Context
I have three tables in my SQL Server database: 1) School, 2) College, 3) University.
Then I have another table: Tags.
Each of the three tables (School, College, University) can have Tags associated with them. For which purpose I have three association tables: SchoolTags, CollegeTags, UniversityTags.
Problem
I am trying to create a user-defined function that will take the name of association table as parameter (i.e. 'SchoolTags') and the Id of the entity (school/college/university) and will return a list of tags associated with that entityId.
The issue I am having is I have got to join Tags with a table whose name will come in as parameter. For that I am creating a dynamic query. And we can not run dynamic queries in SQL Server user-defined functions.
Question
Any idea how can that be acheived?
Note: I want separate association tables as I have created and do not want to convert them into a generic association table and I do not want to add If-Else based on table names in my function so that if a new association table is created, I do not need to update my function.
I am using Microsoft SQL Server.
Whatever language you are using, you would probably use if:
begin
if table = 'school' then
begin
. . .
end;
else if table = 'college' then
. . .
end;
The exact syntax depends on the scripting language for the database you are using.
What you desire is impossible. You cannot pass a table name as a parameter to a UDF and use dynamic sql in the UDF to then create and execute a statement that is specific to the table passed as the argument. You already know that you have no choice but to use if-else statements in your UDF to achieve your goal - it is your pipe-dream of "never having to update (or verify) your code when the schema changes" (yes - I rephrased it to make your issue more obvious) that is a problem.
There are likely to be other ways of implementing some useful functionality - but I suggest that you are thinking too far ahead and trying to implement generic functions without a clear purpose. And that is a very difficult and trouble-prone path that requires sophisticated tsql skills.
And to re-iterate the prior responses, you have a schema problem. You purposely created three different entities - and now you want a common function to use with any of them. So before you spend much time on this particular aspect, you should take some time to think carefully about how you intend to use (i.e., write queries against) these tables. If you find yourself using unions frequently to combine these entities into a common resultset, then you have might have a mismatch between your actual business and your model (schema) of it.
Consider normalizing your database in related, logical groupings for one EducationInstitution table and one JoinEducTags table. Those tables sound like they maintain the same structure but of different typology and hence should be saved in one table with different Type field for School, College, University, etc.
Then, add necessary constraints, primary/foreign keys for the one-to-many relationship between all three sets:
You never want to keep restructuring your schema (i.e., add tables) for each new type. With this approach, your user-defined function would just need to receive value parameters not identifiers like tables to be run in dynamic querying. Finally, this approach scales better with efficient storage. And as you will see normalization saves on complex querying.
Related
I want to create new table with empty columns and specify the datatype later. Is it possible? I try to do so on myscompiler.io and it works. I don't know if it's just possible in such site or is it actually possible to create that once I use other tools to write my SQL.
No. The SQL syntax requires that a table be well-defined, with column names and data types. This is true in every database that I can think of.
You could possibly do what you want in one of three ways:
Your database might support some sort of generic type which you could use to define the column. For instance, SQL Server has a sql_variant type.
You could define the table with a specific type such as a string and change the type later using alter table.
You could define the table with a single primary key column and add columns as you decide what they are.
I don't recommend any of these approaches. Instead, I would suggest that you need to re-think how your application is structured. Tables represent entities and entities have properties. Generally when using databases, these things are known before you start doing any work. There may be some cases where dynamic table creation is useful, but that is definitely not the common approach when using databases.
I'm trying to determine if it's possible to write rules for Postgres to dynamically alter queries at runtime.
Essentially, I have a multitenancy solution that uses a mapping table to determine if a row is owned by a tenant. I've simplified the examples below for easy reading.
In these examples, "car" table is scoped to a particular tenant by a carmapping table which has foreign keys scoped_model_id pointing at the car table and scoping_model_pk pointing at the tenant/customer/whatever model that can generically be considered a "scoping model" used to scope various tables in the application.
Example scoped query:
SELECT "car"."id" FROM "car" WHERE
("car"."id" IN (SELECT U0."scoped_model_id" FROM "carmapping" U0 WHERE U0."scoping_model_pk" IN (1));
Original query:
SELECT "car"."id" FROM "car";
Is there a way to use rules to dynamically add these WHERE clauses?
