1. Know what to log
- Messages (Both Incoming and Outgoing)
- Service and Function Invocations
- User Interactions and Business Stats
- Data Operations
2. Know When to Use Each Log Level
- FATAL – Identifies extremely serious error events that are likely to cause the program to abort. Typically, this leads to catastrophic failures.
- ERROR – Identifies error events that may still let the software run, but with restricted capabilities in the impacted routes.
- WARN – Describes events that are less destructive than errors. They usually do not result in any reduction of the program's functionality or its full failure. They are, however, red signs that must be investigated.
- INFO LEVEL – In the program behavior, it denotes the major event banners and informational messages.
- DEBUG – Denotes particular and detailed data, which is mostly used for debugging. These logs assist us in debugging the code.
- TRACE – To provide the greatest information on a specific event/context, it denotes the most low-level information, such as stack traces of code. These logs allow us to examine variable values as well as complete error stacks.
3. Use English Language and Friendly Log Messages
Certain Unicode characters are not supported by several tools and terminal consoles for printing and saving log messages.
#4 Have a Consistent Structure Across All Logs
A standard log file structure that is consistent across all log files is required for good logging. Each log statement line should reflect a single event and include the timestamp, hostname, service, and project logger name, among other things. The thread or process id, event id, session id, and user id can all be used as additional values.
#5 Understand Metrics
Metrics are a fundamental notion in logging requirements. A metric is
a measurement of a property's worth over time, usually at regular
intervals.
The following is a list of common metric types:
- Meter – Calculates the frequency of events (ex: rate of visitors to your website)
- Timer – Measures the length of time it takes for a procedure to be completed (ex: your web server response time)
- Counter – Integer values are incremented and decremented (ex: number of signed-in users)
- Gauge – An arbitrary value to be measured (ex: CPU)
#6 Make Each Log Message Unique
#7 Always Provide Context
For instance, consider the following two log statements:
- “The database is unavailable.”
- “Failed to Get users' preferences for user id=1. Configuration Database not responding. Please retry again in 3 minutes.”
#8 Reporting Alerts and Exception Handling
If something goes wrong in your code and you already know what to do, don't log in and then set an alarm — it's far too difficult and error-prone
. Instead, create an alert from the code itself.
#9 Write Log Parsers and Proactively Monitor Logs
Most API logging systems include the ability to create custom log parsers and filters. These parsers allow us to store log data in more organized ways, making queries much easier and faster. Log data that is correctly organized can also be supplied into log monitoring and anomaly detection systems to proactively monitor the system and forecast future events.