Last April I was in Bergen Norway for some consulting and training and I also gave my first NServiceBus presentation to a user group. I don’t particularly like giving NServiceBus-specific presentations, preferring to talk about the patterns and concepts of service-based architectures and service buses – NServiceBus is just an implementation. Ultimately, that’s what happened in the presentation – in the first half (or so) I talked about the theory, and in the second I demonstrated that theory with NServiceBus.

Currently, the video is being graciously hosted by Jon Torresdal on his blog, so let’s hope that the bandwidth holds up.

Get it here.

My latest article has been published in issue 21 of the Microsoft Architecture Journal:

EDA: SOA Through The Looking Glass

UPDATE: Unfortunately, Microsoft has removed a bunch of their older stuff, so I’m reposting the content here:

Download as PDF

Introduction

While event-driven architecture (EDA) is a broadly known topic, both giving up ACID integrity guarantees and introducing eventual consistency make many architects uncomfortable. Yet it is exactly these properties that can direct architectural efforts toward identifying coarsely grained business-service boundaries—services that will result in true IT-business alignment.

Business events create natural temporal boundaries across which there is no business expectation of immediate consistency or confirmation. When they are mapped to technical solutions, the loosely coupled business domains on either side of business events simply result in autonomous, loosely coupled services whose contracts explicitly reflect the inherent publish/subscribe nature of the business.

This article will describe how all of these concepts fit together, as well as how they solve thorny issues such as high availability and fault tolerance.

Commands and Events

To understand the difference in nature between “classic” service- oriented architecture (SOA) and event-driven architecture (EDA), we must examine their building blocks: the command in SOA, and the event in EDA.

In the commonly used request/response communication pattern of service consumer to service provider in SOA, the request contains the action that the consumer wants to have performed (the command), and the response contains either the outcome of the action or some reaction to the expressed request, such as “action performed” and “not authorized.”

Commands are often named in imperative, present-tense form—for example, “update customer” and “cancel order.”

In EDA, the connection between event emitters and event consumers is reversed from the previously described SOA pattern. Consumers do not initiate communication in EDA; instead, they receive events that are produced by emitters. The communication is also inherently unidirectional; emitters do not depend on any response from consumers to continue performing their work.

Events are often named in passive, past-tense form—for example, “customer updated” and “order cancelled”—and can represent state changes in the domain of the emitter.

Events can be thought of as mirror images of the commands in a system. However, there might be cases in which the trigger for an event is not an explicit command, but something like a timeout.

Business Processes with Commands and Events

The difference between commands and events becomes even more pronounced as we look at each one as the building block in various business processes.

When we consider commands such as “create customer” and “create order,” we can easily understand how these commands can be combined to create more involved scenarios, such as: “When creating an order, if a customer is not provided, create a new customer.” This can be visualized as services that operate at different layers, as shown in Figure 1.

Figure 1. Commands and service orchestration

One can also understand the justification for having activity services perform all of their work transactionally, thus requiring one service to flow its transactional context into other lower-level services. This is especially important for commands that deal with the updating of data.

When working with commands, in each step of the business process, a higher-level service orchestrates the work of lower-level services.

When we try to translate this kind of orchestration behavior into events, we must consider the fact that events behave as mirror images of commands and represent our rules by using the past tense.

Instead of: “When creating an order, if a customer is not provided, create a new customer.”

We have: “When an order has been created, if a customer was not provided, create a new customer.”

It is clear that these rules are not equivalent. The first rule implies that an order should not be created unless a customer—whether provided or new—is associated with it. The second rule implies that an order can be created even if a customer has not been provided—stipulating the creation as a separate and additional activity.

To make use of EDA, it is becoming clear that we must think about our rules and processes in an event-driven way, as well as how that affects the way in which we structure and store our data.

Event-Driven Business Analysis and Database Design

When we analyze the “When an order has been created, if a customer was not provided, create a new customer” rule, we can see that a clear temporal boundary splits it up into two parts. In a system that has this rule, what we will see is that at a given point in time, an order might exist that does not have a corresponding customer. The rule also states the action that should be taken in such a scenario: the creation of a new customer. There might also be a nonfunctional requirement that states the maximum time that should be allowed for the action to be completed.

From a technical/database perspective, it might appear that we have allowed our data to get into an inconsistent state; however, that is only if we had modeled our database so that the Orders table had a non-nullable column that contained CustomerId—a foreign key to the Customers table. While such an entity-relationship design would be considered perfectly acceptable, we should consider how appropriate it really is, given the requirements of business consistency.

The rule itself indicates the business perspective of consistency; an order that has no connection to a customer is valid, for a certain period of time. Eventually, the business would like a customer to be affiliated with that order; however, the time frame around that can be strict (to a level of seconds) or quite lax (to a level of hours or days). It is also understandable that the business might want to change these time frames in cases in which it might provide a strategic advantage. An entity-relationship design that would reflect these realities would likely have a separate mapping table that connected Orders to Customers—leaving the Orders entity free of any constraint that relates to the Customers entity.

