I’ve come to the conclusion that, for me, ORMs are more detriment than
benefit. In short, they can be used to nicely augment working with SQL
in a program, but they should not replace it.

Some background: For the past 30 months I’ve been working with code
that has to interface with Postgres and to some extent, SQLite. Most
of that has been with SQLAlchemy (which I quite like) and Hibernate (which I don’t). I’ve worked with existing code and data models, as
well as designing my own. Most of the data is event-based storage
(“timelines”) with a heavy emphasis on creating reports.

Much has been written about the Object/Relational Impedance
Mismatch. It’s hard to appreciate it until you live it. Neward, in his
well known essay, lays out many cogent reasons why ORMs turn into
quagmires. In my experience, I’ve had to deal directly with a fair
number of them: entity identity issues, dual-schema problem, data
retrieval mechanism concern, and the partial-object problem. I want to
talk briefly about my experiences with these issues and add one of my
own.

Partial objects, attribute creep, and foreign keys

Perhaps the most subversive issue I’ve had with ORMs is “attribute
creep” or “wide tables”, that is, tables that just keep accruing
attributes. As much as I’d like to avoid it, sometimes it becomes
necessary (although things like Postgres’ hstore can help). For
example, a client may be providing you with lots of data that they
want attached to reports based on various business logic. Furthermore,
you don’t have much insight into this data; you’re just schlepping it
around.

This in and of itself isn’t a terrible thing in a database. It becomes
a real pain point with an ORM. Specifically, the problem starts to
show up in any query that uses the entity directly to create the
query. You may have a Hibernate query like so early on in the project.

query(Foo.class).add(Restriction.eq("x", value))

This may be fine when Foo has five attributes, but becomes a data fire
hose when it has a hundred. This is the equivalent of using SELECT *, which is usually saying more than what is intended. ORMs, however,
encourage this use and often make writing precise projections as
tedious as they are in SQL. (I have optimized such queries by adding
the appropriate projection and reduced the run time from minutes to
seconds; all the time was spent translating the database row into a
Java object.)

Which leads to another bad experience: the pernicious use of foreign
keys. In the ORMs I’ve used, links between classes are represented in
the data model as foreign keys which, if not configured carefully,
result in a large number of joins when retrieving the object. (A
recent count of one such table in my work resulted in over 600
attributes and 14 joins to access a single object, using the preferred
query methodology.)

Attribute creep and excessive use of foreign keys shows me is that in
order to use ORMs effectively, you still need to know SQL. My
contention with ORMs is that, if you need to know SQL, just use SQL
since it prevents the need to know how non-SQL gets translated to SQL.

Data retrieval 🔗

Knowing how to write SQL becomes even more important when you attempt
to actually write queries using an ORM. This is especially important
when efficiency is a concern.

From what I’ve seen, unless you have a really simple data model (that
is, you never do joins), you will be bending over backwards to figure
out how to get an ORM to generate SQL that runs efficiently. Most of
the time, it’s more obfuscated than actual SQL.

And if you elect to keep the query simple, you end up doing a lot of
work in the code that could be done in the database faster. Window
functions are relatively advanced SQL that is painful to write with
ORMs. Not writing them into the query likely means you will be
transferring a lot of extra data from the database to your
application.

In these cases, I’ve elected to write queries using a templating
system and describe the tables using the ORM. I get the convenience of
an application level description of the table with direct use of
SQL. It’s a lot less trouble than anything else I’ve used so far.

Dual schema dangers 🔗

This one seems to be one of those unavoidable redundancies. If you
try to get rid of it, you only make more problems or add excessive
complexity.

The problem is that you end up having a data definition in two places:
the database and your application. If you keep the definition
entirely in the application, you end up having to write the SQL Data
Definition Language (DDL) with the ORM code, which is the same
complication as writing advanced queries in the ORM. If you keep it
in the database, you will probably want a representation in the
application for convenience and to prevent too much “string typing”.

I much prefer to keep the data definition in the database and read it
into the application. It doesn’t solve the problem, but it makes it
more manageable. I’ve found that reflection techniques to get the
data definition are not worth it and I succumb to managing the
redundancy of data definitons in two places.

But the damn migration issue is a real kick in the teeth: changing the
model is no big deal in the application, but a real pain in the
database. After all, databases are persistent whereas application
data is not. ORMs simply get in the way here because they don’t help
manage data migration at all. I work on the principle that the
database’s data definitions aren’t things you should manipulate in the
application. Instead, manipulate the results of queries. That is,
the queries are your API to the database. So instead of thinking
about objects, I think about functions with return types.

Thus, one is forced to ask, should you use an ORM for anything but
convenience in making queries?

Identities 🔗

Dealing with entity identities is one of those things that you have to
keep in mind at all times when working with ORMs, forcing you to write
for two systems while only have the expressivity of one.

When you have foreign keys, you refer to related identities with an
identifier. In your application, “identifier” takes on various
meanings, but usually it’s the memory location (a pointer). In the
database, it’s the state of the object itself. These two things don’t
really get along because you can really only use database identifiers
in the database (the ultimate destination of the data you’re working
with).

What this results in is having to manipulate the ORM to get a database
identifier by manually flushing the cache or doing a partial commit to
get the actual database identifier.

I can’t even call this a leaky abstraction because the work “leak”
implies small amounts of the contents escaping relative to the source.

Transactions 🔗

Something that Neward alludes to is the need for developers to handle
transactions. Transactions are dynamically scoped, which is a powerful
but mostly neglected concept in programming languages due to the
confusion they cause if overused. This leads to a lot of boilerplate
code with exception handlers and a careful consideration of where
transaction boundaries should occur. It also makes you pass session
objects around to any function/method that might have to communicate
with the database.

The concept of a transaction translates poorly to applications due to
their reliance on context based on time. As mentioned, dynamic scoping
is one way to use this in a program, but it is at odds with lexical
scoping, the dominant paradigm. Thus, you must take great care to know
about the “when” of a transaction when writing code that works with
databases and can make modularity tricky (“Here’s a useful function
that will only work in certain contexts”).

Where do I see myself going?

At this point, I’m starting to question the wisdom behind the outright
rejection of stored procedures. It sounds heretical, but it may work
for my use cases. (And hey, with the advent of “devops”, the divide
between the developer and the database administrator is basically
non-existent.)

I’ve found myself thinking about the database as just another data
type that has an API: the queries. The queries return values of some
type, which are represented as some object in the program. By moving
away from thinking of the objects in my application as something to be
stored in a database (the raison d’être for ORMs) and instead thinking
of the database as a (large and complex) data type, I’ve found working
with a database from an application to be much simpler. And wondering
why I didn’t see it earlier.

(It should be made clear that I am not claiming this is how all
applications should deal with a database. All I am saying is that
this fits my use case based on the data I am working with.)

Regardless of whether I find that stored procedures aren’t actually
that evil or whether I keep using templated SQL, I do know one thing:
I won’t fall into the “ORMs make it easy” trap. They are an acceptable
way to represent a data definition, but a poor way to write queries
and a bad way to store object state. If you’re using an RDBMS, bite
the bullet and learn SQL.