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A lot of the applications I write make use of lookup tables, since that was just the way I was taught (normalization and such). The problem is that the queries I make are often more complicated because of this. They often look like this
get all posts that are still open
"SELECT * FROM posts WHERE status_id = (SELECT id FROM statuses WHERE name = 'open')"
Often times, the lookup tables themselves are very short. For instance, there may only be 3 or so different statuses. In this case, would it be okay to search for a certain type by using a constant or so in the application? Something like
get all posts that are still open
"SELECT * FROM posts WHERE status_id = ".Status::OPEN
Or, what if instead of using a foreign id, I set it as an enum and queried off of that?
Thanks.
The answer depends a little if you are limited to freeware such as PostGreSQL (not fully SQL compliant), or if you are thinking about SQL (ie. SQL compliant) and large databases.
In SQL compliant, Open Architecture databases, where there are many apps using one database, and many users using different report tools (not just the apps) to access the data, standards, normalisation, and open architecture requirements are important.
Despite the people who attempt to change the definition of "normalisation", etc. to suit their ever-changing purpose, Normalisation (the science) has not changed.
if you have data values such as {Open; Closed; etc} repeated in data tables, that is data duplication, a simple Normalisation error: if you those values change, you may have to update millions of rows, which is very limited design.
Such values should be Normalised into a Reference or Lookup table, with a short CHAR(2) PK:
O Open
C Closed
U [NotKnown]
The data values {Open;Closed;etc} are no longer duplicated in the millions of rows. It also saves space.
the second point is ease of change, if Closed were changed to Expired, again, one row needs to be changed, and that is reflected in the entire database; whereas in the un-normalised files, millions of rows need to be changed.
Adding new data values, eg. (H,HalfOpen) is then simply a matter of inserting one row.
in Open Architecture terms, the Lookup table is an ordinary table. It exists in the [SQL compliant] catalogue; as long as the FOREIGN KEY relation has been defined, the report tool can find that as well.
ENUM is a Non-SQL, do not use it. In SQL the "enum" is a Lookup table.
The next point relates to the meaningfulness of the key.
If the Key is meaningless to the user, fine, use an {INT;BIGINT;GUID;etc} or whatever is suitable; do not number them incrementally; allow "gaps".
But if the Key is meaningful to the user, do not use a meaningless number, use a meaningful Relational Key.
Now some people will get in to tangents regarding the permanence of PKs. That is a separate point. Yes, of course, always use a stable value for a PK (not "immutable", because no such thing exists, and a system-generated key does not provide row uniqueness).
{M,F} are unlikely to change
if you have used {0,1,2,4,6}, well don't change it, why would you want to. Those values were supposed to be meaningless, remember, only a meaningful Key need to be changed.
if you do use meaningful keys, use short alphabetic codes, that developers can readily understand (and infer the long description from). You will appreciate this only when you code SELECT and realise you do not have to JOIN every Lookup table. Power users too, appreciate it.
Since PKs are stable, particularly in Lookup tables, you can safely code:
WHERE status_code = 'O' -- Open
You do not have to JOIN the Lookup table and obtain the data value Open, as a developer, you are supposed to know what the Lookup PKs mean.
Last, if the database were large, and supported BI or DSS or OLAP functions in addition to OLTP (as properly Normalised databases can), then the Lookup table is actually a Dimension or Vector, in Dimension-Fact analyses. If it was not there, then it would have to be added in, to satisfy the requirements of that software, before such analyses can be mounted.
If you do that to your database from the outset, you will not have to upgrade it (and the code) later.
Your Example
SQL is a low-level language, thus it is cumbersome, especially when it comes to JOINs. That is what we have, so we need to just accept the encumbrance and deal with it. Your example code is fine. But simpler forms can do the same thing.
