Tracing with Spans

A span is the building block of a trace. You might also think of spans as logs with extra functionality — a single row in our live view.

Info

Spans let you add context to your logs and measure code execution time. Multiple spans combine to form a trace, providing a complete picture of an operation's journey through your system.

Example #1

In this example:

1. The outer span measures the time to count the total size of files in the current directory (cwd).
2. Inner spans measure the time to read each individual file.
3. Finally, the total size is logged.

from pathlib import Path
import logfire

logfire.configure()

cwd = Path.cwd()
total_size = 0

with logfire.span('counting size of {cwd=}', cwd=cwd):
    for path in cwd.iterdir():
        if path.is_file():
            with logfire.span('reading {path}', path=path.relative_to(cwd)):
                total_size += len(path.read_bytes())

    logfire.info('total size of {cwd} is {size} bytes', cwd=cwd, size=total_size)

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Example #2

In this example:

1. The outer span sets the topic — the user's birthday
2. The user input is captured in the terminal
3. dob (date of birth) is displayed in the span
4. Logfire calculates the age from the dob and displays age in the debug message

logfire.configure()

with logfire.span('Asking the user for their {question}', question='birthday'):  # (1)!
    user_input = input('When were you born [YYYY-mm-dd]? ')
    dob = date.fromisoformat(user_input)  # (2)!
    logfire.debug('{dob=} {age=!r}', dob=dob, age=date.today() - dob)  # (3)!

1. Spans allow you to nest other Logfire calls, and also to measure how long code takes to run. They are the fundamental building block of traces!
2. Attempt to extract a date from the user input. If any exception is raised, the outer span will include the details of the exception.
3. This will log for example dob=2000-01-01 age=datetime.timedelta(days=8838) with debug level.

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By instrumenting your code with traces and spans, you can see how long operations take, identify bottlenecks, and get a high-level view of request flows in your system — all invaluable for maintaining the performance and reliability of your applications.