Wednesday, February 2, 2011

Escaping the Relational Schema Trap

We're struggling with our Relational Schema. We're not alone, of course, everyone struggles with the relational model. The technology imposes difficult limitations and we work around them.

There's kind of a 4-step process through which the relational schema erodes into irrelevance. The concept of a schema is not irrelevant. It's the rigid relational schema that's a problem.

Many DBA's will say that the relational model is the ultimate in flexibility. They're right, but they're missing the point. The relational database clearly separates the physical storage from the logical model as seen in tables and columns. It's flexible, but the presence of a rigid relational schema limits the pace of business change.

"Clearly," the DBA says, "you don't know how to use ALTER." I beg to differ. I can use ALTER; however, it doesn't permit the broad, sweeping scope of change that the business demands.

In order to attempt to match the pace of business change, we're using an ORM layer. This allows us to fabricate methods and properties left, right and center. We can tackle some pretty big problems with simple code changes. This, however, is no longer helping.

Straws and Camels

When designing a database, we have to be cognizant of the nature and tempo of change. In highly-regulated, very settled business applications (back-office accounting, for example) the data model is well known. Changes are mostly distinctive reporting changes and the tempo is pretty lethargic. It's the back office. Sorry, but innovation rarely happens there.

Each change is just a another hand-full of straw thrown on the camel's back. It happens fairly slowly. And there aren't many surprises. Hacks, workarounds and technical debt accumulates slowly.

In innovative, novel, experimental businesses, however, the nature and tempo are very different. The changes are disruptive, "what are you saying?" kinds of changes. They are "throw out the bathwater, the babies, the cribs and fire the nursemaid" kinds of changes. The tempo is semi-annual reinvent everything. Hacks, workarounds and technical debt get out of control suddenly.

Important Lesson Learned. When the customer misunderstands the offering and asks for something completely senseless, it's good to listen and try to build that -- even if it wasn't what you were offering. In some cases, the original offering was too complex or contrived. In other cases, the offering didn't create enough value. But when you offer [X] and the customer asks how much it will cost for [Y], you have disruptive, sudden, and surprising database changes.
This is bales of hay through onto an unprepared camel. Backs can get broken.


One common coping strategy is SQL ALTER statements to fiddle with the logical model. This has to be coupled with CREATE TABLE AS SELECT scripts to do open-heart surgery on the logical model. Married with modified ORM definitions. This requires some careful "schema versioning" techniques.

Another coping strategy is lots of "Expansion" columns in the tables. These can be renamed and repurposed without physical storage changes. The rows haven't physically changed, but the column name morphed from "EXPANSION_INT_01" to "Some_Real_Attribute". This doesn't prevent the CREATE TABLE AS SELECT scripts to do open-heart surgery. It still requires some careful "schema versioning" techniques to be sure that the ORM layer matches the logical schema.

A third -- and perhaps most popular -- coping strategy is manpower. Just having dedicated DBA's and maintenance programmers is a common way to handle this. Some folks object, saying that a large staff isn't a way to "cope with change" but is a basic "cost of doing business".

It's false, by the way, to claim that dedicated DBA's are essential. A solo developer can design and implement a database and application software with no help at all. Indeed, in most organizations, developers design and build databases, then turn them over to DBA's for operational support. If the nature of change is minor and tempo of change is slow, a solo developer can deal perfectly well with the database. A dedicated DBA is someone we add when the developer gets swamped by too much change.

(Some DBA's like to claim that the developers never get normalization or indexing correct. I counter with the observation that some DBA's don't get this right, either. DBA's aren't essential. They're a popular way to cope with the nature and tempo of change.)

In the ORM world, there are schema migration toolkits. Projects like Storm, this list for Django, Embarcadero Change Manager for Oracle, and numerous others attempt to support the schema evolution and change management problem. All of this is a clever way to cope with a problem inherent in our choice of technology.

Chaos Theory

Rather than invent clever coping mechanisms, let's take a step back. If we're inventing technology to work around the fixed relational schema, it might be time to rethink the relational schema.

"Oh noes," DBA's cry, "we must have a fixed logical model otherwise chaos ensues."

