I am about to create a database to track weight-lifting exercises.
Which approach would you prefer?
Solution A:
Two tables
Exercise (with ID, Name etc.)
Set (with ID, Set_Number, Date, FK_Exercise)
Here, one Exercise and Set have a one-to-many relationship.
Set_Number is supposed to track which set it is on a given date (1st set, 2nd set, 3rd set etc.)
Advantage: One table less to deal with.
Solution B:
Three tables:
Exercise (with ID, Name etc.)
Session (with ID, Date, FK_Exercise)
Set (with ID, Set_Number, FK_Session)
Here, a Session would be something like a connector between Exercise and Set. So basically a sequence of sets on a given day for a given exercise will be pooled in one Session instance.
In this case, Exercise and Session have a one-to-many relationship and Session and Set also have a one-to-many relationship.
Advantage: The Date property will not be redundant for any given day. And logically it makes sense to bundle sets.
A good data model falls out of a proper understanding of the domain. Your domain has three entities:
EXERCISE: particular type of weightlifting move (name and weight)
SET: number of reps of a given EXERCISE (depending on training goal - strength, muscle, endurance?)
SESSION: number of SETs undertaken on a given date
So you need at least three tables. At least, because EXERCISE has two levels of detail: one is the exercise name and the other is the exercise weight . It's quite likely you will need to store SETs of different combination of names and weights (Bicep curl / 10kg, Bicep curl / 15kg, etc) in which case you need a look-up table EXERCISE name and a fourth table SET_EXERCISE to store the weight used for a particular SET of reps.
Having gone through this exercise (o ho!) we can see that your foreign keys are wrong. A SESSION comprises a number of SETs; a SET comprises a number of EXERCISEs (SET_EXERCISEs).
Hence the logical data model should look something like:
EXERCISE (ID, Name, Weight, etc)
SET (ID, FK_Exercise, Reps, etc)
SESSION (ID, FK_Set, Date, etc)
Although this is not quite accurate: SET:SESSION is in fact a many-to- many relationship, as a SESSION will normally comprise more than one SET and a SET can be done in more than one SESSION.
When it comes to a physical data model i.e. tables you should have five tables:
EXERCISE (ID, Name, etc)
SET_EXERCISE (ID, FK_Exercise, FK_Set, Weight, etc)
SET (ID, FK_Set_Exercise, Reps, etc)
SESSION_SET (FK_Set, FK_Session, Set_Number, etc)
SESSION (ID, Date, etc)
The SESSION_SET table is necessary to resolve the many-to-many relationship between SET and SESSION .
The final model has five tables: three tables for the original entities and two intersection tables which join those entities. It so happens that all the relations between the logical entities (EXERCISE, SET, SESSION) have been implemented as intersection tables rather than foreign keys. This doesn't always happen when transforming from a Logical to a Physical data model.
This is not the only way of modelling the domain. As a design activity data modelling is about interpreting the rules to fit the data you need to record. The data is the starting point.
"it seems I didn't make myself clear regarding the Session entity...he naming is probably bad and misleading"
This is why I said the data model follows from a proper understanding of the domain. EXERCISE, SET and SESSION are domain terms. You are of course welcome to make your own definitions of things for your private projects, but in real life data models are a mechanism for communication between Development and Business: the meaning of things is crucial, and must conform to a common understanding. We cannot build a data model where SESSION means something different from what the business understands by "session".
"I also don't understand how a Set can be done in more than one Session?"
A SET is a pattern of EXERCISE for a number of reps. So #1 / benchpress / 130KG / 8 reps is a SET and #2 / benchpress / 100KG / 12 reps is a different SET. If you benchpressed 130KG eight times on Monday and Wednesday then that's the same SET in two different SESSIONs. Maybe it's a layer of detail too far; but if you're going to build a database app to track your workouts instead of using a spreadsheet like most people you might as well build the best data model you can :-)
Again, data modelling is an exercise with a large dose of opinion: if your data model is good enough for your current needs then it is good enough. The thing is, a more rigorous data model is paradoxically more flexible (because enforcing data integrity rules makes it easier to write queries and be sure that the results are correct). What might be good enough now might be a terrible brake on innovation in the future.
