I'm going to use Drupal as my example, but it extends to other situations as well.
I've seen database schema that are abstracted away from what a DBA would implement, most notably with Drupal. For example, When you create a Content Type in Drupal (equivalent of table), it abstracts away the fields, as new tables, in the form of field_{machineName}, which then relates back to the original "parent" table (node_type in drupal).
When I'm dealing with MVC frameworks, like Rails, Django, or Laravel, we don't abstract away the tables, so fields are stored right on the table itself, not related back.
What benefits do you get from implementing an abstracted table rather than a concrete table? Are there situations that this should be used, or is it generally a bad idea? It seems like a bad design choice to me, but I'm a fairly isolated programmer.
A feeble attempt to illustrate my question, using a "Book" example.
EDIT
I see that my diagram isn't exactly accurate. I will post a new one that reflects that node_id should relate to a node table, which then stores a reference to node_type
My 2 cents:
Pros of abstraction :
Can handle any entity type the same exact way.
you can define "Generic UI" & plugin system based on node type
You can define Generic behaviours (like ACL based on node field title) applicable to any model built.
Cons of abstraction:
You cannot see the "final" model directly (however, you may rebuild an image of it)
performance & querying complexity (can be mitigated with "flat" indexation tables)
So i would say :
for "open datamodel" , able to suit any need of data representation , abstraction has many advantages (at the cost of readability & performance). That's the typical case of many "multipurpose meta builders" (like Drupal)
If you know what you are modelizing and are defining an "application" rather than an "application factory" , you'd better use a "specific" datamodel for the application scope.
Another "meta" database construction pattern i like to use is :
Defining entity specific tables with associated "generic" table. (typed base table & open "key/value" property table associated with each entry of the base entity table). So it gives the ability to add "extra info" to existing base entity without having to modify the core model at each iteration. Letting the choice to find out what "properties" to migrate in the base table over time.
Another variant of this is EAV model , used for example in Magento.
IMHO, here are the 2 main reasons why the Drupal schema is build this way
Fields are dynamic, they cab be added and removed from a an entity bundle at any time fron the Web UI. Using separated table ease mutation of the schema.
Field values can be translated, in Drupal 7 the translations is a done at the field level. The title field could be translatable, while the content field may be not.
Note that most of the times, when using the Drupal APIs, you don't have to deal with these tables.
Related
It is safe to say that the EAV/CR database model is bad. That said,
Question: What database model, technique, or pattern should be used to deal with "classes" of attributes describing e-commerce products which can be changed at run time?
In a good E-commerce database, you will store classes of options (like TV resolution then have a resolution for each TV, but the next product may not be a TV and not have "TV resolution"). How do you store them, search efficiently, and allow your users to setup product types with variable fields describing their products? If the search engine finds that customers typically search for TVs based on console depth, you could add console depth to your fields, then add a single depth for each tv product type at run time.
There is a nice common feature among good e-commerce apps where they show a set of products, then have "drill down" side menus where you can see "TV Resolution" as a header, and the top five most common TV Resolutions for the found set. You click one and it only shows TVs of that resolution, allowing you to further drill down by selecting other categories on the side menu. These options would be the dynamic product attributes added at run time.
Further discussion:
So long story short, are there any links out on the Internet or model descriptions that could "academically" fix the following setup? I thank Noel Kennedy for suggesting a category table, but the need may be greater than that. I describe it a different way below, trying to highlight the significance. I may need a viewpoint correction to solve the problem, or I may need to go deeper in to the EAV/CR.
Love the positive response to the EAV/CR model. My fellow developers all say what Jeffrey Kemp touched on below: "new entities must be modeled and designed by a professional" (taken out of context, read his response below). The problem is:
entities add and remove attributes weekly (search keywords dictate future attributes)
new entities arrive weekly (products are assembled from parts)
old entities go away weekly (archived, less popular, seasonal)
The customer wants to add attributes to the products for two reasons:
department / keyword search / comparison chart between like products
consumer product configuration before checkout
The attributes must have significance, not just a keyword search. If they want to compare all cakes that have a "whipped cream frosting", they can click cakes, click birthday theme, click whipped cream frosting, then check all cakes that are interesting knowing they all have whipped cream frosting. This is not specific to cakes, just an example.
