Hopefully you'll see the problem I'm describing in the scenario below. If it's not clear, please let me know.
You've got an application that's broken into three layers,
front end UI layer, could be asp.net webform, or window (used for editing Person data)
middle tier business service layer, compiled into a dll (PersonServices)
data access layer, compiled into a dll (PersonRepository)
In my front end, I want to create a new Person object, set some properties, such as FirstName, LastName according to what has been entered in the UI by a user, and call PersonServices.AddPerson, passing the newly created Person. (AddPerson doesn't have to be static, this is just for simplicity, in any case the AddPerson will eventually call the Repository's AddPerson, which will then persist the data.)
Now the part I'd like to hear your opinion on is validation. Somewhere along the line, that newly created Person needs to be validated. You can do it on the client side, which would be simple, but what if I wanted to validate the Person in my PersonServices.AddPerson method. This would ensure any person I want to save would be validated and removes any dependancy on the UI layer doing the work. Or maybe, validate both in UI and in by business server layer. Sounds good so far right?
So, for simplicity, I'll update the PersonService.AddPerson method to perform the following validation checks
- Check if FirstName and LastName are not empty
- Ensure this new Person doesn't already exist in my repository
And this method will return True if all validation passes and the Person is persisted, False if Validation fails or if the Person is not persisted.
But this Boolean value that AddPerson returns isn't enough for me at the UI layer to give the user a clear reason why the save process failed. So what's a lonely developer to do? Ultimately, I'd like the AddPerson method to be able to ensure what its about to save is valid, and if not, be able to communicate the reasons why it's not invalid to my UI layer.
Just to get your juices flowing, some ways of solving this could be: (Some of these solutions, in my opinion, suck, but I'm just putting them there so you get an understanding of what I'm trying to solve)
Instead of AddPerson returning a boolean, it can return an int (i.e. 0 = Success, Non Zero equals failure and the number indicates the reason why it failed.
In AddPerson, throw custom exceptions when validation fails. Each type of custom exception would have its own error message. In addition, each custom exception would be unique enough to catch in the UI layer
Have AddPerson return some sort of custom class that would have properties indicating whether validation passed or failed, and if it did fail, what were the reasons
Not sure if this can be done in VB or C#, but attach some sort of property to the Person and its underlying properties. This "attached" property could contain things like validation info
Insert your idea or pattern here
And maybe another here
Apologies for the long winded question, but I definately like to hear your opinion on this.
Thanks!
Multiple layers of validation go well with multi-layer apps.
The UI itself can do the simplest and quickest checks (are all mandatory fields present, are they using the appropriate character sets, etc) to give immediate feedback when the user makes a typo.
However the business logic should have the lion's share of validation responsibilities... and for once it's not a problem if this is "repetitious", i.e., if the business layer re-checks something that should already have been checked in the UI -- the BL should check all the business rules (this double checks on UI's correctness, enables multiple different UI clients that may not all be perfect in their checks -- e.g. a special client on a smart phone which may not have good javascript, and so on -- and, a bit, wards against maliciously hacked clients).
When the business logic saves the "validated" data to the DB, that layer should perform its own checks -- DBs are good at that, and, again, don't worry about some repetition -- it's the DB's job to enforce data integrity (you might want different ways to feed data to it one day, e.g. a "bulk loader" to import a number of Persons from another source, and it's key to ensure that all those ways to load data always respect data integrity rules); some rules such as uniqueness and referential integrity are really best enforced in the DB, in particular, for performance reasons too.
When the DB returns an error message (data not inserted as constraint X would be violated) to the business layer, the latter's job is to reinterpret that error in business terms and feed the results to the UI to inform the user; and of course the BL must similarly provide clear and complete info on business rules violation to the UI, again for display to the user.
A "custom object" is thus clearly "the only way to go" (in some scenarios I'd just make that a JSON object, for example). Keeping the Person object around (to maintain its "validation problems" property) when the DB refused to persist it does not look like a sharp and simple technique, so I don't think much of that option; but if you need it (e.g. to enable "tell me again what was wrong" functionality, maybe if the client went away before the response was ready and needs to smoothly restart later; or, a list of such objects for later auditing, &c), then the "custom validation-failure object" could also be appended to that list... but that's a "secondary issue", the main thing is for the BL to respond to the UI with such an object (which could also be used to provide useful non-error info if the insertion did in fact succeed).
