ORIGINAL (see UPDATED QUESTION below)
I am designing a new laboratory database that tests a wide variety of tests on a wide variety of sample types.
The following list is my current candidate for the list of main entities to best model the laboratory work.
For each entity, a 1-to-many relationship exists from that entity to the entity below. In other words, every entity (except REQ) has at least columns for entity_id and parent_id.
Main Entities:
REQ: Request (a form)
SAM: Sample (the material)
TST: Test (requested procedures)
SUB: ** Sub-Test (part of standard test)
TRI: ** Trial (single instance: usually for mean,range, and stddev)
MEA: Measurement (a measured number)
** Not all tests have subtests, and not all tests have trials.
Sub-tests are a set of tests grouped together by a single name for easy referencing. For example, a lot acceptance test (LAT) for a particular product is defined as the following tests: viscosity, %-nitrogen, pH, and density.
A trial is a single experiment performed multiple times for product assurance. For example, fifty bullets might be shot, and each shot is a trial. The accuracy of each bullet might be required to fall within a certain range, and the average accuracy of all fifty bullets might be required to be in a tighter range.
Question: How should I model cases when sub-tests and/or trials are not needed?
Option 1: Use a "blank" sub-test (or trial) if not needed.
Option 2: Consider sub-tests and trials to be tests (and have a test_id as a parent), so that measurements always have a test as a parent.
Option 3: Optional parents for measurement (trial, sub-test, or test) and trials (sub-test or test).
Option x: Any other option worth considering.
FYI: If required to answer the question, I will be using Oracle.
UPDATED QUESTION
In general, my schema is a heirarchy of entities where each entity (except top) must have ONE parent and (except bottom) must have at least one child. What is the best way to handle cases where an internal entity is unnecessary in certain situation, or what is the benefit/drawback to using a particular option?
Option 1 (Dummy): Use a "dummy" entry to indicate entity does not apply in this case.
Option 2 (Rollup): Roll-up optional entities into next higher parent entity.
Option 3 (Pick-a-Parent): Entity (C) below optional entity (B) with required entity (A) must have ONE parent but the parent can be either the optional entity (B) or the next higher one (A).
Option x: Any other option worth considering.
Addressing your simplified question:
Given a hierarchy as you've described, if I found that some levels in the hierarchy were optional, I would question whether a hierarchy really mapped well to my domain. I would consider drawing my relations differently, or redefining the entities in my schema.
I don't think a more detailed answer to the general question is possible in a short space like this, since figuring out the best representation of a domain is a) hard, and b) very specific to the particular domain.
Use Outer Joins. (RIGHT OUTER JOIN and LEFT OUTER JOIN).
They were made specifically for this.
< Edit > This is my first post. Based on the comments, I'll be adding a second post.
Here's my take on an architectural first pass. This stuff generally requires a LOT of back-and-forth with the subject matter experts to get right.
"Test" means one of:
- Take an action, measure results
- Take several actions (subtests), measure results for each
- Make no tests whatsoever (yet you can still have measurements -- ?)
I'd configure this as a "parent" Test table and a child "SubTest" table, where Test can have 0 or more related SubTests, and every SubTest must be related with one and only one Test. (If a test has only one SubTest, enter it in its own table, don't try and track SubTests in the Test table.)
Trials can only exist if there are SubTests. Therefore, Trials are a child of the SubTest table; SubTests can have zero or more Trials, and Trials must be related with one and only one SubTest.
Measures only exist if there are Trials. Therefore, repeat the above, with Measures as a child of Trials.
Can there be SubTests without Trials (or Tests)? If so, then don't enter any Trials.
Can there be Measures without Trials? If no, you don't need any Trials (or SubTests). If yes (?), once again enter some properly labeled dummy/placholder SubTests or Trials as necessary.
Again, this is rudimentary, and more interviews with the folks driving requirements is required.
As others have remarked it is hard for us to give a definitive answer without understanding more about your domain. You have attempted to distill a lot of business rules into a couple of paragraphs but some important information has been lost. Specifically, it is not possible to be sure whether two entities are genuinely distinct without knowing their attributes. Having said all which, let's have a go.
A TEST is a single procedure. Despite containing the word "test" a LAT is not a TEST in its own right but is rather a pre-defined set of such procedures. I would model this scenario as an entity TEST with an optional parent entity, which I would prefer to call TEST_GROUP (as that is what it is) but it is best to use the domain name, SUB_TEST.
A TRIAL appears to be distinct from a TEST, so model it as a separate entity. Therefore you have a choice when it comes to MEASUREMENT: you can have one entity with two optional foreign keys or you can have TEST_MEASUREMENT and TRIAL_MEASUREMENT. Choosing which road to go depends on the characteristics and usage profile.
The following is an initial stab at the entity relationships. This would be the point in the project when the user goes, "Oh no, that is not what I meant at all."
create table sample (
sample_id number not null
, constraint samp_pk primary key (sample_id)
)
/
create table sub_test (
sub_test_id number not null
, sample_id number not null
, constraint subt_pk primary key (sub_test_id)
, constraint subt_samp_fk foreign key (sample_id)
references sample (sample_id)
)
/
create table test (
test_id number not null
, sample_id number not null
, sub_test_id number
, constraint tst_pk primary key (test_id)
, constraint tst_samp_fk foreign key (sample_id)
references sample (sample_id)
, constraint tst_subt_fk foreign key (sub_test_id)
references sub_test (sub_test_id)
)
/
create table trial (
trial_id number not null
, test_id number not null
, constraint trl_pk primary key (trial_id)
, constraint trl_tst_fk foreign key (test_id)
references test (test_id)
)
/
create table measurement (
measurement_id number not null
, trial_id number
, test_id number
, constraint meas_pk primary key (measurement_id)
, constraint meas_tst_fk foreign key (test_id)
references test (test_id)
, constraint meas_trl_fk foreign key (trial_id)
references trial (trial_id)
, constraint measurement_ck check (
(test_id is not null and trial_id is null)
or (test_id is null and trial_id is not null)
)
/
Edit
Addressing your more generic question.
