B log logging program

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HyperLog "Full featured logging program available since Interfaces to most radio's, TNC's, and Callbooks. Runs under DOS and all ""flavors"" of Windows. It is a simple, yet very powerful way of distinguishing log events from each other. If the log levels are used properly in your application all you need is to look at the severity first.

It will tell you if you can continue sleeping during the on-call night or you need to jump out of bed right away and hit another personal best in running between your bedroom and laptop in the living room. You can think of the log levels as a way to filter the critical information about your system state and the one that is purely informative. The log levels can help in reducing the information noise and reduce alert fatigue. Before continuing with the description of the log levels themselves it would be good to know where the log levels come from.

It all started with syslog. In the 80s, the Sendmail a mailer daemon project developed by Eric Allman required a logging solution. This is how Syslog was born.

It was rapidly adopted by other applications in the Unix-like ecosystem and became a standard. Btw — at Sematext we do support Syslog format with Sematext Logs , our log management tool.

Syslog came with the idea of severity levels , which are now defined in the syslog standard. Syslog comes with the following severity levels:. After the 80s programming languages were evolving and different logging frameworks were introduced. Nowadays each programming language has its own logging framework allowing you to save data in various formats like JSON. In most cases you can ship data to different destinations like text file, syslog or Elasticsearch.

But apart from the format and the possible destinations there is one thing that is common to the majority of them — the level of the log event. The names of some of those give you a hint on what they are about. TRACE — the most fine-grained information only used in rare cases where you need the full visibility of what is happening in your application and inside the third-party libraries that you use.

You can use it for example to annotate each step in the algorithm or each individual query with parameters in your code. The DEBUG log level should be used for information that may be needed for diagnosing issues and troubleshooting or when running application in the test environment for the purpose of making sure everything is running correctly.

INFO — the standard log level indicating that something happened, the application entered a certain state, etc. For example, a controller of your authorization API may include an INFO log level with information on which user requested authorization if the authorization was successful or not.

WARN — the log level that indicates that something unexpected happened in the application, a problem, or a situation that might disturb one of the processes. The WARN level should be used in situations that are unexpected, but the code can continue the work.

For example, a parsing error occurred that resulted in a certain document not being processed. ERROR — the log level that should be used when the application hits an issue preventing one or more functionalities from properly functioning. The ERROR log level can be used when one of the payment systems is not available, but there is still the option to check out the basket in the e-commerce application or when your social media logging option is not working for some reason.

FATAL — the log level that tells that the application encountered an event or entered a state in which one of the crucial business functionality is no longer working. The next question that you are probably already asking in your mind is how to make sense of log levels in your log events. This is a scheme that works relatively fine if your program respects the simple responsibility principle.

Log categories in Java logging libraries are hierarchical, so for instance logging with category com. ELORankingComputation would match the top level category com. This would allow the ops engineer to set up a logging configuration that works for all the ranking subsystem by just specifying configuration for this category. But it could at the same time, produce logging configuration for child categories if needed.

If your server is logging with this category my. Of course, this requires a system where you can change logging configuration on the fly. This might probably be the most important best practice. When writing your log entries messages, always anticipate that there are emergency situations where the only thing you have is the log file, from which you have to understand what happened.

Doing it right might be the subtle difference between getting fired and promoted. When a developer writes a log message, it is in the context of the code in which the log directive is to be inserted. Under these conditions, we tend to write messages that infer on the current context. Unfortunately, when reading the log itself this context is absent, and those messages might not be understandable. The reason is that those previous messages might not appear if they are logged in a different category or level.

Or worse, they can appear in a different place or way before in a multi-threaded or asynchronous context. This might seem a strange piece of advice, especially coming from a French guy.

First, I still think English is much more concise than French and better suits technical language. So the upper-layer client of the rank API will be able to log the error with enough context information.

An easy way to keep a context is to use the MDC some of the Java logging library implements. The MDC is a per-thread associative array. The logger configuration can be modified to always print the MDC content for every log line. If your program uses a per-thread paradigm, this can help solve the issue of keeping the context. For instance, this Java example is using the MDC to log per user information for a given request:. In such a situation, you need to log the context manually with every log statement.

Log entries are really good for humans but very poor for machines. Sometimes it is not enough to manually read log files, you need to perform some automated processing for instance for alerting or auditing. You want to centrally store your logs and be able to perform search requests. So what happens when you embed the log context in the string like in this hypothetical logging statement?

Well, this is not easy and very error-prone, just to get access to string parameters your code already knows natively.

Our aforementioned example could be using JSON like this:. Now your log parsers can be much easier to write, indexing becomes straightforward and you can enable all logstash power. Log files should be machine-parsable, no doubt about that. But they should also be human-readable as well.