What logging best practices can help with log management?

Logs can produce immense amounts of data, but logging standards, such as standardized formats and security procedures, can help IT professionals minimize information overload.

IT professionals must build logging best practices into their data collection procedures to ensure that logs produce readable and safe information without affecting performance.

Logs are essential components of effective troubleshooting and support, but they produce so much data, it can be overwhelming. Logging analysis tools are helpful, but establishing logging best practices and standards ensures proper formatting and security.

There is no single approach to creating log files, but there are some guidelines that might help improve log outcomes.

Evaluate logging platforms and legacy applications

Be careful with logging platforms. Many systems, services and applications produce their own log files, but many don't. Assets that don't produce their own logs will typically include a logging or tracing API that can communicate events and metrics to an outside logging package. Organizations that employ a logging package should consider an open or standardized logging tool. For example, the Apache Log4j tool has evolved as a popular logging package for Java platforms.

Also, don't overlook the importance of legacy applications. Logs can be particularly important assets for legacy applications because they are often at fault for operational problems. Make sure that each of your legacy applications delivers meaningful log data in a suitable format that is compatible with your log aggregation and analysis tools.

Standardize formats to make logs more useful

Logging best practices include creating logs with standardized formats.

Logs come in different formats and structures, but each log format needs its own logging tools that include corresponding parsing rules. This is cumbersome and error-prone, which makes it more difficult to find valuable data for analysis.

Try to forego custom or raw text log file formats in favor of standardized, structured formats, such as JavaScript Object Notation and key-value pair. Using standard formats is an example of logging best practices because they are easier to read and more compatible across a wider variety of tools.

Avoid the temptation to add unusual data fields or to collect unique data in each log.

Avoid the temptation to add unusual data fields or to collect unique data in each log. Diverse log schemas make it harder for log analysis tools to locate important data. This creates more complexity when aggregating multiple log files and makes it equally difficult for IT administrators to find similar data in different log files. Instead, adopt a standard schema for log files so every log collects similar data in similar places.

Examine the security and performance ramifications of log data

Evaluate the security importance of log data when integrating logging best practices. Some logs might include sensitive details, which can pose security or compliance risks. When constructing a log file, identify any sensitive data and make the best security choices for the business. For example, administrators can avoid logging sensitive data, scrub data from the logs after a period of time or encrypt logs to protect sensitive data against loss or theft.

Take the time to evaluate the effect of logging on application performance. Writing logs requires network bandwidth and storage I/O, both of which might be limited in a busy organization.

Get up to speed on log management

When managing logs, use log filters to focus on the data that is most pertinent. Effective logging also requires careful planning around business needs and priorities. After capturing the data, design and build thorough log storage policies to ensure data remains secure and accessible.

Writing multiple complex log files can potentially interfere with applications, which might cause user experience issues or other problems that IT staff might need to troubleshoot. Avoid potential lagging performance effects with logging best practices and by writing logs asynchronously to a buffer rather than committing log entries immediately to disk.

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