A report tool would generate:
SELECT p.*,
s.name
FROM posts p,
status s
WHERE p.status_id = s.status_id
AND p.status_id = 'O'
Another Exaple
For banking systems, where we use short codes which are meaningful (since they are meaningful, we do not change them with the seasons, we just add to them), given a Lookup table such as (carefully chosen, similar to ISO Country Codes):
Eq Equity
EqCS Equity/Common Share
OTC OverTheCounter
OF OTC/Future
Code such as this is common:
WHERE InstrumentTypeCode LIKE "Eq%"
And the users of the GUI would choose the value from a drop-down that displays
{Equity/Common Share;Over The Counter},
not {Eq;OTC;OF}, not {M;F;U}.
Without a lookup table, you can't do that, either in the apps, or in the report tool.
For look-up tables I use a sensible primary key -- usually just a CHAR(1) that makes sense in the domain with an additional Title (VARCHAR) field. This can maintain relationship enforcement while "keeping the SQL simple". The key to remember here is the look-up table does not "contain data". It contains identities. Some other identities might be time-zone names or assigned IOC country codes.
For instance gender:
ID Label
M Male
F Female
N Neutral
select * from people where gender = 'M'
Alternatively, an ORM could be used and manual SQL generation might never have to be done -- in this case the standard "int" surrogate key approach is fine because something else deals with it :-)
Happy coding.
Create a function for each lookup.
There is no easy way. You want performance and query simplicity. Ensure the following is maintained. You could create a SP_TestAppEnums to compare existing lookup values against the function and look for out of sync/zero returned.
CREATE FUNCTION [Enum_Post](#postname varchar(10))
RETURNS int
AS
BEGIN
DECLARE #postId int
SET #postId =
CASE #postname
WHEN 'Open' THEN 1
WHEN 'Closed' THEN 2
END
RETURN #postId
END
GO
/* Calling the function */
SELECT dbo.Enum_Post('Open')
SELECT dbo.Enum_Post('Closed')
Question is: do you need to include the lookup tables (domain tables 'round my neck of the woods) in your queries? Presumably, these sorts of tables are usually
pretty static in nature — the domain might get extended, but it probably won't get shortened.
their primary key values are pretty unlikely to change as well (e.g., the status_id for a status of 'open' is unlikely to suddenly get changed to something other than what it was created as).
If the above assumptions are correct, there's no real need to add all those extra tables to your joins just so your where clause can use a friend name instead of an id value. Just filter on status_id directly where you need to. I'd suspect the non-key attribute in the where clause ('name' in your example above) is more likely to get changes than the key attribute ('name' in your example above): you're more protected by referencing the desire key value(s) of the domain table in your join.
Domain tables serve
to limit the domain of the variable via a foreign key relationship,
to allow the domain to be expanded by adding data to the domain table,
to populate UI controls and the like with user-friendly information,
Naturally, you'd need to suck domain tables into your queries where you you actually required the non-key attributes from the domain table (e.g., descriptive name of the value).
YMMV: a lot depends on context and the nature of the problem space.
The answer is "whatever makes sense".
lookup tables involve joins or subqueries which are not always efficient. I make use of enums a lot to do this job. its efficient and fast
Where possible (and It is not always . . .), I use this rule of thumb: If I need to hard-code a value into my application (vs. let it remain a record in the database), and also store that vlue in my database, then something is amiss with my design. It's not ALWAYS true, but basically, whatever the value in question is, it either represents a piece of DATA, or a peice of PROGRAM LOGIC. It is a rare case that it is both.
NOT that you won't find yourself discovering which one it is halfway into the project. But as the others said above, there can be trade-offs either way. Just as we don't always acheive "perfect" normalization in a database design (for reason of performance, or simply because you CAN take thngs too far in pursuit of acedemic perfection . . .), we may make some concious choices about where we locate our "look-up" values.
Personally, though, I try to stand on my rule above. It is either DATA, or PROGRAM LOGIC, and rarely both. If it ends up as (or IN) a record in the databse, I try to keep it out of the Application code (except, of course, to retrieve it from the database . . .). If it is hardcoded in my application, I try to keep it out of my database.