There's a few general pros and cons I can think of, there are situations where one is better than the other:
Option 1, EAV Model:
Pro: less time to design and develop a simple application
Pro: new entities easy to add (might even
be added by users?)
Pro: "generic" interface components
Con: complex code required to validate simple data types
Con: much more complex SQL for simple
reports
Con: complex reports can become almost
impossible
Con: poor performance for large data sets
Option 2, Modelling each entity separately:
Con: more time required to gather
requirements and design
Con: new entities must be modelled and
designed by a professional
Con: custom interface components for each
entity
Pro: data type constraints and validation simple to implement
Pro: SQL is easy to write, easy to
understand and debug
Pro: even the most complex reports are relatively simple
Pro: best performance for large data sets
Option 3, Combination (model entities "properly", but add "extensions" for custom attributes for some/all entities)
Pro/Con: more time required to gather requirements and design than option 1 but perhaps not as much as option 2 *
Con: new entities must be modelled and designed by a professional
Pro: new attributes might be easily added later on
Con: complex code required to validate simple data types (for the custom attributes)
Con: custom interface components still required, but generic interface components may be possible for the custom attributes
Con: SQL becomes complex as soon as any custom attribute is included in a report
Con: good performance generally, unless you start need to search by or report by the custom attributes
* I'm not sure if Option 3 would necessarily save any time in the design phase.
Personally I would lean toward option 2, and avoid EAV wherever possible. However, for some scenarios the users need the flexibility that comes with EAV; but this comes with a great cost.
It is safe to say that the EAV/CR database model is bad.
No, it's not. It's just that they're an inefficient usage of relational databases. A purely key/value store works great with this model.
Now, to your real question: How to store various attributes and keep them searchable?
Just use EAV. In your case it would be a single extra table. index it on both attribute name and value, most RDBMs would use prefix-compression to on the attribute name repetitions, making it really fast and compact.
EAV/CR gets ugly when you use it to replace 'real' fields. As with every tool, overusing it is 'bad', and gives it a bad image.
// At this point, I'd like to take a moment to speak to you about the Magento/Adobe PSD format.
// Magento/PSD is not a good ecommerce platform/format. Magento/PSD is not even a bad ecommerce platform/format. Calling it such would be an
// insult to other bad ecommerce platform/formats, such as Zencart or OsCommerce. No, Magento/PSD is an abysmal ecommerce platform/format. Having
// worked on this code for several weeks now, my hate for Magento/PSD has grown to a raging fire
// that burns with the fierce passion of a million suns.
http://code.google.com/p/xee/source/browse/trunk/XeePhotoshopLoader.m?spec=svn28&r=11#107
The internal models are wacky at best, like someone put the schema into a boggle game, sealed that and put it in a paint shacker...
Real world: I'm working on a midware fulfilment app and here are one the queries to get address information.
CREATE OR REPLACE VIEW sales_flat_addresses AS
SELECT sales_order_entity.parent_id AS order_id,
sales_order_entity.entity_id,
CONCAT(CONCAT(UCASE(MID(sales_order_entity_varchar.value,1,1)),MID(sales_order_entity_varchar.value,2)), "Address") as type,
GROUP_CONCAT(
CONCAT( eav_attribute.attribute_code," ::::: ", sales_order_entity_varchar.value )
ORDER BY sales_order_entity_varchar.value DESC
SEPARATOR '!!!!!'
) as data
FROM sales_order_entity
INNER JOIN sales_order_entity_varchar ON sales_order_entity_varchar.entity_id = sales_order_entity.entity_id
INNER JOIN eav_attribute ON eav_attribute.attribute_id = sales_order_entity_varchar.attribute_id
AND sales_order_entity.entity_type_id =12
GROUP BY sales_order_entity.entity_id
ORDER BY eav_attribute.attribute_code = 'address_type'
Exacts address information for an order, lazily
--
Summary: Only use Magento if:
You are being given large sacks of money
You must
Enjoy pain
I'm surprised nobody mentioned NoSQL databases.
I've never practiced NoSQL in a production context (just tested MongoDB and was impressed) but the whole point of NoSQL is being able to save items with varying attributes in the same "document".
Where performance is not a major requirement, as in an ETL type of application, EAV has another distinct advantage: differential saves.
