That seems simple enough, but all Django Queries seems to be 'SELECT *'
How do I build a query returning only a subset of fields ?
In Django 1.1 onwards, you can use defer('col1', 'col2') to exclude columns from the query, or only('col1', 'col2') to only get a specific set of columns. See the documentation.
values does something slightly different - it only gets the columns you specify, but it returns a list of dictionaries rather than a set of model instances.
Append a .values("column1", "column2", ...) to your query
The accepted answer advising defer and only which the docs discourage in most cases.
only use defer() when you cannot, at queryset load time, determine if you will need the extra fields or not. If you are frequently loading and using a particular subset of your data, the best choice you can make is to normalize your models and put the non-loaded data into a separate model (and database table). If the columns must stay in the one table for some reason, create a model with Meta.managed = False (see the managed attribute documentation) containing just the fields you normally need to load and use that where you might otherwise call defer(). This makes your code more explicit to the reader, is slightly faster and consumes a little less memory in the Python process.
Related
I have multiple small key/value tables in Django, and there value never change
ie: 1->"Active", 2->"Down", 3->"Running"....
and multiple times, I do some get by id and other time by name.
So I'm asking, if it's not more optimize to move them all as Dict (global or in models) ?
thank you
Generally django querysets are slower than dicts, so if you want to write model with one field that has these statuses (active, down, running) it's generally better to use dict until there is need for editability.
Anyway I don't understand this kind of question, the performance benefits are not really high until you got ~10k+ records in single QS, and even by then you can cast the whole model to list by using .values_list syntax. Execution will take approximately part of second.
Also if I understand, these values should be anyway in models.CharField with choices field set, rather than set up by fixture in models.ForeignKey.
For a model in my database I need to store around 300 values for a specific field. What would be the drawbacks, in terms of performance and simplicity in query, if I use Postgres-specific ArrayField instead of a separate table with One-to-Many relationship?
If you use an array field
The size of each row in your DB is going to be a bit large thus Postgres is going to be using a lot more toast tables (http://www.postgresql.org/docs/9.5/static/storage-toast.html)
Every time you get the row, unless you specifically use defer (https://docs.djangoproject.com/en/1.9/ref/models/querysets/#defer) the field or otherwise exclude it from the query via only, or values or something, you paying the cost of loading all those values every time you iterate across that row. If that's what you need then so be it.
Filtering based on values in that array, while possible isn't going to be as nice and the Django ORM doesn't make it as obvious as it does for M2M tables.
If you use M2M
You can filter more easily on those related values
Those fields are postponed by default, you can use prefetch_related if you need them and then get fancy if you want only a subset of those values loaded
Total storage in the DB is going to be slightly higher with M2M because of keys, and extra id fields
The cost of the joins in this case is completely negligible because of keys.
Personally I'd say go with the M2M tables, but I don't know your specific application. If you're going to be working with a massive amount of data it's likely worth grabbing a representative dataset and testing both methods with it.
I'm trying to select all the songs in my Django database whose tag is any of those in a given list. There is a Song model, a Tag model, and a SongTag model (for the many to many relationship).
This is my attempt:
taglist = ["cool", "great"]
tags = Tag.objects.filter(name__in=taglist).values_list('id', flat=True)
song_tags = SongTag.objects.filter(tag__in=list(tags))
At this point I'm getting an error:
DatabaseError: MultiQuery does not support keys_only.
What am I getting wrong? If you can suggest a completely different approach to the problem, it would be more than welcome too!
EDIT: I should have mentioned I'm using Django on Google AppEngine with django-nonrel
You shouldn't use m2m relationship with AppEngine. NoSQL databases (and BigTable is one of them) generally don't support JOINs, and programmer is supposed to denormalize the data structure. This is a deliberate design desicion: while your database will contain redundant data, your read queries will be much simpler (no need to combine data from 3 tables), which in turn makes the design of DB server much simpler as well (of course this is made for the sake of optimization and scaling)
In your case you should probably get rid of Tag and SongTag models, and just store the tag in the Song model as a string. I of course assume that Tag model only contains id and name, if Tag in fact contains more data, you should still have Tag model. Song model in that case should contain both tag_id and tag_name. The idea, as I explained above, is to introduce redundancy for the sake of simpler queries
Please, please let the ORM build the query for you:
song_tags = SongTag.objects.filter(tag__name__in = taglist)
You should try to use only one query, so that Django also generates only one query using a join.