In cases where I can't observe this rule, I DOCUMENT THE CODE with my reasoning, so three years later, some poor soul will be able to ficure out how it broke, if that happens.
The commenters have convinced me of the error of my ways. This answer and the discussion that went along with it, however, remain here for reference.
I think a constant is appropriate here, and a database table is not. As you design your application, you expect that table of statuses to never, ever change, since your application has hard-coded into it what those statuses mean, anyway. The point of a database is that the data within it will change. There are cases where the lines are fuzzy (e.g. "this data might change every few months or so…"), but this is not one of the fuzzy cases.
Statuses are a part of your application's logic; use constants to define them within the application. It's not only more strictly organized that way, but it will also allow your database interactions to be significantly speedier.
15 ECTS credits worth of database design down the bin.. I really can't come up with the best design solution for my problem.
Which is this: Basically I'm making a tool that gathers a lot of information concerning the user. At the most the user would fill in 50 fields of data, ranging from simple checkboxes to text input. I'm designing the db right now (with mySql) and can't decide whether or not to use a single User table with all of those fields, or to have a table for each category of input.
One example would be "type of payment". This one has three options and if I went with the "table" way I would add a table paymentType and give it binary fields for each payment type. Then I would need and id table to identify which paymentType the user has chosen whereas if I use a single user table, the data would already be there.
The site will probably see a lot of users (tv, internet and radio marketing) so I'm concerned which alternative would be the best.
I'll be happy to provide more details if you need more to base a decision.
Thanks for reading.
Read this article "Database Normalization Basics", and come back here if you still have questions. It should help a lot.
The most fundamental idea behind these decisions, as you will see in this article, is that each table should represent one and only one "thing", and each field should relate directly and only to that thing.
In your payment types example, it probably makes sense to break it out into a separate table if you anticipate the need to store additional information about each payment type.
Create your "Type of Payment" table; there's no real question there. That's proper normalization and the power behind using relational databases. One of the many reasons to do so is the ability to update a Type of Payment record and not have to touch the related data in your users table. Your join between the two tables will allow your app to see the updated type of payment info by changing it in just the 1 place.
Regarding your other fields, they may not be as clear cut. The question to ask yourself about each field is "does this field relate only to a user or does it have meaning and possible use in its own right?". If you can never imagine a field having meaning outside of the context of a user you're safe leaving it as a field on the user table, otherwise do the primary key-foreign key relationship and put the information in its own table.
If you are building a form with variable inputs, I wouldn't recommend building it as one table. This is inflexible and dirty.
Normalization is the key, though if you end up with a key/value setup, or effectively a scalar type implementation across many tables and can't cache:
a) the form definition from table data and
b) the joined result of storage (either a caching view or otherwise)
c) or don't build in proper sharding
Then you may hit a performance boundary.
In this KVP setup, you might want to look at something like CouchDB or a less table-driven storage format.
You may also want to look at trickier setups such as serialized object storage and cache-tables if your internal data is heavily relative to other data already in the database
50 columns is a lot. Have you considered a table that stores values like a property sheet? This would only be useful if you didn't need to regularly query the values it contains.
INSERT INTO UserProperty(UserID, Name, Value)
VALUES(1, 'PaymentType', 'Visa')
INSERT INTO UserProperty(UserID, Name, Value)
VALUES(1, 'TrafficSource', 'TV')
I think I figured out a great way of solving this. Thanks to a friend of mine for suggesting this!
I have three tables, Field {IdField, FieldName, FieldType}, FieldInput {IdInput, IdField, IdUser} and User { IdUser, UserName... etc }
This way it becomes very easy to see what a user has answered, the solution is somewhat scalable and it provides a good overview. I will constrain the alternatives in another layer, farther away from the db. I believe it's a tradeoff worth doing.