I have to add functionality to an existing application and I've run into a data situation that I'm not sure how to model. I am being restricted to the creation of new tables and code. If I need to alter the existing structure I think my client may reject the proposal.. although if its the only way to get it right this is what I will have to do.
I have an Item table that can me link to any number of tables, and these tables may increase over time. The Item can only me linked to one other table, but the record in the other table may have many items linked to it.
Examples of the tables/entities being linked to are Person, Vehicle, Building, Office. These are all separate tables.
Example of Items are Pen, Stapler, Cushion, Tyre, A4 Paper, Plastic Bag, Poster, Decoration"
For instance a Poster may be allocated to a Person or Office or Building. In the future if they add a Conference Room table it may also be added to that.
My intital thoughts are:
Item
{
ID,
Name
}
LinkedItem
{
ItemID,
LinkedToTableName,
LinkedToID
}
The LinkedToTableName field will then allow me to identify the correct table to link to in my code.
I'm not overly happy with this solution, but I can't quite think of anything else. Please help! :)
Thanks!
It is not a good practice to store table names as column values. This is a bad hack.
There are two standard ways of doing what you are trying to do. The first is called single-table inheritance. This is easily understood by ORM tools but trades off some normalization. The idea is, that all of these entities - Person, Vehicle, whatever - are stored in the same table, often with several unused columns per entry, along with a discriminator field that identifies what type the entity is.
The discriminator field is usually an integer type, that is mapped to some enumeration in your code. It may also be a foreign key to some lookup table in your database, identifying which numbers correspond to which types (not table names, just descriptions).
The other way to do this is multiple-table inheritance, which is better for your database but not as easy to map in code. You do this by having a base table which defines some common properties of all the objects - perhaps just an ID and a name - and all of your "specific" tables (Person etc.) use the base ID as a unique foreign key (usually also the primary key).
In the first case, the exclusivity is implicit, since all entities are in one table. In the second case, the relationship is between the Item and the base entity ID, which also guarantees uniqueness.
Note that with multiple-table inheritance, you have a different problem - you can't guarantee that a base ID is used by exactly one inheritance table. It could be used by several, or not used at all. That is why multiple-table inheritance schemes usually also have a discriminator column, to identify which table is "expected." Again, this discriminator doesn't hold a table name, it holds a lookup value which the consumer may (or may not) use to determine which other table to join to.
Multiple-table inheritance is a closer match to your current schema, so I would recommend going with that unless you need to use this with Linq to SQL or a similar ORM.
See here for a good detailed tutorial: Implementing Table Inheritance in SQL Server.
Find something common to Person, Vehicle, Building, Office. For the lack of a better term I have used Entity. Then implement super-type/sub-type relationship between the Entity and its sub-types. Note that the EntityID is a PK and a FK in all sub-type tables. Now, you can link the Item table to the Entity (owner).
In this model, one item can belong to only one Entity; one Entity can have (own) many items.
your link table is ok.
the trouble you will have is that you will need to generate dynamic sql at runtime. parameterized sql does not typically allow the objects inthe FROM list to be parameters.
i fyou want to avoid this, you may be able to denormalize a little - say by creating a table to hold the id (assuming the ids are unique across the other tables) and the type_id representing which table is the source, and a generated description - e.g. the name value from the inital record.
you would trigger the creation of this denormalized list when the base info is modified, and you could use that for generalized queries - and then resort to your dynamic queries when needed at runtime.
I was thinking the other day on normalization, and it occurred to me, I cannot think of a time where there should be a 1:1 relationship in a database.
Name:SSN? I'd have them in the same table.
PersonID:AddressID? Again, same table.
I can come up with a zillion examples of 1:many or many:many (with appropriate intermediate tables), but never a 1:1.
Am I missing something obvious?
A 1:1 relationship typically indicates that you have partitioned a larger entity for some reason. Often it is because of performance reasons in the physical schema, but it can happen in the logic side as well if a large chunk of the data is expected to be "unknown" at the same time (in which case you have a 1:0 or 1:1, but no more).
