I have this problem in a table where there are 4 columns which include terms describing the product. I want to make this terms editable (and you can add more) in my app and there are 4 groups of them obviously. I created a table who has all these terms altogether but the product table will have to create 4 relationships with the ID of the terms table.
Is this a good solution?
The main reason I don't want to make 4 different tables for the terms is because there aren't many of them and as the app progresses we might have even more different term groups, thus adding many small tables cluttering the database.
Any suggestion?
Update #1: Here is my current schema http://i.imgur.com/q2a1ldk.png
Have a product table and a terms (product_id, terms_name, terms_description) which will allow you to add as many or as little terms for each product as you want. You just need to retrieve all terms from the terms table with a particular product id.
You could try a mapping table:
apputamenti(id, ...)
term_map (apputamenti_id, term_id)
terms (id, text, type)
So you can add as many terms as you want.
Or if you want to specify the mapping with one more field, change:
term_map (apputamenti_id, term_id, map_type)
so you can use an enum for map_type like enum(tipologia, feedback, target) or whatever your original fields where
Related
I'm creating a real-estate website and i was wondering if there was a better way of organizing my columns or tables, not sure what would be the best way to go about it, i currently have a lot of columns and im worried about performance issues.
The columns are as follows
5 for things like property id, add date, duration, owner/user id.
35 columns for things like title, description, price, energy rating, location, etc.
40 columns for features like swimming-pool, central heating, river front, garage, well, etc.
15 for image locations which are stored on server
15 for the image descriptions
Is 110+ columns bad practice in MySQL? Everything is lightning fast but i'm in localhost at the mo, wont the monstrous size of the tables slow queries? Especially if I have a couple hundred properties?
Am i ok with my current setup? What would best practice be? How do e-commerce websites that have many feature options go about this?
It is not a good practice since the data can be stored in separate tables. What would help you most would be to create an ERD to visualize how you can organize your tables. Even if you do not understand the ins and outs of ERDs, you can still use it to at least organize your thoughts.
It seems that you already have your tables separated based on the bullet points that you made within your question. One thing that I would add to your bullets is maybe breaking down your features into categories and creating a table for each.
For example, swimming pools and riverfront can be placed in a table called
LandscapeFeatures or OutdoorFeatures.
Most likely, the property features would be better stored in a separate table, with one row per property feature, rather than as columns in your main table. I understand this as a many-to-many relationship between a propery and its features, so this suggest two more tables:
properties (property_id (pk), date_added, title, description)
features (feature_id (pk), description)
property_features (property_id (fk), feature_id (fk))
Such structure is much more flexible and easier to query than having one column per feature. As examples:
easy to add features by creating new rows in the features table (while in the old structure you had to create a new column)
easy to aggregate the features, and answer a question like: count how many features each property has
As for images, they should have their ow table too. If an image maby belong to several user, then it's a many-to-many relationship, and you can follow the above pattern. If each image belongs to a single user, one more table is enough:
properties (property_id (pk), date_added, title, description)
features (feature_id (pk), description)
property_features (property_id (fk), feature_id (fk))
images (image_id, location, description, property_id (fk))
One table:
Columns for the dozen or so values that you are most likely to search on.
Devise several composite indexes that involve those columns, starting with the more commonly searched columns.
Devise a TEXT column and put "words" in it for a FULLTEXT index. If this is home sales, consider words like "swimming pool septic tank gazebo Eichler". This will help with certain "boolean" type queries. (If you like this idea, let's discuss how to make use of filtering with indexes and/or fulltext; it gets tricky.)
Put the rest into a JSON (or TEXT column). Do not plan on searching it; instead bring the row(s) into your app code for further filtering after searching by the actual INDEXes
Let's imagine we have two categories of publications - movies and books. Is it better to create one MySQL table for all publications or two different tables and unite them every time we show them in the united feed?
EDIT: The structure is little different. it has some common and uncommon data
Assuming both categories got the same attributes,
there is no difference in query performance (According to Multiple Table Select vs. JOIN (performance) )
In that case the solution with one table uses slightly more storage than the two table solution as you need a further column for differentiation (e.g. type).
EDIT
As your structure is different, you should use the two-table-solution as you get NULL fields if you use the one-table-solution.
