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I'm new to databases and trying to understand why a junction or association table is needed when creating a many-to-many relationship.
Most of what I'm finding on Stackoverflow and elsewhere describe it in either highly technical relational theory terms or it's just described as 'that's the way it's done' without qualifying why.
Are there any relational database designs out there that support having a many-to-many relationship without the use of an association table? Why is it not possible to have, for example, a column on on table that holds the relationships to another and vice a versa.
For example, a Course table that holds a list of courses and a Student table that holds a bunch of student info — each course can have many students and each student can take many classes.
Why is it not possible to have a column on each row in either table (possibly in csv format) that contains the relationships to the others in a list or something similar?
In a relational database, no column holds more than a single value in each row. Therefore, you would never store data in a "CSV format" -- or any other multiple value system -- in a single column in a relational database. Making repeated columns that hold instances of the same item (Course1, Course2, Course3, etc) is also not allowed. This is the very first rule of relational database design and is referred to as First Normal Form.
There are very good reasons for the existence of these rules (it is enormously easier to verify, constrain, and query the data) but whether or not you believe in the benefits the rules are, none-the-less, part of the definition of relational databases.
I do not know the answer to your question, but I can answer a similar question: Why do we use a junction table for many-to-many relationships in databases?
First, if the student table keeps track of which courses the student is in and the course keeps track of which students are in it, then we have duplication. This can lead to problems. What if a student knows it is in a course, but the course doesn't know that it has that student. Every time you made a course change you would have to make sure to change it in both tables. Inevitably this will not happen every time and the data will become inconsistent.
Second, where would we store this information? A list is not a possible type for a field in a database. So do we put a course column in the student table? No, because that would only allow each student to take one course, a many-to-one relationship from students to courses. Do we put a student column in the courses table? No, because then we have one student in each course.
What does work is having a new table that has one student and one course per row. This tells us that a student is in a class without duplicating any data.
"Junction tables" come from ER/ORM presentations/methods/products that don't really understand the relational model.
In the relational model (and in original ER information modeling) application relationships are represented by relations/tables. Each table holds tuples of values that are in that relationship to each other, ie that are so related, ie that satisfy that relationship, ie that participate in the relationship.
A relationship is expressed independently of any particular situation as a predicate, a fill-in-the-(named-)blanks statement. Rows that fill in the named blanks to give a true statement from the predicate in a particular situation go in the table. We pick sufficient predicates (hence base tables) to describe every situation. Both many-to-1 and many-to-many application relationships get tables.
The reason why you don't see a lot of many-to-many relationships along with columns about the participants rather than about their participation in the relationship is that such tables are better split into ones about the participants and one for the relationship. Eg columns in a many-to-many table that are about participants 1. can't say anything about entities that don't participate and 2. say the same thing about an entity every time it participates. Information modeling techniques that focus on identifying independent entity types first then relationships between them tend to lead to designs with few such problems. The reason why you don't see many-to-many relationships in two tables is that that is redundant and susceptible to the error of the tables disagreeing. The problem with collection-valued columns (sequences/lists/arrays) is that you cannot generically query about their parts using usual query notation and implementation because the DBMS doesn't see the parts organized into a table.
See this recent answer or this one.
Sorry for that noob question but is there any real needs to use one-to-one relationship with tables in your database? You can implement all necessary fields inside one table. Even if data becomes very large you can enumerate column names that you need in SELECT statement instead of using SELECT *. When do you really need this separation?
1 to 0..1
The "1 to 0..1" between super and sub-classes is used as a part of "all classes in separate tables" strategy for implementing inheritance.
A "1 to 0..1" can be represented in a single table with "0..1" portion covered by NULL-able fields. However, if the relationship is mostly "1 to 0" with only a few "1 to 1" rows, splitting-off the "0..1" portion into a separate table might save some storage (and cache performance) benefits. Some databases are thriftier at storing NULLs than others, so a "cut-off point" where this strategy becomes viable can vary considerably.