As an example of a logical partition: you have data about an employee, but there is a larger set of data that needs to be collected, if and only if they select to have health coverage. I would keep the demographic data regarding health coverage in a different table to both give easier security partitioning and to avoid hauling that data around in queries unrelated to insurance.
An example of a physical partition would be the same data being hosted on multiple servers. I may keep the health coverage demographic data in another state (where the HR office is, for example) and the primary database may only link to it via a linked server... avoiding replicating sensitive data to other locations, yet making it available for (assuming here rare) queries that need it.
Physical partitioning can be useful whenever you have queries that need consistent subsets of a larger entity.
One reason is database efficiency. Having a 1:1 relationship allows you to split up the fields which will be affected during a row/table lock. If table A has a ton of updates and table b has a ton of reads (or has a ton of updates from another application), then table A's locking won't affect what's going on in table B.
Others bring up a good point. Security can also be a good reason depending on how applications etc. are hitting the system. I would tend to take a different approach, but it can be an easy way of restricting access to certain data. It's really easy to just deny access to a certain table in a pinch.
My blog entry about it.
Sparseness. The data relationship may be technically 1:1, but corresponding rows don't have to exist for every row. So if you have twenty million rows and there's some set of values that only exists for 0.5% of them, the space savings are vast if you push those columns out into a table that can be sparsely populated.
Most of the highly-ranked answers give very useful database tuning and optimization reasons for 1:1 relationships, but I want to focus on nothing but "in the wild" examples where 1:1 relationships naturally occur.
Please note one important characteristic of the database implementation of most of these examples: no historical information is retained about the 1:1 relationship. That is, these relationships are 1:1 at any given point in time. If the database designer wants to record changes in the relationship participants over time, then the relationships become 1:M or M:M; they lose their 1:1 nature. With that understood, here goes:
"Is-A" or supertype/subtype or inheritance/classification relationships: This category is when one entity is a specific type of another entity. For example, there could be an Employee entity with attributes that apply to all employees, and then different entities to indicate specific types of employee with attributes unique to that employee type, e.g. Doctor, Accountant, Pilot, etc. This design avoids multiple nulls since many employees would not have the specialized attributes of a specific subtype. Other examples in this category could be Product as supertype, and ManufacturingProduct and MaintenanceSupply as subtypes; Animal as supertype and Dog and Cat as subtypes; etc. Note that whenever you try to map an object-oriented inheritance hierarchy into a relational database (such as in an object-relational model), this is the kind of relationship that represents such scenarios.
"Boss" relationships, such as manager, chairperson, president, etc., where an organizational unit can have only one boss, and one person can be boss of only one organizational unit. If those rules apply, then you have a 1:1 relationship, such as one manager of a department, one CEO of a company, etc. "Boss" relationships don't only apply to people. The same kind of relationship occurs if there is only one store as the headquarters of a company, or if only one city is the capital of a country, for example.
Some kinds of scarce resource allocation, e.g. one employee can be assigned only one company car at a time (e.g. one truck per trucker, one taxi per cab driver, etc.). A colleague gave me this example recently.
Marriage (at least in legal jurisdictions where polygamy is illegal): one person can be married to only one other person at a time. I got this example from a textbook that used this as an example of a 1:1 unary relationship when a company records marriages between its employees.
Matching reservations: when a unique reservation is made and then fulfilled as two separate entities. For example, a car rental system might record a reservation in one entity, and then an actual rental in a separate entity. Although such a situation could alternatively be designed as one entity, it might make sense to separate the entities since not all reservations are fulfilled, and not all rentals require reservations, and both situations are very common.
I repeat the caveat I made earlier that most of these are 1:1 relationships only if no historical information is recorded. So, if an employee changes their role in an organization, or a manager takes responsibility of a different department, or an employee is reassigned a vehicle, or someone is widowed and remarries, then the relationship participants can change. If the database does not store any previous history about these 1:1 relationships, then they remain legitimate 1:1 relationships. But if the database records historical information (such as adding start and end dates for each relationship), then they pretty much all turn into M:M relationships.
