Pretty powerful logging library in about 1000 lines of code
Plog is a C++ logging library that is designed to be as simple, small and flexible as possible. It is created as an alternative to existing large libraries and provides some unique features as CSV log format and wide string support.
Here is a minimal hello log sample:
#include <plog/Log.h> // Step1: include the headers #include "plog/Initializers/RollingFileInitializer.h" int main() { plog::init(plog::debug, "Hello.txt"); // Step2: initialize the logger // Step3: write log messages using a special macro // There are several log macros, use the macro you liked the most PLOGD << "Hello log!"; // short macro PLOG_DEBUG << "Hello log!"; // long macro PLOG(plog::debug) << "Hello log!"; // function-style macro // Also you can use LOG_XXX macro but it may clash with other logging libraries LOGD << "Hello log!"; // short macro LOG_DEBUG << "Hello log!"; // long macro LOG(plog::debug) << "Hello log!"; // function-style macro return 0; }
And its output:
2015-05-18 23:12:43.921 DEBUG [21428] [main@13] Hello log!
2015-05-18 23:12:43.968 DEBUG [21428] [main@14] Hello log!
2015-05-18 23:12:43.968 DEBUG [21428] [main@15] Hello log!
windows.h
dependencystd
containersTo start using plog you need to make 3 simple steps.
At first your project needs to know about plog. For that you have to:
plog/include
to the project include paths#include <plog/Log.h>
into your cpp/h files (if you have precompiled headers it is a good place to add this include there)The next step is to initialize the Logger. This is done by the following plog::init
function:
Logger& init(Severity maxSeverity, const char/wchar_t* fileName, size_t maxFileSize = 0, int maxFiles = 0);
maxSeverity
is the logger severity upper limit. All log messages have its own severity and if it is higher than the limit those messages are dropped. Plog defines the following severity levels:
enum Severity { none = 0, fatal = 1, error = 2, warning = 3, info = 4, debug = 5, verbose = 6 };
Note Messages with severity level
none
will always be printed.
The log format is determined automatically by fileName
file extension:
The rolling behavior is controlled by maxFileSize
and maxFiles
parameters:
maxFileSize
- the maximum log file size in bytesmaxFiles
- a number of log files to keepIf one of them is zero then log rolling is disabled.
Sample:
plog::init(plog::warning, "c:\\logs\\log.csv", 1000000, 5);
Here the logger is initialized to write all messages with up to warning severity to a file in csv format. Maximum log file size is set to 1'000'000 bytes and 5 log files are kept.
Note See Custom initialization for advanced usage.
Logging is performed with the help of special macros. A log message is constructed using stream output operators <<
. Thus it is type-safe and extendable in contrast to a format string output.
This is the most used type of logging macros. They do unconditional logging.
PLOG_VERBOSE << "verbose"; PLOG_DEBUG << "debug"; PLOG_INFO << "info"; PLOG_WARNING << "warning"; PLOG_ERROR << "error"; PLOG_FATAL << "fatal"; PLOG_NONE << "none";
PLOGV << "verbose"; PLOGD << "debug"; PLOGI << "info"; PLOGW << "warning"; PLOGE << "error"; PLOGF << "fatal"; PLOGN << "none";
PLOG(severity) << "msg";
These macros are used to do conditional logging. They accept a condition as a parameter and perform logging if the condition is true.
PLOG_VERBOSE_IF(cond) << "verbose"; PLOG_DEBUG_IF(cond) << "debug"; PLOG_INFO_IF(cond) << "info"; PLOG_WARNING_IF(cond) << "warning"; PLOG_ERROR_IF(cond) << "error"; PLOG_FATAL_IF(cond) << "fatal"; PLOG_NONE_IF(cond) << "none";
PLOGV_IF(cond) << "verbose"; PLOGD_IF(cond) << "debug"; PLOGI_IF(cond) << "info"; PLOGW_IF(cond) << "warning"; PLOGE_IF(cond) << "error"; PLOGF_IF(cond) << "fatal"; PLOGN_IF(cond) << "none";
PLOG_IF(severity, cond) << "msg";
In some cases there is a need to perform a group of actions depending on the current logger severity level. There is a special macro for that. It helps to minimize performance penalty when the logger is inactive.
