In hybris, I have Created ArchivalOrderModel extends orderModel
Moved Data from OrderModel To ArchivalOrderModel. (Deleted Record from OrderModel)
When I fired query
select {pk} from {order}
In result Im getting Records of ArchivalOrderModel too.
Is there any way to restrict with help of data Modeling definition or Any other suggestion for archival
When we search any type, by default its subtypes will also be retrieved in the result.
If you want to exclude the subtype, you must use the exclamation mark (!) while specifying the type in the query like this.
Select * from {Order!}
Related
I want to know how efficient this filter can be done with django queries. Essentially I have the followig two clases
class Act(models.Model):
Date = models.DateTimeField()
Doc = models.ForeignKey(Doc)
...
class Doc(models.Model):
...
so one Doc can have severals Acts, and for each Doc I want to get the act with the greater Date. I'm only interested in Acts objects.
For example, if a have
act1 = (Date=2021-01-01, Doc=doc1)
act2 = (Date=2021-01-02, Doc=doc1)
act3 = (Date=2021-01-03, Doc=doc2)
act4 = (Date=2021-01-04, Doc=doc2)
act5 = (Date=2021-01-05, Doc=doc2)
I want to get [act2, act5] (the Act with Doc=doc1 with the greater Date and the Act with Doc=doc2 with the greater Date).
My only solution is to make a for over Docs.
Thank you so much
You can do this with one or two queries: the first query will retrieve the latest Act per Doc, and then the second one will then retrieve the acts:
from django.db.models import OuterRef, Subquery
last_acts = Doc.objects.annotate(
latest_act=Subquery(
Act.objects.filter(
Doc_id=OuterRef('pk')
).values('pk').order_by('-Date')[:1]
)
).values('latest_act')
and then we can retrieve the corresponding Acts:
Act.objects.filter(pk__in=last_acts)
depending on the database, it might be more efficient to first retrieve the primary keys, and then make an extra query:
Act.objects.filter(pk__in=list(last_acts))
In salesforce we have two objects. The first is a Component pricing object (Comp_Pricing__c). This has records with component part numbers and pricing in their respective fields, Part_Number & Pkg_Price__c. We also have an object in which we put together proposals with quotes. In this object we use an apex class and trigger to run our calculations to determine quantities of parts needed. We would like to have the apex class, based on a variable (apPart), search through the records, find the corresponding part number and then pull back the price for use in further apex calculations. I believe I will need to run a query on the records but have no idea how to do this. Can I get some help?
list cpFTList = [SELECT Pkg_Price__c FROM Component_Pricing__c WHERE Component_Pricing__c.Part_Number__c = :PRT_Pr_Ft]; Pr_Ft = cpFTList[0].Pkg_Price__c;
This is the final query that works.
Thanks Chiz for the help.
I am looking for select custom fields of "who" object using SOQL in slaesforce:
As I am testing following SOQL, its working fine.
Select t.Who.FirstName, t.Status From Task t Where t.ActivityDate = TODAY AND t.Who.Type = 'Lead'
but while add any custom field of lead object(who object here) in select statement, as mentioned following query, its give error as
No such column 'LeadExtraInfo__c' on entity 'Name'.
even LeadExtraInfo field exist into Lead object
Select t.Who.FirstName, t.Who.LeadExtraInfo__c t.ActivityDate From Task t Where t.ActivityDate = YESTERDAY AND t.Who.Type = 'Lead'
The Who and What relationships on Task are not normal relationships -- they are polymorphic. This means that they can refer to different types of objects.
In the case of Who, it can refer to a Lead or a Contact. As such, you can only get to a limited subset of the fields in the relationship, namely the ones mentioned in this doc page.
To get to other fields on Lead, you'll have to query the Task first to get its WhoId, then query Lead where the Id equals that WhoId value to get those other fields.
I have an Article type structured like this:
type Article struct {
Title string
Content string `datastore:",noindex"`
}
In an administrative portion of my site, I list all of my Articles. The only property I need in order to display this list is Title; grabbing the content of the article seems wasteful. So I use a projection query:
q := datastore.NewQuery("Article").Project("Title")
Everything works as expected so far. Now I decide I'd like to add two fields to Article so that some articles can be unlisted in the public article list and/or unviewable when access is attempted. Understanding the datastore to be schema-less, I think this might be very simple. I add the two new fields to Article:
type Article struct {
Title string
Content string `datastore:",noindex"`
Unlisted bool
Unviewable bool
}
I also add them to the projection query, since I want to indicate in the administrative article list when an article is publicly unlisted and/or unviewable:
q := datastore.NewQuery("Article").Project("Title", "Unlisted", "Unviewable")
Unfortunately, this only returns entries that have explicitly included Unlisted and Unviewable when Put into the datastore.
