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
)
Related
To keep this as short as possible I'm going to use and example.
So let's say I have a simple database that has the following tables:
company - ( "idcompany", "name", "createdOn" )
user - ( "iduser", "idcompany", "name", "dob", "createdOn" )
event - ( "idevent", "idcompany", "name", "description", "date", "createdOn" )
Many users can be linked to a single company as well as multiple events and many events can be linked to a single company. All companies, users and events have columns as show above in common. However, what if I wanted to give my customers the ability to add custom fields to both their users and their events for any unique extra information they wish to store. These extra fields would be on a company wide basis, not on a per record basis ( so a company adding a custom field to their users would add it to all of their users not just one specific user ). The custom fields also need to be sesrchable and have the ability to be reported on, ideally automatically with some sort of report wizard. Considering the database is expected to have lots of traffic as well as lots of custom fields, what is the best solution for this?
My current research and findings in possible solutions:
To have generic placeholder columns such as "custom1", "custom2" etc.
** This is not viable as there will eventually be too many custom columns and there will be too many NULL values stored in the database
To have 3x tables per current table. eg: user, user-custom-field, user-custom-field-value. The user table being the same. The user-custom-field table containing the information about the new field such as name, data type etc. And the user-custom-field-value table containing the value for the custom field
** This one is more of a contender if it were not for its complexity and table size implications. I think it will be impossible to avoid a user-custom-field table if I want to automatically report on these fields as I will have to store the information on how to report on these fields here. However, In order to pull almost any data you would have to do a million joins on the user-custom-field-value table as well as the fact that your now storing column data as rows which in a database expected to have a lot of traffic as well as a lot of custom fields would soon cause a problem.
Create a new user and event table for each new company that is added to the system removing the company id from within those tables and instead using it in the table name ( eg user56, 56 being the company id ). Then allowing the user to trigger DB commands that add the new custom columns to the tables giving them the power to decide if it has a default value or auto increments etc.
** Everytime I have seen this solution it has always instantly been shut down by people saying it would be unmanageable as you would eventually get thousands of tables. However nobody really explains what they mean by unmanageable. Firstly as far as my understanding goes, more tables is actually more efficient and produces faster search times as the tables are much smaller. Secondly, yes I understand that making any common table changes would be difficult but all you would have to do is run a script that changes all your tables for each company. Finally I actually see benefits using this method as it would seperate company data making it impossible for one to accidentally access another's data via a potential bug, plus it would potentially give the ability to back up and restore company data individually. If someone could elaborate on why this is perceived as a bad idea It would be appreciated.
Convert fully or partially to a NoSQL database.
** Honestly I have no experience with schemaless databases and don't really know how dynamic user defined fields on a per record basis would work ( although I know it's possible ). If someone could explain the implications of the switch or differences in queries and potential benefits that would be appreciated.
Create a JSON column in each table that requires extra fields. Then add the extra fields into that JSON object.
** The issue I have with this solution is that it is nearly impossible to filter data via the custom columns. You would not be able to report on these columns and until you have received and processed them you don't really know what is in them.
Finally if anyone has a solution not mentioned above or any thoughts or disagreements on any of my notes please tell me as this is all I have been able to find or figure out for myself.
A typical solution is to have a JSON (or XML) column that contains the user-defined fields. This would be an additional column in each table.
This is the most flexible. It allows:
New fields to be created at any time.
No modification to the existing table to do so.
Supports any reasonable type of field, including types not readily available in SQL (i.e. array).
On the downside,
There is no validation of the fields.
Some databases support JSON but do not support indexes on them.
JSON is not "known" to the database for things like foreign key constraints and table definitions.
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.
I very rarely see ENUM datatypes used in the wild; a developer almost always just uses a secondary table that looks like this:
CREATE TABLE officer_ranks (
id int PRIMARY KEY
,title varchar NOT NULL UNIQUE);
INSERT INTO officer_ranks VALUES (1,'2LT'),(2,'1LT'),(3,'CPT'),(4,'MAJ'),(5,'LTC'),(6,'COL'),(7,'BG'),(8,'MG'),(9,'LTG'),(10,'GEN');
CREATE TABLE officers (
solider_name varchar NOT NULL
,rank int NOT NULL REFERENCES officer_ranks(id) ON DELETE RESTRICT
,serial_num varchar PRIMARY KEY);
But the same thing can also be shown using a user-defined type / ENUM:
CREATE TYPE officer_rank AS ENUM ('2LT', '1LT','CPT','MAJ','LTC','COL','BG','MG','LTG','GEN');
CREATE TABLE officers (
solider_name varchar NOT NULL
,rank officer_rank NOT NULL
,serial_num varchar PRIMARY KEY);
(Example shown using PostgreSQL, but other RDBMS's have similar syntax)
The biggest disadvantage I see to using an ENUM is that it's more difficult to update from within an application. And it might also confuse an inexperienced developer who's used to using a SQL DB simply as a bit bucket.
