The specific example he used to demonstrate worked transparently because of the has_many :through relationship for users on Article, which requires the collaborations table to filter the users, so joins it. Otherwise, you would need to do the join yourself before merging the scope, and things get messy pretty quickly, especially if you end up with table aliases (which the merged relation knows nothing about -- it will still query against the collaborations table).
I added "sifters" to Squeel (http://github.com/ernie/squeel) in order to address the need for a set of reusable conditions on a specific model that could work through an association. I'm not saying Squeel is the solution you're definitely looking for, but I just want to let people know about the things they'll need to look out for when using Relation#merge.
Good point, #merge isn't itself an arel method. I say arel because it's the core sitting behind the #merge / #where / #joins frontend -- but AR::Relation deserves credit too :)
I've never been a fan of Rails' hieroglyphics. "The query is the one you’d hope for" - why do we need to "hope", when the ORM could just allow you to use relational concepts directly ? Here is SQLAlchemy's much less exciting version of what I see here for "merge", just use a hybrid (sorry, we have more verbose config, due to explicit is better than implicit):
from sqlalchemy import Column, Integer, String, ForeignKey, Enum
from sqlalchemy.orm import Session, relationship, backref
from sqlalchemy.ext.declarative import declarative_base, declared_attr
from sqlalchemy.ext.hybrid import hybrid_property
class Base(object):
@declared_attr
def __tablename__(cls):
return cls.__name__.lower()
Base = declarative_base(cls=Base)
class SurrogatePK(object):
id = Column(Integer, primary_key=True)
class Article(SurrogatePK, Base):
headline = Column(String)
@property
def users(self):
return self.collaborations.join("user").with_entities(User)
class User(SurrogatePK, Base):
name = Column(String)
class Collaboration(Base):
article_id = Column(ForeignKey('article.id'),
primary_key=True)
user_id = Column(ForeignKey('user.id'),
primary_key=True)
role = Column(Enum('editor', 'author'))
user = relationship("User", backref="collaborations")
article = relationship("Article",
backref=backref("collaborations", lazy="dynamic"))
@hybrid_property
def editorial(self):
return self.role == 'editor'
sess = Session()
some_article = Article(id=5)
sess.add(some_article)
print some_article.users.filter(Collaboration.editorial)
you get the same "one line, DRY" calling style at the end and equivalent SQL:
SELECT "user".id AS user_id, "user".name AS user_name
FROM collaboration JOIN "user" ON "user".id = collaboration.user_id
WHERE :param_1 = collaboration.article_id AND collaboration.role = :role_1
Hope was definitely a bad choice of word. By that I meant, the sort of tight query you'd hope an ORM would deliver.
Anyhow, it's a matter of taste, but what may appear at first as hieroglyphics actually is straightforward. It's just that concision here means some packed meaning and some assumed knowledge, so you have to know how to read it. In this case, that's an easy trade-off for me.
I'm not familiar with SQLAlchemy, so I find your example equally hard to read, compounded by there being much more code to spelunk through to understand.
Different strokes and all that, though. There's room for plenty of frameworks :)
> I'm not familiar with SQLAlchemy, so I find your example equally hard to read, compounded by there being much more code to spelunk through to understand.
If you remove few column definition and setup code, it actually boils down to just:
class Article(ArticleColumns):
@property
def users(self)
return self.collaborations.join("user").with_entities(User)
class User(UserColumns):
pass
class Collaboration(CollaborationColumns):
user = relationship("User", backref="collaborations")
article = relationship("Article", backref=backref("collaborations", lazy="dynamic"))
@hybrid_property
def editorial(self):
return self.role == 'editor'
Here's one that's been bugging me - anyone want to take a crack at it?
@dev_configs = DeviceConfig.
joins("join (select device_id, max(updated_at) as max_updated_at
from device_configs group by device_id) dc2
on dc2.device_id = device_configs.device_id and
dc2.max_updated_at = device_configs.updated_at").
includes("device").order("devices.updated_at desc")
Each device has many device configurations, and we want to display all devices, along with the latest device configuration, and order the whole thing by when the device was updated. The above works, but is mostly working directly in SQL, rather than with Rails.
In stock ActiveRecord, you're not going to be able to join a derived table without dropping to SQL. :( Still, if you're looking to constrain results to only the latest, you could use the having clause (with some performance penalty).
im not entirely sure of the on syntax there, the API doesnt look clear.
Atleast, those are the two solutions I have though personally - i think it reads better as straight SQL with select_values. That way, you know what is happening.
I've been using Arel directly (with ActiveRecord) for a project. It's 80% of what I want, but then it inexplicably sucks at the last 20%.
The whole point of a relational algebra is that it's closed under all the relevant operations. But Arel's implementation mostly ignores this fact, and you get back different types of objects with incompatible APIs depending on what operations you use and even what order you apply them in.
Concrete example one: "foo.union(bar).union(baz)" explodes because the union operation is not composable.
Concrete example two: you can compose joins from the right but not the left. So "(foo.join(bar)).join(baz)" works but baz.join(foo.join(bar))" explodes -- but not until later when you try to dump it to sql, at which point you get an obscure exception.