Just a quick (and hopefully helpful) comment: when you're wondering where to place validation, try pretending that, soon, you're going to completely recreate your UI layer using a technology you're not yet so familiar with**. Try to keep out of that layer any validation-like business logic that you know for certain you'd have to rewrite in the new technology.
You'll find exceptions - business logic that ends up in your UI layer regardless, but it's a useful consideration nonetheless.
** Mobile dev, Silverlight, Voice XML, whatever - pretending you don't know the technology of your "new" UI layer helps you abstract your concerns and get less mired in implementation details.
The only important points are:
From the perspective of the front-end(s), the Middle Tier must perform all validation, you never know whether someone is going to try circumventing your front-end validation by talking directly to your Middle Tier (for whatever reason)
The Middle Tier may elect to delegate some of that validation to the DB layer (e.g. data integrity constraints)
You may optionally duplicate some validation in the UI, but that should only be for the sake of performance (to avoid round-trips to the Middle Tier for common scenarios, such as missing mandatory fields, incorrectly formatted data, etc.) These checks should never take the place of doing them in the Middle Tier
Validation should be done at all three levels.
When I am in a project I assume I am making a framework, which most of the time is not the case. Each layer is separate and must check all layers input before doing an operation
Each level can have a different way of doing it, it is not necessary they all use the same, but ideally they should all use the same validation with the ability to customize it.
You never want to let bad data into the database. So you can never trust the data you are getting from the business layer. It needs to be checked.
In the business layer you can never trust the UI layer, and you must check it to prevent un-needed calls to the database layer. The UI layer works the same way.
I disagree with David Basarab's comment that the same validations should be present in all layers. This defies the paradigm of responsibility of layers for one reason. Secondly, though the main intention is to make the layers (or components) loosely coupled, it is also important that a level of responsibility (and hence trust) is endowed on the layers. Though it might be necessary to duplicated some validations in UI and Business Layer (since UI layer can be bypassed by hacking attempts), however, it is not advisable to repeat the validations in each layer. Each layer should perform only those validations which they are responsible for. The biggest flaw in repeting validations in all layers is code redundancy, which can cause maintenance nightmare.
A lot of this is more style than substance. I personally favor returning status objects as a flexible and extensible solution. I would say that I think there are a couple classes of validation in play, the first being "does this person data conform to the contract of what a person is?" and the second being "does this person data violate constraints in the database?" I think the first validation can, and should be done at the client. The second should be done at the middle tier. With this division, you may find that the only reasons the save could fail are 1)violates a uniqueness constrains, or 2)something catastrophic. You could then return false for the first case, and throw an exception for the other.
If tier R is closer to the user (or any input stream you don't control) than tier S then tier S should validate all data received from tier R. This does not mean that tier R shouldn't validate data. It's better for the user if the GUI warns him he's making a mistake before he attempts a new transaction. But no matter how bulletproof the validation in your GUI is, the next tier up should not trust that any validation has taken place.
This assumes your database in completely under your control. If not, you have bigger problems.
Also, you could have the UI pass the data needed to build a Person object through some sort of PersonBuilder object, so that object creation is consolidated in the domain/business layer, and you can keep the Person object in a state that is always consistent. This makes more sense for more complex entities, however even for simple ones, it is good to centralize object creation, just like you centralize persistence, etc.
Related
I am new to domain driven design and there is one thing that is bothering me when I'm writing domain model. How to handle domain validation?
I am designing library management system where user can search through books and see if book is on the stock.
If it is not, user can create request for book so some kind of queue is created. Rule is that we don't have any book in stock. Right now I have information about quantity inside book entity and that is not a problem but what if i have different bounded context for requesting books and book catalog. Then I must somehow contact another vertical/service and ask (validate) that book quantity is zero before creating book aggregate.
Also I am checking if user have valid membership card, is book already borrowed by him, do user have active requests for any book.
Things that bother me.
I need to know what exactly to include in aggregate before passing it to domain model because of validations. I am not sure that is safest approach because my validations accuracy will depend from specification/query, etc.
Another very important thing. When application layer method start with execution and something is not valid client will get only validation messages for code that was executed and there is good chance there is more things that are preventing code for execution. This can be really inconvenient if user is filling some form.
First thoughts for solving this problem.
I have command/handler architecture and I am using MediatR so I am thinking to move domain validations between command and handler and that will solve my problems for now but that approach will spread domain knowledge across bounded context and domain model will not be smart enough to guard from not valid actions. More precise I will need to think before executing application method (handler) what I need to validate.