Assuming that the information is mostly static (weekday names, month names, US Army ranks, etc) is there any advantage to using a ENUM?
Example shown using PostgreSQL, but other RDBMS's have similar syntax
That's incorrect. It is not an ISO/IEC/ANSI SQL requirement, so the commercial databases do not provide it (you are supposed to provide Lookup tables). The small end of town implement various "extras", but do not implement the stricter requirements, or the grunt, of the big end of town.
We do not have ENUMs as part of a DataType either, that is absurd.
The first disadvantage of ENUMs is that is it non-standard and therefore not portable.
The second big disadvantage of ENUMs is, that the database is Closed. The hundreds of Report Tools that can be used on a database (independent of the app), cannot find them, and therefore cannot project the names/meanings. If you had a normal Standard SQL Lookup table, that problem is eliminated.
The third is, when you change the values, you have to change DDL. In a Normal Standard SQL database, you simply Insert/Update/Delete a row in the Lookup table.
Last, you cannot easily get a list of the content of the ENUM; you can with a Lookup table. More important, you have a vector to perform any Dimension-Fact queries with, eliminating the need for selecting from the large Fact table and GROUP BY.
I don't see any advantage in using ENUMS.
They are harder to maintain and don't offer anything that a regular lookup table with proper foreign keys wouldn't allow you to do.
A disadvantage of using something like an ENUM is that you can't get a list of all the available values if they don't happen to exist in your data table, unless you hard-code the list of available values somewhere. For example, if in your OFFICERS table you don't happen to have an MG on post there's no way to know the rank exists. Thus, when BG Blowhard is relieved by MG Marjorie-Banks you'll have no way to enter the new officer's rank - which is a shame, as he is the very model of a modern Major General. :-) And what happens when a General of the Army (five-star general) shows up?
For simple types which will not change I've used domains successfully. For example, in one of my databases I've got a yes_no_domain defined as follows:
CREATE DOMAIN yes_no_dom
AS character(1)
DEFAULT 'N'::bpchar
NOT NULL
CONSTRAINT yes_no_dom_check
CHECK ((VALUE = ANY (ARRAY['Y'::bpchar, 'N'::bpchar])));
Share and enjoy.
ENUMS are very-very-very useful! You just have to know how to use them:
An ENUM uses only 2 Bytes of storage.
No need for additional constraint (as replacement for FK).
Cheaper changes of Values compared to natural values in FKs.
No need for additional JOIN
ENUMs are ordered, ex you can compare if Monday < Friday, or January is < June or Project Initiation is < Payroll.
Thus if you have a fixed list of string values, which you want to use, an ENUM is a better solution compared to a lookup table. Let's say you need to List Amino-Acids in your products, with their respective weight. Today there are ~20 Amino Acids. If you would store their full names, you'd need much more space each time then 2 Bytes. The other option is to use artificial keys and to link to a foreign table. But how would the foreign Table look like? Would it have 2 columns: ID and Amino Acid Name? And you would join that table every time? What if your main table has >40 such fields? Querying that table would involve >40 Joins.
If your database hosts 1600 Tables, 400 of which are lookup tables which just replace ENUMs, your devs will waste lots of time navigating through them (in addition to the JOINs). Yes, you can work with prefixes, schemas and such.... but why not just kick those tables out?
ENUMS are Enumerated lists / ordered. That means that if you have values which are ordered, you are actually saving the hassle of maintaining a 3 columns lookup table.
The question is rather: why do I need lookup tables then?
Well, the answer is easy:
When your values are changing often
When you need to store more additional attributes --> The lookup table corresponds to a full fledged data object, and not a lookup list.
When you need it quick and dirty
And now the funny thing:
Lookup Tables and ENUMS are not complete replacements for each other!!!!
If you have a list, where the PK is single-column natural key. The list can grow or the values can change their names (for some reason), then you could define an ENUM and use it for both: PK in lookup and FK in main tables!
Example benefit:
you have to change the name of a lookup key. Without using the ENUM the DBMS will have to cascade the changes to all tables, where you use this value and not just your lookup table. If you are using ENUM, then you just change the value of ENUM, and there are no changes to the data.
A small advantage may lie in the fact, that you have a sort of UDT when creating an ENUM. A user defined type can be reused formally in many other database objects, e.g. in views, other tables, other types, stored procedures (in other RDBMS), etc.