My workaround for now is to simply stop using a projection query:
q := datastore.NewQuery("Article")
All entries are returned, and the entries that never set Unlisted or Unviewable have them set to their zero value as expected. The downside is that the article content is being passed around needlessly.
In this case, that compromise isn't terrible, but I expect similar situations to arise in the future, and it could be a big deal not being able to use projection queries. Projections queries and adding new properties to datastore entries seem like they're not fitting together well. I want to make sure I'm not misunderstanding something or missing the correct way to do things.
It's not clear to me from the documentation that projection queries should behave this way (ignoring entries that don't have the projected properties rather than including them with zero values). Is this the intended behavior?
Are the only options in scenarios like this (adding new fields to structs / properties to entries) to either forgo projection queries or run some kind of "schema migration", Getting all entries and then Puting them back, so they now have zero-valued properties and can be projected?
Projection queries source the data for fields from the indexes not the entity, when you have added new properties pre-existing records do not appear in those indexes you are performing the project query on. They will need to be re-indexed.
You are asking for those specific properties and they don't exist hence the current behaviour.
You should probably think of a projection query as a request for entities with a value in a requested index in addition to any filter you place on a query.
I am working currently on telecom analytics project and newbie in query optimisation. To show result in browser it takes a full minute while just 45,000 records are to be accessed. Could you please suggest on ways to reduce time for showing results.
I wrote following query to find call-duration of a person of age-group:
sigma=0
popn=len(Demo.objects.filter(age_group=age))
card_list=[Demo.objects.filter(age_group=age)[i].card_no
for i in range(popn)]
for card in card_list:
dic=Fact_table.objects.filter(card_no=card.aggregate(Sum('duration'))
sigma+=dic['duration__sum']
avgDur=sigma/popn
Above code is within for loop to iterate over age-groups.
Model is as follows:
class Demo(models.Model):
card_no=models.CharField(max_length=20,primary_key=True)
gender=models.IntegerField()
age=models.IntegerField()
age_group=models.IntegerField()
class Fact_table(models.Model):
pri_key=models.BigIntegerField(primary_key=True)
card_no=models.CharField(max_length=20)
duration=models.IntegerField()
time_8bit=models.CharField(max_length=8)
time_of_day=models.IntegerField()
isBusinessHr=models.IntegerField()
Day_of_week=models.IntegerField()
Day=models.IntegerField()
Thanks
Try that:
sigma=0
demo_by_age = Demo.objects.filter(age_group=age);
popn=demo_by_age.count() #One
card_list = demo_by_age.values_list('card_no', flat=True) # Two
dic = Fact_table.objects.filter(card_no__in=card_list).aggregate(Sum('duration') #Three
sigma = dic['duration__sum']
avgDur=sigma/popn
A statement like card_list=[Demo.objects.filter(age_group=age)[i].card_no for i in range(popn)] will generate popn seperate queries and database hits. The query in the for-loop will also hit the database popn times. As a general rule, you should try to minimize the amount of queries you use, and you should only select the records you need.
With a few adjustments to your code this can be done in just one query.
There's generally no need to manually specify a primary_key, and in all but some very specific cases it's even better not to define any. Django automatically adds an indexed, auto-incremental primary key field. If you need the card_no field as a unique field, and you need to find rows based on this field, use this:
class Demo(models.Model):
card_no = models.SlugField(max_length=20, unique=True)
...
SlugField automatically adds a database index to the column, essentially making selections by this field as fast as when it is a primary key. This still allows other ways to access the table, e.g. foreign keys (as I'll explain in my next point), to use the (slightly) faster integer field specified by Django, and will ease the use of the model in Django.
If you need to relate an object to an object in another table, use models.ForeignKey. Django gives you a whole set of new functionality that not only makes it easier to use the models, it also makes a lot of queries faster by using JOIN clauses in the SQL query. So for you example:
class Fact_table(models.Model):
card = models.ForeignKey(Demo, related_name='facts')
...
The related_name fields allows you to access all Fact_table objects related to a Demo instance by using instance.facts in Django. (See https://docs.djangoproject.com/en/dev/ref/models/fields/#module-django.db.models.fields.related)
With these two changes, your query (including the loop over the different age_groups) can be changed into a blazing-fast one-hit query giving you the average duration of calls made by each age_group:
age_groups = Demo.objects.values('age_group').annotate(duration_avg=Avg('facts__duration'))
for group in age_groups:
print "Age group: %s - Average duration: %s" % group['age_group'], group['duration_avg']
.values('age_group') selects just the age_group field from the Demo's database table. .annotate(duration_avg=Avg('facts__duration')) takes every unique result from values (thus each unique age_group), and for each unique result will fetch all Fact_table objects related to any Demo object within that age_group, and calculate the average of all the duration fields - all in a single query.