IF_PLOG(severity)
Sample:
IF_PLOG(plog::debug) // we want to execute the following statements only at debug severity (and higher) { for (int i = 0; i < vec.size(); ++i) { PLOGD << "vec[" << i << "]: " << vec[i]; } }
It is possible to set the maximum severity not only at the logger initialization time but at any time later. There are special accessor methods:
Severity Logger::getMaxSeverity() const; Logger::setMaxSeverity(Severity severity);
To get the logger use plog::get
function:
Logger* get();
Sample:
plog::get()->setMaxSeverity(plog::debug);
Non-typical log cases require the use of custom initialization. It is done by the following plog::init
function:
Logger& init(Severity maxSeverity = none, IAppender* appender = NULL);
You have to construct an Appender parameterized with a Formatter and pass it to the plog::init
function.
Note The appender lifetime should be static!
Sample:
static plog::ConsoleAppender<plog::TxtFormatter> consoleAppender; plog::init(plog::debug, &consoleAppender);
It is possible to have multiple Appenders within a single Logger. In such case log message will be written to all of them. Use the following method to accomplish that:
Logger& Logger::addAppender(IAppender* appender);
Sample:
static plog::RollingFileAppender<plog::CsvFormatter> fileAppender("MultiAppender.csv", 8000, 3); // Create the 1st appender. static plog::ConsoleAppender<plog::TxtFormatter> consoleAppender; // Create the 2nd appender. plog::init(plog::debug, &fileAppender).addAppender(&consoleAppender); // Initialize the logger with the both appenders.
Here the logger is initialized in the way when log messages are written to both a file and a console.
Refer to MultiAppender for a complete sample.
Multiple Loggers can be used simultaneously each with their own separate configuration. The Loggers differ by their instanceId (that is implemented as a template parameter). The default instanceId is zero. Initialization is done by the appropriate template plog::init
functions:
Logger<instanceId>& init<instanceId>(...);
To get a logger use plog::get
function (returns NULL
if the logger is not initialized):
Logger<instanceId>* get<instanceId>();
All logging macros have their special versions that accept an instanceId parameter. These kind of macros have an underscore at the end:
PLOGD_(instanceId) << "debug"; PLOGD_IF_(instanceId, condition) << "conditional debug"; IF_PLOG_(instanceId, severity)
Sample:
enum // Define log instanceIds. Default is 0 and is omitted from this enum. { SecondLog = 1 }; int main() { plog::init(plog::debug, "MultiInstance-default.txt"); // Initialize the default logger instance. plog::init<SecondLog>(plog::debug, "MultiInstance-second.txt"); // Initialize the 2nd logger instance. // Write some messages to the default log. PLOGD << "Hello default log!"; // Write some messages to the 2nd log. PLOGD_(SecondLog) << "Hello second log!"; return 0; }
Refer to MultiInstance for a complete sample.
For applications that consist of several binary modules, plog instances can be local (each module has its own instance) or shared (all modules use the same instance). In case of shared you have to initialize plog only in one module, other modules will reuse that instance.
Sharing behavior is controlled by the following macros and is OS-dependent:
Macro | OS | Behavior |
---|---|---|
PLOG_GLOBAL | Linux/Unix | Shared |
PLOG_LOCAL | Linux/Unix | Local |
PLOG_EXPORT | Linux/Unix | n/a |
PLOG_IMPORT | Linux/Unix | n/a |
<default> | Linux/Unix | According to compiler settings |
PLOG_GLOBAL | Windows | n/a |
PLOG_LOCAL | Windows | Local |
PLOG_EXPORT | Windows | Shared (exports) |
PLOG_IMPORT | Windows | Shared (imports) |
<default> | Windows | Local |
For sharing on Windows one module should use PLOG_EXPORT
and others should use PLOG_IMPORT
. Also be careful on Linux/Unix: if you don't specify sharing behavior it will be determined by compiler settings (-fvisibility
).
Refer to Shared for a complete sample.
A Logger can work as an Appender for another Logger. So you can chain several loggers together. This is useful for streaming log messages from a shared library to the main application binary.
Important: don't forget to specify PLOG_LOCAL
sharing mode on Linux/Unix systems for this sample.
