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Python/MongoDB Information Platform - Client

Project description



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” <>



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 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, 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.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  []
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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