Option 1 (Dummy)
Never use a dummy record. It's is like using a magic value instead of a null. The solution is worse than the problem it solves.
Option 2 (Rollup)
This can work when the parent and the child have the same attributes. But it is not a viable solution if they have different columns, or if they are different dependencies. Even if they have identical data structures but different business uses it may still be a problem.
Option 3 (Pick-a-Parent)
This would be my preferred solution. The snag is the need for a check constraint to ensure that one (and only one) of the eligible foreign keys has been populated. You also need to guard against allowing too many parents/grandparents/great-grandparents into the mix.
I am not entirely sure I understand the details of your question, but it sounds like you should have the following:
Table Test
test_id, request, sample, test
Table SubTest
subtest_id, test_id (foreign key to Test)
Table Trial
trial_id, trial_name, measurement, subtest_id
So, Test is a collection of subtests (possibly just one subtest), and a subtest is a collection of Trials (possibly just one trial)
I'm not entirely certain I understand your domain, but could you do something like this?
Tests has a parent_test_id column, which can be NULL (when set, this is a subtest).
Trials has a test_id column. (All tests have at least one trial, since you did a thing and had at least one measurement, right?)
Measurements has a trial_id column.
This does seem to violate your premise, since it stipulates that all tests have at least one trial, so it's possible I misunderstand the requirements. How can you have a test with no trials?
Anyway, if necessary, you could put both a trial_id and a test_id on Measurements, possibly with a constraint that one or the other must be NULL (and the other must be set).
I'll take a second stab at this one, based on the feedback from my first post. The key thing to understand is that design and architecture can be highly iterative, and I doubt you'll get the ideal model without a lot of back-and-forth--something that doesn't play out to well on Stack Overflow. Odds are you'll take the ideas posted (APC has some good ones), bounce them around with the people you work with, and come up with something that'll work.
My goal these days when designing databases is to try and produce a fully normalized model. Once you've got that, if it doesn't seem reasonable or practical you can denormalize for efficiency, expediency, or whatever -- but the key thing is you denormalize after you've found the ideal model. If you stop normalization before you get to fully normalized, you haven't denormalized, you've just got a sloppy model.
Here's the entities I see to-date:
What you've labeled as the top-level test, for purposes of clarity here I'm going to call an Exam. You define an exam and all its contents (below), and people contact your laboratory to run these exams on their problems.
For any given exam performed for a customer, you run a bunch of Tests. Any given test may be used by (required by?) any number of exams.
Often, you get a set of Tests that are done together for more than one Exam. If there are properties that apply to the specific set of Tests, you might want to identify each set as its own entity. Call these TestGroups. However, if these are only used associate a specific set of Tests with one or more Exams, you might not get any particular benefit our of defining them as their own entity. (These are your SubTests.)
So, an Exam "has" or "contains" one or more Tests. Alternatively, Exams are related with one or more TestGroups. However, trying to relate an Exam with zero or more TestGroups and zero or more individual Tests will produce an overly complex model (let alone physical implmentation), and I'd really want to avoid that. Perhaps a TestGroup can contain a single Test, so Exams only reference TestGroups? Maybe an Exam can only be related to one TestGroup -- in which case that'd be the "many to many" table relating Exams with Tests. This depends on further discussion of requirements with the subject matter experts.
So you have Exams -- Exam definitions, really -- related somehow or other with multiple Tests. Next up, you have a "paid instance" of an Exam (customer X comes in and pays you to test his Widgets). Call this a CustomerExam; it has all the contact and billing info, identifies the Exam to be run, and thus is related to the Tests to be performed for the customer. (There's probably a Customer entity out there too...?)
Trials are perfomed for the Tests that are part of a CustomerExam. They don't relate with the Exam or the Test, they are an instance of the Trial being performed. (Seems safe to assume that the "meaning/definition" of a Trial would actually be part of a Test--for example, if Test = Is gun accurate, then the work required by a Trial for that Test = fire gun 50 times and measure). So as Trials are performed for the Tests of a given CustomerExam. Are they performed once, or more than once? (Is a trial to fire the gun 50 times, or is each shot counted as a trial? What if they do two rounds of 50 shots?) Whatever, the attributes of the Trial event are stored here -- when it happened, who did it, special notes/circumstances, whatever.
Measures are produced by (or for?) Trials. The meaning/definition of each measure is actually part of the definition of a Trial (which is part of the definition of a Test); the event of the Trial produces specific values for the defined/anticipated Measures. The assumption is that a Trial will generate zero (?) or more Measures, so Measures are their own entity.
Looking back at this, it seems like there's some form of implicit double stucture: a set of tables to define available Exams, Tests, Trials, and Measures (what can be examined, how can it be tested, what shall we measure) and a companion set of tables to track specific instances of each (who wanted it, who did the work, when did they do it, what were the results)
I've got to have way over-anazled this problem. The key thing here is, as with all design sessions, in posing ideas and asking questions, did they generate your own ideas, questions, or answers?
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.