9 Essential Logging Best Practices to Boost System Performance

Logging is no longer a question of whether to record logs, but how to log and what to log, which has become a focus for IT operations teams seeking to improve application performance and ROI.

This article collects several best practices and key points to help you log more intelligently, saving valuable time and resources when troubleshooting issues. (There is an Easter egg at the end.)

1. Understand Your Audience

Before handling logs, you need to recognize that application logs have two very different audiences: humans and machines.

Machines excel at quickly and automatically processing large amounts of structured data, while humans are slower at processing large volumes but handle unstructured data well.

To maximize the value of logs, they must satisfy both human readability and machine‑structured requirements.

2. Log Standardization

The prerequisite for good logging is defining a standard structure for log files, and ensuring this structure is consistent across all system logs.

Each log line represents a single event and should include a timestamp, hostname, service name, and logger name. Additional information may include thread or process ID, event ID, session ID, and user ID.

Other important values may be environment‑specific, such as instance ID, deployment name, application version, or other key‑value pairs related to the event.

Use high‑precision timestamps (millisecond precision if finer granularity is unavailable) and include timezone data. Unless there is a compelling reason otherwise, use the ISO‑8601 standard format.

Professional logging adds a unique ID to every log line. For error logs, add an error ID, which is extremely useful in a log‑management system.

3. Understand Metrics

Metrics are a core concept of logging, describing overall quantitative characteristics.

A metric is the value of a particular attribute at a specific point in time, typically measured at regular intervals.

Common metric types include:

Meter – measures the rate of events (e.g., website visitor rate);

Timer – measures the time a process takes (e.g., web‑server response time);

Counter – increments or decrements an integer value (e.g., number of logged‑in users);

Gauge – measures an arbitrary value (e.g., CPU utilization).

Each metric describes the state of some system attribute. The benefit of metrics is the abundance of data and the ability to correlate different metrics.

When observing metrics, it is advisable to track them over the long term and store metric data separately from logs.

4. Alerting and Troubleshooting

If a bug is discovered and the system is confirmed to be failing, do not set an ALERT on the error log – this is usually complex and error‑prone. Instead, trigger the ALERT directly from the code.

When recording exceptions, stack traces are useful but hard to read. Using Apache ExceptionUtils can summarize stack‑trace information, making it easier to work with.

5. Log Severity Levels

Different events have different severities. This distinction helps you separate critical events from important ones, and irregular events from routine ones.

Do not ignore low‑probability but severe events; they can serve as data points when establishing baseline application behavior.

Your logs should primarily contain Debug, Info, and Warn messages, with only a few Error entries.

6. Always Provide Log Context

Developers log based on code context, but readers of the logs often lack that context and may not have access to the source code.

Consider the following two log lines:

"Database is down."

"Failed to fetch preferences for user ID=1; configuration database did not respond; will retry in 5 minutes."

The second line makes it easy to understand what the application attempted, which component failed, and whether a remedy exists.

Each log line should contain enough information to accurately understand what happened and the application’s state at that moment.

7. Choose a Good Logging Framework and Leverage Its Advanced Features

Do not waste time building your own logging framework. Every programming language has numerous high‑quality logging libraries (except perhaps TrumpScript).

Logging frameworks allow you to configure multiple appenders, each with its own output format and custom log pattern. Standard features include automatic addition of logger name, timestamp, support for multiple severity levels, and filtering by those levels.

Advanced features you may use:

Configure different log‑level thresholds for different components in code;

Use a discard appender to drop low‑level events when the queue is full;

Use a summary appender to record "message repeated X times: [message]" without actually duplicating the log entry;

Set a threshold so that when a higher‑severity log occurs, N lower‑level logs are also emitted.

8. Write Structured Logs (When Appropriate)

Having your appenders write structured logs may impact performance slightly, but the value is significant.

In the future, loading custom tools into a log‑analysis platform or using a log agent will become easier.

9. Log More, Not Less

We often ignore important logs or intentionally keep logs minimal. When answers cannot be found in the logs, you waste additional time figuring out what the system did and then spend considerable effort parsing it.

Log quality is part of code quality and overall system quality.

In summary, adopt a centralized logging solution, automate log processing, apply the techniques above, and keep logging consistently.