I have a table with data that must be filled by users. Once this data is filled, the status changes to 'completed' (status is a field inside data).
My question is, is it good practice to create a table for data to be completed and another one with completed data? Or should I only make one table with both types of data, distinguished by the status?
Not just Django
This is actually a very good general question, not necessarily specific to Django. But Django, through easy use of linked tables (ForeignKey, ManyToMany) is a good use case for One table.
One table, or group of tables
One table has some advantages:
No need to copy the data, just change the Status field.
If there are linked tables then they don't need to be copied
If you want to remove the original data (i.e., avoid keeping redundant data) then this avoids having to worry about deleting the linked data (and deleting it in the right sequence).
If the original add and the status change are potentially done by different processes then one table is much safer - i.e., marking the field "complete" twice is harmless but trying to delete/add a 2nd time can cause a lot of problems.
"or group of tables" is a key here. Django handles linked tables really well, so but doing all of this with two separate groups of linked tables gets messy, and easy to forget things when you change fields or data structures.
One table is the optimal way to approach this particular case. Two tables requires you to enforce data integrity and consistency within your application, rather than relying on the power of your database, which is generally a very bad idea.
You should aim to normalize your database (within reason) and utilize the database's built-in constraints as much as possible to avoid erroneous data, including duplicates, redundancies, and other inconsistencies.
Here's a good write-up on several common database implementation problems. Number 4 covers your 2-table option pretty well.
If you do insist on using two tables (please don't), then at least be sure to use an artificial primary key (IE: a unique value that is NOT just the id) to help maintain integrity. There may be matching id integer values in each table that match, but there should only ever be one version of each artificial primary key value between the two tables. Again, though, this is not the recommended approach, and adds complexity in your application that you don't otherwise need.
Related
Is it an issue in any way that a card many attribute may have a huge number of values? (say 10^6)
I think the intuition/concern here is that we definitely don't want equivalent of a select * on the entity creeping in anywhere.
Of course, the datoms making up a card many are clearly stored individually, so I suspect this simply isn't an issue. However I just wanted to be sure.
Coming from sql, the pivot is clearly a separate table and can be treated as such.
So, I guess it depends entirely on the entity/pull apis that actually assemble the datoms, and/or perhaps on datalog itself (Background: I haven't used the datomic apis for real yet, though I have done the excellent learn datalog tutorial - my current task is translating a few sql schemas i have into datomic, to get started).
I'd like to know if that is simply not a problem in any of the pull or entity apis, that such an attribute can be easily excluded from a result set in effectively all cases, or is there any drawback of a highly numerous card many attribute to be aware of?
Problem domain
I'm working on a rather big application, which uses a hierarchical data model. It takes images, extracts images' features and creates analysis objects on top of these. So the basic model is like Object-(1:N)-Image_features-(1:1)-Image. But the same set of images may be used to create multiple analysis objects (with different options).
Then an object and image can have a lot of other connected objects, like the analysis object can be refined with additional data or complex conclusions (solutions) can be based on the analysis object and other data.
Current solution
This is a sketch of the solution. Stacks represent sets of objects, arrows represent pointers (i.e. image features link to their images, but not vice versa). Some parts: images, image features, additional data, may be included in multiple analysis objects (because user wants to make analysis on different sets of object, combined differently).
Images, features, additional data and analysis objects are stored in global storage (god-object). Solutions are stored inside analysis objects by means of composition (and contain solution features in turn).
All the entities (images, image features, analysis objects, solutions, additional data) are instances of corresponding classes (like IImage, ...). Almost all the parts are optional (i.e., we may want to discard images after we have a solution).
Current solution drawbacks
Navigating this structure is painful, when you need connections like the dotted one in the sketch. If you have to display an image with a couple of solutions features on top, you first have to iterate through analysis objects to find which of them are based on this image, and then iterate through the solutions to display them.
If to solve 1. you choose to explicitly store dotted links (i.e. image class will have pointers to solution features, which are related to it), you'll put very much effort maintaining consistency of these pointers and constantly updating the links when something changes.
