Udi Dahan   Udi Dahan – The Software Simplist
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Archive for the ‘Databases’ Category

DDD & Many to Many Object Relational Mapping

Saturday, January 24th, 2009

many to many The ability to map entity relationships is broadly supported by many O/RM tools. For some reason, though, many developers run into issues when trying to map a many-to-many relationship between entities. Although much has already been written about the technological aspects of it, I thought I’d take more of an architectural / DDD perspective on it here.

Value Objects Don’t Count

While the canonical example presented is Customer -> Address, and has a good treatment here for nHibernate, it isn’t architecturally representative.

Addresses are value objects. What this means is that if we have to instance of the Address class, and they both have the same business data, they are semantically equivalent. Customers, on the other had, are not value objects – they’re entities. If we have two customers with the same business data (both of them called Bob Smith), that does not mean they are semantically equivalent – they are not the same person.

All Entities

Therefore, for our purposes here we’ll use something different. Say we have an entity called Job which is something that a company wants to hire for. It has a title, description, skill level, and a bunch of other data. Say we also have another entity called Job Board which is where the company posts jobs so that applicants can see them, like Monster.com. A job board has a name, description, web site, referral fee, and a bunch of other data.

A job can be posted to multiple job boards. And a job board can have multiple jobs posted. A regular many to many relationship. At this point, we’re not even going to complicate the association.

This is simply represented in the DB with an association table containing two columns for each of the entity tables’ ids.

In the domain model, developers can also represent this with the Job class containing a list of JobBoard instances, and the JobBoard class containing a list of jobs.

It’s intuitive. Simple. Easy to implement. And wrong.

In order to make intelligent DDD choices, we’re going to first take what may seem to be a tangential course, but I assure you that your aggregate roots depend on it.

Moving forward with our example

Let’s say the user picks a job, and then ticks off the job boards where they want the job posted, and clicks submit.

For simplicity’s sake, at this point, let’s ignore the communication with the actual job sites, assuming that if we can get the association into the DB, magic will happen later causing the job to appear on all the sites.

Our well-intentioned developer takes the job ID, and all the job board IDs, opens a transaction, gets the job object, gets the job board objects, adds all the job board objects to the job, and commits, as follows:

   1:          public void PostJobToBoards(Guid jobId, params Guid[] boardIds)
   2:          {
   3:              using (ISession s = this.SessionFactory.OpenSession())
   4:              using (ITransaction tx = s.BeginTransaction())
   5:              {
   6:                  var job = s.Get<Job>(jobId);
   7:                  var boards = new List<JobBoard>();
   9:                  foreach(Guid id in boardIds)
  10:                      boards.Add(s.Get<JobBoard>(id));
  12:                  job.PostTo(boards);
  14:                  tx.Commit();
  15:              }
  16:          }

In this code, Job is our aggregate root. You can see that is the case since Job is the entry point that the service layer code uses to interact with the domain model. Soon we’ll see why this is wrong.

** Notice that in this service layer code, our well-intentioned developer is following the rule that while you can get as many objects as you like, you are only allowed one method call on one domain object. The code called in line 12 is what you’d pretty much expect:

   1:          public void PostTo(IList<JobBoard> boards)
   2:          {
   3:              foreach(JobBoard jb in boards)
   4:              {
   5:                  this.JobBoards.Add(jb);
   6:                  jb.Jobs.Add(this);
   7:              }
   8:          }

Only that as we were committing, someone deleted one of the job boards just then. Or that someone updated the job board causing a concurrency conflict. Or anything that would cause one single association to not be created.

That would cause the whole transaction to fail and all changes to roll back.

Rightly so, thinks our well-intentioned developer.

But users don’t think like well-intentioned developers.

Partial Failures

If I were to go to the grocery store with the list my wife gave me, finding that they’re out of hazelnuts (the last item on the list), would NOT buy all the other groceries and go home empty handed, what do you think would happen?

Right. That’s how users look at us developers. Before running off and writing a bunch of code, we need to understand the business semantics of users actions, including asking about partial failures.

The list isn’t a unit of work that needs to succeed or rollback atomically. It’s actually many units of work. I mean, I wouldn’t want my wife to send me to the store 10 times to buy 10 items, so the list is really just a kind of user shortcut. Therefore, in the job board scenario, each job to job board connection is its own transaction.