Something like this should work:
Song.objects.filter(tags__name__in=taglist)
You may need to change some names from this example (most likely the tags in tags__name__in), see https://docs.djangoproject.com/en/1.3/ref/models/relations/.
I'm trying to make a query using Django's Exclude() and passing to it a list, as in:
(...).exclude(id__in=list(top_vip_deals_filter))
The problem is that, apparently, there is a Limit -- depending on your database --on the size of the list being passed.
Is this correct?
If so, How to overcome this?
If not, is there some explanation to the fact that queries silently fail when the list size is big?
Thanks
If the top_vip_deals_filter comes from the database, you can set an extra where in the query:
(...).extra(where=['model.id not in select blah blah'])
(put your lowercase model name instead of model.)
You can do better if the data model allows you to. If you can do it in SQL, you probably can do it in django.
I have the following model structure:
class Container(models.Model):
pass
class Generic(models.Model):
name = models.CharacterField(unique=True)
cont = models.ManyToManyField(Container, null=True)
# It is possible to have a Generic object not associated with any container,
# thats why null=True
class Specific1(Generic):
...
class Specific2(Generic):
...
...
class SpecificN(Generic):
...
Say, I need to retrieve all Specific-type models, that have a relationship with a particular Container.
The SQL for that is more or less trivial, but that is not the question. Unfortunately, I am not very experienced at working with ORMs (Django's ORM in particular), so I might be missing a pattern here.
When done in a brute-force manner, -
c = Container.objects.get(name='somename') # this gets me the container
items = c.generic_set.all()
# this gets me all Generic objects, that are related to the container
# Now what? I need to get to the actual Specific objects, so I need to somehow
# get the type of the underlying Specific object and get it
for item in items:
spec = getattr(item, item.get_my_specific_type())
this results in a ton of db hits (one for each Generic record, that relates to a Container), so this is obviously not the way to do it. Now, it could, perhaps, be done by getting the SpecificX objects directly:
s = Specific1.objects.filter(cont__name='somename')
# This gets me all Specific1 objects for the specified container
...
# do it for every Specific type
that way the db will be hit once for each Specific type (acceptable, I guess).
I know, that .select_related() doesn't work with m2m relationships, so it is not of much help here.
To reiterate, the end result has to be a collection of SpecificX objects (not Generic).
I think you've already outlined the two easy possibilities. Either you do a single filter query against Generic and then cast each item to its Specific subtype (results in n+1 queries, where n is the number of items returned), or you make a separate query against each Specific table (results in k queries, where k is the number of Specific types).
It's actually worth benchmarking to see which of these is faster in reality. The second seems better because it's (probably) fewer queries, but each one of those queries has to perform a join with the m2m intermediate table. In the former case you only do one join query, and then many simple ones. Some database backends perform better with lots of small queries than fewer, more complex ones.
If the second is actually significantly faster for your use case, and you're willing to do some extra work to clean up your code, it should be possible to write a custom manager method for the Generic model that "pre-fetches" all the subtype data from the relevant Specific tables for a given queryset, using only one query per subtype table; similar to how this snippet optimizes generic foreign keys with a bulk prefetch. This would give you the same queries as your second option, with the DRYer syntax of your first option.
Not a complete answer but you can avoid a great number of hits by doing this
items= list(items)
for item in items:
spec = getattr(item, item.get_my_specific_type())
instead of this :
for item in items:
spec = getattr(item, item.get_my_specific_type())
Indeed, by forcing a cast to a python list, you force the django orm to load all elements in your queryset. It then does this in one query.
I accidentally stubmled upon the following post, which pretty much answers your question :
http://lazypython.blogspot.com/2008/11/timeline-view-in-django.html