A bad example for the table Publications:
In the given example you do not need an additional attribute to differentiate the two types because you could determine the type by Pages or Director.
You should avoid NULL values, especially when they are not meant to be filled with data in the future.
I am trying to build a little system that would assign specific tags to an item, or a person to be precise. So, I have a list of persons and a list of tags. I need to assign 3 specific tags to each person (that correspond to 3 different skills this person might have).
In a nutshell, the output would look like this :
Person 1 | webdesign, ux, jquery
Person 2 | blogging, photography, wordpress
Person 3 | graphic-design, 3d, inventor
...
For now, those lists are stored in two different tables :
persons
-------
person_id
person_name
tags
-------
tag_id
tag_name
My main goal is to avoid repetition and to simply assign 3 existing tags to an existing person.
Could you give me a few hints on how to implement this? I know that a three-table design is common for a tagging system, but is it relevant in my situation?
Thanks for your help.
If you want to ensure that you don't have any duplicates and to be able to add N tags to a person, then to properly implement a normalized design you would need a third table to link the tags to each person
persons_2_tags
--------------
person_id
tag_id
To guarantee uniqueness, you can either use a composite primary key, or add a unique index to the table including both columns.
See an example of the above in this SQL Fiddle.
If you need to enforce the 3 tag limit at the database level, you can add a third column to the persons_2_tags table (tag_number for example) that is an enum with values of 1, 2, 3 and add that to your unique index. Insert logic would need to be handled at the application level, but would be enforced by the index.
Do your requirements specify "exactly" 3 tags?
The third table is recommended to stay normalized. It's a typical Many-to-many relationship. This offeres the greatest amount of flexibility since you can have an unlimited, yet unique list of user/tag pairs.
You could have 3 columns in the user table for each tag. Performance would be improved at the expense of flexibility. Queries like, "List all the users with tag = 'X'" are a little harder. There may be several null values if you allow fewer than 3 tags. Of course in this setup, you'll have to create a new column and a lot of code to expand beyond three columns.
I think that I would probably do the three table design which Jeff O mentioned, however, just to present an alternative view...
If you're just talking about tags, that is, a short string with no other meta data, I don't know that you'd need a tags table. Essentially, the tag itself could be the its id.
persons (person_id, person_name);
tags (person_id, tag);
Yeah, you'd get a bit of repetition there, but they're short strings anyway and it should really make a difference.
This question already has answers here:
Is storing a delimited list in a database column really that bad?
(10 answers)
Closed 4 years ago.
I'm building a database for a real estate company.. Those real estate properties are managed by the sales people using a CodeIgniter website.
How should I proceed with the database design for this table. The fields like address/location, price etc. are pretty straightforward but there are some sections like Kitchen Appliences, Property Usage etc... where the sales people can check multiple values for that field.
Furthermore, a property can have multiple people attached to it (from the table people), this can be an intermediaire, owner, seller, property lawyer etc... Should I use one field for this or just I create an extra table and normalize the bindings?
Is the best way to proceed just using one field and using serialized data or is there a better way for this?
In a relational database you should never, EVER, put more than one value in a single field - this violates first normal form.
With the sections like Kitchen Appliances, Property Usage etc... where the sales people can check multiple values for that field, it depends on whether it will always be the same set of multiple options that can be specified:
If there are always the same set of multiple options, you could include a boolean column on the property table for each of the options.
If there are likely to be a variety of different options applicable to different properties, it makes more sense to set up a separate table to hold the available options, and a link table to hold which options apply to which properties.
The first approach is simpler and can be expected to perform faster, while the latter approach is more flexible.
A similar consideration applies to the people associated with each house; however, given that a single property can have (for example) more than one owner, the second approach seems like the only one viable. I therefore suggest separate tables to hold people details (name, phone number, etc) and people roles (such as owner, seller, etc.) and a link table to link roles to properties and people.
You should create extra table for it....
For example...
Consider the scenario that 1 item may have many categories and 1 category may have many items...
Then you can create table like this...
Create three tables for that....
(1) tblItem :
fields:
itemId (PK)
itemName
etc..
(2) tblCategory :
fields:
categoryId (PK)
categoryName
etc..
(3) tblItemCategory :
fields:
itemId (PK/FK)
categoryId (PK/FK)
So according to your data you can create an extra table for it....