This is more common than you might think.

Once you go looking for cases where the domain is forgiving of partial failures, you may start seeing more and more of them.

Aggregate Roots

In the original transaction where we tried to connect many job boards to a single job, we saw that the single job is the aggregate root. However, once we have multiple transactions, each connecting one job and one job board, the job board is just as likely an aggregate root as the job.

We can do   jobBoard.Post(job);    or     job.PostTo(jobBoard);

But we need just a bit more analysis to come to the right decision.

While we could just leave the bi-directional/circular dependency between them, it would be preferable if we could make it uni-directional instead. To do that, we need to understand their relationship:

If there was no such thing as “job”, would there be meaning to “job board” ? Probably not.

If there was no such thing as “job board”, would there be meaning to “job” ? Probably. Yes. Our company can handle the hiring process of a job regardless of whether the candidate came in through Monster.com or not.

From this we understand that the uni-directional relationship can be modelled as one-to-many from job board to job. The Job class would no longer have a collection of Job Board objects. In fact, it could even be in an assembly separate from Job Board and not reference Job Board in any way. Job Board, on the other hand, would still have a collection of Job objects.

Going back to the code above we see that the right choice is   jobBoard.Post(job);   

Job Board is the aggregate root in this case. Also, the many-to-many mapping has now dissolved, leaving behind a single one-to-many mapping.

Let that sink in a second.

But Wait…

While the GUI showing which jobs are posted on a given job board are well served by the above decision (simply traversing the object graph from Job Board to its collection of Jobs), that’s not the whole story. Another GUI needs to show administrative users which Job Boards a given Job has been posted to. Since we no longer have the domain-level connection, we can’t traverse myJob.JobBoards.

Our only option is to perform a query. That’s not so bad, but not as pretty as object traversal.

The real benefit is in chopping apart the Gordian M-to-N mapping knot and getting a cleaner, more well factored domain model.

That gives us much greater leverage for bigger, system-level decomposition.

We’re now all set to move up to a pub/sub solution between these aggregate roots, effectively upgrading them to Bounded Contexts. From there, we can move to full-blown internet-scale caching with REST for extra scalability on showing a job board with all its jobs.

In Closing

We often look at many-to-many relationships just like any other relationship. And from a purely technical perspective, we’re not wrong. However, the business reality around these relationships is often very different – forgiving of partial failures, to the point of actually requiring them.

Since the business folks who provide us with requirements rarely think of failure scenarios, they don’t specify that “between these two entities here, I don’t want transactional atomicity” (rolling our technical eyes – the idiots [sarcasm, just to make sure you don’t misread me]).

Yet, if we were to spell out what the system will do under failure conditions when transactionally atomic, those same business folks will be rolling our eyes back to us.

What I’ve found surprises some DDD practitioners is how critical this issue really is to arriving at the correct aggregate roots and bounded contexts.

It’s also simple, and practical, so you won’t be offending the YAGNI police.

Related Content

From CRUD to Domain-Driven Fluency

[Podcast] Domain Models, SOA, and The Single Version of the Truth

Sagas Solve Stupid Transaction Timeouts

Monday, June 23rd, 2008

It turns out that there was a subtle, yet dangerous problem in the use of System.Transactions – a transaction could timeout, rollback, and the connection bound to that transaction could still change data in the database. image

Think about that a second.

Scary, isn’t it?

At TechEd Israel I had a discussion with Manu on this very issue, just under a different hat:

What’s the difference between a short-running workflow and a long-running one?

Manu suggested that we look at the actual time that things ran to differentiate between them. I asserted that if any external communication was involved in some part of state-management logic, that logic should automatically be treated as long-running.

Manu’s reasoning was that the complexity involved in writing long-running workflows was not justified for things that ran quickly, even if there was communication involved. Many developers don’t think twice about synchronously calling some web services in the middle of their database transaction logic. In the many Microsoft presentations I’ve been at on WF, not once has it been mentioned that state machines should be used when external communication is involved.

The problem that I have with this guidance is how do you know how quickly a remote call will return?

Do you just run it all locally on your machine, measure, and if it doesn’t take more than a second or so, then you’re OK?