Any suggestions or critics to this solution?
My friend is building a product to be used by different independent medical units.
The database stores a vast collection of measurements taken at different times, like the temperature, blood pressure, etc...
Let us assume these are held in a table called exams with columns temperature, pressure, etc... (as well as id, patient_id and timestamp). Most of the measurements are stored as floats, but some are of other types (strings, integers...)
While many of these measurements are handled by their product, it needs to allow the different medical units to record and process other custom measurements. A very nifty UI allows the administrator to edit these customs fields, specify their name, type, possible range of values, etc...
He is unsure as to how to store these custom fields.
He is leaning towards a separate table (say a table custom_exam_data with fields like exam_id, custom_field_id, float_value, string_value, ...)
I worry that this will make searching both more difficult to achieve and less efficient.
I am leaning towards modifying the exam table directly (while avoiding conflicts on column names with some scheme like prefixing all custom fields with an underscore or naming them custom_1, ...)
He worries about modifying the database dynamically and having different schemas for each medical unit.
Hopefully some people which more experience can weigh in on this issue.
Notes:
he is using Ruby on Rails but I think this question is pretty much framework agnostic, except from the fact that he is only looking for solutions in SQL databases only.
I simplified the problem a bit since the custom fields need to be available for more than one table, but I believe this doesn`t really impact the direction to take.
(added) A very generic reporting module will need to search, sort, generate stats, etc.. of this data, so it is required that this data be stored in the columns of the appropriate type
(added) User inputs will be filtered, for the standard fields as well as for the custom fields. For example, numbers will be checked within a given range (can't have a temperature of -12 or +444), etc... Thus, conversion to the appropriate SQL type is not a problem.
I've had to deal with this situation many times over the years, and I agree with your initial idea of modifying the DB tables directly, and using dynamic SQL to generate statements.
Creating string UserAttribute or Key/Value columns sounds appealing at first, but it leads to the inner-platform effect where you end up having to re-implement foreign keys, data types, constraints, transactions, validation, sorting, grouping, calculations, et al. inside your RDBMS. You may as well just use flat files and not SQL at all.
SQL Server provides INFORMATION_SCHEMA tables that let you create, query, and modify table schemas at runtime. This has full type checking, constraints, transactions, calculations, and everything you need already built-in, don't reinvent it.
It's strange that so many people come up with ad-hoc solutions for this when there's a well-documented pattern for it:
Entity-Attribute-Value (EAV) Model
Two alternatives are XML and Nested Sets. XML is easier to manage but generally slow. Nested Sets usually require some type of proprietary database extension to do without making a mess, like CLR types in SQL Server 2005+. They violate first-normal form, but are nevertheless the fastest-performing solution.
Microsoft Dynamics CRM achieves this by altering the database design each time a change is made. Nasty, I think.
I would say a better option would be to consider an attribute table. Even though these are often frowned upon, it gives you the flexibility you need, and you can always create views using dynamic SQL to pivot the data out again. Just make sure you always use LEFT JOINs and FKs when creating these views, so that the Query Optimizer can do its job better.
I have seen a use of your friend's idea in a commercial accounting package. The table was split into two, first contained fields solely defined by the system, second contained fields like USER_STRING1, USER_STRING2, USER_FLOAT1 etc. The tables were linked by identity value (when a record is inserted into the main table, a record with same identity is inserted into the second one). Each table that needed user fields was split like that.
Well, whenever I need to store some unknown type in a database field, I usually store it as String, serializing it as needed, and also store the type of the data.
This way, you can have any kind of data, working with any type of database.
I would be inclined to store the measurement in the database as a string (varchar) with another column identifying the measurement type. My reasoning is that it will presumably, come from the UI as a string and casting to any other datatype may introduce a corruption before the user input get's stored.
The downside is that when you go to filter result-sets by some measurement metric you will still have to perform a casting but at least the storage and persistence mechanism is not introducing corruption.