1 to 1
The real "1 to 1" vertically partitions the data, which may have implications for caching. Databases typically implement caches at the page level, not at the level of individual fields, so even if you select only a few fields from a row, typically the whole page that row belongs to will be cached. If a row is very wide and the selected fields relatively narrow, you'll end-up caching a lot of information you don't actually need. In a situation like that, it may be useful to vertically partition the data, so only the narrower, more frequently used portion or rows gets cached, so more of them can fit into the cache, making the cache effectively "larger".
Another use of vertical partitioning is to change the locking behavior: databases typically cannot lock at the level of individual fields, only the whole rows. By splitting the row, you are allowing a lock to take place on only one of its halfs.
Triggers are also typically table-specific. While you can theoretically have just one table and have the trigger ignore the "wrong half" of the row, some databases may impose additional limits on what a trigger can and cannot do that could make this impractical. For example, Oracle doesn't let you modify the mutating table - by having separate tables, only one of them may be mutating so you can still modify the other one from your trigger.
Separate tables may allow more granular security.
These considerations are irrelevant in most cases, so in most cases you should consider merging the "1 to 1" tables into a single table.
See also: Why use a 1-to-1 relationship in database design?
My 2 cents.
I work in a place where we all develop in a large application, and everything is a module. For example, we have a users table, and we have a module that adds facebook details for a user, another module that adds twitter details to a user. We could decide to unplug one of those modules and remove all its functionality from our application. In this case, every module adds their own table with 1:1 relationships to the global users table, like this:
create table users ( id int primary key, ...);
create table users_fbdata ( id int primary key, ..., constraint users foreign key ...)
create table users_twdata ( id int primary key, ..., constraint users foreign key ...)
If you place two one-to-one tables in one, its likely you'll have semantics issue. For example, if every device has one remote controller, it doesn't sound quite good to place the device and the remote controller with their bunch of characteristics in one table. You might even have to spend time figuring out if a certain attribute belongs to the device or the remote controller.
There might be cases, when half of your columns will stay empty for a long while, or will not ever be filled in. For example, a car could have one trailer with a bunch of characteristics, or might have none. So you'll have lots of unused attributes.
If your table has 20 attributes, and only 4 of them are used occasionally, it makes sense to break the table into 2 tables for performance issues.
In such cases it isn't good to have everything in one table. Besides, it isn't easy to deal with a table that has 45 columns!
If data in one table is related to, but does not 'belong' to the entity described by the other, then that's a candidate to keep it separate.
This could provide advantages in future, if the separate data needs to be related to some other entity, also.
The most sensible time to use this would be if there were two separate concepts that would only ever relate in this way. For example, a Car can only have one current Driver, and the Driver can only drive one car at a time - so the relationship between the concepts of Car and Driver would be 1 to 1. I accept that this is contrived example to demonstrate the point.
Another reason is that you want to specialize a concept in different ways. If you have a Person table and want to add the concept of different types of Person, such as Employee, Customer, Shareholder - each one of these would need different sets of data. The data that is similar between them would be on the Person table, the specialist information would be on the specific tables for Customer, Shareholder, Employee.
Some database engines struggle to efficiently add a new column to a very large table (many rows) and I have seen extension-tables used to contain the new column, rather than the new column being added to the original table. This is one of the more suspect uses of additional tables.
You may also decide to divide the data for a single concept between two different tables for performance or readability issues, but this is a reasonably special case if you are starting from scratch - these issues will show themselves later.
First, I think it is a question of modelling and defining what consist a separate entity. Suppose you have customers with one and only one single address. Of course you could implement everything in a single table customer, but if, in the future you allow him to have 2 or more addresses, then you will need to refactor that (not a problem, but take a conscious decision).