When you look under the hood, you see that it's having a hard time with this stuff because it doesn't really implement relational algebra. It's mostly just an abstract syntax tree for SQL.
The current version of ARel is not a relational algebra library, it's an SQL compiler according to Aaron Patterson [1]. Many of the concepts and names for internals are based on SQL not RA. There's nothing wrong with that, it's a pragmatic choice for Rails given that ActiveRecord is an RDBMS only abstraction.
I've been working on a relational algebra library for ruby (tentatively) called veritas [2] where the sets are closed under all operations.
Given that it's a higher level abstraction than ARel the trade off is that there's not a 1:1 mapping between it's pure RA ops and SQL operations. I err on the side of producing SQL that will returns the correct results, which means some of the queries are a bit verbose. I consider that only a temporary problem though; I believe I can get to the point where most common queries are identical to what you'd write by hand. I'm focused on correctness before performance.
I've also written an optimizer [3] that takes the RA AST and rewrites it to be smaller and more efficient. It handles lots of the low hanging fruit, but there's still room for improvement. The advantage to this approach is that it simplifies the AST for all targets, not just SQL. There's even room to do per-target optimizations, which is nice because there is often multiple ways to form a query, and some approaches may be more efficient than others on different backends.
16 comments
[ 0.26 ms ] story [ 61.0 ms ] threadNitpicky, but ActiveRecord::Relation is not ARel. ARel is the relational algebra library that underpins ActiveRecord (>=3.0). See http://erniemiller.org/2010/05/11/activerecord-relation-vs-a... for more info.
The specific example he used to demonstrate worked transparently because of the has_many :through relationship for users on Article, which requires the collaborations table to filter the users, so joins it. Otherwise, you would need to do the join yourself before merging the scope, and things get messy pretty quickly, especially if you end up with table aliases (which the merged relation knows nothing about -- it will still query against the collaborations table).
I added "sifters" to Squeel (http://github.com/ernie/squeel) in order to address the need for a set of reusable conditions on a specific model that could work through an association. I'm not saying Squeel is the solution you're definitely looking for, but I just want to let people know about the things they'll need to look out for when using Relation#merge.
Anyhow, it's a matter of taste, but what may appear at first as hieroglyphics actually is straightforward. It's just that concision here means some packed meaning and some assumed knowledge, so you have to know how to read it. In this case, that's an easy trade-off for me.
I'm not familiar with SQLAlchemy, so I find your example equally hard to read, compounded by there being much more code to spelunk through to understand.
Different strokes and all that, though. There's room for plenty of frameworks :)
I mostly wanted to demonstrate that the functionality of Rails' merge() can be considered in other ways that are just as succinct.
If you remove few column definition and setup code, it actually boils down to just:
In which you can now do which generates similar SQL query as #merge.There is a database toolkit in ruby-land that allows you do just that! It's called Sequel.
I've built an example, which I deliberately kept as bare-bones and explicit as possible: https://gist.github.com/1aa977a63b7d6a727497
You can also build something similar on top of ARel which backs ActiveRecord.
[1]: http://docs.sqlalchemy.org/en/latest/core/tutorial.html
as a note - you shouldn't use includes and joins, includes hammers joins.
Simplest tidy up though is a scope for the join, something like
Alternatively maybe something like im not entirely sure of the on syntax there, the API doesnt look clear.Atleast, those are the two solutions I have though personally - i think it reads better as straight SQL with select_values. That way, you know what is happening.
Edit: Instead of #CCC try #EAEAEA. It made a good difference for me.
I'd have great usage of union (which doesn't exist, or doesn't exist in the version of rails I'm stuck in) though.
The whole point of a relational algebra is that it's closed under all the relevant operations. But Arel's implementation mostly ignores this fact, and you get back different types of objects with incompatible APIs depending on what operations you use and even what order you apply them in.
Concrete example one: "foo.union(bar).union(baz)" explodes because the union operation is not composable.
Concrete example two: you can compose joins from the right but not the left. So "(foo.join(bar)).join(baz)" works but baz.join(foo.join(bar))" explodes -- but not until later when you try to dump it to sql, at which point you get an obscure exception.
When you look under the hood, you see that it's having a hard time with this stuff because it doesn't really implement relational algebra. It's mostly just an abstract syntax tree for SQL.
I've been working on a relational algebra library for ruby (tentatively) called veritas [2] where the sets are closed under all operations.
Given that it's a higher level abstraction than ARel the trade off is that there's not a 1:1 mapping between it's pure RA ops and SQL operations. I err on the side of producing SQL that will returns the correct results, which means some of the queries are a bit verbose. I consider that only a temporary problem though; I believe I can get to the point where most common queries are identical to what you'd write by hand. I'm focused on correctness before performance.
I've also written an optimizer [3] that takes the RA AST and rewrites it to be smaller and more efficient. It handles lots of the low hanging fruit, but there's still room for improvement. The advantage to this approach is that it simplifies the AST for all targets, not just SQL. There's even room to do per-target optimizations, which is nice because there is often multiple ways to form a query, and some approaches may be more efficient than others on different backends.
[1] https://github.com/sconover/knit-js#footnotes [2] https://github.com/dkubb/veritas [3] https://github.com/dkubb/veritas-optimizer