The fact of the matter is that we can never know what the response time of a remote call will be. Maybe the remote machine is down. Maybe the remote process is down. Maybe someone changed the firewall settings and now we’re doing 10KB/s instead of 10MB/s. Maybe the local service is down and we’re communicating with the backup on the other side of the Pacific Ocean.

But the thing is, Manu’s right.

Writing long-running workflows (with WF) is more complex than is justified. My guess is that since WF wasn’t specifically designed for long-running workflows only, that this complexity crept in.nservicebus_logo_small

Sagas in nServiceBus were specifically designed for long-running workflows only.

Maybe that’s what kept them simple.

Since all external communication is done via one-way, non-blocking messaging only, each step of a saga runs as quick as if no communication were done at all. This keeps the time the transaction in charge of handling a message is open as short as possible. That, in turn, leads to the database being able to support more concurrent users.

In short, sagas are both more scalable and more robust.

No need to worry about garbaging-up your database.

From CRUD to Domain-Driven Fluency

Friday, February 15th, 2008

I got a question about how to stay away from CRUD based service interfaces when the logic itself is like that, and I’ve found that this shift in thinking really needs more examples, so I’ve decided to put this out there:

For instance, in an HR system, the process of interviewing candidates – wouldn’t you just insert, update, and delete these Appointment objects?

If I were to put on my domain-driven hat, I would describe those requirements differently – interview appointments have a lifecycle: proposed, accepted, cancelled, etc. It seems that only a user of the role HR Interviewer should be able to make appointments for themselves, so the service layer code would probably look something like this:

using (ISession session = SessionFactory.OpenSession())
using (ITransaction tx = session.BeginTransaction())
    ICandidateInterviewer interviewer = session.Get<ICandidateInterviewer>(message.InterviewerId);
    ICandidate candidate = session.Get<ICandidate>(message.CandidateId);


The “ScheduleInterviewWith” method accepts an ICandidate and returns an IAppointment. IAppointment has a method “At” which accepts a DateTime parameter and returns void – just changes the data of the appointment. The state of the appointment at creation time would probably be proposed. The appointment object would probably be added to the list of appointments for that interviewer – that’s what will cause it to be persisted automatically.

Later, when the candidate accepts the meeting, we could have the following method on ICandidate – void Accept(IAppointment); that would obviously check that the candidate is the right person for that interview, the appointment’s current state (not cancelled), etc – finally updating its state. What part of this looks like create, update, delete? If that’s what your service layer to domain interaction looks like, do you now know what your messages will be looking like?CRUD seems to be what most of us are familiar with. Moving to domain-driven thinking takes time and practice, but is well worth it. Contrast this with a more traditional O/R mapping solution:

using (ISession session = SessionFactory.OpenSession())
using (ITransaction tx = session.BeginTransaction())
    ICandidateInterviewer interviewer = session.Get<ICandidateInterviewer>(message.InterviewerId);
    ICandidate candidate = session.Get<ICandidate>(message.CandidateId); 

    Appointment a = new Appointment(); 

    a.Interviewer = interviewer; 

    a.Candidate = candidate;

    a.Time = message.RequestedTime; 



As you can see, we’ve got simpler, more expressive, and more testable code when employing the domain model pattern, than using “just” O/R mapping. I’m not saying that the domain model pattern doesn’t need O/R mapping in the background for it to work. But that’s just it – the persistence gunk needs to be in the background and the business logic needs to be encapsulated.

So, while I’ll agree with Dave that the Domain Model is more lifestyle than pattern, I would argue against these conclusions:

If this post had a point, it’s only to share the idea that Domain Model is a big, big thing. It’s probably overkill in a lot of cases where you have simple applications that have very simple purposes.

As you just saw in the example above, there is no “overkill” to be seen. The domain model in the example wasn’t “a big, big thing”.

The domain model. Use it.

Why not have a better lifestyle?   ;-)

[Presentation files] Asynchronous Systems Architecture for the Web

Monday, January 7th, 2008

We had a great turnout yesterday at the Web Developer Community (not user group <grin/>). I passed on the presentation files and code samples to Noam but figured that the rest of my readers might enjoy them as well.

The (pdf) presentation is here: Asynchronous Systems Architecture for the Web

The code sample is here: Asynchronous User Management Code Sample

In the sample, you can see the use of sagas to manage the user registration process; store user email and hashed password, send a confirmation “email”, when the user clicks the “link”, the web server will take the saga id found in the url, and send a message with that id. This will cause the saga to complete and the user to be written to the “database”.

