I hope the title is clear, please read further and I will explain what I mean.
We having a disagreement with our database designer about high level structure. We are designing a MySQL database and we have a trove of data that will become part of it. Conceptually, the data is complex – there are dozens of different types of entities (representing a variety of real-world entities, you could think of them as product developers, factories, products, inspections, certifications, etc.) each with associated characteristics and with relationships to each other.
I am not an experienced DB designer but everything I know tells me to start by thinking of each of these entities as a table (with associated fields representing characteristics and data populating them), to be connected as appropriate given the underlying relationships. Every example of DB design I have seen does this.
However, the data is currently in a totally different form. There are four tables, each representing a level of data. A top level table lists the 39 entity types and has a long alphanumeric string tying it to the other three tables, which represent all the entities (in one table), entity characteristics (in one table) and values of all the characteristics in the DB (in one table with tens of millions of records.) This works – we have a basic view in php which lets you navigate among the levels and view the data, etc. – but it’s non-intuitive, to say the least. The reason given for having it this way is that it makes the size of the DB smaller, shortens query time and makes expansion easier. But it’s not clear to me that the size of the DB means we should optimize this over, say, clarity of organization.
So the question is: is there ever a reason to structure a DB this way, and what is it? I find it difficult to get a handle on the underlying data – you can’t, for example, run through a table in traditional rows-and-columns format – and it hides connections. But a more “traditional” structure with tables based on entities would result in many more tables, definitely more than 50 after normalization. Which approach seems better?
Many thanks.
OK, I will go ahead and answer my own question based on comments I got and more research they led me to. The immediate answer is yes, there can be a reason to structure a DB with very few tables and with all the data in one of them, it’s an Entity-Attribute-Value database (EAV). These are characterized by:
A very unstructured approach, each fact or data point is just dumped into a big table with the characteristics necessary to understand it. This makes it easy to add more data, but it can be slow and/or difficult to get it out. An EAV is optimized for adding data and for organizational flexibility, and the payment is it’s slower to access and harder to write queries, etc.
A “long and skinny” format, lots of rows, very few columns.
Because the data is “self encoded“ with its own characteristics, it is often used in situations when you know there will be lots of possible characteristics or data points but that most of them will be empty (“sparse data”). A table approach would have lots of empty cells, but an EAV doesn’t really have cells, just data points.
In our particular case, we don’t have sparse data. But we do have a situation where flexibility in adding data could be important. On the other hand, while I don’t think that speed of access will be that important for us because this won’t be a heavy-access site, I would worry about the ease of creating queries and forms. And most importantly I think this structure would be hard for us BD noobs to understand and control, so I am leaning towards the traditional model – sacrificing flexibility and maybe ease of adding new data in favor of clarity. Also, people seem to agree that large numbers of tables are OK as long as they are really called for by the data relationships. So, decision made.