That is the important thing to understand about eventual consistency: It starts by identifying the business elements that do not have to be 100-percent, up-to-the-millisecond consistent, and then reflecting those relaxed constraints in the technical design.

In this case, we could even go so far as to have each of these transactions occur in its own database, as shown in Figure 2.

Figure 2. Event-driven data flows

Benefits of Event-Driven Architecture

Given that EDA requires a rethinking of the core rules and processes of our business, the benefits of the approach must be quite substantial to make the effort worthwhile— and, indeed, they are. By looking at Figure 2, we can see very loose coupling between the two sides of the temporal boundary. Other than the structure of the event that passes from left to right, nothing is shared. Not only that, but after the event is published, the publisher no longer even needs to be online for the subscriber to process the event, so long as we use a durable transport (such as a queue).

These benefits become even more pronounced when we consider integration with other systems. Consider the case in which we want to integrate with a CRM, whether it is onsite or hosted in the cloud. In the EDA approach, if the CRM is unavailable (for whatever reason), the order will still be accepted. Contrasting this with the classic command- oriented service-composition approach, we would see there that the unavailability of the CRM would cause the entire transaction to time out and roll back. The same is true during integration of mainframes and other constrained resources: Even when they are online, they can process only N concurrent transactions (see Figure 3). Because the event publisher does not need to wait for confirmation from any subscriber, any transactions beyond those that are currently being processed by the mainframe wait patiently in the queue, without any adverse impact on the performance of order processing.

Figure 3. Load-leveling effect of queues between publishers and subscribers

If all systems had to wait for confirmation from one another—as is common in the command-oriented approach—to bring one system to a level of 5 nines of availability, all of the systems that it calls would need to have the same level of availability (as would the systems that they call, recursively). While the investment in infrastructure might have business justification for one system (for example, order processing), it can be ruinous to have to multiply that level of investment across the board for nonstrategic systems (for example, shipping and billing).

In companies that are undergoing mergers or acquisitions, the ability to add a new subscriber quickly to any number of events from multiple publishers without having to change any code in those publishers is a big win (see Figure 4). This helps maintain stability of the core environment, while iteratively rolling out bridges between the systems of the two companies. When we look practically at bringing the new subscriber online, we can take the recording of all published events from the audit log and play them to the new subscriber, or perform the regular ETL style of data migration from one subscriber to another.

Figure 4. Adding new subscriber to existing publisher

IT-Business Alignment, SOA, and EDA

One of the more profound benefits that SOA was supposed to bring was an improved alignment between IT and business. While the industry does not appear to have settled on how this exactly is supposed to occur, there is broad agreement that IT is currently not aligned with business. Often, this is described under the title of application “silos.”

To understand the core problem, let us try to visualize this lack of alignment, as shown in Figure 5.

Figure 5. Lack of IT/Business Alignment

What we see in this lack of alignment is that IT boundaries are different from business boundaries, so that it is understandable that the focus of SOA on explicit boundaries (from the four tenets of service orientation) would lead many to believe that it is the solution.

Yet the problem that we see here is while there are explicit technical boundaries between App 1 and App 2, the mapping to business boundaries is wrong.

If SOA is to have any chance of improving IT-business alignment, the connection between the two needs to look more like the one that is shown in Figure 6.

Figure 6. Services aligned with business boundaries

One could describe such a connection as a service “owning” or being responsible for a single business domain, so that anything outside the service could not perform any actions that relate to that domain. Also, any and all data that relates to that domain also would be accessible only within the service. The EDA model that we saw earlier enabled exactly that kind of strict separation and ownership— all the while, providing mechanisms for interaction and collaboration.

We should consider this strong connection when we look at rules such as: “When an order has been created, if a customer was not provided, create a new customer.” The creation of the order as an object or a row in a database has no significance in the business domain. From a business perspective, it could be the acceptance or the authorization of an order that matters.

What SOA brings to EDA in terms of IT-business alignment is the necessity of events to represent meaningful business occurrences.

For example, instead of thinking of an entity that is being deleted as an event, you should look for the business scenario around it— for example, a product that is being discontinued, a discount that is being revoked, or a shipment that is being canceled. Consider introducing a meaningful business status to your entities, instead of the technically common “deleted” column. While the business domain of sales will probably not be very interested in discontinued products and might treat them as deleted, the support domain might need to continue troubleshooting the problems that clients have with those products—for a while, at least. Modern-day collaborative business- analysis methodologies such as value networks can help identify these domains and the event flows between them.

What an EDA/SOA Service Looks Like

In the context of combined EDA and SOA, the word “service” is equivalent to a logical “thing” that can have a database schema, Web Services, and even user-interface (UI) code inside it. This is a very different perspective from the classic approach that considers services as just another layer of the architecture. In this context, services cut across multiple layers, as shown in Figure 7.

Figure 7. Services logically connecting code from different layers

In this model, the processes that are running on various computers serve as generic, composite hosts of service code and have no real logical “meat” to them.