Sample:
// shared library // Function that initializes the logger in the shared library. extern "C" void EXPORT initialize(plog::Severity severity, plog::IAppender* appender) { plog::init(severity, appender); // Initialize the shared library logger. } // Function that produces a log message. extern "C" void EXPORT foo() { PLOGI << "Hello from shared lib!"; }
// main app // Functions imported from the shared library. extern "C" void initialize(plog::Severity severity, plog::IAppender* appender); extern "C" void foo(); int main() { plog::init(plog::debug, "ChainedApp.txt"); // Initialize the main logger. PLOGD << "Hello from app!"; // Write a log message. initialize(plog::debug, plog::get()); // Initialize the logger in the shared library. Note
一键生成PPT和Word,让学习生活更轻松
讯飞智文是一个利用 AI 技术的项目,能够帮助用户生成 PPT 以及各类文档。无论是商业领域的市场分析报告、年度目标制定,还是学生群体的职业生涯规划、实习避坑指南,亦或是活动策划、旅游攻略等内容,它都能提供支持,帮助用户精准表达,轻松呈现各种信息。
深度推理能力全新升级,全面对标OpenAI o1
科大讯飞的星火大模型,支持语言理解、知识问答和文本创作等多功能,适用于多种文件和业务场景,提升办公和日常生活的效率。讯飞星火是一个提供丰富智能服务的平台,涵盖科技资讯、图像创作、写作辅助、编程解答、科研文献解读等功能,能为不同需求的用户提供便捷高效的帮助,助力用户轻松获取信息、解决问题,满足多样化使用场景。
一种基于大语言模型的高效单流解耦语音令牌文本到语音合成模型
Spark-TTS 是一个基于 PyTorch 的开源文本到语音合成项目,由多个知名机构联合参与。该项目提供了高效的 LLM(大语言模型)驱动的语音合成方案,支持语音克隆和语音创建功能,可通过命令行界面(CLI)和 Web UI 两种方式使用。用户可以根据需求调整语音的性别、音高、速度等参数,生成高质量的语音。该项目适用于多种场景,如有声读物制作、智能语音助手开发等。
字节跳动发布的AI编程神器IDE
Trae是一种自适应的集成开发环境(IDE),通过自动化和多元协作改变开发流程。利用Trae,团队能够更快速、精确地编写和部署代码,从而提高编程效率和项目交付速度。Trae具备上下文感知和代码自动完成功能,是提升开发效率的理想工具。
AI助力,做PPT更简单!
咔片是一款轻量化在线演示设计工具,借助 AI 技术,实现从内容生成到智能设计的一站式 PPT 制作服务。支持多种文档格式导入生成 PPT,提供海量模板、智能美化、素材替换等功能,适用于销售、教师、学生等各类人群,能高效制作出高品质 PPT,满足不同场景演示需求。
选题、配图、成文,一站式创作,让内容运营更高效
讯飞绘文,一个AI集成平台,支持写作、选题、配图、排版和发布。高效生成适用于各类媒体的定制内容,加速品牌传播,提升内容营销效果。
专业的AI公文写作平台,公文写作神器
AI 材料星,专业的 AI 公文写作辅助平台,为体制内工作人员提供高效的公文写作解决方案。拥有海量公文文库、9 大核心 AI 功能,支持 30 + 文稿类型生成,助力快速完成领导讲话、工作总结、述职报告等材料,提升办公效率,是体制打工人的得力写作神器。
OpenAI Agents SDK,助力开发者便捷使用 OpenAI 相关功能。
openai-agents-python 是 OpenAI 推出的一款强大 Python SDK,它为开发者提供了与 OpenAI 模型交互的高效工具,支持工具调用、结果处理、追踪等功能,涵盖多种应用场景,如研究助手、财务研究等,能显著提升开发效率,让开发者更轻松地利用 OpenAI 的技术优势。
高分辨率纹理 3D 资产生成
Hunyuan3D-2 是腾讯开发的用于 3D 资产生成的强大工具,支持从文本描述、单张图片或多视角图片生成 3D 模型,具备快速形状生成能力,可生成带纹理的高质量 3D 模型,适用于多个领域,为 3D 创作提供了高效解决方案。
一个具备存储、管理和客户端操作等多种功能的分布式文件系统相关项目。
3FS 是一个功能强大的分布式文件系统项目,涵盖了存储引擎、元数据管理、客户端工具等多个模块。它支持多种文件操作,如创建文件和目录、设置布局等,同时具备高效的事件循环、节点选择和协程池管理等特性。适用于需要大规模数据存储和管理的场景,能够提高系统的性能和可靠性,是分布式存储领域的优质解决方案。
最新AI工具、AI资讯
独家AI资源、AI项目落地
微信扫一扫关注公众号