I am putting together a schema for a database. The goal of the database is to track applications in our department. I have a repeated problem that I am trying to solve.
For example, I have an "Applications" table. I want to keep track if any application uses a database or a bug tracking system so right now I have fields in the Applications table called
Table: Applications
UsesDatabase (bit)
Database_ID (int)
UsesBugTracking (bit)
BugTracking_ID (int)
Table: Databases:
id
name
Table: BugTracking:
id
name
Should I consolidate the "uses" column with the respective ID columns so there is only one bug tracking column and only one database column in the applications table?
Any best practice here for database design?
NOTE: I would like to run reports like "Percent of Application that use bug tracking" (although I guess either approach could generate this data.)
You could remove the "uses" fields and make the id columns nullable, and let a null value mean that it doesn't use the feature. This is a common way of representing a missing value.
Edit:
To answer your note, you can easily get that statistics like this:
select
count(*) as TotalApplications,
count(Database_ID) as UsesDatabase,
count(BugTracking_ID) as UsesBugTracking
from
Applications
Why not get rid of the two Use fields and simply let a NULL value in the _ID fields indicate that the record does not use that application (bug tracking or database)
Either solution works. However, if you think you may want to occasionally just get a list of applications which do / do not have databases / bugtracking consider that having the flag fields reduces the query by one (or two) joins.
Having the bit fields is slightly denormalized, as you have to keep two fields in sync to keep one piece of data updated, but I tend to prefer them for cases like this for the reason I gave in the prior paragraph.
Another option would be to have the field nullable, and put null in it for those entries which do not have DBs / etc, but then you run into problems with foreign key constraints.
I don't think there is any one supreme right way, just consider the tradeoffs and go with what makes sense for your application.
I would use 3 tables for the objects: Application, Database, and BugTracking. Then I would use 2 join tables to do 1-to-many joins: ApplicationDatabases, and ApplicationBugTracking.
The 2 join tables would have both an application_id and the id of the other table. If an application used a single database, it would have a single ApplicationDatabases record joining them together. Using this setup, an application could have 0 database (no records for this app in the ApplicationDatabases table), or many databases (multiple records for this app in the ApplicationDatabases table).
"Should i consolidate the "uses" column"
If I look at your problem statement, then there either is no "uses" column at all, or there are two. In either case, it is wrong of you to speak of "THE" uses column.
May I politely suggest that you learn to be PRECISE when asking questions ?
Yes using null in the foreign key fields should be fine - it seems superfluous to have the bit fields.
Another way of doing it (though it might be considered evil by database people ^^) is to default them to 0 and add in an ID 0 data row in both bugtrack and database tables with a name of "None"... when you do the reports, you'll have to do some more work unless you present the "None" values as they are as well with a neat percentage...
To answer the edited question-
Yes, the fields should be combined, with NULL meaning that the application doesn't have a database (or bug tracker).
Related
My problem relates to DB schema developing and is as follows.
I am developing a purchasing module, in which I want to use for purchasing items and SERVICES.
Following is my EER diagram, (note that service has very few specialized attributes – max 2)
My problem is to keep products and services in two tables or just in one table?
One table option –
Reduces complexity as I will only need to specify item id which refers to item table which will have an “item_type” field to identify whether it’s a product or a service
Two table option –
Will have to refer separate product or service in everywhere I want to refer to them and will have to keep “item_type” field in every table which refers to either product or service?
Currently planning to use option 1, but want to know expert opinion on this matter. Highly appreciate your time and advice. Thanks.
I'd certainly go to the "two tables" option. You see, you have to distinguish Products and Services, so you may either use switch(item_type) { ... } in your program or entirely distinct code paths for Product and for Service. And if a need for updating the DB schema arises, switch is harder to maintain.