When we look at the code in each of the layers in light of the business domain that it addresses, we tend to see fairly tight coupling between a screen, its logic, and the data that it shows. The places in which we see loose coupling is between screens, logic, and data from different business domains; there is hardly any coupling (if at all) between the screen that shows employee details and the one that is used to cancel an order. The fact that both are screens and are categorized in the UI “layer” appears not to have much technical significance (if any business significance). Much the same can be said for the code that hooks those screens to the data, as well as the data structures themselves.

Any consistency concerns that might have arisen by this separation have already been addressed by the business acceptance of eventual consistency. If there are business demands that two pieces of data that have been allocated to different services always be consistent, this indicates that service boundaries are not aligned with business boundaries and must be changed.

This is extremely valuable. Architects can explain to the business the ramifications of their architectural decisions in ways that the business can understand—“There might be a couple of seconds during which these two bits of data are not in sync. Is that a problem?”—and the answer to those kinds of question is used to iterate the architecture, so as to bring it into better alignment with the business.

As soon as service boundaries reflect business boundaries, there is great flexibility within each service; each can change its own database schema without having to worry about breaking other services, or even choose to change vendors and technology to such things as object or XML databases. Interoperability between services is a question of how event structures are represented, as well as how publish/subscribe is handled. This can be done by using basic enterprise service bus (ESB) functionality, such things as the Atom Publishing Protocol, or a mix.

Integration of legacy applications in this environment occurs within the context of a service, instead of identifying them as services in their own right. Use of Web Services to ease the cost of integration continues to make sense; however, from the perspective of a business domain, it really is nothing more than an implementation detail.

Conclusion

EDA is not a technical panacea to Web Services–centric architectures. In fact, attempting to employ EDA principles on purely technical domains that implement command-centric business analysis will almost certainly fail. The introduction of eventual consistency without the ratification of business stakeholders is poorly advised.

However, if in the process of architecture we work collaboratively with the business, map out the natural boundaries that are inherent in the organization and the way in which it works, and align the boundaries of our services to them, we will find that the benefits of EDA bring substantial gains to the business in terms of greater flexibility and shorter times to market, while its apparent disadvantages become addressed in terms of additional entity statuses and finer-grained events.

By itself, EDA ignores the IT-business alignment of SOA—so critical to getting boundaries and events right. Classic SOA has largely ignored the rock-solid foundation of publish/subscribe events—dead Web Services eventing and notification standards notwithstanding. It is only in the fusing of these two approaches that they overcome the weaknesses of each other and create a whole that is greater than the sum of its parts.

Interestingly enough, even though we have almost literally turned the classic command-driven services on their heads, the service- oriented tenets of autonomy and explicit boundaries have only become more pronounced, and the goal of IT-business alignment is now within our grasp.

Beyond just being a sound theoretical foundation, this architecture has weathered the trials of production in domains such as finance, travel and hospitality, aerospace, and many others—each with its own challenging constraints and nonfunctional demands. Organizations have maximized the effectiveness of their development teams by structuring them in accordance with these same service boundaries, instead of the more common technical specialization that corresponds to layered architectures. These loosely coupled service teams were able to wring the most out of their agile methodologies, as competition for specialized shared resources was eliminated.

Oracle once named this approach SOA 2.0. Maybe it really is the next evolutionary step.

Yesterday me and Scott virtually sat down to have a chat about NServiceBus and service buses in general. While we didn’t get in to many of the more advanced parts, you may find it an interesting introduction to the topic as well as saving yourself the costly mistake of implementing a broker instead of a bus (yes – they’re actually two different things).

Take a listen.

When working with clients, I run into more than a couple of people that have difficulty with event-driven architecture (EDA). Even more people have difficulty understanding what sagas really are, let alone why they need to use them. I’d go so far to say that many people don’t realize the importance of how sagas are persisted in making it all work (including the Workflow Foundation team).

The common e-commerce example

We accept orders, bill the customer, and then ship them the product.

Fairly straight-forward.

Since each part of that process can be quite complex, let’s have each step be handled by a service:

Sales, Billing, and Shipping. Each of these services will publish an event when it’s done its part. Sales will publish OrderAccepted containing all the order information – order Id, customer Id, products, quantities, etc. Billing will publish CustomerBilledForOrder containing the customer Id, order Id, etc. And Shipping will publish OrderShippedToCustomer with its data.

So far, so good. EDA and SOA seem to be providing us some value.

Where’s the saga?

Well, let’s consider the behavior of the Shipping service. It shouldn’t ship the order to the customer until it has received the CustomerBilledForOrder event as well as the OrderAccepted event. In other words, Shipping needs to hold on to the state that came in the first event until the second event comes in. And this is exactly what sagas are for.

Let’s take a look at the saga code that implements this. In order to simplify the sample a bit, I’ll be omitting the product quantities.

