The insights database

collect_insights rebuilds the insights database from your own data. It is a normal SQLite file, so you can read it with anything that speaks SQL. Here are the tables.

Table	One row per	Main columns
`insights_app`	module with an `insights.py`	`uuid`, `module`, `label`
`insights_counter`	counter	`app_id`, `label`, `question`, `desc`, `value`, `trend`, `created_at`
`insights_gauge`	gauge	the same as a counter, `value` is a decimal number
`insights_bucket`	chart	`app_id`, `label`, `question`, `desc`, `type`, `xlabel`, `xformat`, `ylabel`, `yformat`, `title`
`insights_bucketvalue`	point or bar of a chart	`bucket_id`, `timestamp`, `xvalue`, `yvalue`, `category`

The label is the name of your function and is unique per app, so two apps can both have a count_users.
The uuid of an app is derived from its module path, so a dashboard URL stays the same when you rebuild the database.
type of a bucket is 1 for a time series, 3 for a scatter plot, 4 for a bar chart and 5 for a horizontal bar chart.
trend is a JSON list with the latest 60 values of the trend, empty without a trend= function.
In insights_bucketvalue the timestamp is only filled for a time series, and a bucket has at most one value per timestamp.

Querying #

sqlite3 db/insights.db "
  select a.label, c.label, c.value
  from insights_counter c join insights_app a on a.uuid = c.app_id
"

Shop|count_orders|40

The values of a chart:

sqlite3 db/insights.db "
  select category, xvalue from insights_bucketvalue v
  join insights_bucket b on b.id = v.bucket_id
  where b.label = 'by_status' order by xvalue desc
"

From Python, the standard library is enough:

import sqlite3

with sqlite3.connect("db/insights.db") as db:
    for label, value in db.execute("select label, value from insights_counter"):
        print(label, value)

Or use the models of Django Insights from a shell of your project, they live on the insights database:

from django_insights.models import Counter

Counter.objects.get(label="count_orders").value

The file holds answers, not your data: counts, averages and the points of the charts. That is what makes it safe to share on request. Check what your insights return before you hand it out, a category can be an email address if you group by one.

Querying #

Sharing #