Getting integration right is the single most important aspect of the technology associated with microservices in my opinion. Do it well, and your microservices retain their autonomy, allowing you to change and release them independent of the whole. Get it wrong, and disaster awaits. Hopefully once you’ve read this chapter you’ll learn how to avoid some of the biggest pitfalls that have plagued other attempts at SOA and could yet await you in your journey to microservices.
There is a bewildering array of options out there for how one microservice can talk to another. But which is the right one: SOAP? XML-RPC? REST? Protocol buffers? We’ll dive into those in a moment, but before we do, let’s think about what we want out of whatever technology we pick.
Every now and then, we may make a change that requires our consumers to also change. We’ll discuss how to handle this later, but we want to pick technology that ensures this happens as rarely as possible. For example, if a microservice adds new fields to a piece of data it sends out, existing consumers shouldn’t be impacted.
If you have been in the IT industry for more than 15 minutes, you don’t need me to tell you that we work in a space that is changing rapidly. The one certainty is change. New tools, frameworks, and languages are coming out all the time, implementing new ideas that can help us work faster and more effectively. Right now, you might be a .NET shop. But what about in a year from now, or five years from now? What if you want to experiment with an alternative technology stack that might make you more productive?
I am a big fan of keeping my options open, which is why I am such a fan of microservices. It is also why I think it is very important to ensure that you keep the APIs used for communication between microservices technology-agnostic. This means avoiding integration technology that dictates what technology stacks we can use to implement our microservices.
We want to make it easy for consumers to use our service. Having a beautifully factored microservice doesn’t count for much if the cost of using it as a consumer is sky high! So let’s think about what makes it easy for consumers to use our wonderful new service. Ideally, we’d like to allow our clients full freedom in their technology choice, but on the other hand, providing a client library can ease adoption. Often, however, such libraries are incompatible with other things we want to achieve. For example, we might use client libraries to make it easy for consumers, but this can come at the cost of increased coupling.
We don’t want our consumers to be bound to our internal implementation. This leads to increased coupling. This means that if we want to change something inside our microservice, we can break our consumers by requiring them to also change. That increases the cost of change—the exact result we are trying to avoid. It also means we are less likely to want to make a change for fear of having to upgrade our consumers, which can lead to increased technical debt within the service. So any technology that pushes us to expose internal representation detail should be avoided.
Now that we’ve got a few guidelines that can help us select a good technology to use for integration between services, let’s look at some of the most common options out there and try to work out which one works best for us. To help us think this through, let’s pick a real-world example from MusicCorp.
Customer creation at first glance could be considered a simple set of CRUD operations, but for most systems it is more complex than that. Enrolling a new customer may need to kick off additional processes, like setting up financial payments or sending out welcome emails. And when we change or delete a customer, other business processes might get triggered as well.
So with that in mind, we should look at some different ways in which we might want to work with customers in our MusicCorp system.
By far the most common form of integration that I or any of my colleagues see in the industry is database (DB) integration. In this world, if other services want information from a service, they reach into the database. And if they want to change it, they reach into the database! This is really simple when you first think about it, and is probably the fastest form of integration to start with—which probably explains its popularity.
Figure 4-1 shows our registration UI, which creates customers by performing SQL operations directly on the database. It also shows our call center application that views and edits customer data by running SQL on the database. And the warehouse updates information about customer orders by querying the database. This is a common enough pattern, but it’s one fraught with difficulties.
First, we are allowing external parties to view and bind to internal implementation details. The data structures I store in the DB are fair game to all; they are shared in their entirety with all other parties with access to the database. If I decide to change my schema to better represent my data, or make my system easier to maintain, I can break my consumers. The DB is effectively a very large, shared API that is also quite brittle. If I want to change the logic associated with, say, how the helpdesk manages customers and this requires a change to the database, I have to be extremely careful that I don’t break parts of the schema used by other services. This situation normally results in requiring a large amount of regression testing.
Second, my consumers are tied to a specific technology choice. Perhaps right now it makes sense to store customers in a relational database, so my consumers use an appropriate (potentially DB-specific) driver to talk to it. What if over time we realize we would be better off storing data in a nonrelational database? Can it make that decision? So consumers are intimately tied to the implementation of the customer service. As we discussed earlier, we really want to ensure that implementation detail is hidden from consumers to allow our service a level of autonomy in terms of how it changes its internals over time. Goodbye, loose coupling.
Finally, let’s think about behavior for a moment. There is going to be logic associated with how a customer is changed. Where is that logic? If consumers are directly manipulating the DB, then they have to own the associated logic. The logic to perform the same sorts of manipulation to a customer may now be spread among multiple consumers. If the warehouse, registration UI, and call center UI all need to edit customer information, I need to fix a bug or change the behavior in three different places, and deploy those changes too. Goodbye, cohesion.
Remember when we talked about the core principles behind good microservices? Strong cohesion and loose coupling—with database integration, we lose both things. Database integration makes it easy for services to share data, but does nothing about sharing behavior. Our internal representation is exposed over the wire to our consumers, and it can be very difficult to avoid making breaking changes, which inevitably leads to a fear of any change at all. Avoid at (nearly) all costs.