Since I didn’t have an email component on my laptop, and I’m guessing you don’t either, the saga just writes the url to the console. Copy and paste it from there into the browser, and you’re good to go.

A Word on TimeoutExceptions

One other thing that I want to call to your attention. When stepping-through the code in the debugger, you’re liable to spend more time than the Transaction Coordinator likes, which will cause it to rollback and try the message again. This is supposed to happen and occurs by design.

When you’re actually working with a database in a high performance environment, there will be cases where one transaction locks a page of a table and may cause other transactions to either timeout or be chosen as victims and just tossed. The behavior that best handles this scenario is just to retry the transaction.

However, you don’t have to write ugly code that checks for the specific error codes of each specific database for your code to work properly. The infrastructure will automatically do that for you – just let the exception happen. No need to write any try-catch code.

The sample is built on the newly released version of nServiceBus (1.6.1) but already contains all the binaries so you don’t have to set anything up yourself.

What’s coming for nServiceBus

We’re working towards a 2.0 release in the June-July timeframe which, beyond having the necessary documentation, web site, samples and everything any self-respecting open-source project has, is going to have some amazing grid-style features that will make all the message-priority & dynamic-routing stuff look “so last year”. Stay tuned.

Handling messages out of order

Saturday, December 15th, 2007

I wanted to follow up on my recent post, “In order messaging a myth?” by showing the exact code that solves the issue. I have a podcast waiting to come online that deals with the specifics, so keep your eye out for that too.

The important thing to note is that if we just automatically return the message to the queue, we may get “stuck” with that message if the first PolicyCreatedMessage never arrived. This opens us up to a Denial-of-Service attack by quite simply flooding us with a bunch of messages that never get cleaned up.

Anyway, the general idea is to first try the regular happy path, and only if we see that prerequisite data isn’t available, do we see if another thread may be working on that data. This is done by decreasing the isolation level of our transaction from the regular ReadCommitted to ReadUncommitted. This will enable our thread to see if some other thread inserted the policy in to the Policies table but hasn’t committed its transaction yet.

    public class PolicyApprovedMessageHandler : BaseDBMessageHandler<PolicyApprovedMessage>


        public override void Handle(PolicyApprovedMessage message)


            bool policyExists = true;


            using (ISession s = OpenSession())

            using (ITransaction tx = s.BeginTransaction(IsolationLevel.ReadCommitted))


                Policy p = s.Get<Policy>(message.PolicyId);


                if (p != null)






                    policyExists = false;



            if (!policyExists) // check to make sure

                using (ISession s = OpenSession())

                using (ITransaction tx = s.BeginTransaction(IsolationLevel.ReadUncommitted))


                    Policy p = s.Get<Policy>(message.PolicyId);


                    if (p != null) // another thread hasn’t committed its tx yet, so try message again later







The next step will be how we take this code and make it generic, so that we don’t have write the same code over and over again for the different kinds of message handlers we have.

But that will have to wait until the next installment 🙂

TechEd Persistent Domain Models Video now Online

Thursday, November 15th, 2007

You can now find my talk from TechEd – ARC401 Designing High Performance, Persistent Domain Models – online. You have to log in to the TechEd site first, otherwise the DRM will block the video.


Watch in Media Player

Asynchronous, High-Performance Login for Web Farms

Saturday, November 10th, 2007

Often during my consulting engagements I run into people who say, "some things just can’t be made asynchronous" even after they agree about the inherent scalability that asynchronous communications pattern bring. One often-cited example is user authentication – taking a username and password combo and authenticating it against some back-end store. For the purpose of this post, I’m going to assume a database. Also, I’m not going to be showing more advanced features like ETags to further improve the solution.

The Setup

Just so that the example is in itself secure, we’ll assume that the password is one-way hashed before being stored. Also, given a reasonable network infrastructure our web servers will be isolated in the DMZ and will have to access some application server which, in turn, will communicate with the DB. There’s also a good chance for something like round-robin load-balancing between web servers, especially for things like user login.

Before diving into the meat of it, I wanted to preface with a few words. One of the commonalities I’ve found when people dismiss asynchrony is that they don’t consider a real deployment environment, or scaling up a solution to multiple servers, farms, or datacenters.