I've implemented a number of applications where an over-arching requirement was the ability to see the history of a domain object from its first "version" to it's current state. If that domain object has a large number of attributes, that means each change requires a new row be inserted into it's corresponding table (not an update because the history would be lost, but an insert). Let's say this domain object is a Person, and I have 500k Persons to track with an average of 100+ changes over the Persons life-cycle to various attributes. Couple that with the fact that rare is the application that has only 1 major domain object and you'll quickly surmize that the size of the database would quickly grow out of control.
An easy solution is to save only the differential changes to the major domain objects rather than repeatedly saving redundant information.
All models change over time to reflect new business needs. Period. Using EAV is but one of the tools in our box to use; but it should never be automatically classified as "bad".
I'm struggling with the same issue. It may be interesting for you to check out the following discussion on two existing ecommerce solutions: Magento (EAV) and Joomla (regular relational structure):
https://forum.virtuemart.net/index.php?topic=58686.0
It seems, that Magento's EAV performance is a real showstopper.
That's why I'm leaning towards a normalized structure. To overcome the lack of flexibility I'm thinking about adding some separate data dictionary in the future (XML or separate DB tables) that could be edited, and based on that, application code for displaying and comparing product categories with new attributes set would be generated, together with SQL scripts.
Such architecture seems to be the sweetspot in this case - flexible and performant at the same time.
The problem could be frequent use of ALTER TABLE in live environment. I'm using Postgres, so its MVCC and transactional DDL will hopefully ease the pain.
I still vote for modeling at the lowest-meaningful atomic-level for EAV. Let standards, technologies and applications that gear toward certain user community to decide content models, repetition needs of attributes, grains, etc.
If it's just about the product catalog attributes and hence validation requirements for those attributes are rather limited, the only real downside to EAV is query performance and even that is only a problem when your query deals with multiple "things" (products) with attributes, the performance for the query "give me all attributes for the product with id 234" while not optimal is still plenty fast.
One solution is to use the SQL database / EAV model only for the admin / edit side of the product catalog and have some process that denormalizes the products into something that makes it searchable. Since you already have attributes and hence it's rather likely that you want faceting, this something could be Solr or ElasticSearch. This approach avoids basically all downsides to the EAV model and the added complexity is limited to serializing a complete product to JSON on update.
EAV has many drawbacks:
Performance degradation over time
Once the amount of data in the application grows beyond a certain size, the retrieval and manipulation of that data is likely to become less and less efficient.
The SQL queries are very complex and difficult to write.
Data Integrity problems.
You can't define foreign keys for all the fields needed.
You have to define and maintain your own metadata.
I have a slightly different problem: instead of many attributes with sparse values (which is possibly a good reason to use EAV), I want to store something more like a spreadsheet. The columns in the sheet can change, but within a sheet all cells will contain data (not sparse).
I made a small set of tests to benchmark two designs: one using EAV, and the other using a Postgres ARRAY to store cell data.
EAV
Array
Both schemas have indexes on appropriate columns, and the indexes are used by the planner.
It turned out the array-based schema was an order of magnitude faster for both inserts and queries. From quick tests, it seemed that both scaled linearly. The tests aren't very thorough, though. Suggestions and forks welcome - they're under an MIT licence.
I am using POJO (Plain Old Java Object) classes for mapping the relational database and using Apache Solr to index the database.
I don't know whether I can re-use pojo classes for Apache Solr or not.
Since mapping classes are too specific and are designed with foreign key relationship in mind, it is very difficult to use the classes with Solr (a single schema search server), but creating new POJO classes for Apache Solr is also difficult.
So I want to know which is the better design approach for reusing.
Also I would like to know the pitfalls of reusing the same POJO class.
SOLR is very different from a relational database...basically it is something like a big table (with several differences like multivalued columns).
Now, I see your problem a step behind the concrete implementation (POJO)...
First, you have to de-normalize your table(s)...that's the real hard thing you need to do when working with SOLR. I mean passing from your ER to SOLR schema. Once did that, you can use Solrj to map entity with pojo, but this is only the last part of the story.
Still about denormalization: doesn't make sense do a raw translation of a set of POJO mapped on top of a relational database. Relational databases are general-purpose: their design approach is data-centric. I mean, first decide how to store your data and after that SQL clients will be able to get what they need.
