Question

I’m working with the new version of a third party application. In this version, the database structure is changed, they say “to improve performance”.

The old version of the DB had a general structure like this:

TABLE ENTITY
(
    ENTITY_ID,
    STANDARD_PROPERTY_1,
    STANDARD_PROPERTY_2,
    STANDARD_PROPERTY_3,
    ...
)

TABLE ENTITY_PROPERTIES
(
    ENTITY_ID,
    PROPERTY_KEY,
    PROPERTY_VALUE
)

so we had a main table with fields for the basic properties and a separate table to manage custom properties added by user.

The new version of the DB insted has a structure like this:

TABLE ENTITY
(
    ENTITY_ID,
    STANDARD_PROPERTY_1,
    STANDARD_PROPERTY_2,
    STANDARD_PROPERTY_3,
    ...
)

TABLE ENTITY_PROPERTIES_n
(
    ENTITY_ID_n,
    CUSTOM_PROPERTY_1,
    CUSTOM_PROPERTY_2,
    CUSTOM_PROPERTY_3,
    ...
)

So, now when the user add a custom property, a new column is added to the current ENTITY_PROPERTY table until the max number of columns (managed by application) is reached, then a new table is created.

So, my question is: Is this a correct way to design a DB structure? Is this the only way to “increase performances”? The old structure required many join or sub-select, but this structute don’t seems to me very smart (or even correct)…

Answer 1

I have seen this done before on the assumed (often unproven) “expense” of joining – it is basically turning a row-heavy data table into a column-heavy table. They ran into their own limitation, as you imply, by creating new tables when they run out of columns.

I completely disagree with it.

Personally, I would stick with the old structure and re-evaluate the performance issues. That isn’t to say the old way is the correct way, it is just marginally better than the “improvement” in my opinion, and removes the need to do large scale re-engineering of database tables and DAL code.

These tables strike me as largely static… caching would be an even better performance improvement without mutilating the database and one I would look at doing first. Do the “expensive” fetch once and stick it in memory somewhere, then forget about your troubles (note, I am making light of the need to manage the Cache, but static data is one of the easiest to manage).

Or, wait for the day you run into the maximum number of tables per database 🙂

Others have suggested completely different stores. This is a perfectly viable possibility and if I didn’t have an existing database structure I would be considering it too. That said, I see no reason why this structure can’t fit into an RDBMS. I have seen it done on almost all large scale apps I have worked on. Interestingly enough, they all went down a similar route and all were mostly “successful” implementations.