I think it would be optimize way....
If you want your data to be in third normal form, then you should think in terms of adding extra tables, rather than encoding multiple values into various fields. However it depends how far your brief goes.
Hello, stackoverflow community!
I am working on a rather large database-driven web application. The underlying database is growing in complexity as more components are being added, but so far I've had absolutely no trouble normalizing the data quite nicely.
However, this final component implies a table that can hold products.
Each product has a category, and depending on the category, has different fields.
Making a table for each product category doesn't seem right, as there are currently five types, and they still have quite a lot of fields in common. (but in weird ways - a few general fields such as description and price are common to all 5 categories, but some attributes are shared between 1 and 2, others 3,4,5 and so on).
I'm trying to steer away from the EAV model for obvious performance reasons.
The thing is that according to what product type the user wants to enter into the database there is a somewhat (but not completely) different field structure - all of them have a name and general description, but other attributes such as "area covered" can be applied only to certain categories such as seeds and pesticides, but not fuel, which would have a diesel/gasoline boolean and a bunch of other fuel-related attributes.
Should I just extract the core features in a table, and make another five for each category type? That would be a bit hard to expand in the future.
My current idea would be to have the product table contain all the fields from all the possible categories, and then just have another table to describe which category from the product table has which fields.
product: id | type | name | description | price | composition | area covered | etc.
fields: id | name (contains a list of the fields in the above table)
product-fields: id | product_type | field_id (links a bunch of fields to the product table based on the product type)
I reckon this wouldn't be too slow, easy to search (no need to actually join the other tables, just perform the search on the main product table based on some inputs) and it would facilitate things like form generation and data validation with just one lightweight additional query /join. (fetch a product from the db and join a concatenated list of the fields actually used in a string - split that and display the proper form fields based on what it contains, i.e. the fields actually associated with that product.
Thanks for your trouble!
Andrei Bârsan
EAV can actually be quite good at storing data and fetching that databack again when you know the key. It also excels in it's ability to add fields without changing the schema. But where it's quite poor is when you need the equivilent of WHERE field1 = x and field2 = y.
So while I agree the data behaviour is important (how many products share the same fields, etc), the use of that data is also important.
Which fields need searching, which fields are always just data storage, etc
In most cases I'd suggest keeping all fields that need searching, in combination with each other, in the same table.
In practice this often leads to a single table solution.
New fields require schema changes, new indexes, etc
Potential for sparsely populated data, using more space than is 'required'
Allows simple queries, simple indexing and often the fastest queries
Often, though not always, the space overhead is marginal
Where the sparse-data overheads reach a critical point, I would then head towards additional tables grouped by what fields they contain. More specifically, I would not create tables by product. This is on the dual assumption that most/all fields will be shared across at least some products, and that those fields will need searching.
This gives a schema more like...
Main_table ( PK, Product_Type, Field1, Field2, Field3 )
Geo_table ( PK, county, longitute, latitude )
Value ( PK, cost, sale_price, tax )
etc
You may also have a meta-data table describing which product types have which fields, etc.
What this schema allows is a more densly populated set of tables, which can be easily indexed and so quickly searched, while minimising table clutter and joins by grouping related fields.
In the end, there isn't a true answer, it's all a balancing act. My general rule of thumb is to stay with a single table until I actually have a real and pressing reason not to, not just a theoretical one.
In my experience unless you are writing a a complete framework that can render fully described fields (we are talking about a lot of metadata describing each field) it is not worth separating field definitions from the main object. Modern frameworks (like Grails) allow for virtual zero pain adding a new column to a domain/Model class and table.
If your common field overlap is about 80% between all object types I would put them all in 1 table and use Table per Hierarchy inheritance model, where a descriminator field helps you tell your object types apart. On the other hand if you have 20% overlap of common fields then go with Table per Class inheritance model with base class and table containing common fields. And other joint tables hang off the base.
Should I just extract the core features in a table, and make another five for each category type? That would be a bit hard to expand in the future.
This is called a SuperType - SubType relationship. It works very well if most of your queries are one of two types:
If you will be querying mostly the SupetType table and only drilling down into the SubType table infrequently.
If you will be querying the database after being filtered to a specific SubType.