• Richie Bartlett

    Interesting read. (Supporting reason not to use Doctrine in ZF…)

    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

    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

    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
    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

    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

    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?


    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

    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.


    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

    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.

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