We're rolling out Graphite and statsd at [[http://saymedia.com][work]],
and I've spend some time debugging our setup. Most of the time, the only
thing I need is =tcpdump= to verify that a host is sending correctly the
various metrics.

But today, thanks to a
[[http://if.andonlyif.net/blog/2013/01/the-case-of-the-disappearing-metrics.html][stupid
reason]], I've learned about another way to debug
[[http://graphite.readthedocs.org/en/latest/carbon-daemons.html][carbon]]:
the manhole. The idea of the manhole is to give you a access to a REPL
attached to the live process. When my boss told me about it, I was at
first surprised to see this in a Python application. I've already been
exposed to this kind of debugging thanks to Clojure, where it's not
uncommon to connect a REPL to your live application (for example, Heroku
[[https://devcenter.heroku.com/articles/debugging-clojure][document how
to connect to a remote live REPL in your application]]). When I first
heard of that I was very skeptical (give access to a /live/ environment,
and let the developer mess with the process ?!). But I've learned to
love it and I feel naked when I'm working in an environment where this
is not available. So I was happy to jump and take a look at that
feature.

Since it's not very well documented and I had a hard time finding some
information, let me share here the basics.

First you'll need to configure Carbon's to allow the connection:

#+BEGIN_EXAMPLE
    ENABLE_MANHOLE = True # by default it's set to False
    MANHOLE_INTERFACE = 127.0.0.1
    MANHOLE_PORT = 7222
    MANHOLE_USER = admin
    MANHOLE_PUBLIC_KEY = <your public SSH key, the string, not the path to the key>
#+END_EXAMPLE

Now you can restart carbon, and connect to the Python shell with
=ssh admin@127.0.0.1 -p7222=. This manhole is useful to get an idea of
the data structure your process is handling, or to get an idea of what's
going on (is there a lot of keys being held in memory? Is the queue size
for one metric huge? etc).

From here, you can execute Python code to examine the data of the
process:

#+BEGIN_SRC python
    >>> from carbon.cache import MetricCache
    >>> print MetricCache['PROD.apps.xxx.yyy.zzz]
    [(1357861603.0, 93800.0), (1357861613.0, 98200.0), (1357861623.0, 91900.0)]
#+END_SRC

The
[[https://github.com/graphite-project/carbon/blob/master/lib/carbon/cache.py#L19][=MetricCache=]]
class is a Python dictionary where you can access your keys. You can
also list all the metrics with the size of their queue with
=MetricCache.counts()=.

Or even force the daemon to write to disk all the data points:

#+BEGIN_SRC python
    >>> from carbon.writer import writeCachedDataPoints
    >>> writeCachedDataPoints()
#+END_SRC

Before doing any of that, I would recommend to read the code of carbon.
It's pretty short and quiet straight forward, especially the code of the
[[https://github.com/graphite-project/carbon/blob/master/lib/carbon/writer.py][writer]].

Of course, you have to know what you're doing when you're executing code
from a REPL in a live environment.