[Python-Dev] An yocto change proposal in logging module to simplify structured logs support

Ludovic Gasc gmludo at gmail.com
Sun May 24 19:22:17 CEST 2015


1. The problem

For now, when you want to write a log message, you concatenate the data
from your context to generate a string: In fact, you convert your
structured data to a string.
When a sysadmin needs to debug your logs when something is wrong, he must
write regular expressions to extract interesting data.

Often, he must find the beginning of the interesting log and follow the
path. Sometimes, you can have several requests in the same time in the log,
it's harder to find interesting log.
In fact, with regular expressions, the sysadmin tries to convert the log
lines strings to structured data.

2. A possible solution

You should provide a set of regular expressions to your sysadmins to help
them to find the right logs, however, another approach is possible:
structured logs.
Instead of to break your data structure to push in the log message, the
idea is to keep the data structure, to attach that as metadata of the log
For now, I know at least Logstash and Journald that can handle structured
logs and provide a query tool to extract easily logs.

3. A concrete example with structured logs

As most Web developers, we build HTTP daemons used by several different
human clients in the same time.
In the Python source code, to support structured logs, you don't have a big
change, you can use "extra" parameter for that, example:

    [handle HTTP request]
    LOG.debug('Receive a create_or_update request', extra={'request_id':

                     'account_id': account_id,

                     'aiohttp_request': request,

                     'payload': str(payload)})
   [create data in database]
    LOG.debug('Callflow created', extra={'account_id': account_id,
                                             'aiopg_cursor': cur,
                                             'results': row})

Now, if you want, you can enhance the structured log with a custom logging
Handler, because the standard journald handler doesn't know how to handle
aiohttp_request or aiopg_cursor.
My example is based on journald, but you can write an equivalent version
with python-logstash:
from systemdream.journal.handler import JournalHandler

class Handler(JournalHandler):
    # Tip: on a system without journald, use socat to test:
    # socat UNIX-RECV:/run/systemd/journal/socket STDIN
    def emit(self, record):
        if record.extra:
            # import ipdb; ipdb.set_trace()
            if 'aiohttp_request' in record.extra:
                record.extra['http_method'] =
                record.extra['http_path'] =
                record.extra['http_headers'] =
            if 'aiopg_cursor' in record.extra:
                record.extra['pg_query'] =
                record.extra['pg_status_message'] =
                record.extra['pg_rows_count'] =

And you can enable this custom handler in your logging config file like

And now, with journalctl, you can easily extract logs, some examples:
Logs messages from 'lg' account:
    journalctl ACCOUNT_ID=lg
All HTTP requests that modify the 'lg' account (PUT, POST and DELETE):
    journalctl ACCOUNT_ID=lg HTTP_METHOD=PUT
Retrieve all logs from one specific HTTP request:
    journalctl REQUEST_ID=130b8fa0-6576-43b6-a624-4a4265a2fbdd
All HTTP requests with a specific path:
    journalctl HTTP_PATH=/v1/accounts/lg/callflows
All logs of "create" function in the file "example.py"
   journalctl CODE_FUNC=create CODE_FILE=/path/example.py

If you already do a troubleshooting on a production system, you should
understand the interest of this:
In fact, it's like to have SQL queries capabilities, but it's logging
We use that since a small time on one of our critical daemon that handles a
lot of requests across several servers, it's already adopted from our
support team.

4. The yocto issue with the Python logging module

I don't explain here a small part of my professional life for my pleasure,
but to help you to understand the context and the usages, because my patch
for logging is very small.
If you're an expert of Python logging, you already know that my Handler
class example I provided above can't run on a classical Python logging,
because LogRecord doesn't have an extra attribute.

extra parameter exists in the Logger, but, in the LogRecord, it's merged as
attributes of LogRecord:

It means, that when the LogRecord is sent to the Handler, you can't
retrieve the dict from the extra parameter of logger.
The only way to do that without to patch Python logging, is to rebuild by
yourself the dict with a list of official attributes of LogRecord, as is
done in python-logstash:
At least to me, it's a little bit dirty.

My quick'n'dirty patch I use for now on our CPython on production:

diff --git a/Lib/logging/__init__.py b/Lib/logging/__init__.py
index 104b0be..30fa6ef 100644
--- a/Lib/logging/__init__.py
+++ b/Lib/logging/__init__.py
@@ -1382,6 +1382,7 @@ class Logger(Filterer):
         rv = _logRecordFactory(name, level, fn, lno, msg, args, exc_info,
+        rv.extra = extra
         if extra is not None:
             for key in extra:
                 if (key in ["message", "asctime"]) or (key in rv.__dict__):

At least to me, it should be cleaner to add "extra" as parameter
of _logRecordFactory, but I've no idea of side effects, I understand that
logging module is critical, because it's used everywhere.
However, except with python-logstash, to my knowledge, extra parameter
isn't massively used.
The only backward incompatibility I see with a new extra attribute of
LogRecord, is that if you have a log like this:
    LOG.debug('message', extra={'extra': 'example'})
It will raise a KeyError("Attempt to overwrite 'extra' in LogRecord")
exception, but, at least to me, the probability of this use case is near to

Instead of to "maintain" this yocto patch, even it's very small, I should
prefer to have a clean solution in Python directly.

Thanks for your remarks.

Ludovic Gasc (GMLudo)
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