There are two notable exceptions to the historical note: First, some relationships change so rarely that historical information would normally not be stored. For example, most IS-A relationships (e.g. product type) are immutable; that is, they can never change. Thus, the historical record point is moot; these would always be implemented as natural 1:1 relationships. Second, the reservation-rental relationship store dates separately, since the reservation and the rental are independent events, each with their own dates. Since the entities have their own dates, rather than the 1:1 relationship itself having a start date, these would remain as 1:1 relationships even though historical information is stored.
Your question can be interpreted in several ways, because of the way you worded it. The responses show this.
There can definitely be 1:1 relationships between data items in the real world. No question about it. The "is a" relationship is generally one to one. A car is a vehicle.
One car is one vehicle. One vehicle might be one car. Some vehicles are trucks, in which case one vehicle is not a car. Several answers address this interpretation.
But I think what you really are asking is... when 1:1 relationships exist, should tables ever be split? In other words, should you ever have two tables that contain exactly the same keys? In practice, most of us analyze only primary keys, and not other candidate keys, but that question is slightly diferent.
Normalization rules for 1NF, 2NF, and 3NF never require decomposing (splitting) a table into two tables with the same primary key. I haven't worked out whether putting a schema in BCNF, 4NF, or 5NF can ever result in two tables with the same keys. Off the top of my head, I'm going to guess that the answer is no.
There is a level of normalization called 6NF. The normalization rule for 6NF can definitely result in two tables with the same primary key. 6NF has the advantage over 5NF that NULLS can be completely avoided. This is important to some, but not all, database designers. I've never bothered to put a schema into 6NF.
In 6NF missing data can be represent by an omitted row, instead of a row with a NULL in some column.
There are reasons other than normalization for splitting tables. Sometimes split tables result in better performance. With some database engines, you can get the same performance benefits by partitioning the table instead of actually splitting it. This can have the advantage of keeping the logical design easy to understand, while giving the database engine the tools needed to speed things up.
I use them primarily for a few reasons. One is significant difference in rate of data change. Some of my tables may have audit trails where I track previous versions of records, if I only care to track previous versions of 5 out of 10 columns splitting those 5 columns onto a separate table with an audit trail mechanism on it is more efficient. Also, I may have records (say for an accounting app) that are write only. You can not change the dollar amounts, or the account they were for, if you made a mistake then you need to make a corresponding record to write adjust off the incorrect record, then create a correction entry. I have constraints on the table enforcing the fact that they cannot be updated or deleted, but I may have a couple of attributes for that object that are malleable, those are kept in a separate table without the restriction on modification. Another time I do this is in medical record applications. There is data related to a visit that cannot be changed once it is signed off on, and other data related to a visit that can be changed after signoff. In that case I will split the data and put a trigger on the locked table rejecting updates to the locked table when signed off, but allowing updates to the data the doctor is not signing off on.
Another poster commented on 1:1 not being normalized, I would disagree with that in some situations, especially subtyping. Say I have an employee table and the primary key is their SSN (it's an example, let's save the debate on whether this is a good key or not for another thread). The employees can be of different types, say temporary or permanent and if they are permanent they have more fields to be filled out, like office phone number, which should only be not null if the type = 'Permanent'. In a 3rd normal form database the column should depend only on the key, meaning the employee, but it actually depends on employee and type, so a 1:1 relationship is perfectly normal, and desirable in this case. It also prevents overly sparse tables, if I have 10 columns that are normally filled, but 20 additional columns only for certain types.
The most common scenario I can think of is when you have BLOB's. Let's say you want to store large images in a database (typically, not the best way to store them, but sometimes the constraints make it more convenient). You would typically want the blob to be in a separate table to improve lookups of the non-blob data.
In terms of pure science, yes, they are useless.
In real databases it's sometimes useful to keep a rarely used field in a separate table: to speed up queries using this and only this field; to avoid locks, etc.
Rather than using views to restrict access to fields, it sometimes makes sense to keep restricted fields in a separate table to which only certain users have access.