The second reason is NULLs. I'd advise avoid them as much as you can — they create more problems than they solve. With two tables you can declare all fields non-NULL and forget about NULL-processing. With one table option, you have to manually write code to ensure that if item_type=product, then Product-specific fields are not NULL, and Service-specific ones are, and that if item_type=service, then Service-specific fields are not NULL, and Product-specific ones are. That's not quite pleasant work, and the DBMS can't do it for you (there is no NOT NULL IF another_field = value column constraint in SQL or anything like this).
Go with two tables. It's easier to support. I once saw a DB where everything, every single piece of data went in just two tables — there were pages and pages of code to make sure that necessary fields are not NULL.
If I were to implement I would have gone for the Two table option, It's kinda like the first rule of normalization of the schema. To remove multi-valued attributes. Using item_type is not recommended. Once you create separate tables you dont need to use the item_type you can just use the foreign key relationship.
Consider reading this article :
http://en.wikipedia.org/wiki/Database_normalization
It should help.
I'm looking at the best practice approach here. I have a web page that has several drop down options. The drop downs are not related, they are for misc. values (location, building codes, etc). The database right now has a table for each set of options (e.g. table for building codes, table for locations, etc). I'm wondering if I could just combine them all into on table (called listOptions) and then just query that one table.
Location Table
LocationID (int)
LocatValue (nvarchar(25))
LocatDescription (nvarchar(25))
BuildingCode Table
BCID (int)
BCValue (nvarchar(25))
BCDescription (nvarchar(25))
Instead of the above, is there any reason why I can't do this?
ListOptions Table
ID (int)
listValue (nvarchar(25))
listDescription (nvarchar(25))
groupID (int) //where groupid corresponds to Location, Building Code, etc
Now, when I query the table, I can pass to the query the groupID to pull back the other values I need.
Putting in one table is an antipattern. These are differnt lookups and you cannot enforce referential integrity in the datbase (which is the ciorrect place to enforce it as applications are often not the only way data gets changed) unless they are in separate tables. Data integrity is FAR more important than saving a few minutes of development time if you need an additonal lookup.
If you plan to use the values later in some referencing FKeys - better use separate tables.
But why do you need "all in one" table? Which problem it solves?
You could do this.
I believe that is your master data and it would not be having any huge amounts of rows that it might create and performance problems.
Secondly, why would you want to do it once your app is up and running. It should have thought about earlier. The tables might be used in a lot of places and it's might be a lot of coding and most importantly testing.
Can you throw further light into your requirements.
You can keep them in separate tables and have your stored procedure return one set of data with a "datatype" key that signifies which set of values go with what option.
However, I would urge you to consider a much different approach. This suggestion is based on years of building data driven websites. If these drop-down options don't change very often then why not build server-side include files instead of querying the database. We did this with most of our websites. Think about it, each time the page is presented you query the database for the same list of values... that data hardly ever changes.
In cases when that data did have the tendency to change, we simply added a routine to the back end admin that rebuilt the server-side include file whenever an add, change or delete was done to one of the lookup values. This reduced database I/O's and spead up the load time of all our websites.
We had approximately 600 websites on the same server all using the same instance of SQL Server (separate databases) our total server database I/O's were drastically reduced.
Edit:
We simply built SSI that looked like this...
<option value="1'>Blue</option>
<option value="2'>Red</option>
<option value="3'>Green</option>
With single table it would be easy to add new groups in favour of creating new tables, but for best practices concerns you should also have a group table so you can name those groups in the db for future maintenance
The best practice depends on your requirements.
Do the values of location and building vary frequently? Where do the values come from? Are they imported from external data? Do other tables refer the unique table (so that I need a two-field key to preper join the tables)?
For example, I use unique table with hetorogeneus data for constants or configuration values.
But if the data vary often or are imported from external source, I prefer use separate tables.
What is the best way to store settings for certain objects in my database?