SOLR works in a different way: in order to determine moreless exactly your schema you should know your search requirements (i.e. queries). The schema is not general-purpose (like a database) but is tiered on top of search requirements. That's the reason why you could index an atrtribute or not, decide what kind of analysis needs a particular field, multivalued, monovalued, stemming, etc etc etc
So basically, it's all about denormalization and query requirements.
In my project (ASP.NET MVC + NHibernate) I have all my entities, lets say Documents, described by set of custom metadata. Metadata is contained in a structure that can have multiple tags, categories etc. These terms have the most importance for users seeking the document they want, so it has an impact on views as well as underlying data structures, database querying etc.
From view side of application, what interests me the most are the string values for the terms. Ideally I would like to operate directly on the collections of strings like that:
class MetadataAsSeenInViews
{
public IList<string> Categories;
public IList<string> Tags;
// etc.
}
From model perspective, I could use the same structure, do the simplest-possible ORM mapping and use it in queries like "fetch all documents with metadata exactly like this".
But that kind of structure could turn out useless if the application needs to perform complex database queries like "fetch all documents, for which at least one of categories is IN (cat1, cat2, ..., catN) OR at least one of tags is IN (tag1, ..., tagN)". In that case, for performance reasons, we would probably use numeric keys for categories and tags.
So one can imagine a structure opposite to MetadataAsSeenInViews that operates on numeric keys and provide complex mappings of integers to strings and other way round. But that solution doesn't really satisfy me for several reasons:
it smells like single responsibility violation, as we're dealing with database-specific issues when just wanting to describe Document business object
database keys are leaking through all layers
it adds unnecessary complexity in views
and I believe it doesn't take advantage of what can good ORM do
Ideally I would like to have:
single, as simple as possible metadata structure (ideally like the one at the top) in my whole application
complex querying issues addressed only in the database layer (meaning DB + ORM + at less as possible additional code for data layer)
Do you have any ideas how to structure the code and do the ORM mappings to be as elegant, as effective and as performant as it is possible?
I have found that it is problematic to use domain entities directly in the views. To help decouple things I apply two different techniques.
Most importantly I'm using separate ViewModel classes to pass data to views. When the data corresponds nicely with a domain model entity, AutoMapper can ease the pain of copying data between them, but otherwise a bit of manual wiring is needed. Seems like a lot of work in the beginning but really helps out once the project starts growing, and is especially important if you haven't just designed the database from scratch. I'm also using an intermediate service layer to obtain ViewModels in order to keep the controllers lean and to be able to reuse the logic.
The second option is mostly for performance reasons, but I usually end up creating custom repositories for fetching data that spans entities. That is, I create a custom class to hold the data I'm interested in, and then write custom LINQ (or whatever) to project the result into that. This can often dramatically increase performance over just fetching entities and applying the projection after the data has been retrieved.
Let me know if I haven't been elaborate enough.
The solution I've finally implemented don't fully satisfy me, but it'll do by now.
I've divided my Tags/Categories into "real entities", mapped in NHibernate as separate entities and "references", mapped as components depending from entities they describe.
So in my C# code I have two separate classes - TagEntity and TagReference which both carry the same information, looking from domain perspective. TagEntity knows database id and is managed by NHibernate sessions, whereas TagReference carries only the tag name as string so it is quite handy to use in the whole application and if needed it is still easily convertible to TagEntity using static lookup dictionary.
That entity/reference separation allows me to query the database in more efficient way, joining two tables only, like select from articles join articles_tags ... where articles_tags.tag_id = X without joining the tags table, which will be joined too when doing simple fully-object-oriented NHibernate queries.
Really newbie question coming up. Is there a standard (or good) way to deal with not needing all of the information that a database table contains loaded into every associated object. I'm thinking in the context of web pages where you're only going to use the objects to build a single page rather than an application with longer lived objects.
For example, lets say you have an Article table containing id, title, author, date, summary and fullContents fields. You don't need the fullContents to be loaded into the associated objects if you're just showing a page containing a list of articles with their summaries. On the other hand if you're displaying a specific article you might want every field loaded for that one article and maybe just the titles for the other articles (e.g. for display in a recent articles sidebar).
Some techniques I can think of:
Don't worry about it, just load everything from the database every time.