So I am really curious. Is there any clear way of handling domain validations inside domain model?
Is there any clear way of handling domain validations inside domain model?
Yes; they require work and careful thinking.
One aspect of careful thinking is to distinguish message validation from domain logic. Message validation is an isolated thing, a message is valid or not according to the schema of the message -- are all of the required fields present, is the data in the right form, are the numbers in the allowed range, and so on. Really, we're asking the question "did the client fill out the form correctly?"
Integrating a valid message with previously known information (aka, the "state" of the domain model) is a domain logic concern. State is chosen deliberately - the domain model is a state machine for the bookkeeping of your domain.
Depending on your domain, and the information that is available, there can be states that mean that the client doesn't get what they want. "The road less traveled" doesn't mean that things are invalid.
Furthermore, if your system is distributed (different pieces of data are the responsibility of different authorities), then any locally cached copies of that data are necessarily stale, and may be out of date. See Pat Helland's Memories, Guesses, and Apologies. That we will sometimes produce an incorrect answer is an inevitable consequence of distributing the work. If we're responsible, then we performed a cost benefit analysis to ensure that the expected benefits of distributing the work offset the expected risks.
Let's say I've got a domain class, which has functions, that are to be called in a sequence. Each function does its job but if the previous step in the sequence is not done yet, it throws an error. The other way is that each function completes the step required for it to run, and then executes its own logic. I feel that this way is not a good practice, since I am adding multiple responsibilities, and the caller wont know what all operations can happen when he invokes a method.
My question is, how to handle dependent scenarios in DDD. Is it the responsibility of the caller to invoke the methods in the right sequence? Or do we make the methods handle the dependent operations before it's own logic?
Is it the responsibility of the caller to invoke the methods in the right sequence?
It's ok if those methods have a business meaning. For example the client may book a flight, and then book a hotel room. Both of those is something the client understands, and it is the client's logic to call them in this sequence. On the other hand, inserting the reservation into the database, then committing (or whatever) is technical. The client should not have to deal with that at all. Or "initializing" an object, then calling other methods, then calling "close".
Requiring a sequence of technical calls is a form of temporal coupling, it is considered a bad practice, and is not directly related to DDD.
The solution is to model the problem better. There is probably a higher level use-case the caller wants achieved with this call sequence. So instead of publishing the individual "steps" required, just support the higher use-case as a whole.
In general you should always design with the goal to get any sequence of valid calls to actually mean something (as far as the language allows).
Update: A possible model for the mentioned "File" domain:
public interface LocalFile {
RemoteFile upload();
}
public interface RemoteFile {
RemoteFile convert(...);
LocalFile download();
}
From my point of view, what you are describing is the orchestration of domain model operations. That's the job of the application layer, the layer upon domain model. You should have an application service that would call the domain model methods in the right sequence, and it also should take into account whether some step has left any task undone, and in such case, tell the next step to perform it.
TLDR; Scroll to the bottom for the answer, but the backstory will give some good context.
If the caller into your domain must know the order in which to call things, then you have missed an opportunity to encapsulate business logic in your domain, which is a symptom of an anemic domain.
#RobertBräutigam made a very good point:
Requiring a sequence of technical calls is a form of temporal coupling, it is considered a bad practice, and is not directly related to DDD.
This is true, but it is worse when you do it with your domain model because non-domain concerns get intermixed with domain concerns. Intent becomes lost in a sea of non business logic. If you can, you look for a higher-order aggregate that encapsulates the ordering. To borrow Robert's example, rather than booking a flight then a hotel room, and forcing that on the client, you could have a Vacation aggregate take both and validate it.
I know that sounds wrong in your case, and I suspect you're right. There's a clear dependency that can't happen all at once, so we can't be the end of the story. When you have a clear dependency with intermediate transactions that must occur before the "final" state, we have... orchestration (think sagas, distributed transactions, domain events and all that goodness).
What you describe with file operations spans across transactions. The manipulation (state change) of a domain is transactional at each point in a distributed transaction, but is not transactional overall. So when #choquero70 says
you are describing is the orchestration of domain model operations. That's the job of the application layer, the layer upon domain model.
that's also correct. Orchestration is key. Each step must manipulate the state of the domain once, and once only, and leave it in a valid state, but it OK for there to be multiple steps.