   1:      public class ShippingSaga : Saga<ShippingSagaData>,

   2:          ISagaStartedBy<OrderAccepted>,

   3:          ISagaStartedBy<CustomerBilledForOrder>

   4:      {

   5:          public void Handle(OrderAccepted message)

   6:          {

   7:              this.Data.ProductIdsInOrder = message.ProductIdsInOrder;

   8:          }

   9:   

  10:          public void Handle(CustomerBilledForOrder message)

  11:          {

  12:               this.Bus.Send<ShipOrderToCustomer>(

  13:                  (m =>

  14:                  {

  15:                      m.CustomerId = message.CustomerId;

  16:                      m.OrderId = message.OrderId;

  17:                      m.ProductIdsInOrder = this.Data.ProductIdsInOrder;

  18:                  }

  19:                  ));

  20:   

  21:              this.MarkAsComplete();

  22:          }

  23:   

  24:          public override void Timeout(object state)

  25:          {

  26:              

  27:          }

  28:      }

First of all, this looks fairly simple and straightforward, which is good.
It’s also wrong, which is not so good.

One problem we have here is that events may arrive out of order – first CustomerBilledForOrder, and only then OrderAccepted. What would happen in the above saga in that case? Well, we wouldn’t end up shipping the products to the customer, and customers tend not to like that (for some reason).

There’s also another problem here. See if you can spot it as I go through the explanation of ISagaStartedBy<T>.

Saga start up and correlation

The “ISagaStartedBy<T>” that is implemented for both messages indicates to the infrastructure (NServiceBus) that when a message of that type arrives, if an existing saga instance cannot be found, that a new instance should be started up. Makes sense, doesn’t it? For a given order, when the OrderAccepted event arrives first, Shipping doesn’t currently have any sagas handling it, so it starts up a new one. After that, when the CustomerBilledForOrder event arrives for that same order, the event should be handled by the saga instance that handled the first event – not by a new one.

I’ll repeat the important part: “the event should be handled by the saga instance that handled the first event”.

Since the only information we stored in the saga was the list of products, how would we be able to look up that saga instance when the next event came in containing an order Id, but no saga Id?

OK, so we need to store the order Id from the first event so that when the second event comes along we’ll be able to find the saga based on that order Id. Not too complicated, but something to keep in mind.

Let’s look at the updated code:

   1:      public class ShippingSaga : Saga<ShippingSagaData>,

   2:          ISagaStartedBy<OrderAccepted>,

   3:          ISagaStartedBy<CustomerBilledForOrder>

   4:      {

   5:          public void Handle(CustomerBilledForOrder message)

   6:          {

   7:              this.Data.CustomerHasBeenBilled = true;

   8:   

   9:              this.Data.CustomerId = message.CustomerId;

  10:              this.Data.OrderId = message.OrderId;

  11:   

  12:              this.CompleteIfPossible();

  13:          }

  14:   

  15:          public void Handle(OrderAccepted message)

  16:          {

  17:              this.Data.ProductIdsInOrder = message.ProductIdsInOrder;

  18:   

  19:              this.Data.CustomerId = message.CustomerId;

  20:              this.Data.OrderId = message.OrderId;

  21:   

  22:              this.CompleteIfPossible();

  23:          }

  24:   

  25:          private void CompleteIfPossible()

  26:          {

  27:              if (this.Data.ProductIdsInOrder != null && this.Data.CustomerHasBeenBilled)

  28:              {

  29:                  this.Bus.Send<ShipOrderToCustomer>(

  30:                     (m =>

  31:                     {

  32:                         m.CustomerId = this.Data.CustomerId;

  33:                         m.OrderId = this.Data.OrderId;

  34:                         m.ProductIdsInOrder = this.Data.ProductIdsInOrder;

  35:                     }

  36:                     ));

  37:                  this.MarkAsComplete();

  38:              }

  39:          }

  40:      }

And that brings us to…

Saga persistence

We already saw why Shipping needs to be able to look up its internal sagas using data from the events, but what that means is that simple blob-type persistence of those sagas is out. NServiceBus comes with an NHibernate-based saga persister for exactly this reason, though any persistence mechanism which allows you to query on something other than saga Id would work just as well.

Let’s take a quick look at the saga data that we’ll be storing and see how simple it is:

   1:      public class ShippingSagaData : ISagaEntity

   2:      {

   3:          public virtual Guid Id { get; set; }

   4:          public virtual string Originator { get; set; }

   5:          public virtual Guid OrderId { get; set; }

   6:          public virtual Guid CustomerId { get; set; }

   7:          public virtual List<Guid> ProductIdsInOrder { get; set; }

   8:          public virtual bool CustomerHasBeenBilled { get; set; }

   9:      }

You might have noticed the “Originator” property in there and wondered what it is for. First of all, the ISagaEntity interface requires the two properties Id and Originator. Originator is used to store the return address of the message that started the saga. Id is for what you think it’s for. In this saga, we don’t need to send any messages back to whoever started the saga, but in many others we do. In those cases, we’ll often be handling a message from some other endpoint when we want to possibly report some status back to the client that started the process. By storing that client’s address the first time, we can then “ReplyToOriginator” at any point in the process.