I can also think of an interesting case not mentioned in other answers where splitting the table could be useful:
Imagine, again, you have customers with a single address each, but this time it is optional to have an address. Of course you could implement that as a bunch of NULL-able columns such as ZIP,state,street. But suppose that given that you do have an address the state is not optional, but the ZIP is. How to model that in a single table? You could use a constraint on the customer table, but it is much easier to divide in another table and make the foreign_key NULLable. That way your model is much more explicit in saying that the entity address is optional, and that ZIP is an optional attribute of that entity.
not very often.
you may find some benefit if you need to implement some security - so some users can see some of the columns (table1) but not others (table2)..
of course some databases (Oracle) allow you to do this kind of security in the same table, but some others may not.
You are referring to database normalization. One example that I can think of in an application that I maintain is Items. The application allows the user to sell many different types of items (i.e. InventoryItems, NonInventoryItems, ServiceItems, etc...). While I could store all of the fields required by every item in one Items table, it is much easier to maintain to have a base Item table that contains fields common to all items and then separate tables for each item type (i.e. Inventory, NonInventory, etc..) which contain fields specific to only that item type. Then, the item table would have a foreign key to the specific item type that it represents. The relationship between the specific item tables and the base item table would be one-to-one.
Below, is an article on normalization.
http://support.microsoft.com/kb/283878
As with all design questions the answer is "it depends."
There are few considerations:
how large will the table get (both in terms of fields and rows)? It can be inconvenient to house your users' name, password with other less commonly used data both from a maintenance and programming perspective
fields in the combined table which have constraints could become cumbersome to manage over time. for example, if a trigger needs to fire for a specific field, that's going to happen for every update to the table regardless of whether that field was affected.
how certain are you that the relationship will be 1:1? As This question points out, things get can complicated quickly.
Another use case can be the following: you might import data from some source and update it daily, e.g. information about books. Then, you add data yourself about some books. Then it makes sense to put the imported data in another table than your own data.
I normally encounter two general kinds of 1:1 relationship in practice:
IS-A relationships, also known as supertype/subtype relationships. This is when one kind of entity is actually a type of another entity (EntityA IS A EntityB). Examples:
Person entity, with separate entities for Accountant, Engineer, Salesperson, within the same company.
Item entity, with separate entities for Widget, RawMaterial, FinishedGood, etc.
Car entity, with separate entities for Truck, Sedan, etc.
In all these situations, the supertype entity (e.g. Person, Item or Car) would have the attributes common to all subtypes, and the subtype entities would have attributes unique to each subtype. The primary key of the subtype would be the same as that of the supertype.
"Boss" relationships. This is when a person is the unique boss or manager or supervisor of an organizational unit (department, company, etc.). When there is only one boss allowed for an organizational unit, then there is a 1:1 relationship between the person entity that represents the boss and the organizational unit entity.
The main time to use a one-to-one relationship is when inheritance is involved.
Below, a person can be a staff and/or a customer. The staff and customer inherit the person attributes. The advantage being if a person is a staff AND a customer their details are stored only once, in the generic person table. The child tables have details specific to staff and customers.
In my time of programming i encountered this only in one situation. Which is when there is a 1-to-many and an 1-to-1 relationship between the same 2 entities ("Entity A" and "Entity B").
When "Entity A" has multiple "Entity B" and "Entity B" has only 1 "Entity A"
and
"Entity A" has only 1 current "Entity B" and "Entity B" has only 1 "Entity A".
For example, a Car can only have one current Driver, and the Driver can only drive one car at a time - so the relationship between the concepts of Car and Driver would be 1 to 1. - I borrowed this example from #Steve Fenton's answer
Where a Driver can drive multiple Cars, just not at the same time. So the Car and Driver entities are 1-to-many or many-to-many. But if we need to know who the current driver is, then we also need the 1-to-1 relation.
Another use case might be if the maximum number of columns in the database table is exceeded. Then you could join another table using OneToOne
I am having a hard time trying to figure out when to use a 1-to-1 relationship in db design or if it is ever necessary.