Have several different, possibly inherited, classes for each table and create the appropriate one for the situation (e.g. SummaryArticle, FullArticle).
Use one class but set unused properties to null at creation if that field is not needed and be careful.
Give the objects access to the database so they can load some fields on demand.
Something else?
All of the above seem to have fairly major disadvantages.
I'm fairly new to programming, very new to OOP and totally new to databases so I might be completely missing the obvious answer here. :)
(1) Loading the whole object is, unfortunately what ORMs do, by default. That is why hand tuned SQL performs better. But most objects don't need this optimization, and you can always delay optimization until later. Don't optimize prematurely (but do write good SQL/HQL and use good DB design with indexes). But by and large, the ORM projects I've seen resultin a lot of lazy approaches, pulling or updating way more data than needed.
2) Different Models (Entities), depending on operation. I prefer this one. May add more classes to the object domain, but to me, is cleanest and results in better performance and security (especially if you are serializing to AJAX). I sometimes use one model for serializing an object to a client, and another for internal operations. If you use inheritance, you can do this well. For example CustomerBase -> Customer. CustomerBase might have an ID, name and address. Customer can extend it to add other info, even stuff like passwords. For list operations (list all customers) you can return CustomerBase with a custom query but for individual CRUD operations (Create/Retrieve/Update/Delete), use the full Customer object. Even then, be careful about what you serialize. Most frameworks have whitelists of attributes they will and won't serialize. Use them.
3) Dangerous, special cases will cause bugs in your system.
4) Bad for performance. Hit the database once, not for each field (Except for BLOBs).
You have a number of methods to solve your issue.
Use Stored Procedures in your database to remove the rows or columns you don't want. This can work great but takes up some space.
Use an ORM of some kind. For .NET you can use Entity Framework, NHibernate, or Subsonic. There are many other ORM tools for .NET. Ruby has it built in with Rails. Java uses Hibernate.
Write embedded queries in your website. Don't forget to parametrize them or you will open yourself up to hackers. This option is usually frowned upon because of the mingling of SQL and code. Also, it is the easiest to break.
From you list, options 1, 2 and 4 are probably the most commonly used ones.
1. Don't worry about it, just load everything from the database every time: Well, unless your application is under heavy load or you have some extremely heavy fields in your tables, use this option and save yourself the hassle of figuring out something better.
2. Have several different, possibly inherited, classes for each table and create the appropriate one for the situation (e.g. SummaryArticle, FullArticle): Such classes would often be called "view models" or something similar, and depending on your data access strategy, you might be able to get hold of such objects without actually declaring any new class. Eg, using Linq-2-Sql the expression data.Articles.Select(a => new { a .Title, a.Author }) will give you a collection of anonymously typed objects with the properties Title and Author. The generated SQL will be similar to select Title, Author from Article.
4. Give the objects access to the database so they can load some fields on demand: The objects you describe here would usaly be called "proxy objects" and/or their properties reffered to as being "lazy loaded". Again, depending on your data access strategy, creating proxies might be hard or easy. Eg. with NHibernate, you can have lazy properties, by simply throwing in lazy=true in your mapping, and proxies are automatically created.
Your question does not mention how you are actually mapping data from your database to objects now, but if you are not using any ORM framework at the moment, do have a look at NHibernate and Entity Framework - they are both pretty solid solutions.
It is safe to say that the EAV/CR database model is bad. That said,
Question: What database model, technique, or pattern should be used to deal with "classes" of attributes describing e-commerce products which can be changed at run time?
In a good E-commerce database, you will store classes of options (like TV resolution then have a resolution for each TV, but the next product may not be a TV and not have "TV resolution"). How do you store them, search efficiently, and allow your users to setup product types with variable fields describing their products? If the search engine finds that customers typically search for TVs based on console depth, you could add console depth to your fields, then add a single depth for each tv product type at run time.
There is a nice common feature among good e-commerce apps where they show a set of products, then have "drill down" side menus where you can see "TV Resolution" as a header, and the top five most common TV Resolutions for the found set. You click one and it only shows TVs of that resolution, allowing you to further drill down by selecting other categories on the side menu. These options would be the dynamic product attributes added at run time.