The manufacturing sample that comes with NServiceBus shows how this works.

Saga Lookup

Earlier, we saw the need to search for sagas based on order Id. The way to hook into the infrastructure and perform these lookups is by implementing “IFindSagas<T>.Using<M>” where T is the type of the saga data and M is the type of message. In our example, doing this using NHibernate would look like this:

   1:      public class ShippingSagaFinder : 

   2:          IFindSagas<ShippingSagaData>.Using<OrderAccepted>,

   3:          IFindSagas<ShippingSagaData>.Using<CustomerBilledForOrder>

   4:      {

   5:          public ShippingSagaData FindBy(CustomerBilledForOrder message)

   6:          {

   7:              return FindBy(message.OrderId)

   8:          }

   9:   

  10:          public ShippingSagaData FindBy(OrderAccepted message)

  11:          {

  12:              return FindBy(message.OrderId)

  13:          }

  14:   

  15:          private ShippingSagaData FindBy(Guid orderId)

  16:          {

  17:              return sessionFactory.GetCurrentSession().CreateCriteria(typeof(ShippingSagaData))

  18:                  .Add(Expression.Eq("OrderId", orderId))

  19:                  .UniqueResult<ShippingSagaData>();

  20:          }

  21:   

  22:          private ISessionFactory sessionFactory;

  23:   

  24:          public virtual ISessionFactory SessionFactory

  25:          {

  26:              get { return sessionFactory; }

  27:              set { sessionFactory = value; }

  28:          }

  29:      }

For a performance boost, we’d probably index our saga data by order Id.

On concurrency

Another important note is that for this saga, if both messages were handled in parallel on different machines, the saga could get stuck. The persistence mechanism here needs to prevent this. When using NHibernate over a database with the appropriate isolation level (Repeatable Read – the default in NServiceBus), this “just works”. If/When implementing your own saga persistence mechanism, it is important to understand the kind of concurrency your business logic can live with.

Take a look at Ayende’s example for mobile phone billing to get a feeling for what that’s like.

Summary

In almost any event-driven architecture, you’ll have services correlating multiple events in order to make decisions. The saga pattern is a great fit there, and not at all difficult to implement. You do need to take into account that events may arrive out of order and implement the saga logic accordingly, but it’s really not that big a deal. Do take the time to think through what data will need to be stored in order for the saga to be fault-tolerant, as well as a persistence mechanism that will allow you to look up that data based on event data.

If you feel like giving this approach a try, but don’t have an environment handy for this, download NServiceBus and take a look at the samples. It’s really quick and easy to get set up.

I’ve been to too many clients where I’ve been brought in to help them with their problems around service versioning when the solution I propose is simply to have version N+1 of the system be backwards-compatible with version N. If two adjacent versions of a given system aren’t compatible with each other, it is practically impossible to solve versioning issues.

Here’s what happens when versions aren’t compatible:

Admins stop the system from accepting any new requests, and wait until all current requests are done processing. They take a backup/snapshot of all relevant parts of the system (like data in the DB). Then, bring down the system – all of it. Install the new version on all machines. Bring everything back up. Let the users back in.

If, heaven-forbid, problems were uncovered with the new version (since some problems only appear in production), the admins have to roll back to the previous version – once again bringing everything down.

This scenario is fairly catastrophic for any company that requires not-even high availability, but pretty continuous availability – like public facing web apps.

If adjacent versions were compatible with each other, we could upgrade the system piece-meal – machine by machine, where both the old and new versions will be running side by side, communicating with each other. While the system’s performance may be sub-optimal, it will continue to be available throughout upgrades as well as downgrades.

This isn’t trivial to do.

It impacts how you decide what is (and more importantly, what isn’t) nullable.

It may force you to spread certain changes to features across more versions (aka releases).

As such, you can expect this to affect how you do release and feature planning.

However, if you do not take these factors into account, it’s almost a certainty that your versioning problems will persist and no technology (new or old) will be able to solve them.

Coming next… Units of versioning – inside and outside a service.

There’s been some recent discussion as to the “cost” of messaging:

Greg Young asserts:

“I believe that this shows there to be a rather negligible cost associated with the use of such a model. There is however a small cost, this cost however I believe only exists when one looks at the system in isolation.”

Ayende adds his perspective:

“The cost of messaging, and a very real one, comes when you need to understand the system. In a system where message exchange is the form of communication, it can be significantly harder to understand what is going on.”

Of course, both these intelligent fellows are right. The reason for the apparent disparity in viewpoints has to do with which part of the following graph you look at. Ayende zooms in on the left side:

As systems get larger, though, the only way to understand them is by working at higher levels of abstraction. That’s where messaging really shines, as the incremental complexity remains the same by maintaining the same modularity as before:

In Ayende’s post, he follows the design I described a while back on using messaging for user management and login for a high-scale web scenario. In his comments, he agrees with the above stating:

“I certainly think that a similar solution using RPC would be much more complex and likely more brittle.”