Each of those individual points along the timeline are valid moments in the state of your domain.
So, back to your model. If you expose a single interface with multiple possible calls to all steps, then you leave yourself open to things being called out of order. Make this impossible or at least improbable. Orchestration is not just about what to do, but what to prevent from happening. Create smaller interfaces/classes to avoid accidentally increasing the "surface area" of what could be misused accidentally.
In this way, you are guiding the caller on what to do next by feeding them valid intermediate states. But, and this is the important part, the burden on what to call in what order is not on the caller. Sure, the caller could know what to do, but why force it.
Your basic algorithm is the same: upload, transform, download.
Is it the responsibility of the caller to invoke the methods in the right sequence?
Not exactly. Is the responsibility of the caller to choose from legitimate choices given the state of your domain. It's "your" responsibility to present these choices via business methods on your correctly modeled moment/interval aggregate suitable for the caller to use.
Or do we make the methods handle the dependent operations before it's own logic?
If you've setup orchestration correctly, this won't be necessary. But it does make sense to validate anyway.
On a side note, each step of the orchestration you do should be very linear in nature. I tell my developers to be suspicious of an orchestration step that has an if statement in it. If there's an if it's likely better to be part of another orchestration step or encapsulated in business logic.
So, I'm working on a CQRS/ES project in which we are having some doubts about how to handle trivial problems that would be easy to handle in other architectures
My scenario is the following:
I have a customer CRUD REST API and each customer has unique document(number), so when I'm registering a new customer I have to verify if there is another customer with that document to avoid duplicity, but when it comes to a CQRS/ES architecture where we have eventual consistency, I found out that this kind of validations can be very hard to address.
It is important to notice that my problem is not across microservices, but between the command application and the query application of the same microservice.
Also we are using eventstore.
My current solution:
So what I do today is, in my command application, before saving the CustomerCreated event, I ask the query application (using PostgreSQL) if there is a customer with that document, and if not, I allow the event to go on. But that doesn't guarantee 100%, right? Because my query can be desynchronized, so I cannot trust it 100%. That's when my second validation kicks in, when my query application is processing the events and saving them to my PostgreSQL, I check again if there is a customer with that document and if there is, I reject that event and emit a compensating event to undo/cancel/inactivate the customer with the duplicated document, therefore finishing that customer stream on eventstore.
Altough this works, there are 2 things that bother me here, the first thing is my command application relying on the query application, so if my query application is down, my command is affected (today I just return false on my validation if query is down but still...) and second thing is, should a query/read model really be able to emit events? And if so, what is the correct way of doing it? Should the command have some kind of API for that? Or should the query emit the event directly to eventstore using some common shared library? And if I have more than one view/read? Which one should I choose to handle this?
Really hope someone could shine a light into these questions and help me this these matters.
For reference, you may want to be reviewing what Greg Young has written about Set Validation.
I ask the query application (using PostgreSQL) if there is a customer with that document, and if not, I allow the event to go on. But that doesn't guarantee 100%, right?
That's exactly right - your read model is stale copy, and may not have all of the information collected by the write model.
That's when my second validation kicks in, when my query application is processing the events and saving them to my PostgreSQL, I check again if there is a customer with that document and if there is, I reject that event and emit a compensating event to undo/cancel/inactivate the customer with the duplicated document, therefore finishing that customer stream on eventstore.
This spelling doesn't quite match the usual designs. The more common implementation is that, if we detect a problem when reading data, we send a command message to the write model, telling it to straighten things out.
This is commonly referred to as a process manager, but you can think of it as the automation of a human supervisor of the system. Conceptually, a process manager is an event sourced collection of messages to be sent to the command model.
You might also want to consider whether you are modeling your domain correctly. If documents are supposed to be unique, then maybe the command model should be using the document number as a key in the book of record, rather than using the customer. Or perhaps the document id should be a function of the customer data, rather than being an arbitrary input.
as far as I know, eventstore doesn't have transactions across different streams
Right - one of the things you really need to be thinking about in general is where your stream boundaries lie. If set validation has significant business value, then you really need to be thinking about getting the entire set into a single stream (or by finding a way to constrain uniqueness without using a set).
How should I send a command message to the write model? via API? via a message broker like Kafka?
That's plumbing; it doesn't really matter how you do it, so long as you are sure that the command runs within its own transaction/unit of work.
