10 Python Logging Format Best Practices

Logging is an important part of any Python application. Here are 10 best practices to make sure your logs are as useful as possible.

Logging is a critical part of any software development project. It allows developers to track events and understand what is happening with their software in real-time. Python’s logging module provides a powerful and flexible way to add logging to your applications.

In this article, we will discuss 10 best practices for working with Python’s logging module. We will cover topics such as how to configure logging, how to format log messages, and how to integrate logging with third-party services. By the end of this article, you will have a solid understanding of how to use Python’s logging module to its full potential.

1. Use the default logging format

The default logging format includes the date and time, the log level, the name of the logger, and the message. This information is important because it allows you to quickly see when the message was logged, what the log level is, where the message came from, and what the message is.

If you use a custom logging format, you will lose this valuable information. For example, if you only include the message in your custom logging format, you will not be able to see the date and time, the log level, or the name of the logger. This can make debugging difficult because you will not have all of the information that you need to troubleshoot the issue.

It is also important to note that the default logging format is compatible with most logging libraries. This means that you can switch between different logging libraries without having to worry about changing your logging format.

2. Don’t use string formatting to create log messages

String formatting is the process of substituting values into a string, usually in order to generate a message. For example, you might use string formatting to create a log message like this:

“User {} logged in at {}”.format(user_id, timestamp)

While this may seem like a convenient way to create a log message, it can actually lead to some serious problems.

The first problem is that string formatting is slow. This may not be a big deal if you’re only logging a few messages, but it can start to add up if you’re logging hundreds or even thousands of messages.

The second problem is that string formatting can cause your logs to be cluttered with unnecessary information. For example, if you’re logging a user’s ID and timestamp, you probably don’t need to include the user’s ID in the message itself. It would be much better to just include it in the metadata of the log message.

The third problem is that string formatting can make it difficult to parse and understand your logs. This is because the format of the log message can change from one message to the next, making it hard to write scripts or tools that can reliably parse them.

For these reasons, it’s best to avoid using string formatting to create log messages. Instead, you should use the LogRecord class (from the logging module) to create log messages. This class allows you to specify all of the necessary information (such as the message, level, and timestamp) in a single object, which makes it much easier to work with.

3. Logging should be as fast as possible

When an application is running in production, every millisecond counts. If logging is taking up too much time, it can slow down the entire application and impact performance. In some cases, it can even cause the application to crash.

That’s why it’s important to make sure that logging is as fast as possible. One way to do this is to use a logging format that is optimized for performance. For example, JSON is a popular choice because it can be parsed quickly and easily.

Another way to improve logging performance is to use a logging library that is designed for high performance. For example, Python’s standard logging library is not particularly fast. However, there are other libraries available that are much faster.

Finally, it’s also important to consider what you’re logging. If you’re logging too much information, it can impact performance. Therefore, it’s important to only log the information that is actually needed.

4. Make sure your logs are useful

If you’re troubleshooting an issue, the first thing you’ll want to do is check the logs. But if your logs are full of useless information, it’ll be very difficult to find the needle in the haystack that is the root cause of your issue.

Therefore, it’s important to only log the information that is actually useful for debugging purposes. This means avoiding things like logging every single request made to your API, or every single SQL query executed.

Instead, focus on logging only the information that would be relevant for debugging an issue. For example, if you’re having an issue with a particular API endpoint, you might want to log the input data and the output data for that endpoint. Or if you’re having an issue with a particular SQL query, you might want to log the query itself, as well as the input data and the output data.

By doing this, you can avoid cluttering up your logs with useless information, and make them much more useful for debugging purposes.

5. Use a library for structured logging

When you’re logging unstructured data, it can be difficult to parse and extract the information you need. This is because unstructured data is just a string of characters with no defined structure.

On the other hand, structured logging uses a predefined format that includes fields for different pieces of information. This makes it much easier to parse and extract the information you need from your logs.

There are many different libraries available for structured logging in Python. Some of the most popular ones include:




No matter which library you choose, make sure it’s one that you’re comfortable with and that will meet your needs.

6. Use a JSON formatter

When your logs are in JSON format, it’s much easier to parse and ingest them into a centralized logging system. This is because there’s no need to worry about log formats –– everything is in a standard format that can be easily parsed by the logging system.

JSON also allows for more structured data, which means you can include additional information in your logs (such as metadata). This is valuable for troubleshooting and debugging purposes.

Finally, using a JSON formatter will make it easier to migrate to a different logging system in the future, should you need to.

7. Include contextual information in your logs

Suppose you have a log message that says “invalid input.” By itself, this message is not very helpful. But if you include contextual information like the input that was invalid, the user who provided it, and when it happened, suddenly the message becomes much more useful.

Now, suppose you have hundreds or even thousands of these messages. It would be very difficult to go through them all and try to figure out what happened just based on the message alone. But if you have the relevant context included, it’s much easier to understand what’s going on and take appropriate action.

So, always remember to include contextual information in your Python logs. It will save you a lot of time and effort in the long run.

8. Don’t include sensitive data in your logs

If your logs are ever exposed (e.g. to a hacker), then you don’t want them to have access to sensitive data like passwords, credit card numbers, etc. By not including this data in your logs, you reduce the risk of a data breach.

To avoid accidentally logging sensitive data, you should use a whitelist approach when configuring your Python logging. This means only allowing certain data to be logged, and excluding everything else.

For example, let’s say you have a login form that includes a password field. When a user submits the form, you might want to log the username (but not the password). In this case, you would add the username field to your whitelist, and exclude the password field.

Configuring your Python logging in this way is a good best practice, as it helps to protect your sensitive data in the event that your logs are ever exposed.

9. Use a standard set of fields

If you’re working on a project with other developers, it’s important to have a consistent format for your logs. This makes it easier to search and parse the logs, and also makes it easier to spot errors.

A standard set of fields might include the timestamp, the log level, the name of the logger, and the message. You can also add additional fields, such as the thread ID or the request ID, if they’re relevant to your application.

It’s also a good idea to use a consistent date/time format for your timestamps. The ISO 8601 format is a good choice, as it’s easy to parse and sort.

10. Rotate your logs

When you don’t rotate your logs, they can grow infinitely large. This is a problem because:

Your disk will eventually fill up
It becomes harder to find the information you’re looking for when your logs are very large
Rotating your logs means that you periodically archive your old logs and start fresh with new ones. There are many ways to do this, but one popular method is to use the logrotate utility.

Logrotate is a tool that helps you manage your log files. It can compress old logs, delete them after a certain age, and even send emails when certain conditions are met (e.g., when a log file gets too big).

To use logrotate, you first need to create a configuration file. This file tells logrotate how often to rotate your logs and what to do with the old ones. Here’s an example:

/var/log/myapp.log {
rotate 4

This config file will rotate your myapp.log file every week. It will keep four weeks of logs and compress the old ones. The delaycompress option ensures that the most recent log file is not compressed, which is important if you need to quickly look at it.

Once you have your config file set up, you can test it by running logrotate with the -d option:

logrotate -d /etc/logrotate.conf

If everything looks good, you can run it without the -d option to actually rotate your logs.

logrotate /etc/logrotate.conf

You can also add logrotate to your crontab so that it runs automatically. For example, you could add the following line to run logrotate every day at 3:00 am:

0 3 * * * logrotate /etc/logrotate.conf


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