I am developing a website for a dealer of wine and other alcoholic beverages. Obviously, each wine is made in a country that must be modeled in the Wine table.
But many times, a Wine also has a Region (Languedoc, Rioja, Bougogne etc.), these regions are of course in a parent-child relationship with a country.
THe following options exist:
-Giving the Wine table only a reference to a region
Problem is that some wines/whiskeys do not mention a region, only a country
-Giving the wine table 2 separate FK references, to a Country and a Region table. This introduces a circular reference and a redundacny problem becase country and region are already related.
-Using a Location table and a single FK referernce from the Wine table to the Location table. THe Location table is in fact a region or a country (maybe even a city) so it has a field "location_type" and a parent FK field, referring to its own PK. For the top-level Country entries, the parent id is null.
This is the example I have found somewhere in the internet. It will make however the queries more complex.
Is this a known problem, and are there any suggestions?
TIA, Klaas
I'm also working on an application in this domain. Common for wine, there is also the concept of sub-region or appellation, so you can have wines from France-Bourgogne-Cote d'Or, for example. I went with the second option you described, having FK references to Country, Region, and Subregion. Only the Country field is required, while the others are nullable. The potential issues with referential integrity are compounded with this model, but it greatly facilitates effective query based on these fields, which is kind of the point of capturing this information in the first place.
I think you might want to look at this from a dimensional analysis perspective, rather than a strict entity-relationship one. That is, a but of denormalization may be just what you are looking for. I would recommend Ralph Kimball's books on dimensional data warehouses, since they often solve this type of problem.
In your case, just create a "location" dimension that contains all the fields that you might be interested in, at the lowest level of granularity:
Region
Country
SubCountry
Are the two obvious ones. You might also have hillside, city, whatever.
To take an example, you would have the following rows:
Barolo/Italy/Piemonte
NULL/Italy/Piemonte
NULL/Italy/NULL
The wines would be connected to this table.
Now, you have a maintenance problem for this table. However, the universe of wines and official regions is well known and very slowly changing, so I don't see this as a problem.
Good point about creating a location dimension. This type of model addresses analysis more effectively, but is more complicated for transactional systems. This gets into the question of whether you're optimizing your model to handle CRUD-type transactions, or for aggregated data analysis.
On the whole, I assume that Klaus is looking at modeling for a transactional system with basic query, rather than an analysis-based application like a data warehouse.
I've been learning database design and I'm confused about 1:1 relationships. From what I understand, you can simply add columns to the appropriate table. Can someone provide a real world example of where a 1:1 relationship was either necessary or provided some significant benefit? I.e., where would I use a 1:1 relationship and what would it look like?
I'll give you a real practical example.
In the medical billing world, doctors who want to get paid by medicare handle billing by creating a dictation report for each visit with a patient. This might actually be a recorded audio dictation transcribed by a secretary, but more often it's just a written description of what they did and talked about with the patient, along with history, impressions, and so forth. A licensed medical coder will then read this dictation and decide what the doctor is allowed to bill.
Separate from the dictation, there is demographic information about the patient involved: name, age, billing address, etc. This information must be strictly separate from information about the dictation, to prevent coders from allowing bias to cloud their billing judgements or violating patients' privacy.
This data is often kept well-normalized with a 1:many relationship in the data systems at the point of origin, and only the right parts are displayed to the right people at the right times. However, a significant number of offices out-source their billing function to a third party. This way a small clinic, for example, doesn't have to keep a licensed medical coder on staff; one coder at the billing office can handle the needs of many clinics. When the data is sent from the clinic to the billing office, the patient demographic information and the dictations need to come over as separate pieces, possibly at separate times. At this point, they'll likely be stored in completely separate tables with a 1:1 relationship and a shared ID field to match them up later.
In this case, the 1:1 relationship has very little to do with the data model. You could probably match up the records at the time of import, and as a bill moves through the system eventually the provincial patient information received in the clinic's demographic record will be matched to a real person so the 1:many relationship can be restored. Otherwise you'd get a separate statement on a separate account for each visit to the doctor.
Instead, it has almost everything to do with the systems design. There are likely entirely different people building and using the billing part verses the coding part at our imaginary billing service. This way, each side can each have full control of it's own fiefdom, and you are sure that no one, not even a developer, is breaking any privacy rules.