I can't tell you the best way but I can tell you how Drupal achieves a sort of schemaless structure while still using the standard RDBMSs available today.
The general idea is that there's a schema table with a list of fields. Each row really only has two columns, the 'table':String column and the 'column':String column. For each of these columns it actually defines a whole table with just an id and the actual data for that column.
The trick really is that when you are working with the data it's never more than one join away from the bundle table that lists all the possible columns so you end up not losing as much speed as you might otherwise think. This will also allow you to expand much farther than just a few medical companies unlike the custom_ prefix you were proposing.
MySQL is very fast at returning row data for short rows with few columns. In this way this scheme ends up fairly quick while allowing you lots of flexibility.
As to search, my suggestion would be to index the page content instead of the database content. Use Solr to parse through rendered pages and hold links to the actual page instead of trying to search through the database using clever SQL.
Define two new tables: custom_exam_schema and custom_exam_data.
custom_exam_data has an exam_id column, plus an additional column for every custom attribute.
custom_exam_schema would have a row to describe how to interpret each of the columns of the custom_exam_data table. It would have columns like name, type, minValue, maxValue, etc.
So, for example, to create a custom field to track the number of fingers a person has, you would add ('fingerCount', 'number', 0, 10) to custom_exam_schema and then add a column named fingerCount to the exam table.
Someone might say it's bad to change the database schema at run time, but I'd argue that configuring these custom fields is part of set up and won't happen too often. Still, this method lets you handle changes at any time and doesn't risk messing around with your core table schemas.
lets say that your friend's database has to store data values from multiple sources such as demogrphic values, diagnosis, interventions, physionomic values, physiologic exam values, hospitalisation values etc.
He might have as well to define choices, lets say his database is missing the race and the unit staff need the race of the patient (different races are more unlikely to get some diseases), they might want to use a drop down with several choices.
I would propose to use an other table that would have these choices or would you just use a "Custom_field_choices" table, which at some point is exactly the same but with a different name.
Considering that the database :
- needs to be flexible
- that data from multiple tables can be added and be customized
- that you might want to keep the integrity of the main structure of your database for distribution and uniformity purpose
- that data MUST have a limit and alarms and warnings
- that data must have units ( 10 kg or 10 pounds) ?
- that data can have a selection of choices
- that data can be with different rights (from simple user to admin)
- that these data might be needed to generate reports without modifying the code (automation)
- that these data might be needed to make cross reference analysis within the system without modifying the code
the custom table would be my solution, modifying each table would end up being too risky.
I would store those custom fields in a table where each record ( dataType, dataValue, dataUnit ) would use in one row. So there would be a relation oneToMany from one sample to the data. You can also create a table to record all the kind of cutsom types you would use. For example:
create table DataType
(
id int primary key,
name varchar(100) not null unique
description text,
uri varchar(255) //<-- can be used for an ONTOLOGY
)
create table DataRecord
(
id int primary key,
sample_id int not null,//<-- reference to the sample
dataType_id int not null, //<-- references DataType
value varchar(100),//<-- the value as string
unit varchar(50)//<-- g, mg/ml, etc... but it could also be a link to a table describing the units just like DataType
)
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It it ok to have a table with just one column? I know it isn't technically illegal, but is it considered poor design?
EDIT:
Here are a few examples:
You have a table with the 50 valid US state codes, but you have no need to store the verbose state names.
An email blacklist.
Someone mentioned adding a key field. The way I see it, this single column WOULD be the primary key.
In terms of relational algebra this would be a unary relation, meaning "this thing exists"
Yes, it's fine to have a table defining such a relation: for instance, to define a domain.
The values of such a table should be natural primary keys of course.
A lookup table of prime numbers is what comes to my mind first.
Yes, it's certainly good design to design a table in such a way as to make it most efficient. "Bad RDBMS Design" is usually centered around inefficiency.