Further discussion:
So long story short, are there any links out on the Internet or model descriptions that could "academically" fix the following setup? I thank Noel Kennedy for suggesting a category table, but the need may be greater than that. I describe it a different way below, trying to highlight the significance. I may need a viewpoint correction to solve the problem, or I may need to go deeper in to the EAV/CR.
Love the positive response to the EAV/CR model. My fellow developers all say what Jeffrey Kemp touched on below: "new entities must be modeled and designed by a professional" (taken out of context, read his response below). The problem is:
entities add and remove attributes weekly (search keywords dictate future attributes)
new entities arrive weekly (products are assembled from parts)
old entities go away weekly (archived, less popular, seasonal)
The customer wants to add attributes to the products for two reasons:
department / keyword search / comparison chart between like products
consumer product configuration before checkout
The attributes must have significance, not just a keyword search. If they want to compare all cakes that have a "whipped cream frosting", they can click cakes, click birthday theme, click whipped cream frosting, then check all cakes that are interesting knowing they all have whipped cream frosting. This is not specific to cakes, just an example.
There's a few general pros and cons I can think of, there are situations where one is better than the other:
Option 1, EAV Model:
Pro: less time to design and develop a simple application
Pro: new entities easy to add (might even
be added by users?)
Pro: "generic" interface components
Con: complex code required to validate simple data types
Con: much more complex SQL for simple
reports
Con: complex reports can become almost
impossible
Con: poor performance for large data sets
Option 2, Modelling each entity separately:
Con: more time required to gather
requirements and design
Con: new entities must be modelled and
designed by a professional
Con: custom interface components for each
entity
Pro: data type constraints and validation simple to implement
Pro: SQL is easy to write, easy to
understand and debug
Pro: even the most complex reports are relatively simple
Pro: best performance for large data sets
Option 3, Combination (model entities "properly", but add "extensions" for custom attributes for some/all entities)
Pro/Con: more time required to gather requirements and design than option 1 but perhaps not as much as option 2 *
Con: new entities must be modelled and designed by a professional
Pro: new attributes might be easily added later on
Con: complex code required to validate simple data types (for the custom attributes)
Con: custom interface components still required, but generic interface components may be possible for the custom attributes
Con: SQL becomes complex as soon as any custom attribute is included in a report
Con: good performance generally, unless you start need to search by or report by the custom attributes
* I'm not sure if Option 3 would necessarily save any time in the design phase.
Personally I would lean toward option 2, and avoid EAV wherever possible. However, for some scenarios the users need the flexibility that comes with EAV; but this comes with a great cost.
It is safe to say that the EAV/CR database model is bad.
No, it's not. It's just that they're an inefficient usage of relational databases. A purely key/value store works great with this model.
Now, to your real question: How to store various attributes and keep them searchable?
Just use EAV. In your case it would be a single extra table. index it on both attribute name and value, most RDBMs would use prefix-compression to on the attribute name repetitions, making it really fast and compact.
EAV/CR gets ugly when you use it to replace 'real' fields. As with every tool, overusing it is 'bad', and gives it a bad image.
// At this point, I'd like to take a moment to speak to you about the Magento/Adobe PSD format.
// Magento/PSD is not a good ecommerce platform/format. Magento/PSD is not even a bad ecommerce platform/format. Calling it such would be an
// insult to other bad ecommerce platform/formats, such as Zencart or OsCommerce. No, Magento/PSD is an abysmal ecommerce platform/format. Having
// worked on this code for several weeks now, my hate for Magento/PSD has grown to a raging fire
// that burns with the fierce passion of a million suns.
http://code.google.com/p/xee/source/browse/trunk/XeePhotoshopLoader.m?spec=svn28&r=11#107
The internal models are wacky at best, like someone put the schema into a boggle game, sealed that and put it in a paint shacker...
Real world: I'm working on a midware fulfilment app and here are one the queries to get address information.
CREATE OR REPLACE VIEW sales_flat_addresses AS
SELECT sales_order_entity.parent_id AS order_id,
sales_order_entity.entity_id,
CONCAT(CONCAT(UCASE(MID(sales_order_entity_varchar.value,1,1)),MID(sales_order_entity_varchar.value,2)), "Address") as type,
GROUP_CONCAT(
CONCAT( eav_attribute.attribute_code," ::::: ", sales_order_entity_varchar.value )
ORDER BY sales_order_entity_varchar.value DESC
SEPARATOR '!!!!!'
