_callback(self._executor_callback)
def _executor_callback(self, worker):
worker_exception = worker.exception()
if worker_exception:
print("Worker return exception: ", self.worker_exception)
class TimedRotatingFileHandlerTest:
"""
TimedRotatingFileHandler 测试
"""
def __init__(self):
import logging
import logging.config
log_conf = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'default': {
'format': '%(asctime)s - %(process)d-%(threadName)s - '
'%(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s',
'datefmt': "%Y-%m-%d %H:%M:%S"
},
},
'handlers': {
'file': {
'level': 'DEBUG',
'class': 'logging.handlers.TimedRotatingFileHandler',
'backupCount': 100,
'when': 's',
'delay': True,
'filename': 'log2/test.log',
'encoding': 'utf-8',
'formatter': 'default',
}
},
'root': {
'handlers': ['file'],
'level': 'DEBUG',
},
}
import os
file_path = os.path.split(log_conf.get("handlers").get("file").get("filename"))[0]
if not os.path.exists(file_path):
os.makedirs(file_path)
logging.config.dictConfig(log_conf)
self.logger = logging.getLogger(__name__)
def write_log(self, index):
self.logger.debug('debug-%s' % index)
self.logger.info('info-%s' % index)
self.logger.warning('警告-%s' % index)
self.logger.error('报错-%s' % index)
self.logger.critical('严重-%s' % index)
def mutil_thread_write_log(self):
with ThreadPoolExecutor(100) as thread_pool:
for i in range(100000):
thread_pool.submit(self.write_log, i).add_done_callback(self._executor_callback)
def _executor_callback(self, worker):
worker_exception = worker.exception()
if worker_exception:
print("Worker return exception: ", self.worker_exception)
if __name__ == "__main__":
print("50W日志写入测试")
begin_time = time.time()
# 多进程写入日志,进程数与CPU核心数一致,使用文件锁实现进程并发控制,防止脏数据以及日志丢失
# 每个进程100个线程共需写入五千行日志,由于GIL原因,并发只存在一个线程,但是会存在线程上下文切换,使用线程锁防止脏数据和日志丢失
ConcurrentTimedRotatingFileHandlerTest().mutil_process_write_log()
use_time = time.time() - begin_time
print("ConcurrentTimedRotatingFileHandler 耗时:%s秒" % use_time)
begin_time = time.time()
# 每个进程100个线程共需写入所有日志,由于GIL原因,并发只存在一个线程,但是会存在线程上下文切换,同样需要锁机制防止脏数据和日志丢失
TimedRotatingFileHandlerTest().mutil_thread_write_log()
use_time = time.time() - begin_time
print("TimedRotatingFileHandler 耗时:%s秒" % use_time)
压测结果
经验证,日志内容完整,按照时间切割正确
环境
CPU:Intel® Core™ i9-7940X
内存:64G
磁盘:三星 970Pro 1T
输出
50W日志写入测试
ConcurrentTimedRotatingFileHandler 耗时:84.82415437698364秒
TimedRotatingFileHandler 耗时:100.73775053024292秒
Metadata
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