Method one: Using a single table
Table: Company {CompanyID, CompanyName, AutoEmail, AutoEmailAddress, AutoPrint, AutoPrintPrinter}
Method two: Using two tables
Table Company {CompanyID, COmpanyName}
Table2 CompanySettings{CompanyID, utoEmail, AutoEmailAddress, AutoPrint, AutoPrintPrinter}
I would take things a step further...
Table 1 - Company
CompanyID (int)
CompanyName (string)
Example
CompanyID 1
CompanyName "Swift Point"
Table 2 - Contact Types
ContactTypeID (int)
ContactType (string)
Example
ContactTypeID 1
ContactType "AutoEmail"
Table 3 Company Contact
CompanyID (int)
ContactTypeID (int)
Addressing (string)
Example
CompanyID 1
ContactTypeID 1
Addressing "name#address.blah"
This solution gives you extensibility as you won't need to add columns to cope with new contact types in the future.
SELECT
[company].CompanyID,
[company].CompanyName,
[contacttype].ContactTypeID,
[contacttype].ContactType,
[companycontact].Addressing
FROM
[company]
INNER JOIN
[companycontact] ON [companycontact].CompanyID = [company].CompanyID
INNER JOIN
[contacttype] ON [contacttype].ContactTypeID = [companycontact].ContactTypeID
This would give you multiple rows for each company. A row for "AutoEmail" a row for "AutoPrint" and maybe in the future a row for "ManualEmail", "AutoFax" or even "AutoTeleport".
Response to HLEM.
Yes, this is indeed the EAV model. It is useful where you want to have an extensible list of attributes with similar data. In this case, varying methods of contact with a string that represents the "address" of the contact.
If you didn't want to use the EAV model, you should next consider relational tables, rather than storing the data in flat tables. This is because this data will almost certainly extend.
Neither EAV model nor the relational model significantly slow queries. Joins are actually very fast, compared with (for example) a sort. Returning a record for a company with all of its associated contact types, or indeed a specific contact type would be very fast. I am working on a financial MS SQL database with millions of rows and similar data models and have no problem returning significant amounts of data in sub-second timings.
In terms of complexity, this isn't the most technical design in terms of database modelling and the concept of joining tables is most definitely below what I would consider to be "intermediate" level database development.
I would consider if you need one or two tables based onthe following criteria:
First are you close the the record storage limit, then two tables definitely.
Second will you usually be querying the information you plan to put inthe second table most of the time you query the first table? Then one table might make more sense. If you usually do not need the extended information, a separate ( and less wide) table should improve performance on the main data queries.
Third, how strong a possibility is it that you will ever need multiple values? If it is one to one nopw, but something like email address or phone number that has a strong possibility of morphing into multiple rows, go ahead and make it a related table. If you know there is no chance or only a small chance, then it is OK to keep it one assuming the table isn't too wide.
EAV tables look like they are nice and will save futue work, but in reality they don't. Genreally if you need to add another type, you need to do future work to adjust quesries etc. Writing a script to add a column takes all of five minutes, the other work will need to be there regarless of the structure. EAV tables are also very hard to query when you don;t know how many records you wil need to pull becasue normally you want them on one line and will get the information by joining to the same table multiple times. This causes performance problmes and locking especially if this table is central to your design. Don't use this method.
It depends if you will ever need more information about a company. If you notice yourself adding fields like companyphonenumber1 companyphonenumber2, etc etc. Then method 2 is better as you would seperate your entities and just reference a company id. If you do not plan to make these changes and you feel that this table will never change then method 1 is fine.
Usually, if you don't have data duplication then a single table is fine.
In your case you don't so the first method is OK.
I use one table if I estimate the data from the "second" table will be used in more than 50% of my queries. Use two tables if I need multiple copies of the data (i.e. multiple phone numbers, email addresses, etc)
I have a table that has a bunch of fields. The fields can be broken into logical groups - like a job's project manager info. The groupings themselves aren't really entity candidates as they don't and shouldn't have their own PKs.