However, I have found that most cases of single column design could benefit from an additional column. For example, State Codes can typically have the Full State name spelled out in a second column. Or a blacklist can have notes associated. But, if your design really does not need that information, then it's perfectly ok to have the single column.
I've used them in the past. One client of mine wanted to auto block anyone trying to sign up with a phone number in this big list he had so it was just one big blacklist.
If there is a valid need for it, then I don't see a problem. Maybe you just want a list of possibilities to display for some reason and you want to be able to dynamically change it, but have no need to link it to another table.
One case that I found sometimes is something like this:
Table countries_id, contains only one column with numeric ID for each country.
Table countries_description, contains the column with country ID, a column With language ID and a column with the localized country name.
Table company_factories, contains information for each factory of the company, including the country in Wich is located.
So to maintain data coherence and language independent data in the tables the database uses this schema with tables with only one column to allow foreign keys without language dependencies.
In this case I think the existence of one column tables are justified.
Edited in response to the comment by: Quassnoi
(source: ggpht.com)
In this schema I can define a foreign key in the table company_factories that does not require me to include Language column on the table, but if I don't have the table countries_id, I must include Language column on the table to define the foreign key.
There would be rare cases where a single-column table makes sense. I did one database where the list of valid language codes was a single-column table used as a foreign key. There was no point in having a different key, since the code itself was the key. And there was no fixed description since the language code descriptions would vary by language for some contexts.
In general, any case where you need an authoritative list of values that do not have any additional attributes is a good candidate for a one-column table.
I use single-column tables all the time -- depending, of course, on whether the app design already uses a database. Once I've endured the design overhead of establishing a database connection, I put all mutable data into tables where possible.
I can think of two uses of single-column tables OTMH:
1) Data item exists. Often used in dropdown lists. Also used for simple legitimacy tests.
Eg. two-letter U.S. state abbreviations; Zip codes that we ship to; words legal in Scrabble; etc.
2) Sparse binary attribute, ie., in a large table, a binary attribute that will be true for only a very few records. Instead of adding a new boolean column, I might create a separate table containing the keys of the records for which the attribute is true.
Eg. employees that have a terminal disease; banks with a 360-day year (most use 365); etc.
-Al.
Mostly I've seen this in lookup type tables such as the state table you described. However, if you do this be sure to set the column as the primary key to force uniqueness. If you can't set this value as unique, then you shouldn't be using one column.
No problem as long as it contains unique values.
I would say in general, yes. Not sure why you need just one column. There are some exceptions to this that I have seen used effectively. It depends on what you're trying to achieve.
They are not really good design when you're thinking of the schema of the database, but really should only be used as utility tables.
I've seen numbers tables used effectively in the past.
The purpose of a database is to relate pieces of information to each other. How can you do that when there is no data to relate to?
Maybe this is some kind of compilation table (i.e. FirstName + LastName + Birthdate), though I'm still not sure why you would want to do that.
EDIT: I could see using this kind of table for a simple list of some kind. Is that what you are using it for?
Yes as long as the field is the primary key as you said it would be. The reason is because if you insert duplicate data those rows will be readonly. If you try to delete one of the rows that are duplicated. it will not work because the server will not know which row to delete.
The only use case I can conceive of is a table of words perhaps for a word game. You access the table just to verify that a string is a word: select word from words where word = ?. But there are far better data structures for holding a list of words than a relational database.
Otherwise, data in a database is usually placed in a database to take advantage of the relationships between various attributes of the data. If your data has no attributes beyond its value how will these relationship be developed?
So, while not illegal, in general you probably should not have a table with just one column.
All my tables have at least four tech fields, serial primary key, creation and modification timestamps, and soft delete boolean. In any blacklist, you will also want to know who did add the entry. So for me, answer is no, a table with only one column would not make sense except when prototyping something.
Yes that is perfectly fine. but an ID field couldn't hurt it right?
