Python/MongoDB Information Platform - Server
Python/MongoDB Information Platform and Data Warehouse
Metrique help bring data into an intuitive, indexable data object collection that supports quick snapshotting, advanced ad-hoc querying, including (mongodb) aggregations and mapreduce, along with python, ipython, pandas, numpy, matplotlib, and so on, is fully integrated with the scientific python computing stack. I hope so anyway. :)
Author: “Chris Ward” <firstname.lastname@example.org>
You must first install MongoDB. Then, to continue, make sure it’s started.
Metrique (suggested) Install virtualenv and create a new virtual environment for metrique. Activate it.
python-pip install metrique -r requirements.txt
Make sure you have gcc and python-devel libraries installed
If you see ‘Connection reset by peer’ error, try option: –use-mirrors
If you see any other error, Google.
You should now be ready to go.
Run metrique-server-config.py if you changed any defaults.
To start metrique, run:
$[/metrique/server/bin] metrique-server start [2|1|0] [1|0]
Where argv are debug on+/on/off and async on/off respectively.
It’s suggested to run :mod:metrique-server-setup after install as well, especially if you changed any default values of your mongo or metrique servers, they’re hosted on a different ip than localhost.
Client If the metrique server is running on anything other than http://127.0.0.1, run metrique-client-setup.
Then, launch a python shell. We suggest ipython notebook.
As of this time, :mod:cubes can be found in global metrique namespace or local to the running user.
To quickly make those cubes available in sys.path:
IN  from metrique.client.cubes import set_cube_path IN  set_cube_path() # defaults to '~/.metrique/cubes'
Then, to load a cube for extraction, query or administration, import:
IN  from git_repo.gitrepo import Commit IN  g = Commit(config_file=None, uri=None)
Ping the server to ensure your connected. If all is well, metriqe server should pong your ping!:
IN  g.ping() OUT  pong!
Try running an example ::mod:git_commit etl job, for example:
IN  g.extract("git_commit")
Then, analyse away:
IN  q = c.query.fetch('git_commit', 'author, committer_ts') IN  q.groupby(['author']).size().plot(kind='barh') OUT  <matplotlib.axes.AxesSubplot at 0x6f77ad0>