I can also think of situations where you have an OO model in which you use inheritance, and the inheritance tree has to be persisted to the DB.
For instance, you have a class Bird and Fish which both inherit from Animal.
In your DB you could have an 'Animal' table, which contains the common fields of the Animal class, and the Animal table has a one-to-one relationship with the Bird table, and a one-to-one relationship with the Fish table.
In this case, you don't have to have one Animal table which contains a lot of nullable columns to hold the Bird and Fish-properties, where all columns that contain Fish-data are set to NULL when the record represents a bird.
Instead, you have a record in the Birds-table that has a one-to-one relationship with the record in the Animal table.
1-1 relationships are also necessary if you have too much information. There is a record size limitation on each record in the table. Sometimes tables are split in two (with the most commonly queried information in the main table) just so that the record size will not be too large. Databases are also more efficient in querying if the tables are narrow.
In SQL it is impossible to enforce a 1:1 relationship between two tables that is mandatory on both sides (unless the tables are read-only). For most practical purposes a "1:1" relationship in SQL really means 1:0|1.
The inability to support mandatory cardinality in referential constraints is one of SQL's serious limitations. "Deferrable" constraints don't really count because they are just a way of saying the constraint is not enforced some of the time.
It's also a way to extend a table which is already in production with less (perceived) risk than a "real" database change. Seeing a 1:1 relationship in a legacy system is often a good indicator that fields were added after the initial design.
Most of the time, designs are thought to be 1:1 until someone asks "well, why can't it be 1:many"? Divorcing the concepts from one another prematurely is done in anticipation of this common scenario. Person and Address don't bind so tightly. A lot of people have multiple addresses. And so on...
Usually two separate object spaces imply that one or both can be multiplied (x:many). If two objects were truly, truly 1:1, even philosophically, then it's more of an is-relationship. These two "objects" are actually parts of one whole object.
If you're using the data with one of the popular ORMs, you might want to break up a table into multiple tables to match your Object Hierarchy.
I have found that when I do a 1:1 relationship its totally for a systemic reason, not a relational reason.
For instance, I've found that putting the reserved aspects of a user in 1 table and putting the user editable fields of the user in a different table allows logically writing those rules about permissions on those fields much much easier.
But you are correct, in theory, 1:1 relationships are completely contrived, and are almost a phenomenon. However logically it allows the programs and optimizations abstracting the database easier.
extended information that is only needed in certain scenarios. in legacy applications and programming languages (such as RPG) where the programs are compiled over the tables (so if the table changes you have to recompile the program(s)). Tag along files can also be useful in cases where you have to worry about table size.
Most frequently it is more of a physical than logical construction. It is commonly used to vertically partition a table to take advantage of splitting I/O across physical devices or other query optimizations associated with segregating less frequently accessed data or data that needs to be kept more secure than the rest of the attributes on the same object (SSN, Salary, etc).
The only logical consideration that prescribes a 1-1 relationship is when certain attributes only apply to some of the entities. However, in most cases there is a better/more normalized way to model the data through entity extraction.
The best reason I can see for a 1:1 relationship is a SuperType SubType of database design. I created a Real Estate MLS data structure based on this model. There were five different data feeds; Residential, Commercial, MultiFamily, Hotels & Land.
I created a SuperType called property that contained data that was common to each of the five separate data feeds. This allowed for very fast "simple" searches across all datatypes.
I create five separate SubTypes that stored the unique data elements for each of the five data feeds. Each SuperType record had a 1:1 relationship to the appropriate SubType record.
If a customer wanted a detailed search they had to select a Super-Sub type for example PropertyResidential.
In my opinion a 1:1 relationship maps a class Inheritance on a RDBMS.
There is a table A that contains the common attributes, i.e. the partent class status
Each inherited class status is mapped on the RDBMS with a table B with a 1:1 relationship
to A table, containing the specialized attributes.
The table namend A contain also a "type" field that represents the "casting" functionality
Bye
Mario
You can create a one to one relationship table if there is any significant performance benefit. You can put the rarely used fields into separate table.