For now, to group them, the fields have prefixes (PmFirstName for example) but I'm considering breaking them out into multiple tables with 1:1 relations on the main table.
Is there anything I should watch out for when I do this? Is this just a poor choice?
I can see that maybe my queries will get more complicated with all the extra joins but that can be mitigated with views right? If we're talking about a table with less than 100k records is this going to have a noticeable effect on performance?
Edit: I'll justify the non-entity candidate thoughts a little further. This information is entered by our user base. They don't know/care about each other. So its possible that the same user will submit the same "projectManager name" or whatever which, at this point, wouldn't be violating any constraint. Its for us to determine later on down the pipeline if we wanna correlate entries from separate users. If I were to give these things their own key they would grow at the same rate the main table grows - since they are essentially part of the same entity. At no pt is a user picking from a list of available "project managers".
So, given the above, I don't think they are entities. But maybe not - if you have further thoughts please post.
I don't usually use 1 to 1 relations unless there is a specific performance reason for it. For example storing an infrequently used large text or BLOB type field in a separate table.
I would suspect that there is something else going on here though. In the example you give - PmFirstName - it seems like maybe there should be a single pm_id relating to a "ProjectManagers" or "Employees" table. Are you sure none of those groupings are really entity candidates?
To me, they smell unless for some rows or queries you won't be interested in the extra columns. e.g. if for a large portion of your queries you are not selecting the PmFirstName columns, or if for a large subset of rows those columns are NULL.
I like the smells tag.
I use 1 to 1 relationships for inheritance-like constructs.
For example, all bonds have some basic information like CUSIP, Coupon, DatedDate, and MaturityDate. This all goes in the main table.
Now each type of bond (Treasury, Corporate, Muni, Agency, etc.) also has its own set of columns unique to it.
In the past we would just have one incredibly wide table with all that information. Now we break out the type-specific info into separate tables, which gives us much better performance.
For now, to group them, the fields have prefixes (PmFirstName for example) but I'm considering breaking them out into multiple tables with 1:1 relations on the main table.
Create a person table, every database needs this. Then in your project table have a column called PMKey which points to the person table.
Why do you feel that the group of fields are not an entity candidates? If they are not then why try to identify them with a prefix?
Either drop the prefixes or extract them into their own table.
It is valuable splitting them up into separate tables if they are separate logical entities that could be used elsewhere.
So a "Project Manager" could be 1:1 with all the projects currently, but it makes sense that later you might want to be able to have a Project Manager have more than one project.
So having the extra table is good.
If you have a PrimaryFirstName,PrimaryLastName,PrimaryPhone, SecondaryFirstName,SecondaryLastName,SEcondaryPhone
You could just have a "Person" table with FirstName, LastName, Phone
Then your original Table only needs "PrimaryId" and "SecondaryId" columns to replace the 6 columns you previously had.
Also, using SQL you can split up filegroups and tables across physical locations.
So you could have a POST table, and a COMMENT Table, that have a 1:1 relationship, but the COMMENT table is located on a different filegroup, and on a different physical drive with more memory.
1:1 does not always smell. Unless it has no purpose.
In a recent project I have seen a tables from 50 to 126 columns.
Should a table hold less columns per table or is it better to separate them out into a new table and use relationships? What are the pros and cons?
Generally it's better to design your tables first to model the data requirements and to satisfy rules of normalization. Then worry about optimizations like how many pages it takes to store a row, etc.
I agree with other posters here that the large number of columns is a potential red flag that your table is not properly normalized. But it might be fine in this case. We can't tell from your description.
In any case, splitting the table up just because the large number of columns makes you uneasy is not the right remedy. Is this really causing any defects or performance bottleneck? You need to measure to be sure, not suppose.
A good rule of thumb that I've found is simply whether or not a table is growing rows as a project continues,
For instance:
On a project I'm working on, the original designers decided to include site permissions as columns in the user table.