Really? How come we're always altering that schema? How come we're always adding tables and restructuring the tables?

"Oh that? That's 'controlled change'," the DBA responds.

No, that's slow chaos.

Here's how it plays out. We have a disruptive change. We negotiate with the DBA's to restructure the database. And the test database. And the QA database. We do the development database without any help from the DBA's. We fix the ORM layers. We unit test the changes.

Then we plan and coordinate the production rollout of this change with the DBA's. Note. We already made the change in development. We're not allowed to make the change in production. The DBA's then suggest design alternatives. Normalization isn't "right". Or there are physical changes that need to be declared in the table definitions. We redo the development database. And the ORM layer. And rerun the unit tests.

Because the production database couldn't be touched -- and we had paying customers -- we copied production data into a development database and started doing "production" in development. Now that we're about to make the official production change, we have two databases. The official database content is out-of-date. The development database is a mixture of live production and test data. Sigh.

Rethinking Schema

If the schema is a problem, perhaps we can live without it. Enter NoSQL databases.

Here's how you start down the slippery slope.

Phase I. You need a fairly radical database change. Rather than wait weeks for the DBA's, you ask for a single "BLOB" column. You take the extra data elements for the radical change, JSON encode them, and store the JSON representation in the BLOB field. Now you have a "subschema" buried inside a single BLOB column.

Since this is a simple ALTER, the DBA's will do it without a lot of negotiation or delay. You have a hybrid database with a mixture of schema and noSQL.

Phase II. You need an even more radical change. Rather than wait weeks for the DBA's, you ask for a few tables that have just a primary key and a BLOB column. You've basically invented a document-structured database inside SQL, bypassing the SQL schema entirely.

Phase III. While waiting for the Phase II changes to be implemented, you convert the customer data from their obscure, stupid format into a simple sequential file of JSON documents and write your own simple map-reduce algorithms in Python. Sure, performance is poor, but you're up and running without any database overheads.

Phase IV. Start looking for alternatives.

This MongoDB looks really nice. PyMongo offers lots of hints and guidance.

At least one person is looking at mango, a MongoDB database adapter for Django. For us, this isn't the best idea. We use OpenAM for identity management, so our Users and Sessions are simply cloned from OpenAM by an authentication backend that gets the user from OpenAM. SQLite works fine for this.

We think we can use Django's ORM and a relational database for User and Session. For everything else, we need to look closely and MongoDB.

Wins and Losses

The big win is the ability to handle disruptive change a little bit more gracefully.

The big loss in switching away from the Django ORM is we lose the built-in admin pages. We have to build admin Forms and view functions. While this is a bit of a burden, we've already customized every model form heavily. Switching from ModelForm to Form and adding the missing fields isn't much additional work.

The biggest issue with document-oriented data models is assuring that the documents comply with some essential or core schema. Schemas are inescapable. The question is more a matter of how the schema limits change. Having a Django Form to validate JSON documents for the "essential" features is far more flexible than having a Django Model class and a mapping to a relational database.

Schema migration becomes a non-issue until we have to expand the essential schema, which changes the validation rules, and may render old documents retroactively invalid. This is not a new problem -- Relational folks cope with this, also -- but if it's the only problem, then we may have streamlined the process of making disruptive business changes.


  1. You can take a look at mongoengine ( It's an "ORM" for MongoDB made to be extremely similar to Django ORM.

    In the last version, they integrate the authentication with MongoDB (I haven't test it)

  2. "Oh that? That's 'controlled change'," the DBA responds.
    No, that's slow chaos.


    BTW, awesome blog post!

  3. Before looking at MongoDB and their ilk, ask if ACID (atomicity, consistency, isolation, durability) [ie, the transaction thing] is a business requirement.

    Check out "Dropping ACID with MongoDB"

  4. Article: Real World NoSQL: Amazon SimpleDB at Netflix By GUY HARRISON of GigaOm - February 4, 2011

    Relational database transactions were depreciated in favour of SimpleDB’s optimistic concurrency mechanism, which allows modifications to proceed only if an item is unchanged since it was last accessed

  5. Check out

    How FriendFeed uses MySQL to store schema-less data
    By Bret Taylor · February 27, 2009