This release is a pre-release and may not be stable for production use.
Metrique
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” <cward@redhat.com>
Sources: https://github.com/drpoovilleorg/metrique
Installation
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.
Install metrique:
python-pip install metrique -r requirements.txt
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.
Default: ~/.metrique/cubes
To quickly make those cubes available in sys.path:
IN [] from metrique.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 [4] g.extract("git_commit")
Then, analyse away:
IN [5] q = c.query.fetch('git_commit', 'author, committer_ts')
IN [6] q.groupby(['author']).size().plot(kind='barh')
OUT [6] <matplotlib.axes.AxesSubplot at 0x6f77ad0>
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