If you can select only the columns you need in a query is there ever a point to break up a table into 1-to-1 relationships. I guess updating a large table has more impact on performance than a smaller table and I'm sure it depends on how heavily the table is used for certain operations (read/ writes)
So when designing a database schema how do you factor in 1-to-1 relationships? What criteria do you use to determine if you need one, and what are the benefits over not using one?
From the logical standpoint, a 1:1 relationship should always be merged into a single table.
On the other hand, there may be physical considerations for such "vertical partitioning" or "row splitting", especially if you know you'll access some columns more frequently or in different pattern than the others, for example:
You might want to cluster or partition the two "endpoint" tables of a 1:1 relationship differently.
If your DBMS allows it, you might want to put them on different physical disks (e.g. more performance-critical on an SSD and the other on a cheap HDD).
You have measured the effect on caching and you want to make sure the "hot" columns are kept in cache, without "cold" columns "polluting" it.
You need a concurrency behavior (such as locking) that is "narrower" than the whole row. This is highly DBMS-specific.
You need different security on different columns, but your DBMS does not support column-level permissions.
Triggers are typically table-specific. While you can theoretically have just one table and have the trigger ignore the "wrong half" of the row, some databases may impose additional limits on what a trigger can and cannot do. For example, Oracle doesn't let you modify the so called "mutating" table from a row-level trigger - by having separate tables, only one of them may be mutating so you can still modify the other from your trigger (but there are other ways to work-around that).
Databases are very good at manipulating the data, so I wouldn't split the table just for the update performance, unless you have performed the actual benchmarks on representative amounts of data and concluded the performance difference is actually there and significant enough (e.g. to offset the increased need for JOINing).
On the other hand, if you are talking about "1:0 or 1" (and not a true 1:1), this is a different question entirely, deserving a different answer...
See also: When I should use one to one relationship?
Separation of duties and abstraction of database tables.
If I have a user and I design the system for each user to have an address, but then I change the system, all I have to do is add a new record to the Address table instead of adding a brand new table and migrating the data.
EDIT
Currently right now if you wanted to have a person record and each person had exactly one address record, then you could have a 1-to-1 relationship between a Person table and an Address table or you could just have a Person table that also had the columns for the address.
In the future maybe you made the decision to allow a person to have multiple addresses. You would not have to change your database structure in the 1-to-1 relationship scenario, you only have to change how you handle the data coming back to you. However, in the single table structure you would have to create a new table and migrate the address data to the new table in order to create a best practice 1-to-many relationship database structure.
Well, on paper, normalized form looks to be the best. In real world usually it is a trade-off. Most large systems that I know do trade-offs and not trying to be fully normalized.
I'll try to give an example. If you are in a banking application, with 10 millions passbook account, and the usual transactions will be just a query of the latest balance of certain account. You have table A that stores just those information (account number, account balance, and account holder name).
Your account also have another 40 attributes, such as the customer address, tax number, id for mapping to other systems which is in table B.
A and B have one to one mapping.
In order to be able to retrieve the account balance fast, you may want to employ different index strategy (such as hash index) for the small table that has the account balance and account holder name.
The table that contains the other 40 attributes may reside in different table space or storage, employ different type of indexing, for example because you want to sort them by name, account number, branch id, etc. Your system can tolerate slow retrieval of these 40 attributes, while you need fast retrieval of your account balance query by account number.
Having all the 43 attributes in one table seems to be natural, and probably 'naturally slow' and unacceptable for just retrieving single account balance.
It makes sense to use 1-1 relationships to model an entity in the real world. That way, when more entities are added to your "world", they only also have to relate to the data that they pertain to (and no more).
That's the key really, your data (each table) should contain only enough data to describe the real-world thing it represents and no more. There should be no redundant fields as all make sense in terms of that "thing". It means that less data is repeated across the system (with the update issues that would bring!) and that you can retrieve individual data independently (not have to split/ parse strings for example).