) as data
FROM sales_order_entity
INNER JOIN sales_order_entity_varchar ON sales_order_entity_varchar.entity_id = sales_order_entity.entity_id
INNER JOIN eav_attribute ON eav_attribute.attribute_id = sales_order_entity_varchar.attribute_id
AND sales_order_entity.entity_type_id =12
GROUP BY sales_order_entity.entity_id
ORDER BY eav_attribute.attribute_code = 'address_type'
Exacts address information for an order, lazily
--
Summary: Only use Magento if:
You are being given large sacks of money
You must
Enjoy pain
I'm surprised nobody mentioned NoSQL databases.
I've never practiced NoSQL in a production context (just tested MongoDB and was impressed) but the whole point of NoSQL is being able to save items with varying attributes in the same "document".
Where performance is not a major requirement, as in an ETL type of application, EAV has another distinct advantage: differential saves.
I've implemented a number of applications where an over-arching requirement was the ability to see the history of a domain object from its first "version" to it's current state. If that domain object has a large number of attributes, that means each change requires a new row be inserted into it's corresponding table (not an update because the history would be lost, but an insert). Let's say this domain object is a Person, and I have 500k Persons to track with an average of 100+ changes over the Persons life-cycle to various attributes. Couple that with the fact that rare is the application that has only 1 major domain object and you'll quickly surmize that the size of the database would quickly grow out of control.
An easy solution is to save only the differential changes to the major domain objects rather than repeatedly saving redundant information.
All models change over time to reflect new business needs. Period. Using EAV is but one of the tools in our box to use; but it should never be automatically classified as "bad".
I'm struggling with the same issue. It may be interesting for you to check out the following discussion on two existing ecommerce solutions: Magento (EAV) and Joomla (regular relational structure):
https://forum.virtuemart.net/index.php?topic=58686.0
It seems, that Magento's EAV performance is a real showstopper.
That's why I'm leaning towards a normalized structure. To overcome the lack of flexibility I'm thinking about adding some separate data dictionary in the future (XML or separate DB tables) that could be edited, and based on that, application code for displaying and comparing product categories with new attributes set would be generated, together with SQL scripts.
Such architecture seems to be the sweetspot in this case - flexible and performant at the same time.
The problem could be frequent use of ALTER TABLE in live environment. I'm using Postgres, so its MVCC and transactional DDL will hopefully ease the pain.
I still vote for modeling at the lowest-meaningful atomic-level for EAV. Let standards, technologies and applications that gear toward certain user community to decide content models, repetition needs of attributes, grains, etc.
If it's just about the product catalog attributes and hence validation requirements for those attributes are rather limited, the only real downside to EAV is query performance and even that is only a problem when your query deals with multiple "things" (products) with attributes, the performance for the query "give me all attributes for the product with id 234" while not optimal is still plenty fast.
One solution is to use the SQL database / EAV model only for the admin / edit side of the product catalog and have some process that denormalizes the products into something that makes it searchable. Since you already have attributes and hence it's rather likely that you want faceting, this something could be Solr or ElasticSearch. This approach avoids basically all downsides to the EAV model and the added complexity is limited to serializing a complete product to JSON on update.
EAV has many drawbacks:
Performance degradation over time
Once the amount of data in the application grows beyond a certain size, the retrieval and manipulation of that data is likely to become less and less efficient.
The SQL queries are very complex and difficult to write.
Data Integrity problems.
You can't define foreign keys for all the fields needed.
You have to define and maintain your own metadata.
I have a slightly different problem: instead of many attributes with sparse values (which is possibly a good reason to use EAV), I want to store something more like a spreadsheet. The columns in the sheet can change, but within a sheet all cells will contain data (not sparse).
I made a small set of tests to benchmark two designs: one using EAV, and the other using a Postgres ARRAY to store cell data.
EAV
Array
Both schemas have indexes on appropriate columns, and the indexes are used by the planner.
It turned out the array-based schema was an order of magnitude faster for both inserts and queries. From quick tests, it seemed that both scaled linearly. The tests aren't very thorough, though. Suggestions and forks welcome - they're under an MIT licence.