I have a table in a DB (Postgres based), which acts like a superclass in object-oriented programming. It has a column 'type' which determines, which additional columns should be present in the table (sub-class properties). But I don't want the table to include all possible columns (all properties of all possible types).
So I decided to make a table, containg the 'key' and 'value' columns (i.e. 'filename' = '/file', or 'some_value' = '5'), which contain any possible property of the object, not included in the superclass table. And also made one related table to contain the available 'key' values.
But there is a problem with such architecture - the 'value' column should be of a string data type by default, to be able to contain anything. But I don't think converting to and from strings is a good decision. What is the best way to bypass this limitation?
The design you're experimenting with is a variation of Entity-Attribute-Value, and it comes with a whole lot of problems and inefficiencies. It's not a good solution for what you're doing, except as a last resort.
What could be a better solution is what fallen888 describes: create a "subtype" table for each of your subtypes. This is okay if you have a finite number of subtypes, which sounds like what you have. Then your subtype-specific attributes can have data types, and also a NOT NULL constraint if appropriate, which is impossible if you use the EAV design.
One remaining weakness of the subtype-table design is that you can't enforce that a row exists in the subtype table just because the main row in the superclass table says it should. But that's a milder weakness than those introduced by the EAV design.
edit: Regarding your additional information about comments-to-any-entity, yes this is a pretty common pattern. Beware of a broken solution called "polymorphic association" which is a technique many people use in this situation.
How about this instead... each sub-type gets its own DB table. And the base/super table just has a varchar column that holds the name of the sub-type DB table. Then you can have something like this...
Entity
------
ID
Name
Type
SubTypeName (value of this column will be 'Dog')
Dog
---
VetName
VetNumber
etc
If you don't want your (sub-)table names to be varchar values in the base table, you can also just have a SubType table whose primary key will be in the base table.
The only workaround (while retaining your strucure) is to have separate tables:
create table IntProps(...);
create table StringProps(...);
create table CurrencyProps(...);
But I do not think that this is a good idea...
One common approach is having the key-value table contain multiple columns, one for each data type, i.e. StringValue, DecimalValue, etc.
Just know you're trading queryability and performance for a database schema you don't need to change. You could also consider ORM mapping or an object database.
You could have a per type key/value table. The available table would need to encode the availability of a specific key/type pair to point to the correctly typed key/value table.
This seems like a highly inefficient architecture in for a row based relational databases however.
Perhaps you should take a look at a column oriented relational database?
Thanks for the answers. I'll explain a little bit more specifically what i need.
There's a need to program a blog+forum website, and I've been looking at the WordPress DB structure.
There's a strong need for the ability to place comments to any kind of 'object', like a blog entry, or a video file attachment to it. The above DB structure being very easy to scale and to fulfill all our needs was the reason of its choice.
But that's not late to change it, cause this is in stage of early engineering. Also our model smells now like a completely tree-hierarchy based DB. For now I'll accept Bill Karwin's and fallen888 answers, but maybe I'm going in a totally wrong direction?
about the user being able to add a new field to the table:
I admire all these people making comments.
I used to be interested in this kind of thing a few years ago, but have written little code recently (apart from a little bit of PHP and MYSQL).
I think it's fine if you want to keep going - you may end up with something new.
Sorry to pour any cold water on the scheme - I admire your efforts. My personal belief is that if you go far enough in this direction, you will end up with a system that interprets more of natural language than SQL does. (Around 1970, SQL was actually spelt Sequel, and it actually stood for "structured english query language", but after they standardized it in the 1970's - I think someone said that Oracle was the first commercial implementation, 19079, the "English" got dropped off, because I guess they decided that it was only a tiny subset of English.
I have run out of steam in this area, because I haven't got a job. Without an easy job that pays the bills, where I can experiment with these ideas, it's a bit hard to concentrate on this area.
Best wishes to all.
sorry, I wrote 19079 above, I meant the year 1979. Oracle got their first contract writing a database for the CIA.