1:1 relationships don't really make sense if you're into normalization as anything that would be 1:1 would be kept in the same table.
In the real world though, it's often different. You may want to break your data up to match your applications interface.
Possibly if you have some kind of typed objects in your database.
Say in a table, T1, you have the columns C1, C2, C3… with a one to one relation. It's OK, it's in normalized form. Now say in a table T2, you have columns C1, C2, C3, … (the names may differ, but say the types and the role is the same) with a one to one relation too. It's OK for T2 for the same reasons as with T1.
In this case however, I see a fit for a separate table T3, holding C1, C2, C3… and a one to one relation from T1 to T3 and from T2 to T3. I even more see a fit if there exist another table, with which there already exist a one to multiple C1, C2, C3… say from table A to multiple rows in table B. Then, instead of T3, you use B, and have a one to one relation from T1 to B, the same for from T2 to B, and still the same one to multiple relation from A to B.
I believe normalization do not agree with this, and that may be an idea outside of it: identifying object types and move objects of a same type to their own storage pool, using a one to one relation from some tables, and a one to multiple relation from some other tables.
It is unnecessary great for security purposes but there better ways to perform security checks. Imagine, you create a key that can only open one door. If the key can open any other door, you should ring the alarm. In essence, you can have "CitizenTable" and "VotingTable". Citizen One vote for Candidate One which is stored in the Voting Table. If citizen one appear in the voting table again, then their should be an alarm. Be advice, this is a one to one relationship because we not refering to the candidate field, we are refering to the voting table and the citizen table.
Example:
Citizen Table
id = 1, citizen_name = "EvryBod"
id = 2, citizen_name = "Lesly"
id = 3, citizen_name = "Wasserman"
Candidate Table
id = 1, citizen_id = 1, candidate_name = "Bern Nie"
id = 2, citizen_id = 2, candidate_name = "Bern Nie"
id = 3, citizen_id = 3, candidate_name = "Hill Arry"
Then, if we see the voting table as so:
Voting Table
id = 1, citizen_id = 1, candidate_name = "Bern Nie"
id = 2, citizen_id = 2, candidate_name = "Bern Nie"
id = 3, citizen_id = 3, candidate_name = "Hill Arry"
id = 4, citizen_id = 3, candidate_name = "Hill Arry"
id = 5, citizen_id = 3, candidate_name = "Hill Arry"
We could say that citizen number 3 is a liar pants on fire who cheated Bern Nie. Just an example.
When you are dealing with a database from a third party product, then you probably don't want to alter their database as to prevent tight coupling. but you may have data that corresponds 1:1 with their data
Anywhere were two entirely independent entities share a one-to-one relationship. There must be lots of examples:
person <-> dentist (its 1:N, so its wrong!)
person <-> doctor (its 1:N, so it's also wrong!)
person <-> spouse (its 1:0|1, so its mostly wrong!)
EDIT: Yes, those were pretty bad examples, particularly if I was always looking for a 1:1, not a 0 or 1 on either side. I guess my brain was mis-firing :-)
So, I'll try again. It turns out, after a bit of thought, that the only way you can have two separate entities that must (as far as the software goes) be together all of the time is for them to exist together in higher categorization. Then, if and only if you fall into a lower decomposition, the things are and should be separate, but at the higher level they can't live without each other. Context, then is the key.
For a medical database you may want to store different information about specific regions of the body, keeping them as a separate entity. In that case, a patient has just one head, and they need to have it, or they are not a patient. (They also have one heart, and a number of other necessary single organs). If you're interested in tracking surgeries for example, then each region should be a unique separate entity.
In a production/inventory system, if you're tracking the assembly of vehicles, then you certainly want to watch the engine progress differently from the car body, yet there is a one to one relationship. A care must have an engine, and only one (or it wouldn't be a 'car' anymore). An engine belongs to only one car.
In each case you could produce the separate entities as one big record, but given the level of decomposition, that would be wrong. They are, in these specific contexts, truly independent entities, although they might not appear so at a higher level.
Paul.