I feel quite conservative in saying the most enterprise solutions fall on the right side of the intersection in the graph.

That being said, don’t underestimate the learning curve developers go through with messaging. While the mechanics are similar, the mindset is very different. Think about it like this:

You’ve driven a car for years in the US. It’s practically second nature. Then you fly to the UK, rent a car, and all of a sudden, your brain is in meltdown. (or vice versa for those going from the UK to the US)

Summary

If you are going down the messaging route, please be aware that there are shades of gray there as well. You don’t have to implement your user management and login the way I outlined in my post if you don’t require such high levels of scalability, but even lower levels of scalability can benefit from messaging.

Just as there isn’t a single correct design for non-messaging solutions, the same is true for those using messaging. Finding the right balance is tricky, and critical.

When the code is simple in every part of the system, and the asynchronous interactions are what provide for the necessary complexity the problem domain requires, that’s when you know you’ve got it just right.

From Integrated Simplicity:

The question of how web-based (or 3rd party) consumers can work with pub/sub based services comes up a lot.

Many developers are used to implementing web services exposing methods on them like GetAllCustomers.

When moving to pub/sub and other more loosely coupled messaging patterns, developers look to implement the same pattern, opting for something like duplex GetCustomersRequest and GetCustomersResponse. The reasoning is simple and straightforward – it is difficult to push data over the web to consumers.

However, there are still ways to disconnect the preparation of the data from its usage thus gaining many of the advantages of pub/sub.

By employing REST principles and modelling our customer list as an explicit resource, web-based consumers would simply perform regular HTTP GET operations on the URI to get the list of customers.

The resource itself could be a simple XML file – it wouldn’t need to be dynamic at all.

You can get all the scalability benefits of pub/sub for web based consumers. All you need is a bit of REST 🙂

In the architectural principle of fully self contained messages, events “can – instantly and in future – be interpreted as the respective event without the need to rely on additional data stores that would need to be in time-sync with the event during message-processing.”

Also, “passing reference data in a message makes the message-consuming systems dependent on the knowledge and availability of actual persistent data that is stored “somewhere”. This data must separately be accessed for the sake of understanding the event that is represented by the message.”

The discussion of self-contained events can be compared to integration databases vs application databases.

Centralized Integration – Pros & Cons

If everything in a system can access a central datastore, it is enough for one party to publish an event containing only the ID of an entity that that party previously entered/updated. Upon receiving that event, a subscriber would go to the central datastore and get the fields its interested in for that ID. The advantage of this approach is that the minimal amount of data necessary crosses the network, as subscribers only retrieve the fields that interest them. Martin Fowler describes the disadvantages as:

“An integration database needs a schema that takes all its client applications into account. The resulting schema is either more general, more complex or both. The database usually is controlled by a separate group to the applications and database changes are more complex because they have to be negotiated between the database group and the various applications.”

This is far from being aligned with the principle of autonomy so important to SOA. In that respect, the architectural principle of self-contained messages points us away from those problems and towards more autonomous services.

However, once we have these autonomous business services in place, we may find that we don’t need 100% fully self-contained messages anymore.

A Real-World Example

Let’s say we have 3 business services, Sales, Fulfillment, and Billing.

Sales publishes an OrderAccepted event when it accepts an order. That event contains all the order information.

Both Fulfillment and Billing are subscribed to this event, and thus receive it.

Fulfillment does not ship products to the customer until the customer has been billed, so it just stores the order information internally, and is done.

Billing starts the process of billing the customer for their order, possibly joining several orders into a single bill. After completing this process, it publishes a CustomerBilled event containing all billing information, as well as the IDs of the orders in that bill. It does not put all the order information in that event, as it is not the authoritative owner of that data.

When Fulfillment receives the CustomerBilled event, it uses the IDs of the orders contained in the event to find the order information it previously stored internally. It does not need to call the Sales service for this information or contact some central Master Data Management system. It uses the data it has, and goes about fulfilling the orders and shipping the products to the customer, finally publishing its own OrderShipped event.

Notice, as well, that in the original OrderAccepted event there were the IDs of products the customer ordered. These product IDs originated from another service, Merchandising, responsible for the product catalog. The same thing can be said for the customer ID originating from another service – Customer Care.

The Issue of Time

One could argue that since subscribers use previously cached data when processing new events, that data might not be up to date. Also, we may have race conditions between our services. In the above example, if Billing was extremely fast and more highly available than Fulfillment. Billing could have received the OrderAccepted event, processed it, and published the CustomerBilled event before Fulfillment had received the OrderAccepted event. In short, the CustomerBilled and OrderAccepted messages could be out of order in Fulfillment’s queue.

What would Fulfillment do when trying to process the CustomerBilled message when it doesn’t have the order information?

Well, it knows that the world is parallel and non-sequential, so it does NOT return/log an error, but rather puts that message in the back of the queue to be processed again later (or maybe in some other temporary holding area). This enables the OrderAccepted message to be processed before the CustomerBilled message is retried. When the retry occurs, well, everything’s OK – it’s worked itself out over time.