True one-to-one relationships seldom
occur in the real world. This type of
relationship is often created to get
around some limitation of the database
management software rather than to
model a real-world situation. In
Microsoft Access, one-to-one
relationships may be necessary in a
database when you have to split a
table into two or more tables because
of security or performance concerns or
because of the limit of 255 columns
per table. For example, you might keep
most patient information in
tblPatient, but put especially
sensitive information (e.g., patient
name, social security number and
address) in tblConfidential (see
Figure 3). Access to the information
in tblConfidential could be more
restricted than for tblPatient. As a
second example, perhaps you need to
transfer only a portion of a large
table to some other application on a
regular basis. You can split the table
into the transferred and the
non-transferred pieces, and join them
in a one-to-one relationship.
That's a quote from here: Fundamentals of Relational Database Design
And here's a similar question on SO.
Another reason I can see for using a 1:1 (where I have used it in the past) is if you have a table with a lot of columns, and only a few of them are involved in very intensive and frequent queries which need to be fast, I would break it into two tables that are related 1:1 where I could query the lightweight table and get good performance, but still have the other data related to it easily with a simple join.
I belief tables should be designed with the domain background. So if those columns form two different entities, they should not be mixed in one table. From my experience 1:1 relationships tend to evolve into 1:n relationships over time.
For example you may want to store the postal address of a person. But after some time, you are required to store more than one address per person. Refactoring programs from a 1:1 relationship into 1:n is usually a lot easier than extracting some columns from an old table into a new one.
Many database systems allow defining of access permissions per table in a very easy way. But defining permissions on individual columns is often quite painful.
It's useful if X has a 1:1 relationship with Y and Z also has a 1:1 relationship with Y. Y can be abstracted out into a shared table rather than duplicating in both X and Z.
EDIT: A real world example would be Customers, Companies, and Addresses. There can be a N:N relationship between Customer and Company. But both Customer and Company have 1:1 relationships with Address. Some Address rows could be related to both a Customer and a Person.
First, because they are talking about Access (Jet, Ace, whatever) -- credit to #Richard DesLonde for spotting this -- then they are probably talking about 1:0..1 relationships. I do not believe true 1:1 relationships are workable in Access because it has no mechanism for deferring constraints nor executing multiple statements in a SQL PROCEDURE. Most Access practitioners are satisfied to use a 1:0..1 relationship to model a true 1:1 relationship, so I guess the authors are satisfied to use the term "1:1" informally to refer to both.
Of course, 1:1 and 1:1..0 relationships are common enough in the real world. I rather think they are trying to convey the (valid) point that some 1:1 and 1:1..0 relationships are invented in a data model for business purposes.
Consider a "natural person" (i.e. human) and a "corporation". They have no attributes in common (sure, both have a "name" but their domains are different e.g. "natural person name" has sub atomic domains for "family name", "given name" and "title", etc).
However, in a given data model distinct entity types may play the same role. For example, both a "natural person" and a "corporation" can be the officer of a "corporation". In the data model, we could have two distinct entity types "natural person officers" and "corporate officers" that are likely to have many attributes in common and from the same domains e.g. appointment date, termination date, etc; further, they business rules would be the same e.g. appointment date must be before termination date. Also, both would participate in equivalent relationships e.g. "natural person representing", etc.
The data model could be 'split' at high level, resulting in pairs of very similar tables e.g. "natural person officer" and "corporate officer", "natural person officer natural person representing" and "corporate officer natural person representing", etc.
However, another approach is to model the common attributes and relationships using a fabricated entity type. For example, both a "natural person" and a "corporation" could be considered to be a "legal person" (aside: there is such a concept of "legal person" in law but does this mean the same as existing in the real world?!)
Therefore, we could have a superclass table for "legal persons" and subtype tables for "natural persons" and "corporations" respectively. The "officers" table would reference the "legal persons" table. All subsequent relationship tables could reference the "officers" table, which would half the number of tables from this point down.
There are practical problems to such a 'subclassing' approach. Because a "natural person" and a "corporation" has no attributes in common, they have no common key, therefore the "legal persons" table would need to have an artificial key, with all the problems that this entails, especially if it needs to be exposed in the application. Also, because the relationships between "legal persons", "natural persons" and "corporations" are truly 1:1 some DBMSs, as Access, will lack the necessary functionality to effectively implement them and many will have to settle for making them 1:0..1.
A 1:1 relationship is an abstract concept that you model in your data, but at the database level (assuming RDBMS) doesn't really exist. You always have a foreign key on one table pointing to another, so technically the parent table being pointed to by the FK could have multiple children. This is something you'll want to enforce in your business logic.
A good example of a 1:1 relationship in a modeling sense would be the relationship between employee and person. You have a person with certain data, then you have extra attributes on that same person that you put on an employee. A good way to think of this in OO programming terms is as inherited classes. The Employee class, inherits from Person. In fact may ORM systems will model the 1:1 relationship in the database with each table having a shared primary key.
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.