My idea
I'd like to build a more extensible (2) and flexible (1) data model. The first idea was to use a relational model, separating objects and their relations. And why not use RDBMS here - sqlite seems an appropriate engine to me. So complex relations will be accessible by simple (left)JOIN's on the database: pseudocode "images JOIN images_to_image_features JOIN image_features JOIN image_features_to_objects JOIN objects JOIN solutions JOIN solution_features") and then fetching actual C++ objects for solution features from global storage by ID.
The question
So my primary question is
Is using RDBMS an appropriate solution for problems I described, or it's not worth it and there are better ways to organize information in my app?
If RDBMS is ok, I'd appreciate any advice on using RDBMS and relational approach to store C++ objects' relationships.
You may want to look at Semantic Web technologies, such as RDF, RDFS and OWL that provide an alternative, extensible way of modeling the world. There are some open-source triple stores available, and some of the mainstream RDBMS also have triple store capabilities.
In particular take a look at Manchester Universities Protege/OWL tutorial: http://owl.cs.manchester.ac.uk/tutorials/protegeowltutorial/
And if you decide this direction is worth looking at further, I can recommend "SEMANTIC WEB for the WORKING ONTOLOGIST"
Just based on the diagram, I would suggest that an RDBMS solution would indeed work. It has been years since I was a developer on an RDMS (called RDM, of course!), but I was able to renew my knowledge and gain very many valuable insights into data structure and layout very similar to what you describe by reading the fabulous book "The Art of SQL" by Stephane Faroult. His book will go a long way to answer your questions.
I've included a link to it on Amazon, to ensure accuracy: http://www.amazon.com/The-Art-SQL-Stephane-Faroult/dp/0596008945
You will not go wrong by reading it, even if in the end it does not solve your problem fully, because the author does such a great job of breaking down a relation in clear terms and presenting elegant solutions. The book is not a manual for SQL, but an in-depth analysis of how to think about data and how it interrelates. Check it out!
Using an RDBMS to track the links between data can be an efficient way to store and think about the analysis you are seeking, and the links are "soft" -- that is, they go away when the hard objects they link are deleted. This ensures data integrity; and Mssr Fauroult can answer what to do to ensure that remains true.
I don't recommend RDBMS based on your requirement for an extensible and flexible model.
Whenever you change your data model, you will have to change DB schema and that can involve more work than change in code.
Any problems with DB queries are discovered only at runtime. This can make a lot of difference to the cost of maintenance.
I strongly recommend using standard C++ OO programming with STL.
You can make use of encapsulation to ensure any data change is done properly, with updates to related objects and indexes.
You can use STL to build highly efficient indexes on the data
You can create facades to get you the information easily, rather than having to go to multiple objects/collections. This will be one-time work
You can make unit test cases to ensure correctness (much less complicated compared to unit testing with databases)
You can make use of polymorphism to build different kinds of objects, different types of analysis etc
All very basic points, but I reckon your effort would be best utilized if you improve the current solution rather than by look for a DB based solution.
http://www.boost.org/doc/libs/1_51_0/libs/multi_index/doc/index.html
"you'll put very much effort maintaining consistency of these pointers
and constantly updating the links when something changes."
With the help of Boost.MultiIndex you can create almost every kind of index on a "table". I think the quoted problem is not so serious, so the original solution is manageable.
Specifically thinking of web apps,
(1) why are relationships(ie:foreign keys) in RDBMS even useful?
The web apps I write have logic built-in that validates user input against required fields. I see no real use for foreign keys and thus no real use for relational databases.
Besides, if I were to put all the required field validation logic in the RDBMS(ie:MySQL) it would simply return a vague error. At least with PHP-based validation I know which field is missing and I can notify the user(though with Javascript-based validation this would almost NEVER happen anyway).
(2) Was there a point in the past where RDBMS were useful for some reason or is there a reason they are useful now that I'm not aware of?
I really need some insight on this topic. I'm simply can't come up with a good answer.
I will come at this from a different angle.
I work at a place where we had a database that had no foreign key constraints, default values, or other data checks whatsoever in their initial records database. The lead engineer's excuse for this was something similar to what you have described above. "The application will ensure the referential integrity".
The problem is, we did not have a standard data layer (like an object relational mapping) over the top of the database. We had multiple programmatic sources that fed into the same tables. It was funny because after a while, you could tell which parts of the code created which rows in the table. Sometimes the links lined up, sometimes they didn't. Sometimes the links were NULL (when they shouldn't be), and sometimes they were 0. We even had a few cyclic records which was fun.
