I am new speaking about modelling databases. But I give my best to learn as much as possible by my own. Therefore I want to ask you, whether my first attemp make sense for the following example:
So I modeled the database as followed:
The databse is about medicine. There are several medicine items which should be dosed depending on the age of the patient. Every medicine item can belong to one ore more groups (or none).
This is just a test case to show what I learned so far. So every tip to improve my skills is welcome!
Thanks a lot!
The relationtable table name is just a placeholder, right? It should be more descriptive, maybe dosage?
Something tells me that age ranges will greatly vary. Some medicines have different rules for children under 3 years, other under 5, 10, and so on. Instead of creating a separate table, just include two extra columns (start and end) in relationtable. It will be much easier to query and I won't consider this a denormalization.
Talking about age and dose tables - get rid of unit column and use normalized, fixed unit. Years for age and mg for doses. This will make querying much simpler. Don't be afraid to use floating numbers, e.g. 0.5 to represent six months.
I agree with what Tomasz write and would like to add:
If the relationtable is the correct way to go depends on some knowledge not contained in the table. It sounds strange that one medicine can be part of different groups and that the dosage depends on that relation. I would expect that a medicine can belong to different groups (resulting in a medicine2group mapping table) and that their exist different dosages depending on the age for a medicine (so you get dosage4age table, combining the existing age and dose tables. That new table would directly reference the medicine)
Which version is correct can not be told from the table alone.
As a rule of thumb: I get skeptical when a table without a proper name and concept links more then two other table. It is possible but often hints at concept hiding somewhere.
In order to check if the proposed model is correct, ask the business experts if the table is still correct if you replace Antibiotika with Superantibiotika in one of the first three rows. If it is, this means that the dosage does not depend on the group and should not be linked to it, so the model proposed by me would be more correct.
If the altered table is not correct, your model might be the better one, but I would listen carefully about the explanation why it isn't correct.
Related
Today I was designing a database for a potential personal project of mine. Since I couldn't decide what would be a better option I asked my teacher Databases, unfortunately he couldn't tell me which of the two options is better than the other and why.
I designed the database for a dummy data generator. Since I want to generate multilangual data I thought of these tables. (But its a simplification of the tables).
(first and last)names: id, name
streets: id, name
languages: id, name
Each names.name and streets.name originates from a language, sometimes a name can have multiple origins (ex: Nick is both a Dutch as an English name).
Each language has multiple names and streets.
These two rules result in a Many-to-Many relationship. At the moment I've got only two tables, but I know I will get between 10 and 20 of these kind of tables.
The regular way one would do this is just make 10 to 20 Many-to-Many relationship tables.
Another idea I came up with was just one Many-to-Many table with a third column which specifies which table the id relates to.
At the moment I've got the design on my other PC so I will update it with my ideas visualized after dinner (2 hours or so).
Which idea is better and why?
To make the project idea a bit clearer:
It is always a hassle to create good and enough realistic looking working data for projects. This application will generate this data for you and return the needed SQL so you only have to run the queries.
The user comes to the site to get the data. He states his tablename, his columnnames and then he can link the columnnames to types of data, think of:
* Firstname
* Lastname
* Email adress (which will be randomly generated from the name of the person)
* Adress details (street, housenumber, zipcode, place, country)
* A lot more
Then, after linking columns with the types the user can set the number of rows he wants to make. The application will then choose a country at random and generate realistic looking data according to the country they live in.
That's actually an excellent question. This sort of thing leads to a genuine problem in database design and there is a real tradeoff. I don't know what rdbms you are using but....
Basically you have four choices, all of them with serious downsides:
1. One M-M table with check constraints that only one fkey can be filled in besides language and one column per potential table. Ick....
2. One M-M table per relationship. This makes things quite hard to manage over time especially if you need to change something from an int to a bigint at some point.
3. One M-M table with a polymorphic relationship. You lose a lot of referential integrity checks when you do this and to make it safe, have fun coding (and testing!) triggers.
4. Look carefully at the advanced features in your rdbms for a solution. For example in postgresql this can be solved with table inheritance. The downside is that you lose portability and end up in advanced territory.
Unfortunately there is no single definite answer. You need to consider the tradeoffs carefully and decide what makes sense for your project. If I was just working with one RDBMS, I would do the last one. But if not, I would probably do one table per relationship and focus on tooling to manage the problems that come up. But the former preference is about my level of knowledge and confidence, and the latter is a bit more of a personal opinion.
So I hope this helps you look at the tradeoffs and select what is right for you.
In our database design we have a couple of tables that describe different objects but which are of the same basic type. As describing the actual tables and what each column is doing would take a long time I'm going to try to simplify it by using a similar structured example based on a job database.
So say we have following tables:
These tables have no connections between each other but share identical columns. So the first step was to unify the identical columns and introduce a unique personId:
Now we have the "header" columns in person that are then linked to the more specific job tables using a 1 to 1 relation using the personId PK as the FK. In our use case a person can only ever have one job so the personId is also unique across the Taxi driver, Programmer and Construction worker tables.