The Synchronous Solution

In the synchronous solution, each one of our web servers will be contacting the app server for each user login request. In other words, the load on the app server and, consequently, on the database server will be proportional to the number of logins. One property of this load is its data locality, or rather, the lack of it. Given that user U logged in, the DB won’t necessarily gain any performance benefits by loading all username/password data into memory for the same page as user U. Another property is that this data is very non-volatile – it doesn’t change that often.

I won’t go to far into the synchronous solution since its been analysed numerous times before. The bottom line is that the database is the bottleneck. You could use sharding solutions. Many of the large sites have numerous read-only databases for this kind of data, with one master for updates – replicating out to the read-only replicas. That’s great if you’re using a nice cheap database like mySql (of LAMP), not so nice if you’re running Oracle or MS Sql Server.

Regardless of what you’re doing in your data tier, you’re there. Wouldn’t it be nice to close the loop in the web servers? Even if you are using Apache, that’s going to be less iron, electricity, and cooling all around. That’s what the asynchronous solution is all about – capitalizing on the low cost of memory to save on other things.

The Asynchronous Solution

In the asynchronous solution, we cache username/hashed-password pairs in memory on our web servers, and authenticate against that. Let’s analyse how much memory that takes:

Usernames are usually 12 characters or less, but let’s take an average of 32 to be sure. Using Unicode we get to 64 bytes for the username. Hashed passwords can run between 256 and 512 bits depending on the algorithm, divide by 8 and you have 64 bytes. That’s about 128 bytes altogether. So we can safely cache 8 million of these with 1GB of memory per web server. If you’ve got a million users, first of all, good for you 🙂 Second, that’s just 128 MB of memory – relatively nothing even for a cheap 2GB web server.

Also, consider the fact that when registering a new user we can check if such a username is already taken at the web server level. That doesn’t mean it won’t be checked again in the DB to account for concurrency issues, but that the load on the DB is further reduced. Other things to notice include no read-only replicas and no replication. Simple. Our web servers are the "replicas".

The Authentication Service

What makes it all work is the "Authentication Service" on the app server. This was always there in the synchronous solution. It is what used to field all the login requests from the web servers, and, of course, allowed them to register new users and all the regular stuff. The difference is that now it publishes a message when a new user is registered (or rather, is validated – all a part of the internal long-running workflow). It also allows subscribers to receive the list of all username/hashed-password pairs. It’s also quite likely that it would keep the same data in memory too.

The same message can be used to publish both single updates, and returning the full list when using NServiceBus. Let’s define the message:

public class UsernameInUseMessage : IMessage
    private string username;
    public string Username
        get { return username; }
        set { username = value; }

    private byte[] hashedPassword;
    public byte[] HashedPassword
        get { return hashedPassword; }
        set { hashedPassword = value; }

And the message that the web server sends when it wants the full list:

public class GetAllUsernamesMessage : IMessage


And the code that the web server runs on startup looks like this (assuming constructor injection):


public class UserAuthenticationServiceAgent

    public UserAuthenticationServiceAgent(IBus bus) 
        this.bus = bus;
        bus.Send(new GetAllUsernamesMessages());


And the code that runs in the Authentication Service when the GetAllUsernamesMessage is received:


public class GetAllUsernamesMessageHandler : BaseMessageHandler<GetAllUsernamesMessage>
    public override void Handle(GetAllUsernamesMessage message)


And the class on the web server that handles a UsernameInUseMessage when it arrives:


public class UsernameInUseMessageHandler : BaseMessageHandler<UsernameInUseMessage>
    public override void Handle(UsernameInUseMessage message)
        WebCache.SaveOrUpdate(message.Username, message.HashedPassword); 

When the app server sends the full list, multiple objects of the type UsernameInUseMessage are sent in one physical message to that web server. However, the bus object that runs on the web server dispatches each of these logical messages one at a time to the message handler above.

