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8.2 日志
本节对日志模块(logging module)进行简单的介绍。
logging 模块
logging
模块是用于记录诊断信息的 Python 标准库模块。日志模块非常庞大,具有许多复杂的功能。我们将会展示一个简单的例子来说明其用处。
再探异常
在本节练习中,我们创建这样一个
parse()
函数:
# fileparse.pydef parse(f, types=None, names=None, delimiter=None):records = []for line in f:line = line.strip()if not line: continuetry:records.append(split(line,types,names,delimiter))except ValueError as e:print(\"Couldn\'t parse :\", line)print(\"Reason :\", e)return records
请看到
try-except
语句,在
except
块中,我们应该做什么?
应该打印警告消息(warning message)?
try:records.append(split(line,types,names,delimiter))except ValueError as e:print(\"Couldn\'t parse :\", line)print(\"Reason :\", e)
还是默默忽略警告消息?
try:records.append(split(line,types,names,delimiter))except ValueError as e:pass
任何一种方式都无法令人满意,通常情况下,两种方式我们都需要(用户可选)。
使用 logging
logging
模块可以解决这个问题:
# fileparse.pyimport logginglog = logging.getLogger(__name__)def parse(f,types=None,names=None,delimiter=None):...try:records.append(split(line,types,names,delimiter))except ValueError as e:log.warning(\"Couldn\'t parse : %s\", line)log.debug(\"Reason : %s\", e)
修改代码以使程序能够遇到问题的时候发出警告消息,或者特殊的
Logger
对象。
Logger
对象使用
logging.getLogger(__name__)
创建。
日志基础
创建一个记录器对象(logger object)。
log = logging.getLogger(name) # name is a string
发出日志消息:
log.critical(message [, args])log.error(message [, args])log.warning(message [, args])log.info(message [, args])log.debug(message [, args])
不同方法代表不同级别的严重性。
所有的方法都创建格式化的日志消息。
args
和
%
运算符 一起使用以创建消息。
logmsg = message % args # Written to the log
日志配置
配置:
# main.py...if __name__ == \'__main__\':import logginglogging.basicConfig(filename = \'app.log\', # Log output filelevel = logging.INFO, # Output level)
通常,在程序启动时,日志配置是一次性的(译注:程序启动后无法重新配置)。该配置与日志调用是分开的。
说明
日志是可以任意配置的。你可以对日志配置的任何一方面进行调整:如输出文件,级别,消息格式等等,不必担心对使用日志模块的代码造成影响。
练习
练习 8.2:将日志添加到模块中
在
fileparse.py
中,有一些与异常有关的错误处理,这些异常是由错误输入引起的。如下所示:
# fileparse.pyimport csvdef parse_csv(lines, select=None, types=None, has_headers=True, delimiter=\',\', silence_errors=False):\'\'\'Parse a CSV file into a list of records with type conversion.\'\'\'if select and not has_headers:raise RuntimeError(\'select requires column headers\')rows = csv.reader(lines, delimiter=delimiter)# Read the file headers (if any)headers = next(rows) if has_headers else []# If specific columns have been selected, make indices for56cfiltering and set output columnsif select:indices = [ headers.index(colname) for colname in select ]headers = selectrecords = []for rowno, row in enumerate(rows, 1):if not row: # Skip rows with no datacontinue# If specific column indices are selected, pick them outif select:row = [ row[index] for index in indices]# Apply type conversion to the rowif types:try:row = [func(val) for func, val in zip(types, row)]except ValueError as e:if not silence_errors:print(f\"Row {rowno}: Couldn\'t convert {row}\")print(f\"Row {rowno}: Reason {e}\")continue# Make a dictionary or a tupleif headers:record = dict(zip(headers, row))else:record = tuple(row)records.append(record)return records
请注意发出诊断消息的
语句。使用日志操作来替换这些
语句相对来说更简单。请像下面这样修改代码:
# fileparse.pyimport csvimport logginglog = logging.getLogger(__name__)def parse_csv(lines, select=None, types=None, has_headers=True, delimiter=\',\', silence_errors=False):ad8\'\'\'Parse a CSV file into a list of records with type conversion.\'\'\'if select and not has_headers:raise RuntimeError(\'select requires column headers\')rows = csv.reader(lines, delimiter=delimiter)# Read the file headers (if any)headers = next(rows) if has_headers else []# If specific columns have been selected, make indices for filtering and set output columnsif select:indices = [ headers.index(colname) for colname in select ]headers = selectrecords = []for rowno, row in enumerate(rows, 1):if not row: # Skip rows with no datacontinue# If specific column indices are selected, pick them outif select:row = [ row[index] for index in indices]# Apply type conversion to the rowif types:try:row = [func(val) for func, val in zip(types, row)]except ValueError as e:if not silence_errors:log.warning(\"Row %d: Couldn\'t convert %s\", rowno, row)log.debug(\"Row %d: Reason %s\", rowno, e)continue# Make a dictionary or a tupleif headers:record = dict(zip(headers, row))else:record = tuple(row)records.append(record)return records
完成修改后,尝试在错误的数据上使用这些代码:
>>> import report>>> a = report.read_portfolio(\'Data/missing.csv\')Row 4: Bad row: [\'MSFT\', \'\', \'51.23\']Row 7: Bad row: [\'IBM\', \'\', \'70.44\']>>>
如果你什么都不做,则只会获得
WARNING
级别以上的日志消息。输出看起来像简单的打印语句。但是,如果你配置了日志模块,你将会获得有关日志级别,模块等其它信息。请按以下步骤操作查看:
>>> import logging>>> logging.basicConfig()>>> a = report.read_portfolio(\'Data/missing.csv\')WARNING:fileparse:Row 4: Bad row: [\'MSFT\', \'\', \'51.23\']WARNING:fileparse:Row 7: Bad row: [\'IBM\', \'\', \'70.44\']>>>
你会发现,看不到来自于
log.debug()
操作的输出。请按以下步骤修改日志级别(译注:因为日志配置是一次性的,所以该操作需要重启命令行窗口):
>>> logging.getLogger(\'fileparse\').level = logging.DEBUG>>> a = report.read_portfolio(\'Data/missing.csv\')WARNING:fileparse:Row 4: Bad row: [\'MSFT\', \'\', \'51.23\']DEBUG:fileparse:Row 4: Reason: invalid literal for int() with base 10: \'\'WARNING:fileparse:Row 7: Bad row: [\'IB564M\', \'\', \'70.44\']DEBUG:fileparse:Row 7: Reason: invalid literal for int() with base 10: \'\'>>>
只留下 critical 级别的日志消息,关闭其它级别的日志消息。
>>> logging.getLogger(\'fileparse\').level=logging.CRITICAL>>> a = report.read_portfolio(\'Data/missing.csv\')>>>
练习 8.3:向程序添加日志
要添加日志到应用中,你需要某种机制来实现在主模块中初始化日志。其中一种方式使用看起来像下面这样的代码:
# This file sets up basic configuration of the logging module.# Change settings here to adjust logging output as needed.import logginglogging.basicConfig(filename = \'app.log\', # Name of the log file (omit to use stderr)filemode = \'w\', # File mode (use \'a\' to append)level = logging.WARNING, # Logging level (DEBUG, INFO, WARNING, ERROR, or CRITICAL))
再次说明,你需要将日志配置代码放到程序启动步骤中。例如,将其放到
report.py
程序里的什么位置?
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