While this structure works we now have the use case where in our application we get the personId and want to get the data of the respective job table. This gets us to the problem that we can't immediately know what kind of job the person with this personId is doing.
A few options we came up with to solve this issue:
Deal with it in the backend
This means just leaving the architecture as it is and look for the right table in the backend code. This could mean looking through every table present and/or construct a semi-complicated join select in which we have to sift through all columns to find the ones which are filled.
All in all: Possible but means a lot of unecessary selects. We also would like to keep such database oriented logic in the actual database.
Using a Type Field
This means adding a field column in the Person table filled for example with numbers to determine the correct child table like:
So you could add a 0 in Type if it's a taxi driver, a 1 if it's a programmer and so on...
While this greatly reduced the amount of backend logic we then have to make sure that the numbers we use in the Type field are known in the backend and don't ever change.
Use separate IDs for each table
That means every job gets its own ID (has to be nullable) in Person like:
Now it's easy to find out which job each person has due to the others having an empty ID.
So my question is: Which one of these designs is the best practice? Am i missing an obvious solution here?
Bill Karwin made a good explanation on a problem similar to this one. https://stackoverflow.com/a/695860/7451039
We've now decided to go with the second option because it seem to come with the least drawbacks as described by the other commenters and posters. As there was no actual answer portraying the second option as a solution i will try to summarize our reasoning:
Against Option 1:
There is no way to distinguish the type from looking at the parent table. As a result the backend would have to include all logic which includes scanning all tables for the that contains the id. While you can compress most of the logic into a single big Join select it would still be a lot more logic as opposed to the other options.
Against Option 3:
As #yuri-g said this one is technically not possible as the separate IDs could not setup as primary keys. They would have to be nullable and as a result can't be indexed, essentially rendering the parent table useless as one of the reasons for it was to have a unique personID across the tables.
Against a single table containing all columns:
For smaller use cases as the one i described in the question this might me viable but we are talking about a bunch of tables with each having roughly 2-6 columns. This would make this option turn into a column-mess really quickly.
Against a flat design with a key-value table:
Our properties have completly different data types, different constraints and foreign key relations. All of this would not be possible/difficult in this design.
Against custom database objects containt the child specific properties:
While this option that #Matthew McPeak suggested might be a viable option for a lot of people our database design never really used objects so introducing them to the mix would likely cause confusion more than it would help us.
In favor of the second option:
This option is easy to use in our table oriented database structure, makes it easy to distinguish the proper child table and does not need a lot of reworking to introduce. Especially since we already have something similar to a Type table that we can easily use for this purpose.
Third option, as you describe it, is impossible: no RDBMS (at least, of I personally know about) would allow you to use NULLs in PK (even composite).
Second is realistic.
And yes, first would take up to N queries to poll relatives in order to determine the actual type (where N is the number of types).
Although you won't escape with one query in second case either: there would always be two of them, because you cant JOIN unless you know what exactly you should be joining.
So basically there are flaws in your design, and you should consider other options there.
Like, denormalization: line non-shared attributes into the parent table anyway, then fields become nulls for non-correpondent types.
Or flexible, flat list of attribute-value pairs related through primary key (yes, schema enforcement is a trade-off).
Or switch to column-oriented DB: that's a case for it.
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'm trying to design a "recipe box" database and I'm having trouble getting it right. I have no idea if I'm on the right track or not, but here's what I have.
recipes(recipeID, etc.)
ingredient(ingredientID, etc.)
recipeIngredient(recipeID, ingredientID, amount)
category(categoryID, name)
recipeCategory(recipeID, categoryID, name, etc.)
So I have a couple of questions.
How am I doing so far? Is this design okay from what you all know?
How would I implement the preparation steps? Should I create an additional many-to-many implementation (something like preparation(prepID, etc.) and recipePrep(recipeID, prepID)) or just add the directions in the recipes table? I would like this to be an ordered list in the UI (webpage).
Thank you for your help.
Have you looked at any of the existing schemas out there, such as this one at DatabaseAnswers?
some thoughts:
You might want to use the same table for Recipe and Ingredient, with a type indicator column. The reason is that Recipes can contain sub-recipes. Let's call the combined table "Item". Then your RecipeIngredient table would look like
RecipeIngredient (RecipeId, ItemId, Amount).
I'd expect that the table would also have a sequencing column.
If you want to do any calculations with these recipes (e.g., scaling, nutritional analysis, production planning) then your quantities will need to specify a unit of measure. You can do that explicitly (by having a separate column for uofm) or you can use a text field for quantity and expect the user to enter values like "1 cup", or "2 tbs". If you take that approach, you'll need to make sure that what they enter is recognizable, and parse it every time you need to use it. This can become surprising complex, especially if you want to represent recipe yields in a formalized manner.
Assuming you want 1:M from recipe to category, I'm still not sure why your RecipeCategory table would have a Name column. I'd think that the name comes from the Category definition.
I agree with Dave that it's unlikely that you'd reuse preparation steps from recipe to recipe, and so a RecipePreparationSteps table (or something like it) would be more appropriate.
However, recipes are often presented with ingredients and instructions intermixed. eg.
