mini webapp for viewing tabular reports
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Kohlrabi is a mini webapp, based off of Tornado, for viewing tabular report data. You can try running it like this:

python kohlrabi/ -c config.yaml.example

Customizing Kohlrabi

Out of the box, Kohlrabi includes a module kohlrabi.modules.example that demonstrates some example reports. These are meant to be an inspiration for the types of reports you might want to put into Kohlrabi, and how to create a new class. However, they're probably not very useful to most people; most people will need to customize what data they store in Kohlrabi to be slightly or entirely different.

The way customization works in Kohlrabi is to create a new Python module in the same format as the one in kohlrabi.modules.example (look at the source code). In the configuration file, you'll specify this as your module; this module should be something available in sys.path that can be imported using Python's __import__ directive. Any SQLAlchemy tables in this module with the metaclass ReportMeta will be detected by Kohlrabi as a potential data source, which you can upload data for.

Reports are uploaded to Kohlrabi by making an HTTP POST request to the Kohlrabi server, indicating the date, the data for the report, and the data source.

The next section will cover this in more detail.

Adding New Reports

It's easiest to explain this with an example. Suppose the report module specified by the config module variable has the following code in it:

from sqlalchemy import *
from kohlrabi.db import *

class DailySignups(Base):
    __tablename__ = 'daily_signups_report'
    __metaclass__ = ReportMeta

    id = Column(Integer, primary_key=True)
    date = Column(Date, nullable=False)
    referrer = Column(String, nullable=False)
    clickthroughs = Column(Integer, nullable=False, default=0)
    signups = Column(Integer, nullable=False, default=0)

    display_name = 'Daily Signups'
    html_table = [
        ReportColumn('Referrer', 'referrer'),
        ReportColumn('Click-Throughs', 'clickthroughs'),
        ReportColumn('Signups', 'signups'),

    def report_data(cls, date):
        return session.query(cls).filter( == date).order_by(

This is a data source that will track users who sign up on your site, based on the HTTP Referrer header. The table has three columns: referrer will track the domain that referred the initial visit to your site, clickthroughs will track who many people came to the site from that referrer, and signups will track how many of those people actually signed up.

The next step is to create the table in your Kohlrabi SQLite database. If you don't do this, Kohlrabi will automatically create the table, but the table won't have any indexes. In most cases you should probably at least add an index on the date column, and probably an index on the full set of columns you plan on querying from the report_data method:

CREATE TABLE daily_signups_report (
    date DATE NOT NULL,
    referrer VARCHAR NOT NULL,
    clickthroughs INTEGER NOT NULL,
    signups INTEGER NOT NULL,
    PRIMARY KEY (id)
CREATE INDEX daily_signups_date_idx ON daily_signups_report (date, signups);

OK, that's all the setup you need to do on the Kohlrabi side of things: create a Python SQLAlchemy class, and create a table in your SQLite database. The second step is to write a report that generates data to store in Kohlrabi. You can do this however you want, in any language you want. This report should finish by making a normal HTTP POST request to your Kohlrabi instance, with URL /upload, and the following POST parameters:

  • date -- the date for this data, in the format YYYY-MM-DD
  • table -- the name of the Python class you defined earlier (in this example, DailySignups)
  • data -- A JSON list of dictionaries mapping column names (excluding id and date) to their respective values

For instance, if we were running Kohlrabi on http://localhost:8888, then the following Python code would generate a sample report for 2001-01-1:

import json
import urllib

    urllib.urlencode({'date': '2010-01-01',
                      'data': json.dumps([{'referrer': '',
                                           'clickthroughs': 100,
                                           'signups': 7},
                                          {'referrer': '',
                                           'clickthroughs': 500,
                                           'signups': 42}]),
                      'table': 'DailySignups'}))

Just to reiterate: because the interface to Kohlrabi is a normal HTTP request using JSON, you can use any language to send data to Kohlrabi. You can use Java, Ruby, a bash script, etc. Whatever works for you.


This section describes the parameters that can be placed in the config file. The config file should be in YAML format. You can specify the path to the configuration file by invoking kohlrabi/ with the -c option, e.g. python kohlrabi/ -c /etc/kohlrabi.yaml.

  • database -- this is a string with the SQLAlchemy-style path to the database. An example would be sqlite:///foo.sqlite for a foo.sqlite file in the current directory, or sqlite:////tmp/foo.sqlite for /tmp/foo.sqlite. You can also use MySQL, PostgreSQL, etc. For more details, refer to the upstream SQLAlchemy Documentation on creating engines.
  • debug -- forces debug mode. You can also invoke with the --debug option to get the same effect (debug mode is enabled if either switch is on).
  • module -- this is the name of the Python database module you're using. For instance, a valid value might be kohlrabi.modules.example. See below for more details on setting this value.
  • path_prefix -- this changes the prefix used for all URLs internally within kohlrabi. For instance, you might run kohlrabi via Apache's mod_proxy ProxyPass, and all URLs should be prefixed with /kohlrabi/. The use of initial/trailing slashes here is not signficant, they'll be added if you omit them.

Running custom database modules is achieved by specifying a module in your config file, or invoking with the -m options. Internally, this is implemented by Kohlrabi by using Python's __import__ bulitin to load the module. This means that if the location of the module is in a place that's not in your default Python path, you should export that location via the PYTHONPATH environment setting when invoking Kohlrabi.

For instance, suppose Kohlrabi is installed somewhere like /usr/lib/python2.7/site-packages (or in any other site package directory). For you custom module report, you created a file named /tmp/kohlrabi_reports/ When invoking Kohlrabi, you should run it like so:

PYTHONPATH="/tmp:$PYTHONPATH" python kohlrabi/ -m my_report

This will ensure that the __import__ statement is able to find the module you specified.