Another advantage is for documentation of the allowed values of a field. Examples:
A yes/no field
A male/female field
A mr/mrs/ms/dr field
Probably a matter of taste. I prefer ENUMs for these kinds of fields, rather than foreign keys to lookup tables for such simple concepts.
Yet another advantage may be that when you use code generation or ORMs like jOOQ in Java, you can use that ENUM to generate a Java enum class from it, instead of joining the lookup table, or working with the ENUM literal's ID
It's a fact, though, that only few RDBMS support a formal ENUM type. I only know of Postgres and MySQL. Oracle or DB2 don't have it.
Advantages:
Type safety for stored procedures: will raise a type error if argument can not be coerced into the type. Like: select court_martial('3LT') would raise a type error automatically.
Custom coalition order: In your example, officers could be sorted without a ranking id.
Generally speaking, enum is better for things that don't change much, and it uses slightly fewer resources, since there's no FK checks or anything like to execute on insert etc.
Using a lookup table is more elegant and or traditional and it's much easier to add and remove options than an enum. It's also easier to mass change the values than an enum.
Well, you don't see, because usually developers are using enums in programming languages such as Java, and the don't have their counterparts in database design.
In database such enums are usually text or integer fields, with no constraints. Database enums will not be translated into Java/C#/etc. enums, so the developers see no gain in this.
There are very many very good database features which are rarely used because most ORM tools are too primitive to support them.
Another benefit of enums over a lookup table is that when you write SQL functions you get type checking.
My application has one table called 'events' and each event has approx 30 standard fields, but also user defined fields that could be any name or type, in an 'eventdata' table. Users can define these event data tables, by specifying x number of fields (either text/double/datetime/boolean) and the names of these fields. This 'eventdata' (table) can be different for each 'event'.
My current approach is to create a lookup table for the definitions. So if i need to query all 'event' and 'eventdata' per record, i do so in a M-D relaitionship using two queries (i.e. select * from events, then for each record in 'events', select * from 'some table').
Is there a better approach to doing this? I have implemented this so far, but most of my queries require two distinct calls to the DB - i cannot simply join my master 'events' table with different 'eventdata' tables for each record in in 'events'.
I guess my main question is: can i join my master table with different detail tables for each record?
E.g.
SELECT E.*, E.Tablename
FROM events E
LEFT JOIN 'E.tablename' T ON E._ID = T.ID
If not, is there a better way to design my database considering i have no idea on how many user defined fields there may be and what type they will be.
There are four ways of handling this.
Add several additional fields named "Custom1", "Custom2", "Custom3", etc. These should have a datatype of varchar(?) or similiar
Add a field to hold the unstructured data (like an XML column).
Create a table of name /value pairs which are associated with some type of template. Let them manage the template. You'll have to use pivot tables or similiar to get the data out.
Use a database like MongoDB or another NoSql style product to store this.
The above said, The first one has the advantage of being fast but limits the number of custom fields to the number you defined. Older main frame type applications work this way. SalesForce CRM used to.
The second option means that each record can have it's own custom fields. However, depending on your database there are definite challenges here. Tried this, don't recommend it.
The third one is generally harder to code for but allows for extreme flexibility. SalesForce and other applications have gone this route; including a couple I'm responsible for. The downside is that Microsoft apparently acquired a patent on doing things this way and is in the process of suing a few companies over it. Personally, I think that's bullcrap; but whatever. Point is, use at your own risk.
The fourth option is interesting. We've played with it a bit and the performance is great while coding is pretty darn simple. This might be your best bet for the unstructured data.
Those type of joins won't work because you will need to pivot the eventdata table to make it columns instead of rows. Therefore it depends on which database technology you are using.
Here is an example with MySQL: How to pivot a MySQL entity-attribute-value schema
My approach would be to avoid using a different table for each event, if that's possible.
I would use something like:
Event (EventId, ..., ...)
EventColumnType (EventColumnTypeId, EventTypeId, ColumnName)
EventColumnData (EventColumnTypeId, Data)
You are them limited to the type of data you can store (everything would have to be strings, for example), but you the number of events and columns are unrestricted.
What I'm getting from your description is you have an event table, and then a separate EventData table for each and every event.
Rather than that, why not have a single EventCustomFields table that contains a foreign key to the event table, a field Name (event+field being the PK) and a field value.
Sure it's not the best. You'd be stuck serializing the value or storing everything as a string. And you'd still be stuck doing two queries, one for the event table and one to get it's custom fields, but at least you wouldn't have a new table for every event in the system (yuck x10)
Another, (arguably worse) option is to serialize the custom fields into a single column of the and then deserialize when you need. So your query would be something like
Select E.*, C.*
From events E, customFields C
Where E.ID = C.ID
Is it possible to just impose a limit on your users? I know the tables underneath Sharepoint 2007 had a bunch of columns for custom data that were just named like CustomString1, CustomDate2, etc. That may end up easier than some of the approaches above, where everything is in one column (though that's an approach I've taken as well), and I would think it would scale up better.