In the case where we retry again and again and things don’t work themselves out (maybe the OrderAccepted event was lost), we move that message off to a different queue for something else to resolve the conflict (maybe a person, maybe software). If/when the conflict is resolved (got the Sales system / messaging system to replay the OrderAccepted event), the conflict resolver returns the CustomerBilled message to the queue, and now everything works just fine.

As all of this is occurring, the only thing that’s visible to external parties is that it happens to be taking longer than usual for the OrderShipped event to be published. In other words, time is the only difference.

Summary

The problem of non-self-contained events is mitigated first and foremost by business services in SOA, and the apparent issue of time-synchronization by business logic inside these services.

Don’t be afraid to put IDs in your messages and events.

Do be afraid of using those IDs to access datastores shared by multiple “services”.

Using IDs to correlated current events to data from previous events is not only OK, it’s to be expected.

The architectural principle of fully self-contained messages steers us away from the problems of Integration Databases and towards Application Databases, autonomous services, and a better SOA implementation. From there, following the principle of autonomy from a business perspective, will lead us to services not publishing data in their messages that is owned by other services, taking us the next step of our journey to SOA.

Related Content

[Podcast] Message Ordering – Is it cost effective?

Don’t EDA between existing systems

[Podcast] Handling dependencies between subscribers in SOA

One of the most common questions I get on the topic of pub/sub messaging is what happens if a notification is lost. Interestingly enough, there are some who almost entirely write-off this pattern because of this issue, preferring the control of request/response-exception. So, what should be done about lost messages? The short answer is durable messaging. The long answer is design.

Durable Messaging

In order to prevent a message from being lost when it is sent from a publisher to a subscriber, the message is written to disk on the publisher side, and then forwarded to the subscriber, where it is also written to disk. This store-and-forward mechanism enables our systems to gracefully recover from either side being temporarily unavailable.

In my MSDN article on this topic, I outlined some problems with this approach. These problems are exacerbated for publishers. Imagine a publisher with 40 subscribers, publishing 10 messages a second, each containing 1MB of XML. If 10 of the subscribers are unavailable, that’s 100MB of data being written to the publisher’s disk every second, 6GB every minute. That’s liable to bring down a publisher before an administrator brews a cup of coffee.

Publishers have no choice but to throw away messages after a certain period of time.

Publisher Contracts

The whole issue of contracts and schema is considered one of the better understand parts of SOA. Unfortunately, the operational aspects of service contracts is hardly ever taken into account.

On top of the schema of the messages a service publishers, additional information is needed in the contract:

1. How big will this message be?
2. How often will it be published?
3. How long will this message be stored if a subscriber is unavailable?

This first two pieces of information are important for subscribers to do load and capacity planning. The last one is the most important as it dictates the required availability and fault-tolerance characteristic of subscribers.

For Example

In the canonical retail scenario, when our sales service accepts an order, it publishes an order accepted event. Other services subscribed to this event include shipping, billing, and business intelligence.

While shipping and billing are highly available and able to keep up with the rate at which orders are accepted, the business intelligence service is not. BI has two main parts to it – a nightly batch that does the number crunching, and a UI for reporting off of the results of that number crunching. Some even do the reporting in a semi-offline fashion, emailing reports back to the user when they’re ready.

Furthermore, nobody’s going to invest in servers for making BI highly available.

And wasn’t the whole point of this publish/subscribe messaging to keep our services autonomous? That not all services have to have the same level uptime?

Houston, do we have a problem.?

Data Freshness

There is a glimmer of light in all this doom and gloom.

Not all services have the same data freshness requirements.

The business intelligence service above doesn’t need to know about orders the second they’re accepted. A daily roll-up would be fine, and an hourly roll-up bring us that much closer to “real time business intelligence”.

So, while BI is ready to accept the sales message schema, it would like a slightly different contract around it – less messages per unit of time, more data in each message.

From the operational perspective of the sales service, it would be cost effective to have less “online” subscribers. It could even take things a few steps further. Instead of using the regular messaging backbone for transmitting these hourly messages, it could use FTP. The data could even be zipped to take up even less space. Since the total data size is less than the corresponding online stream, is stored on cheaper, large storage, and the number of subscribers for this zipped, hourly update is fairly small, these messages can be kept around far longer.

If you’ve heard about consumer-driven contracts, this is it.

Note that we’re still talking about the same logical message schema.

Summary

It’s not that lost notifications aren’t a problem.

It’s that they feed the design process in such a way that the resulting service ecosystem is set up in such a way that notifications won’t get lost. I know that that sounds kind of recursive, but that’s how it works. Either subscribers take care of their SLA allowing them to process the online stream of events, or they should subscribe to a different pipe (which will have different SLA requirements, but maybe they can deal with those).

It make sense to have multiple pipes for the same logical schema.

It’s practically a necessity to make pub/sub a feasible solution.