To work out how to do this, you should research "Database Normalisation" (or Normalization), "Normal Form" and "first, second and third normal form". This describes how to break down your data. A version with an example is always helpful. Perhaps try this tutorial.
Often people are talking about a 1:0..1 relationship and call it a 1:1. In reality, a typical RDBMS cannot support a literal 1:1 relationship in any case.
As such, I think it's only fair to address sub-classing here, even though it technically necessitates a 1:0..1 relationship, and not the literal concept of a 1:1.
A 1:0..1 is quite useful when you have fields that would be exactly the same among several entities/tables. For example, contact information fields such as address, phone number, email, etc. that might be common for both employees and clients could be broken out into an entity made purely for contact information.
A contact table would hold common information, like address and phone number(s).
So an employee table holds employee specific information such as employee number, hire date and so on. It would also have a foreign key reference to the contact table for the employee's contact info.
A client table would hold client information, such as an email address, their employer name, and perhaps some demographic data such as gender and/or marital status. The client would also have a foreign key reference to the contact table for their contact info.
In doing this, every employee would have a contact, but not every contact would have an employee. The same concept would apply to clients.
Just a few samples from past projects:
a TestRequests table can have only one matching Report. But depending on the nature of the Request, the fields in the Report may be totally different.
in a banking project, an Entities table hold various kind of entities: Funds, RealEstateProperties, Companies. Most of those Entities have similar properties, but Funds require about 120 extra fields, while they represent only 5% of the records.
Full disclosure...Trying feverishly here to learn more about databases so I am putting in the time and also tried to get this answer from the source to no avail.
Barry Williams from databaseanswers has this schema posted.
Clients and Fees Schema
I am trying to understand the split of address tables in this schema. Its clear to me that the Addresses table contains the details of a given address. The Client_Addresses and Staff_Addresses tables are what gets me.
1) I understand the use of Primary Foreign Keys as shown but I was under the assumption that when these are used you don't have a resident Primary Key in that same table (date_address_from in this case). Can someone explain the reasoning for both and put it into words how this actually works out?
2) Why would you use date_address_from as the primary key instead of something like client_address_id as the PK? What if someone enters two addresses in one day would there be conflicts in his design? If so or if not, what?
3) Along the lines of normalization...Since both date_address_from and date_address_to are the same in the Client_Addresses and Staff_Addresses table should those fields just not be included in the main Address table?
Evaluation
First an Audit, then the specific answers.
This is not a Data Model. This is not a Database. It is a bucket of fish, with each fish drawn as a rectangle, and where the fins of one fish are caught in the the gills of another, there is a line. There are masses of duplication, as well as masses of missing elements. It is completely unworthy of using as an example to learn anything about database design from.
There is no Normalisation at all; the files are very incomplete (see Mike's answer, there are a hundred more problem like that). The other_details and eg.s crack me up. Each element needs to be identified and stored: StreetNo, ApartmentNo, StreetName, StreetType, etc. not line_1_number_street, which is a group.
Customer and Staff should be normalised into a Person table, with all the elements identified.
And yes, if Customer can be either a Person or an Organisation, then a supertype-subtype structure is required to support that correctly.
So what this really is, the technically accurate terms, is a bunch of flat files, with descriptions for groups of fields. Light years distant from a database or a relational one. Not ready for evaluation or inspection, let alone building something with. In a Relational Data Model, that would be approximately 35 normalised tables, with no duplicated columns.
Barry has (wait for it) over 500 "schemas" on the web. The moment you try to use a second "schema", you will find that (a) they are completely different in terms of use and purpose (b) there is no commonality between them (c) let's say there was a customer file in both; they would be different forms of customer files.
He needs to Normalise the entire single "schema" first,
then present the single normlaised data model in 500 sections or subject areas.
I have written to him about it. No response.