My point is, you never know when you are going to need to write a quick script to batch import records, or write a new subsystem that references the same tables. It behooves us as programmers to program as defensively as possible. We can't assume that those who come after us will know as much (if anything) about how our schema should be used.
I'm not much of an SQL lover, but even I must say that the relational structure has its advantages.
It doesn't only allow validation. By providing the database with metadata describing the relations between the actual pieces information stored, a great number of optimizations are possible.
This makes it possible to quickly retrieve large, complex datasets. It also reduces the number of queries needed to make modifications and keep the data coherent, since most of the "book-keeping" is carried out automatically on the DB side of the connection.
One incredibly useful feature of foreign keys in most relational databases are cascades.
Suppose you have a families table and a persons table. Each family can have multiple people, but a person can only belong in one family (one-to-many relationship). If you have foreign keys and you delete a family row, the database can automatically update all the related people, either by deleting them or setting their foreign keys to null.
If you do not have this constraint, you must handle this situation yourself, in your own code.
RDBMSs are still very useful. Not sure why you wouldn't think so. Foreign key constraints can be used to maintain referential integrity (in other words, to provide a simple way to express 1:1, 1:many and many:many relationships. RDBMSs are also useful because there was a rich theory accompanying practical developments, unlike previous DBMSs. In particular, relational calculus/algebra are nice since they allow for good query optimization, normalization, etc.
Not sure if that really answers your question. Wikipedia might list some advantages of RDBMSs.
(1) why are relationships(ie:foreign keys) in RDBMS even useful?
First off, I think you are talking about foreign key CONSTRAINTS. Foreign keys are just a logical design feature that says that this entity matches up with that one.
The reason foreign key constraints are useful are:
They help you adhere to the DRY (Don't repeat yourself) principle. Sure your app validates the relationship, but does it do it in several places? Are there multiple apps that access the same DB? Do you have to repeat the logic in each app? Hey, you could pull that logic out and use a common DLL for access to that data that enforces that logic.Better yet, what if that was built into the RDMBS so I didn't have to write custom code to do something so routine? Bam. Foreign key constraints.
If your app enforces the foreign key validations, how do you force users who are working directly in the DB to honor your rules? I know, I know. You shouldn't let users into the back-end directly, but you just try telling that to the data analysts when they have a project for corporate and you are the bottleneck.
As to the vague error. Wouldn't your argument be better stated as RDBMS X has vague errors when data fails foreign key constraint checks? The way you have generalized it, you could also argue that we should use paper ledgers instead of computers because the constraint had a vague error.
(2) Was there a point in the past where RDBMS were useful for some reason or is there a reason they are useful now that I'm not aware of?
Yeah, that would be now, yesterday and probably long into the future.
I could go on forever about the reasons, but here is the big one...
It provides a common structured file format that is easy to extend, leverage by other applications. You may be too young to remember when every dang system had it's own proprietary structured file format, but it sucked. Plus, it forced you re-invent the wheel constantly in terms of things like indexing, a query language, locking, etc.
"I see no real use for foreign keys and thus no real use for
relational databases"
Judging by this remark, you seem to be underestimating what a relational database is for. Foreign key constraints aren't a defining feature of relational databases and certainly aren't the only reason for using such databases. The relational database model is a powerful and effective way to represent data and it remains so even if you decide you don't want to implement a foreign key constraint. I will therefore assume the question you really meant to ask is: Why are foreign keys useful in relational databases?
A foreign key constraint is just one kind of data integrity constraint. You can of course implement integrity rules outside the database but the DBMS is designed and optimised to do the job for you and is generally the most efficient place to do it because it is closest to the data structures. If you did it outside the database then you would have at least an extra round trip to retrieve the necessary data. You would also have to replicate the DBMS's locking/concurrency model in your application code.
The database optimiser can take advantage of constraints in the database to improve the performance of queries. It can't do that if the rules only exist in your application code.
If you have many applications sharing the same database then implementing data integrity rules in every application is impractical and expensive to maintain. Centralising the constraint logic makes more sense.
Various CASE tools and DBA tools will take advantage of database constraints, can reverse engineer them and use them to assist development and maintenance tasks.
