Why …

… Structured Logging?

I believe the widespread use of format strings in logging is based on two presumptions:

  • The first level consumer of a log message is a human.

  • The programmer knows what information is needed to debug an issue.

I believe these presumptions are no longer correct in server side software.

Paul Querna

Structured logging means that you don’t write hard-to-parse and hard-to-keep-consistent prose in your log entries. Instead, you log events that happen in a context of key-value pairs.


More general advice about production-grade logging can be found in the later chapter on Logging Best Practices.

… structlog?

Easier Logging

You can stop writing prose and start thinking in terms of an event that happens in the context of key-value pairs:

>>> from structlog import get_logger
>>> log = get_logger()
>>> log.info("key_value_logging", out_of_the_box=True, effort=0)
2020-11-18 09:17:09 [info     ] key_value_logging    effort=0 out_of_the_box=True

Each log entry is a meaningful dictionary instead of an opaque string now!

That said, structlog is not taking anything away from you. You can still use string interpolation using positional arguments:

>>> log.info("Hello, %s!", "world")
2022-10-10 07:19:25 [info     ] Hello, world!

Data Binding

Since log entries are dictionaries, you can start binding and re-binding key-value pairs to your loggers to ensure they are present in every following logging call:

>>> log = log.bind(user="anonymous", some_key=23)
>>> log = log.bind(user="hynek", another_key=42)
>>> log.info("user.logged_in", happy=True)
2020-11-18 09:18:28 [info     ] user.logged_in    another_key=42 happy=True some_key=23 user=hynek

You can also bind key-value pairs to context variables that look global, but are local to your thread or asyncio context – which usually means your web request.

Powerful Pipelines

Each log entry goes through a processor pipeline that is just a chain of functions that receive a dictionary and return a new dictionary that gets fed into the next function. That allows for simple but powerful data manipulation:

def timestamper(logger, log_method, event_dict):
    """Add a timestamp to each log entry."""
    event_dict["timestamp"] = time.time()
    return event_dict

There are plenty of processors for most common tasks coming with structlog:


structlog is completely flexible about how the resulting log entry is emitted. Since each log entry is a dictionary, it can be formatted to any format:

  • A colorful key-value format for local development,

  • JSON of logfmt for easy parsing,

  • or some standard format you have parsers for like nginx or Apache httpd.

Internally, formatters are processors whose return value (usually a string) is passed into loggers that are responsible for the output of your message. structlog comes with multiple useful formatters out-of-the-box.


structlog is also flexible with the final output of your log entries:

  • A built-in lightweight printer like in the examples above. Easy to use and fast.

  • Use the standard library’s or Twisted’s logging modules for compatibility. In this case structlog works like a wrapper that formats a string and passes them off into existing systems that won’t know that structlog even exists.

    Or the other way round: structlog comes with a logging formatter that allows for processing third party log records.

  • Don’t format it to a string at all! structlog passes you a dictionary and you can do with it whatever you want. Reported use cases are sending them out via network or saving them to a database.

Highly Testable

structlog is thoroughly tested and we see it as our duty to help you to achieve the same in your applications. That’s why it ships with a test helpers to introspect your application’s logging behavior with little-to-no boilerplate.