It is important to note also, that he has used some unrecognisable diagramming convention. The problem with these nice interesting pictures is that they convey some things but they do not convey the important things about a database or a design. It is no surprise that a learner is confused; it is not clear to experienced database professionals. There is a reason why there is a standard for modelling Relational databases, and for the notation in Data Models: they convey all the details and subtleties of the design.
There is a lot that Barry has not read about yet: naming conventions; relations; cardinality; etc, too many to list.
The web is full of rubbish, anyone can "publish". There are millions of good- and bad-looking "designs" out there, that are not worth looking at. Or worse, if you look, you will learn completely incorrect methods of "design". In terms of learning about databases and database design, you are best advised to find someone qualified, with demonstrated capability, and learn from them.
Answer
He is using composite keys without spelling it out. The PK for client_addresses is client_id, address_id, date_address_from). That is not a bad key, evidently he expects to record addresses forever.
The notion of keeping addresses in a separate file is a good one, but he has not provided any of the fields required to store normalised addresses, so the "schema" will end up with complete duplication of addresses; in which case, he could remove addresses, and put the lines back in the client and staff files, along with their other_details, and remove three files that serve absolutely no purpose other than occupying disk space.
You are thinking about Associative Tables, which resolve the many-to-many relations in Databases. Yes, there, the columns are only the PKs of the two parent tables. These are not Associative Tables or files; they contain data fields.
It is not the PK, it is the third element of the PK.
The notion of a person being registered at more than one address in a single day is not reasonable; just count the one address they slept the most at.
Others have answered that.
Do not expect to identify any evidence of databases or design or Normalisation in this diagram.
1) In each of those tables the primary key is a compound key consisting of three attributes: (staff_id, address_id, date_address_from) and (client_id, address_id, date_address_from). This presumably means that the mapping of clients/staff to addresses is expected to change over time and that the history of those changes is preserved.
2) There's no obvious reason to create a new "id" attribute in those tables. The compound key does the job adequately. Why would you want to create the same address twice for the same client on the same date? If you did then that might be a reason to modify the design but that seems like an unlikely requirement.
3) No. The apparent purpose is that they are the applicable dates for the mapping of address to client/staff - not dates applicable to the address alone.
3) Along the lines of
normalization...Since both
date_address_from and date_address_to
are the same in the Client_Addresses
and Staff_Addresses table should those
fields just not be included in the
main Address table?
No. But you did find a problem.
The designer has decided that clients and staff are two utterly different things. By "utterly different", I mean they have no attributes in common.
That's not true, is it? Both clients and staff have addresses. I'm sure most of them have telephones, too.
Imagine that someone on staff is also a client. How many places is that person's name stored? That person's address? Can you hear Mr. Rogers in the background saying, "Can you spell 'update anomaly'? . . . I knew you could."
The problem is that the designer was thinking of clients and staff as different kinds of people. They're not. "Client" describes a business relationship between a service provider (usually, that is, not a retailer) and a customer, which might be either a person or a company. "Staff" describes a employment relationship between a company and a person. Not different kinds of people--different kinds of relationships.
Can you see how to fix that?
This 2 extra tables enables you to have address history per one person.
You can have them both in one table, but since staff and client are separated, it is better to separate them as well (b/c client id =1 and staff id =1 can't be used on the same table of address).
there is no "single" solution to a design problem, you can use 1 person table and then add a column to different between staff and client. BUT The major Idea is that the DB should be clear, readable and efficient, and not to save tables.
about 2 - the pk is combined, both clientID, AddressID and from.
so if someone lives 6 month in the states, then 6 month in Israel, and then back to the states, to the same address - you need only 2 address in address table, and 3 in the client_address.
The idea of heaving the from_Date as part of the key is right, although it doesn't guaranty data integrity - as you also need manually to check that there isn't overlapping dates between records of the same person.
about 3 - no (look at 2).
Viewing the data model, i think:
1) PF means that the field is both part of the primary key of the table and foreign key with other table.