The answer to your main question is: no. You can't have different rows in the result set with different columns. The result set is kind of like a table, so each row has to have the same columns. You can fake it with padding and dummy columns, but that's probably not much better.
You could try defining a fixed event data table, with (say) ten of each type of column. Then you'd store the usage metadata in a separate table and just read that in at system startup. The metadata would tell you that event type "foo" has a field "name" mapped to column string0 in the event data table, a field named "reporter" mapped to column string1, and a field named "reportDate" mapped to column date0. It's ugly and wastes space, but it's reasonably flexible. If you're in charge of the database, you can even define a view on the table so to the client it looks like a "normal" table. If the clients create their own tables and just stick the table name in the event record, then obviously this won't fly.
If you're really hardcore you can write a database procedure to query the table structures and serialize everything to a lilst of key/type/value tuples and return that in one long string as the last column, but that's probably not much handier than what you're doing now.
Database design question for y'all. I have a form (like, the paper kind) that has several entry points for data. This form has changed, and is expected to change over years. It is being turned into a computer app, so that we can, among other things, quit wasting paper. (And minor things, like have all the data in one central store that can be queried, etc.) I'd like to store all of the forms data in a database, and have it be pretty agnostic as to the changes.
Originally, I was just considering each field to be a string -- and I had a table something like this:
FormId int (FK)
FieldName nvarchar(64)
FieldValue nvarchar(128)
...something like that. It was actually a bit more 3NFy in that FieldName was in another table, associated with an artificial key, so that the field names weren't duplicated all over the place.
However, I'd like to extend this to numeric and drop-down data. I could just store numeric data as strings, but that seems like a pretty crappy idea. Same with drop downs.
I could stop using a table, and actually use columns on the main form table (the one that FormId above references), but that means adding a column for each new item as they come along, and older forms would just be null. (And, unless I stored it, I wouldn't know when that column was created. With the string table above, it's implicit.)
I could extend the table above to something like:
FormId int (FK)
FieldName nvarchar(64)
FieldValueType int -- enum as to which of the columns below are valid (or just let nulls imply that)
FieldValue nvarchar(128)
FieldValueInt int
Combos would have to be in a OTLT (one true lookup table), which I have reservations about, but perhaps it's needed here?
Any advice on StackOverflow? I'm using MSSQL, but this is really a more general question.
Use Nulls. Proper database design is a complicated subject; you may do well to pick up a good reference and do some research on the whole thing (I gather this is a good book on the topic). In general, it sounds like you would be well served by starting with a single table that encapsulates all the fields in your form, and then putting it through the normalization process. And yes, use nulls and do NOT use an int to enumerate which columns are set to valid values; that is exactly what nulls are for.
You could have a separate table for each datatype.
I.e. to fetch an entire form you'd do an N-way join using the form id where N is the number of distinct datatypes you support (+ perhaps extras depending on the info you want - e.g. dropdown values would probably be stored in another table / your fieldname lookup / etc.)
But the design should probably also depend on how you intend to use the data, which you've said nothing about. And it would also depend on just how fast the rate of change is for these forms . . .
By creating a table with a description of your forms, you are actually defining a metadata structure. That's daunting. You would need a lot of the infrastructure needed for proper table description. I think the vendors of your database system spent a lot of effort in doing all that.
At first I thought - what a nice idea! Build your own compatibility-aware table description system!
But then I thought - I'm too stupid to do that on my own. There must be a database system capable of doing that.
So I conclude, not being a db expert, define proper defaults for 'new fields' in new form versions. Handle the compatibility issue in your business logic.
I would strongly advise against having a "generic table" like you describe.
You are essentially reinventing the relational database, which is not a good idea: Queries and updates will be very painful with your structure, and you will not be able to use the more advanced features like foreign keys and triggers, should you need them.
Just make a table(s) with columns for the data fields, and if a form does not have a field, let it be null.
Or, probably even better, have a "base table" (field that are in every form), and give names/version numbers to updated forms, and have a new table for the new columns that this version adds, then use a synthetic PK to join these new tables to your base table.
I.e.:
base table: id(numeric,PK), name, birthday, town
addresstable1: street, number, postal code, country, base_table_id (foreign key)
addresstable2: po box no, po box code, base_table_id (FK)
and so on.
That way you avoid loads of null fields; your tables are not so wide (always desirable), and your records are implicitly versioned, because the list of tables that have a record belonging to a record in your base table tells you which fields the original form had, hence what kind of form was used originally.