Intro text
some ingredients.
prep instructions
some more ingredients
baking instructions.
To accomodate that, you need to cleverly set sequencing values in the RecipeIngredient and RecipePreparation step tables so that you can combine data from both in the proper order for presentation. Another approach would be, instead of these two tables, use a "RecipeLine" table such that each row can represent either an instruction OR an ingredient. I think that may be what you were suggesting. Purists would frown on this kind of table overloading, but I'm not a purist.
This is a topic I happen to know a lot about, so ask anything.
Looks like a good start. A few thoughts:
Don't see a need for a recpieCategory table. One-to-many between recipe and category should do fine.
A PreparationSteps table should contain 1-n steps for each recipe. I wouldn't try to reuse steps between recipes.
I am wondering when and when not to pull a data structure into a separate database table when it appears in several tables.
I have pulled the 12 attribute address structure into a separate table because I have a couple of different entities containing a single address in this format.
But how about my 3 attribute person name structure (given, middle, surname)?
Should this be put into its own table referenced with a foreign key for all the entities containing a name... e.g. the company table has a contact person name, the citizen table has a person name etc.
Are these best left as attributes in the main tables or should they be extracted?
I would usually keep the address on the Person table, unless there was an unusual need for absolutely uniform addresses on each entity, or if an entity could have an arbitrary number of addresses, or if addresses need to be shared between entities, or if it was a large enterprise product where I know I have to invest in infrastructure all over the place or I will end up gutting everything down the road.
Having your addresses in a seperate table is interesting because it's flexible, but in the context of a small project lacking a special need like the ones mentioned above, it's probably a slight waste. Always be aware of the balance between complexity and flexibility. Flexibility is important, but be discriminating... It's easy to invest way too much there!
In concrete terms, the times that I experimented with (for instance) one-to-one relationships for things like addresses, I ended up refactoring them back into the table because it introduced a bunch of headaches including more complex queries, dealing with situations where the address does not exist, etc. More entities also increases your cognitive load -- it makes the project harder to think about. In my case, it was an unecessary cost because there was no concrete need and, in truth, not even a gain in flexibility.
So, based on my experiences, I would "try" to keep the addresses in the same table, and I would definitely keep the names on them - again, unless there was a special need.
So to paraphrase Einstein, make it as simple as possible and no simpler. But in the short term, experiment. It's the best way to learn these lessons.
It's about not repeating information, so you don't want to store the same information in two places when one will do.
Another useful rule of thumb is one entity per table. If you find that one table contains, say, "person" AND "order" then you probably should split those into two tables.
And (putting myself at risk of repeating information...) you might find it helpful to review some database design basics, there are plenty of related questions here on stackoverflow.
Start with these...
What is normalisation?
What is important to keep in mind when designing a database
How many fields is 'too many'?
More tables or more columns?
Creating a person entity across your data model will give you this present and future advantages -
The same person occurring as a contact, or individual in different contexts. Saves redundancy.
Info can be maintained and kept current with far-less effort.
Easier to search for a person and identify them - i.e. is it the same John Smith?
You can expand the information - i.e. maintain addresses for this person far more easily.
Programming will be more consistent and debugging will be easier as well.
Moves you closer to a 'self-documenting' system.
As a counterpoint to the other (entirely valid) replies: within your application's current structure, how likely will it be for a given individual (not just name, the actual "person" -- multiple people could be "John Smith") to appear in more than one table? The less likely this is to happen, the less likely you are to get benefits from normalization.
Another way to think of it is entities. Outside of labels (names), is their any overlap between "customer" entity and an "employee" entity?
Extract them. Your aim should be to have no repeating data in your database.
Read about Normalization
It really depends on the problem you are trying to solve. In general it is probably a good idea to have some sort of 'person' table which holds details of people. However, there are occasions where that is potentially a very bad idea.
One example would be if you are holding details of prescriptions written out to people by a doctor. In some countries it is a legal requirment that the prescription details are held with the name in which they were prescribed NOT the name the person is going under currently. For instance a woman might be prescribed a drug as miss X, but then she gets married and becomes Mrs Y. If you had a person table that was linked to the prescriptions table you would now have the wrong details and would possibly face legal consequences. In that case you would need to probably copy the relevant details of the person into the prescription table, even though this would be duplicating data.
So again - it depends on the problem you are trying to solve. Don't just blindly follow what people consider to be best practices. Understand your data and any issues surrounding it, then try to follow best practices that fit.
Depends on what you're using the database for.
If you want fast queries on your tables you should de-normalize your tables. Having to run multiple JOIN's will take longer and make your queries more complex.
On the other hand if your intention is to have a flexible storage database which is not meant to be hit with a ton of fast-response queries, then normalizing the tables by splitting them out into multiple xref'ed tables will provide more flexibility in your design and reduce the need for submitting duplicated data.
Since de-normalization is "optimization", I would suggest you normalize the tables first, index them properly and see if you're getting any bottlenecks on your queries. If so, flatten the affected tables where needed.
You should really consider your whole database structure and do a ER diagram (entity relationship diagram) first. OF COURSE there should be another table called "Person" where the concept of a person is stored...