I am working on a reporting feature that allows users for querying
arbitrary models and fields, and present the result as a table. For
example, consider the following object model:
Package
|
v
Device <- Component
^ ^
| |
|---- Interface <---2--- Connection
^^^
/ | \
/ | \
/ | \
Sampling IP Policy
(The dash is the direction of a ForeignKey.)
To produce a report, I chose a three-step process:
1. The user interfaces returns a list of fields to be included in the
report, such as
args = dict('Device.hostname__contains': 'localhost',
'Package.name__icontains': 'unix', 'IP.address__contains': '192')
2. Given the list of args, find the shortest path that connects all
required models. For the example above, the result is a tuple:
path = Device, Package, Interface, IP
3. In theory, I could now perform the following SQL request:
SELECT * FROM myapp_device d
LEFT JOIN myapp_package pa ON pa.device_id=d.id
LEFT JOIN myapp_interface ifc ON ifc.device_id=d.id
LEFT JOIN myapp_ip ip ON ip.interface_id=ifc.id;
But of course, I want to avoid the raw SQL. I considered the following
options:
- Using Device.objects.select_related() does not work, because Device has a
1:n relation to Package (and also to Unit), which Django's select_related()
does not support.
- Using prefetch_related() does not work, because it prefetches everything,
which is too much in our case (>100 million rows if a user queries on all
tables), and it does not provide us with a total of the number of rows
selected. In practice, I want to count(*) everything for displaying a
total, and fetch only a subset, using LIMIT.
Our tests showed that the raw SQL query with LEFT JOIN is fast enough for
production, regardless of what fields and objects are being queried. The
craziest query I built took about 20 seconds, which is ok for what we are
trying to do.
Any other options?
-Samuel
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