So what I do today is, in my command application, before saving the CustomerCreated event, I ask the query application (using PostgreSQL) if there is a customer with that document, and if not, I allow the event to go on. But that doesn't guarantee 100%, right? Because my query can be desynchronized, so I cannot trust it 100%.
No, you cannot safely rely on the query side, which is eventually consistent, to prevent the system to step into an invalid state.
You have two options:
You permit the system to enter in a temporary, pending state and then, eventually, you will bring it into a valid permanent state; for this you could allow the command to pass, yield CustomerRegistered event and using a Saga/Process manager you verify against a uniquely-indexed-by-document-collection and issue a compensating command (not event!), i.e. UnregisterCustomer.
Instead of sending a command, you create&start a Saga/Process that preallocates the document in a uniquely-indexed-by-document-collection and if successfully then send the RegisterCustomer command. You can model the Saga as an entity.
So, in both solution you use a Saga/Process manager. In order for the system to be resilient you should make sure that RegisterCustomer command is idempotent (so you can resend it if the Saga fails/is restarted)
You've butted up against a fairly common problem. I think the other answer by VoicOfUnreason is worth reading. I just wanted to make you aware of a few more options.
A simple approach I have used in the past is to create a lookup table. Your command tries to register the key in a unique constraint table. If it can reserve the key the command can go ahead.
Depending on the nature of the data and the domain you could let this 'problem' occur and raise additional events to mark it. If it is something that's important to the business/the way the application works then you can deal with it either manually or at the time via compensating commands. if the latter then it would make sense to use a process manager.
In some (rare) cases where speed/capacity is less of an issue then you could consider old-fashioned locking and transactions. Admittedly these are much better suited to CRUD style implementations but they can be used in CQRS/ES.
I have more detail on this in my blog post: How to Handle Set Based Consistency Validation in CQRS
I hope you find it helpful.
I have recently been building an application on top of Greg Young EventStore as my peristance layer and I have been pondering how big should I allow an event to get?
For example I have an UK Address Aggregate with the following fields
UK_Address
-BuildingName
-Street
-Locality
-Town
-Postcode
Now I'm building the UI using React/Redux and was thinking should I create a single FAT addressUpdated Event contatining all the above fields?
Or should I Create a event for each of the different fields? and batch them within the client until the Save event is fired? buildingNameUpdated Event, streetUpdated Event, localityUpdated Event.
I'm not sure if the answer is as black and white ask I have asked it what I really would like to know is what conditions/constraints could you use to make the decision?
should I create a event for each of the different fields?
No. The representations of your events are part of the API -- so you want to use spellings that make sense at the level of the business, not at the level of the implementation.
Now I'm building the UI using React/Redux and was thinking should I create a single FAT updateAddress Event containing all the above fields?
You don't need to constrain the data that you send to your UI to match that which is in the persistence store. The UI is just a cached representation of a read model; there's no reason that representation needs to have the same form as what is in your event store.
Consider the React model itself -- your code makes changes to the "in memory" representation of your data, and then the library computes the new DOM and replaces it, which in turn causes the browser to update its view, which in turn causes the pixels on the screen to change.
So taking a fat event from the store, and breaking it into field level events for the UI is fine. Taking multiple events from the store and aggregating them into a single message for the UI is also fine. Taking events from the event store and transforming them into a spelling that the UI will recognize is also fine.
Do you have any comment regarding Arien answer regarding keeping fields that need to be consistent together? so regardless of when your snapshop the current state of the world it would be in a valid state?
I don't believe that this makes sense, and I'm not sure if it is possible in general.
It doesn't make sense, because "valid state" is a write model concern only; events are things that have happened, its too late to vote on whether they are valid or not. For instance, if you deploy a new model, with a new invariant, it still needs to respect the history of what happened before. So you can build a snapshot for that new model, but the snapshot may not be "valid". Too bad.
Given that, I don't think it makes sense to worry over whether each individual event in a commit leaves the snapshot in a valid state.
In particular, if a particular transaction involves multiple entities, it is very likely that the domain language will suggest an event for each entity (we "debit cash" and "credit accounts receivable"). The entities themselves, of course, are capable of changing independently of each other -- it's the aggregate that maintains the balance.
You have to bundle al the information together in one event when this data has to be consistent with each other.
So when you update one field of an address you probably get an unwanted address.
This will happen when the client has not processed all the events at a certain time due to eventual consistency.