So, when it comes time to actually authenticate a user, this the web page (or controller, if you’re doing MVC) would call:

public class UserAuthenticationServiceAgent
    public bool Authenticate(string username, string password)
        byte[] existingHashedPassword = WebCache[username];
        if (existingHashedPassword != null)
            return existingHashedPassword == this.Hash(password);

        return false;


When registering a new user, the web server would of course first check its cache, and then send a RegisterUserMessage that contained the username and the hashed password.

public class RegisterUserMessage : IMessage
    private string username;
    public string Username
        get { return username; }
        set { username = value; }

    private string email;
    public string Email
        get { return email; }
        set { email = value; }

    private byte[] hashedPassword;
    public byte[] HashedPassword
        get { return hashedPassword; }
        set { hashedPassword = value; }


When the RegisterUserMessage arrives at the app server, a new long-running workflow is kicked off to handle the process:

public class RegisterUserWorkflow :
    BaseWorkflow<RegisterUserMessage>, IMessageHandler<UserValidatedMessage>
    public void Handle(RegisterUserMessage message)
        //send validation request to message.Email containing this.Id (a guid)
        // as a part of the URL

    /// <summary>
    /// When a user clicks the validation link in the email, the web server
    /// sends this message (containing the workflow Id)
    /// </summary>
    /// <param name="message"></param>
    public void Handle(UserValidatedMessage message)
        // write user to the DB

        this.Bus.Publish(new UsernameInUseMessage(
            message.Username, message.HashedPassword));

That UsernameInUseMessage would eventually arrive at all the web servers subscribed.

Performance/Security Trade-Offs

When looking deeper into this workflow we realize that it could be implemented as two separate message handlers, and have the email address take the place of the workflow Id. The problem with this alternate, better performing solution has to do with security. By removing the dependence on the workflow Id, we’ve in essence stated that we’re willing to receive a UserValidatedMessage without having previously received the RegisterUserMessage.

Since the processing of the UserValidatedMessage is relatively expensive – writing to the DB and publishing messages to all web servers, a malicious user could perform a denial of service (DOS) attack without that many messages, thus flying under the radar of many detection systems. Spoofing a guid that would result in a valid workflow instance is much more difficult. Also, since workflow instances would probably be stored in some in-memory, replicated data grid the relative cost of a lookup would be quite small – small enough to avoid a DOS until a detection system picked it up.

Improved Bandwidth & Latency

The bottom line is that you’re getting much more out of your web tier this way, rather than hammering your data tier and having to scale it out much sooner. Also, notice that there is much less network traffic this way. Not such a big deal for usernames and passwords, but other scenarios built in the same way may need more data. Of course, the time it takes us to log a user in is much shorter as well since we don’t have to cross back and forth from the web server (in the DMZ) to the app server, to the db server.

The important thing to remember in this solution is doing pub/sub. NServiceBus merely provides a simple API for designing the system around pub/sub. And publishing is where you get the serious scalability. As you get more users, you’ll obviously need to get more web servers. The thing is that you probably won’t need more database servers just to handle logins. In this case, you also get lower latency per request since all work needed to be done can be done locally on the server that received the request.

ETags make it even better

For the more advanced crowd, I’ll wrap it up with the ETags. Since web servers do go down, and the cache will be cleared, what we can do is to write that cache to disk (probably in a background thread), and "tag" it with something that the server gave us along with the last UsernameInUseMessage we received. That way, when the web server comes back up, it can send that ETag along with its GetAllUsernamesMessage so that the app server will only send the changes that occurred since. This drives down network usage even more at the insignificant cost of some disk space on the web servers.

And in closing…

Even if you don’t have anything more than a single physical server today, and it acts as your web server and database server, this solution won’t slow things down. If anything, it’ll speed it up. Regardless, you’re much better prepared to scale out than before – no need to rip and replace your entire architecture just as you get 8 million Facebook users banging down your front door.

So, go check out NServiceBus and get the most out of your iron.

Fetching Strategy NHibernate Implementation Available

Sunday, September 16th, 2007

A couple of months ago I put out a post discussing one way to implement custom fetching strategies. Anyway, I finally got around to putting my money where my mouth was…

So, I’ve implemented the pattern in NHibernate, adding the following method to ISession:

T Create<T>();

As well as adding the following interface to the NHibernate package:

public interface IFetchingStrategy<T>
ICriteria AddFetchJoinTo(ICriteria criteria);

All this enables you to have a stronger separation between your service layer classes and your domain model class, as well as for you to express each service-level use case as a domain concept – an interface.

Once you have such an interface, you can create a fetching strategy for that use case and define exactly how deep of an object graph you want to load so that you only hit the DB once for that use case.