In practice the meaning and function of a database constraint versus some procedural code that validates data only on entry is very different. If X is implemented in a database constraint then I know it is valid for every piece of data in the database. If X is implemented in the application when data is entered then I only know it applies to future data - I can't be sure it applies to everything already in the database (maybe X was only implemented today and didn't apply to the data entered yesterday).
Because they maintain the integrity of the database. If you have all your business logic in the application then in theory they are not needed, but are still useful as a safeguard against bad data.
I'm writing an app in Django where I'd like to make use of implicit inheritence when using ForeignKeys. As far as I'm concerned the only way to handle this nicely is to use django_polymorphic library (no single table inheritence in Django, WHY OH WHY??).
I'd like to know about the performance implications of this solution. What kind of joins are performed when doing polymorphic queries? Does it have to hit the database multiple times as compared to regular queries (the infamous N+1 queries problem)? The docs warn that "the type of queries that are performed aren't handled efficiently by the modern RDBMs"? However it doesn't really tell what those queries are. Any statistics, experiences would be really helpful.
EDIT:
Is there any way of retrieving a list of objects, each being an instance of its actual class with a constant number of queries ?? I thought this is what the aforementioned library does, however now I got confused and I'm not that certain anymore.
Django-Typed-Models is an alternative to Django-Polymorphic which takes a simple & clean approach to solving the single table inheritance issue. It works off a 'type' attribute which is added to your model. When you save it, the class is persisted into the 'type' attribute. At query time, the attribute is used to set the class of the resulting object.
It does what you expect query-wise (every object returned from a queryset is the downcasted class) without needing special syntax or the scary volume of code associated with Django-Polymorphic. And no extra database queries.
In Django inherited models are internally represented through an OneToOneField. If you are using select_related() in a query Django will follow a one to one relation forwards and backwards to include the referenced table with a join; so you wouldn't need to hit the database twice if you are using select_related.
Ok, I've digged a little bit further and found this nice passage:
https://github.com/bconstantin/django_polymorphic/blob/master/DOCS.rst#performance-considerations
So happily this library does something reasonably sane. That's good to know.
I have a model, say, Item. I want to store arbitrary amount of attributes on it, like title, description, release_date. And i want them to be not just strings but have python type, so string, boolean, datetime etc.
What are my options here? EAV pattern with separate name-value table won't work because of the same DB type across all values. JSONField can probably help, but it doesn't know about datetime, for example. Also i was looking at PickeField, it fits perfectly, but i'm a bit concerned about performance.
You have a couple of options and none of them are great. Some of them have been discussed before on Stack Overflow.
Firstly, as you suggested, you have the entity-attribute-value design pattern.
You can add DB type checking by having a table for VARCHARs and one for INTs and one for BOOLEANs and so on.
EAV makes selects very painful. You have to query a number of tables to actually get an object and if you have to use values from the EAV table in the lookup you will run into performance issues as the size increases.
In general, however, EAV should really only be used for very sparse data where another option simply does not work.
There is a Django package for this on PyPI, but I haven't used it.
I have seen some pretty large scale commercial products that use this approach when a lot of flexibility is absolutely required
A slightly better approach is to have a table whose schema changes and a metadata table that describes that table. For dense data where most items have most of the attributes, this has a lot of advantages over EAV. This approach is sometimes called dynamic tables or dynamic rows.
INSERTs, UPDATEs and DELETEs are much faster since everything is in 1-2 tables
Type checking and potentially constraints can be added
However, this approach leaves a very complex database that can be harder to work with
I don't know any way that Django would use its ORM with this kind of database since your models would be changing on the fly.
You are altering your database with ALTER TABLE on the fly. You better be very careful with your transactions
A good approach if you don't need to perform lookups based on these dynamic attributes is to store dynamic data in a JSONField or better yet a schema validated XMLField. However, lookups will be painful if you have to lookup based on a dynamic attribute that is part of your JSON or XML.
The best approach depends on how sparse your data is and how you'll be looking up that data. Also, a very good question to ask is if you absolutely need this flexibility. I've worked on some projects where we decided we needed EAV but since the project went into production attributes are rarely added and rarely removed so we got all the disadvantages and none of the boons.