2) In the same way, the primary key of Staff_Addresses is {staff_id,address_id,date_adderess_from} not just date_adderess_from
3) The same that 2)
In reference to Staff_Addresses table, the Primary Key on date_address_from basically prevents a record with the same staff_id/address_id entered more than once. Now, i'm no DBA, but i like my PKs to be integers or guids for performance reasons/faster indexing. If i were to do this i would make a new column, say, Staff_Address_Id and make it the PK column and put a unique constraint on staff_id/address_id/date_address_from.
As for your last concern, Addresses table is really a generic address storage structure. It shouldn't care about date ranges during which someone resided there. It's better to be left to specific implementations of an address such as Client/Staff addresses.
Hope this helps a little.
I have a table that has a bunch of fields. The fields can be broken into logical groups - like a job's project manager info. The groupings themselves aren't really entity candidates as they don't and shouldn't have their own PKs.
For now, to group them, the fields have prefixes (PmFirstName for example) but I'm considering breaking them out into multiple tables with 1:1 relations on the main table.
Is there anything I should watch out for when I do this? Is this just a poor choice?
I can see that maybe my queries will get more complicated with all the extra joins but that can be mitigated with views right? If we're talking about a table with less than 100k records is this going to have a noticeable effect on performance?
Edit: I'll justify the non-entity candidate thoughts a little further. This information is entered by our user base. They don't know/care about each other. So its possible that the same user will submit the same "projectManager name" or whatever which, at this point, wouldn't be violating any constraint. Its for us to determine later on down the pipeline if we wanna correlate entries from separate users. If I were to give these things their own key they would grow at the same rate the main table grows - since they are essentially part of the same entity. At no pt is a user picking from a list of available "project managers".
So, given the above, I don't think they are entities. But maybe not - if you have further thoughts please post.
I don't usually use 1 to 1 relations unless there is a specific performance reason for it. For example storing an infrequently used large text or BLOB type field in a separate table.
I would suspect that there is something else going on here though. In the example you give - PmFirstName - it seems like maybe there should be a single pm_id relating to a "ProjectManagers" or "Employees" table. Are you sure none of those groupings are really entity candidates?
To me, they smell unless for some rows or queries you won't be interested in the extra columns. e.g. if for a large portion of your queries you are not selecting the PmFirstName columns, or if for a large subset of rows those columns are NULL.
I like the smells tag.
I use 1 to 1 relationships for inheritance-like constructs.
For example, all bonds have some basic information like CUSIP, Coupon, DatedDate, and MaturityDate. This all goes in the main table.
Now each type of bond (Treasury, Corporate, Muni, Agency, etc.) also has its own set of columns unique to it.
In the past we would just have one incredibly wide table with all that information. Now we break out the type-specific info into separate tables, which gives us much better performance.
For now, to group them, the fields have prefixes (PmFirstName for example) but I'm considering breaking them out into multiple tables with 1:1 relations on the main table.
Create a person table, every database needs this. Then in your project table have a column called PMKey which points to the person table.
Why do you feel that the group of fields are not an entity candidates? If they are not then why try to identify them with a prefix?
Either drop the prefixes or extract them into their own table.
It is valuable splitting them up into separate tables if they are separate logical entities that could be used elsewhere.
So a "Project Manager" could be 1:1 with all the projects currently, but it makes sense that later you might want to be able to have a Project Manager have more than one project.
So having the extra table is good.
If you have a PrimaryFirstName,PrimaryLastName,PrimaryPhone, SecondaryFirstName,SecondaryLastName,SEcondaryPhone
You could just have a "Person" table with FirstName, LastName, Phone
Then your original Table only needs "PrimaryId" and "SecondaryId" columns to replace the 6 columns you previously had.
Also, using SQL you can split up filegroups and tables across physical locations.
So you could have a POST table, and a COMMENT Table, that have a 1:1 relationship, but the COMMENT table is located on a different filegroup, and on a different physical drive with more memory.
1:1 does not always smell. Unless it has no purpose.