It would need to be capable of determining what scoping_model_pk is active in the current request - whether that is passed to Postgres as an additional piece of metadata for all db cursor connections or embedded as a noop string somehow within the SQL statement itself. And will also need to determine what the mapping table is for the model currently targeted by the SELECT statement - in this case, carmapping.
We can do this easily at the application level within the ORM, but it would be best to do at the db level for consistency (because we have some code not using the ORM and don't want to have to remember to update any raw SQL code).
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.
I want to design a table for items.
There are many types of items, all share several fields.
Each type of item has it's own fields.
I want to store the uncommon fields in a separate table.
I thought of something like :
----Items
+Item_id
+Item_Type_Id
+Item_Serial
...
----Item_types
+Item_Type_Id
+Item_Name
...
----Item_Fields
+Item_Field_Id
+Item_Type_Id
+Field_Name
...
----Field_Values
+Field_Value_Id
+Item_Field_Id
+Item_Id
+Value
...
The pro is having the ability to add fields and values without changing the tables.
The con is that i have to transpose the field names and values in order to see all info for an item.
Any better suggestions? Or perhaps a simple (not stored procedure) way to join the tables to get a flat info?
I tried to use PIVOT (I'm using SQL 2005) but with no luck.
Thanks.
I wrote a stored proc to make PIVOT more useful. Here is the source:
http://dot-dash-dot.com/files/pivot_query.sql
and some examples how to use it:
http://dot-dash-dot.com/files/pivot_query_examples.sql
For your data, the query would just be the raw data joining those tables above to produce a raw listing of:
set #myQuery = '
Select Item_Id, Item_Name, Field_Name, Value From ...
';
Then your call to pivot_query would be:
exec pivot_query #myQuery, 'Item_Id, Item_Name', 'Field_Name', 'max(Value)'
like that.
One other option is to store items in XML format in one single field. Depending on your usage scenario, it may work well. Or it may not.
I believe there has to be some grouping of values.
For example lets say your items are objects in a room. Then different types of objects have different attributes. For example books have publication date and number of pages, chairs have color pattern and height, etc.
In this example, you make an item table, a book table and a chair table.
You could make an "additional values" table that holds generic information as above, but what you really want to do is figure out the "types" of the different groups of attributes and then make every one of those types it's own table.
Is there a set of values that all items have? There has to be at least one which is a type field (this describes where the other information is stored. I expect every item will also have a name and a description. This is the information to go in the item table.
Then you make additional tables for the different types itembook, itemchair etc. There may even be some overlap. For example itembook, itemhardback, itempaperback would be 3 tables used to describe books.
I believe this is the best solution to your problem. It will still allow you to extend, but it does put a framework around your data.
Of course there are systems that do it the way you describe, but unless you are building a tool that others are going to reuse for many different projects, it makes sense to design the system for the task at hand. You end up falling into the over designing trap otherwise. (IMHO)
On the other hand, if you are going to go the totally generic direction I suggest you use one of the systems that already exist that work in this way (entity framework, app framework, etc) Use someone else's don't start from scratch.
I'm not too sure how you want to retrieve the info, but something like the below may work. (It's probably close to what Hogan mentioned.)
If you want to retrieve data for a type, you can just JOIN two tables.
If you want to retrieve data for all types (with all fields), you can LEFT JOIN all tables.
----Items
+Item_id
+Item_Type_Id
+Item_Common_Field1
+Item_Common_Field1
...
----Item_Type_A
+Item_id
+Item_Type_A_Specific_Field1
+Item_Type_A_Specific_Field2
...
----Item_Type_B
+Item_id
+Item_Type_B_Specific_Field1
...
If you add these columns to the table, you can make them sparse columns to avoid the space taken by unspecified uncommon fields.
But I would not call this a best practice. (see comments under your question)
I don't want to be accused of being the always-uses-the-latest-useless-technology guy, but depending on your use case, this might be a good case for a nosql database - Tokyo, Mongo, SimpleDB, etc. Or as Developer Art suggested, you could just serialize the different fields into a single column. It's not the worst thing in the world.
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
)