Example:
Change address (City=1, Street=1, Housenumber=1) to (City=2, Street=2, Housenumber=2)
When you do this with 3 events and you have just processed one at the time of reading you could get the address: (City=2, Street=1, Housenumber=1).
If puzzled, give a try to a solution that is easier to implement. I guess "FAT" event will be easier: you will end up spending less time for implementing/debugging/supporting.
It is usually referred as YAGNI-KISS-Occam's Razor principles.
In theory and I find it to be a good rule of thumb is to have your commands and events reflecting the intent of the user staying true to DDD. You can find a good explanation of the pros and cons about event granularity here: https://medium.com/#hugo.oliveira.rocha/what-they-dont-tell-you-about-event-sourcing-6afc23c69e9a
Imagine I have an application that request to the user a name, a category list. When user click on save button, application will save name and category to a database.
I have a layer that get name and category from UI. This layer check if there is a name (a string with length > 0). If this is correct, it will pass name a category to another layer. Note: category is a radiobutton list where one item is always selected.
On this second layer, application will select a suitable class to save name, depending on category.
On last layer, a class will save this name on a database. On this class I will check if name is empty or not.
My question is: where is the right place to check method's input parameters? on every layer? Maybe, I'm going to use these layers on others developments.
Is my example correct? Maybe, I can left validation on database layer and raise an exception to UI layer.
In general, in terms of the larger question about validating input that is ultimately persisted, it is best to:
Convert the input parameters to a
fully encapsulated business object as
soon as possible after you receive
it.
Validate early and fail fast then to
wait until you get to a lower layer
-- waste of resources, waste of time, possibly more complex (more things to
roll back).
Validate the business logic once and make that part of
your object instantiation process. (but note that validation of view logic and persistence logic may need to be done at the other layers and is separate from business logic)
Model how your object is persisted
using an ORM (e.g., Hibernate) so
that you could work purely at the
object level in memory and leave
persistence as an implementation
detail. Focus the business logic at
the object layer.
And in terms of method validation itself, I agree with Oded -- at every layer, and it should be done immediate upon method entry. Again, this is part of the fail fast methodology. But, as I noted above, this doesn't mean you validate business logic at every method (or every layer). I'm just referring to the basic practice of validating inputs (either by assertions or explicit checks and exceptions thrown).
where is the right place to check method's input parameters? on every layer?
Yes, on every layer. This is called defense in depth.
There are different reasons to do so on each layer:
UI/Client code: keep things responsive and avoid roundtrips when data is invalid
Business layer: Ensure business rules are kept
Data layer: Ensure that valid data is passed through
I disagree with the recommendation about every layer, all the time. It sounds too dogmatic to me.
All design represents a choice between functionality and cost. Your validation choices should reflect that as well.
I think your decisions should take into account the sharing and reuse of layers.
If the database is shared by more than one application, then the database cannot depend on every application validating properly. In that case, the database must validate to protect itself.
In this day and age of SQL injection attacks, I think binding and validating prior to reaching the persistence tier is a must. The schema should do what it must to ensure referential and business integrity (e.g. unique constraints on columns and required "not null"), but other validations should be done prior to hitting the database.
If the database is wholly owned by one and only one application, and there's a service that is the only gateway into the data, then validation can be done by the service. The cost of duplicating the validation on the database layer can be waived.
Likewise between the UI and the service layer.
Double validation on client and service tiers is common because the service is shared by many clients in a service oriented architecture. Today your service might be used by a browser based UI; suddenly there's a flock of mobile apps along side it that clamor for services, too. In that case the service absolutely must validate and bind each and every request.
No manufacturer polishes every surface to mirror quality. Sometimes a rough cast surface is permissible, because the benefit of grinding and polishing is negligible and the cost is too great.
Same with software.
I don't like dogmatic statements. It's better to understand the implications of your choices. Know the rules and when it's appropriate to break them.
If you're going for very loose coupling where other applications will also make use of these same layers, then I would recommend doing input checking at every step. Since each class/method should have no knowledge of expectation of the other classes/methods which ran before them in the stack, each one should enforce its requirements individually.
If the UI requires that a value be in the text box when the button is clicked, it should validate accordingly.
If the business logic requires that a name never be null/empty, it shouldn't allow a null/empty value to be placed in that property.
If the data layer has a constraint on that field requiring that a value be present, it should check that a value is present before trying to persist the data.