Related Content

MSDN article on messaging and lost messages

Durable messaging dilemmas

Additional logic required for service autonomy

More in depth example on events and pub/sub between services

Consumer-Driven Contracts

There’s been some discussion on the SOA yahoo group around the connection between SOA, EDA, and CEP (complex event processing) since Jack’s original post on the topic. I’ve been waiting for the right opportunity to jump in and it seems to have come.

Dennis asked this:

There are different design choices in a SOA, even when you already have identified the services. I have a simple example that I would like to share:

Imagine a order-to-cash process. One part of that process is to register an order. Suppose we have two services, Order Service and Inventory Service. The task is to register the order and make a corresponding reservation of the stock level. I would be pleased to have the groups view on the following 3 design options (A, B, C):

A.
1. The “process/application” sends a message (sync or async) to “registerOrder” on the Order Service.
2. The “process/application” sends another message (sync or async) to “reserveStock” on the the Inventory Service.

B.
1. The “process/application” sends a message (sync or async) to “registerOrder” on the Order Service.
2. The Order Service sends a message (sync or async) to “reserveStock” on the the Inventory Service.

C.
1. The “process/application” sends a message (sync or async) to “registerOrder” on the Order Service.
2. The Order Service publishes an “orderReceived” event.
3. The Inventory Service subscribes to the “orderReceived” event .

On the whole “already identified the services” thing – naming a service doesn’t mean much. It’s all about allocating responsibility, and until that’s been done, those “services” don’t give us very much information.

Business Services

If we were to view this example in light of business services, and look at the business events that make up this process, maybe we’d get a different perspective.

Three business services: Sales, Inventory, and Shipping.

In Sales, many applications and people may operate, including the person and the application he used to submit the order. When the order is submitted and goes through all the internal validation stuff, Sales raises an OrderTentativelyAccepted event.

Inventory and Orders

Inventory, which is subscribed to this event, checks if it has everything in stock for the order. For every item in the order on stock, it allocates that stock to the order and publishes the InventoryAllocatedToOrder event for it. For items/quantities not in stock, it starts a long running process which watches for inventory changes.

When an InventoryChanged event occurs, it matches that against orders requiring allocation – if it finds one that requires stock, based on some logic to choose which order gets precedence, it publishes the InventoryAllocatedToOrder event.

Sales, which is subscribed to the InventoryAllocatedToOrder event, upon receiving all events pertaining to the order tentatively accepted, will publish an OrderAccepted event.

Orders and Shipping

When Inventory receives the OrderAccepted event, it generates the pick list to bring all the stock from the warehouses to the loading docks, finally publishing the PickListGenerated event containing target docks.

When Shipping receives the PickListGenerated event, it starts the yard management necessary to bring the needed kinds of trucks to the docks.

What else is possible

I could go on, talking about things like the maximum amount of time stock of various kinds can wait to be loaded on trucks, subscribing to earlier events to employ all kinds of optimization and prediction algorithms, having a Customer Care service notifying the customer about what’s going on with their order (probably different for different kinds of customers and preferred communication definitions). Obviously, we’d need a Billing service to handle the various kinds of billing procedures, whether or not the customer has credit, pays upon delivery, etc.

It turns out that many business domains map very well to this join of SOA and EDA.

What an ESB is for

When we have these kinds of business services primarily publishing events and subscribing to those of other services, you don’t need much else from your “enterprise service bus”. All sorts of transformation, routing, and orchestration capabilities don’t come into play at all.

In all truthfullness, those bits of functionality are really just a historical artifact of their broker heritage.

Don’t get me wrong, sometimes a broker is a nice thing to have – behind a service boundary in order to perform some complex integration between existing legacy applications.

Just keep that stuff in its place – not between services.

Complex Event Processing

We can look at how Sales transitions an order from being tentatively accepted to being accepted as requiring event correlation around InventoryAllocatedToOrder events. This isn’t exactly “complex” in its own right. If there were some kind of CEP engine that did this for us out of the box, it might be a possible technology choice for implementing this logic within our service.

As we add more concerns, like time, we may find new ways to make use of this engine. For instance, if the time to provide the order to the customer is approaching, we may choose to split the order into two – accepting one for which we have all the stock allocated, and leaving the second as tentatively accepted.

Summary

While it is difficult to move forward on service responsibility without discussing the events it raises and those it subscribe to, the whole issue of CEP can be postponed for a while.

Although there aren’t many who would say that EDA is necessary for driving down coupling in SOA, or that SOA won’t likely provide much value without EDA, or that SOA is necessary for providing the right boundaries for EDA, it’s been my experience that that is exactly the case.

CEP, while being a challenging engineering field, and managing the technical risks around it necessary for a project to succeed in some circumstances, and really shines when used under the SOA/EDA umbrella, it should not be taken by itself and used at the topmost architectural levels.

Related Content

SOA and Enterprise Processes

How client interaction fits with SOA

Time and SOA

Durable Messaging for Fault-Tolerant Services

And if you’re wondering about how to handle all that complexity inside services (different kinds of billing, periodic tests for electronics inventory, etc), you might like listening to this podcast about business components.