The nice thing is that its all configured with Spring. In other words, if you the entry for your fetching strategy class exists, you get the improved performance, if it doesn’t, you don’t. All without touching your service layer classes.

Just as an example, when I’m in the use case modeled by “ICustomer”, I want to get all the customer’s orders, and their orderlines. This would be done by having a class like this:

public class CustomerFetchingStrategy : IFetchingStrategy<ICustomer>
public ICriteria AddFetchJoinTo(ICriteria criteria)
criteria.SetFetchMode(“orders”, FetchMode.Eager).
SetFetchMode(“orders.orderLines”, FetchMode.Eager);

return criteria;

And the configuration would look like this (as a part of the regular spring template):

<object id=”CustomerFetchingStrategy” type=”Domain.Persistence.CustomerFetchingStrategy, Domain.Persistence” />

If you want to take a look at the full solution, you can find it here. For some reason, the combined file was too big for the upload on my blog so it’s split into two. Unzip both packages into the same directory. You’ll find a file called “db_scripts.sql” which contains the schema for the DB. Don’t forget to update your connection string in the “hibernate.cfg.xml”. If you’re looking for the changes I made to the NHibernate source, you can find it in the “Updated NHibernate Files” directory. The only real change is to the “SessionImpl.cs” file.

Relevant NHibernate and Spring binaries.

Source code of example.

BTW, there is some intelligent thread-safe caching going on in SessionImpl now so that you get a much smaller performance hit (in terms of code that uses reflection) on subsequent usages of the same interfaces.

Let me know what you think.

Performant and Explicit Domain Models

Monday, June 4th, 2007

Some Technical Difficulties

Ayende and I had an email conversation that started with me asking what would happen if I added an Order to a Customer’s “Orders” collection, when that collection was lazy loaded. My question was whether the addition of an element would result in NHibernate hitting the database to fill that collection. His answer was a simple “yes”. In the case where a customer can have many (millions) of Orders, that’s just not a feasible solution. The technical solution was simple – just define the Orders collection on the Customer as “inverse=true”, and then to save a new Order, just write:

session.Save( new Order(myCustomer) );

Although it works, it’s not “DDD compliant” 🙂

In Ayende’s post Architecting for Performance he quoted a part of our email conversation. The conclusion I reached was that in order to design performant domain models, you need to know the kinds of data volumes you’re dealing with. It affects both internals and the API of the model – when can you assume cascade, and when not. It’s important to make these kinds of things explicit in the Domain Model’s API.

How do you make “transparent persistence” explicit?

The problem occurs around “transparent persistence”. If we were to assume that the Customer object added the Order object to its Orders collection, then we wouldn’t have to explicitly save orders it creates, so we would write service layer code like this:

using (IDBScope scope = this.DbServices.GetScope(TransactionOption.On))
IOrderCreatingCustomer c = this.DbServices.Get<IOrderCreatingCustomer>(msg.CustomerId);


On the other hand, if we designed our Domain Model around the million orders constraint, we would need to explicitly save the order, so we would write service layer code like this:

using (IDBScope scope = this.DbServices.GetScope(TransactionOption.On))
IOrderCreatingCustomer c = this.DbServices.Get<IOrderCreatingCustomer>(msg.CustomerId);
IOrder o = c.CreateOrder(message.OrderAmount);


But the question remains, how do we communicate these guidelines to service layer developers from the Domain Model? There are a number of ways, but it’s important to decide on one and use it consistently. Performance and correctness require it.

Solution 1: Explicitness via Return Type

The first way is a little subtle, but you can do it with the return type of the “CreateOrder” method call. In the case where the Domain Model wishes to communicate that it handles transparent persistence by itself, have the method return “void”. Where the Domain Model wishes to communicate that it will not handle transparent persistence, have the method return the Order object created.

Another way to communicate the fact that an Order has been created that needs to be saved is with events. There are two sub-ways to do so:

Solution 2: Explicitness via Events on Domain Objects

The first is to just define the event on the customer object and have the service layer subscribe to it. It’s pretty clear that when the service layer receives a “OrderCreatedThatRequiresSaving” event, it should save the order passed in the event arguments.