So now, we are constantly adding more columns as new features are implemented on the site. obviously this is not optimal. A better solution would be to have a table containing permissions and a join table between users and permissions to assign them.
However, for other more archival information, or tables that simply don't have to grow or need to be cached/minimize pages/can be filtered effectively, having a large table doesn't hurt too much as long as it doesn't hamper maintenance of the project.
At least that is my opinion.
Usually excess columns points to improper normalization, but it is hard to judge without having some more details about your requirements.
I can picture times when it might be necessary to have this many, or more columns. Examples would be if you had to denormalize and cache data - or for a type of row with many attributes. I think the keys are to avoid select * and make sure you are indexing the right columns and composites.
If you had an object detailing the data in the database, would you have a single object with 120 fields, or would you be looking through the data to extract data that is logically distinguishable? You can inline Address data with Customer data, but it makes sense to remove it and put it into an Addresses table, even if it keeps a 1:1 mapping with the Person.
Down the line you might need to have a record of their previous address, and by splitting it out you've removed one major problem refactoring your system.
Are any of the fields duplicated over multiple rows? I.e., are the customer's details replicated, one per invoice? In which case there should be one customer entry in the Customers table, and n entries in the Invoices table.
One place where you need to not fix broken normalisation is where you have a facts table (for auditing, etc) where the purpose is to aggregate data to run analyses on. These tables are usually populated from the properly normalised tables however (overnight for example).
It sounds like you have potential normalization issues.
If you really want to, you can create a new table for each of those columns (a little extreme) or group of related columns, and join it on the ID of each record.
It could certainly affect performance if people are running around with a lot of "Select * from GiantTableWithManyColumns"...
Here are the official statistics for SQL Server 2005
http://msdn.microsoft.com/en-us/library/ms143432.aspx
Keep in mind these are the maximums, and are not necessarily the best for usability.
Think about splitting the 126 columns into sections.
For instance, if it is some sort of "person" table
you could have
Person
ID, AddressNum, AddressSt, AptNo, Province, Country, PostalCode, Telephone, CellPhone, Fax
But you could separate that into
Person
ID, AddressID, PhoneID
Address
ID, AddressNum, AddressSt, AptNo, Province, Country, PostalCode
Phone
ID, Telephone, Cellphone, fax
In the second one, you could also save yourself from data replication by having all the people with the same address have the same addressId instead of copying the same text over and over.
The UserData table in SharePoint has 201 fields but is designed for a special purpose.
Normal tables should not be this wide in my opinion.
You could probably normalize some more. And read some posts on the web about table optimization.
It is hard to say without knowing a little bit more.
Well, I don't know how many columns are possible in sql but one thing for which I am very sure is that when you design table, each table is an entity means that each table should contain information either about a person, a place, an event or an object. So till in my life I don't know that a thing may have that much data/information.
Second thing that you should notice is that that there is a method called normalization which is basically used to divide data/information into sub section so that one can easily maintain database. I think this will clear your idea.
I'm in a similar position. Yes, there truly is a situation where a normalized table has, like in my case, about 90, columns: a work flow application that tracks many states that a case can have in addition to variable attributes to each state. So as each case (represented by the record) progresses, eventually all columns are filled in for that case. Now in my situation there are 3 logical groupings (15 cols + 10 cols + 65 cols). So do I keep it in one table (index is CaseID), or do I split into 3 tables connected by one-to-one relationship?
Columns in a table1 (merge publication)
246
Columns in a table2 (SQL Server snapshot or transactional publication)
1,000
Columns in a table2 (Oracle snapshot or transactional publication)
995
in a table, we can have maximum 246 column
http://msdn.microsoft.com/en-us/library/ms143432.aspx
A table should have as few columns as possible.....
in SQL server tables are stored on pages, 8 pages is an extent
in SQL server a page can hold about 8060 bytes, the more data you can fit on a page the less IOs you have to make to return the data
You probably want to normalize (AKA vertical partitioning) your database