The second realizes that the call to the customer object may come from some other domain object and that the service layer doesn’t necessarily know what can happen as the result of calling some method on the aggregate root. The change of state as the result of that method call may permeate the entire object graph. If each object in the graph raises its own events, its calling object will have to propagate that event to its parent – resulting in defining the same events in multiple places, and each object being aware of all things possible with its great-grandchild objects. That is clearly bad.

What [ThreadStatic] is for

So, the solution is to use thread-static events.

[Sidebar] Thread-static events are just static events defined on a static class, where each event has the ThreadStaticAttribute applied to it. This attribute is important for server-side scenarios where multiple threads will be running through the Domain Model at the same time. The easiest thread-safe way to use static data is to apply the ThreadStaticAttribute.

Solution 3: Explicitness via Static Events

Each object raises the appropriate static event according to its logic. In our example, Customer would call:


And the service layer would write:

DomainModelEvents.OrderCreatedThatRequiresSaving +=
delegate(object sender, OrderEventArgs e) { this.DbServices.Save(e.Order); };

The advantage of this solution is that it requires minimal knowledge of the Domain Model for the Service Layer to correctly work with it. It also communicates that anything that doesn’t raise an event will be persisted transparently behind the appropriate root object.

Statics and Testability

I know that many of you are wondering if I am really advocating the use of statics. The problem with most static classes is that they hurt testability because they are difficult to mock out. Often statics are used as Facades to hide some technological implementation detail. In this case, the static class is an inherent part of the Domain Model and does not serve as a Facade for anything.

When it comes to testing the Domain Model, we don’t have to mock anything out since the Domain Model is independent of all other concerns. This leaves us with unit testing at the single Domain Class level, which is pretty useless unless we’re TDD-ing the design of the Domain Model, in which case we’ll still be fiddling around with a bunch of classes at a time. Domain Models are best tested using State-Based Testing; get the objects into a given state, call a method on one of them, assert the resulting state. The static events don’t impede that kind of testing at all.

What if we used Injection instead of Statics?

Also, you’ll find that each Service Layer class will need to subscribe to all the Domain Model’s events, something that is easily handled by a base class. I will state that I have tried doing this without a static class, and injecting that singleton object into the Service Layer classes, and in that setter having them subscribe to its events. This was also pulled into a base class. The main difference was that the Dependency Injection solution required injecting that object into Domain Objects as well. Personally, I’m against injection for domain objects. So all in all, the static solution comes with less overhead than that based on injection.


In summary, beyond the “technical basics” of being aware of your data volumes and designing your Domain Model to handle each use case performantly, I’ve found these techniques useful for designing its API as well as communicating my intent around persistence transparency. So give it a try. I’d be grateful to hear your thoughts on the matter as well as what else you’ve found that works.

Related posts:

NHibernate will rule, because Ayende already does

Sunday, May 20th, 2007

First I find out that NHibernate does support “Persistence by Reachability”, even though the docs say it doesn’t. Next, Ayende makes it support multiple queries in a single DB roundtrip, something I’ve been asking all the other O/R mappers out there to do. To top it off, he’s got his sights set on solving the issues I raised in my talk on Complex Business Logic with DDD and O/R Mapping at DevTeach. That’s right, he’s going to give me my decorators and state machines.

I love you, Oren.

I know that the ADO.NET Entity Framework guys are open to this as well, but I’m pretty sure that the “Entity Model” thinking will hold them back. You just can’t divorce data and behavior – not when employing state machines or decorators.

I’m sold.


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“So far, I heard about Service Oriented architecture all over. Everyone mentions it – the big buzz word. But, when I actually asked someone for what does it really mean, no one managed to give me a complete satisfied answer. Finally in his excellent course “Advanced Distributed Systems”, I got the answers I was looking for. Udi went over the different motivations (principles) of Services Oriented, explained them well one by one, and showed how each one could be technically addressed using NService bus. In his course, Udi also explain the way of thinking when coming to design a Service Oriented system. What are the questions you need to ask yourself in order to shape your system, place the logic in the right places for best Service Oriented system.

I would recommend this course for any architect or developer who deals with distributed system, but not only. In my work we do not have a real distributed system, but one PC which host both the UI application and the different services inside, all communicating via WCF. I found that many of the architecture principles and motivations of SOA apply for our system as well. Enough that you have SW partitioned into components and most of the principles becomes relevant to you as well. Bottom line – an excellent course recommended to any SW Architect, or any developer dealing with distributed system.”

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