For the rest of the chapter, we’ll explore different styles of integration that involve collaborating services, which themselves hide their own internal representations.
Before we start diving into the specifics of different technology choices, we should discuss one of the most important decisions we can make in terms of how services collaborate. Should communication be synchronous or asynchronous? This fundamental choice inevitably guides us toward certain implementation detail.
With synchronous communication, a call is made to a remote server, which blocks until the operation completes. With asynchronous communication, the caller doesn’t wait for the operation to complete before returning, and may not even care whether or not the operation completes at all.
Synchronous communication can be easier to reason about. We know when things have completed successfully or not. Asynchronous communication can be very useful for long-running jobs, where keeping a connection open for a long period of time between the client and server is impractical. It also works very well when you need low latency, where blocking a call while waiting for the result can slow things down. Due to the nature of mobile networks and devices, firing off requests and assuming things have worked (unless told otherwise) can ensure that the UI remains responsive even if the network is highly laggy. On the flipside, the technology to handle asynchronous communication can be a bit more involved, as we’ll discuss shortly.
These two different modes of communication can enable two different idiomatic styles of collaboration: request/response or event-based. With request/response, a client initiates a request and waits for the response. This model clearly aligns well to synchronous communication, but can work for asynchronous communication too. I might kick off an operation and register a callback, asking the server to let me know when my operation has completed.
With an event-based collaboration, we invert things. Instead of a client initiating requests asking for things to be done, it instead says this thing happened and expects other parties to know what to do. We never tell anyone else what to do. Event-based systems by their nature are asynchronous. The smarts are more evenly distributed—that is, the business logic is not centralized into core brains, but instead pushed out more evenly to the various collaborators. Event-based collaboration is also highly decoupled. The client that emits an event doesn’t have any way of knowing who or what will react to it, which also means that you can add new subscribers to these events without the client ever needing to know.
So are there any other drivers that might push us to pick one style over another? One important factor to consider is how well these styles are suited for solving an often-complex problem: how do we handle processes that span service boundaries and may be long running?
As we start to model more and more complex logic, we have to deal with the problem of managing business processes that stretch across the boundary of individual services. And with microservices, we’ll hit this limit sooner than usual. Let’s take an example from MusicCorp, and look at what happens when we create a customer:
A new record is created in the loyalty points bank for the customer.
Our postal system sends out a welcome pack.
We send a welcome email to the customer.
This is very easy to model conceptually as a flowchart, as we do in Figure 4-2.
When it comes to actually implementing this flow, there are two styles of architecture we could follow. With orchestration, we rely on a central brain to guide and drive the process, much like the conductor in an orchestra. With choreography, we inform each part of the system of its job, and let it work out the details, like dancers all finding their way and reacting to others around them in a ballet.
Let’s think about what an orchestration solution would look like for this flow. Here, probably the simplest thing to do would be to have our customer service act as the central brain. On creation, it talks to the loyalty points bank, email service, and postal service as we see in Figure 4-3, through a series of request/response calls. The customer service itself can then track where a customer is in this process. It can check to see if the customer’s account has been set up, or the email sent, or the post delivered. We get to take the flowchart in Figure 4-2 and model it directly into code. We could even use tooling that implements this for us, perhaps using an appropriate rules engine. Commercial tools exist for this very purpose in the form of business process modeling software. Assuming we use synchronous request/response, we could even know if each stage has worked.
The downside to this orchestration approach is that the customer service can become too much of a central governing authority. It can become the hub in the middle of a web, and a central point where logic starts to live. I have seen this approach result in a small number of smart “god” services telling anemic CRUD-based services what to do.
With a choreographed approach, we could instead just have the customer service emit an event in an asynchronous manner, saying Customer created. The email service, postal service, and loyalty points bank then just subscribe to these events and react accordingly, as in Figure 4-4. This approach is significantly more decoupled. If some other service needed to reach to the creation of a customer, it just needs to subscribe to the events and do its job when needed. The downside is that the explicit view of the business process we see in Figure 4-2 is now only implicitly reflected in our system.
This means additional work is needed to ensure that you can monitor and track that the right things have happened. For example, would you know if the loyalty points bank had a bug and for some reason didn’t set up the correct account? One approach I like for dealing with this is to build a monitoring system that explicitly matches the view of the business process in Figure 4-2, but then tracks what each of the services does as independent entities, letting you see odd exceptions mapped onto the more explicit process flow. The flowchart we saw earlier isn’t the driving force, but just one lens through which we can see how the system is behaving.
In general, I have found that systems that tend more toward the choreographed approach are more loosely coupled, and are more flexible and amenable to change. You do need to do extra work to monitor and track the processes across system boundaries, however. I have found most heavily orchestrated implementations to be extremely brittle, with a higher cost of change. With that in mind, I strongly prefer aiming for a choreographed system, where each service is smart enough to understand its role in the whole dance.
There are quite a few factors to unpack here. Synchronous calls are simpler, and we get to know if things worked straightaway. If we like the semantics of request/response but are dealing with longer-lived processes, we could just initiate asynchronous requests and wait for callbacks. On the other hand, asynchronous event collaboration helps us adopt a choreographed approach, which can yield significantly more decoupled services—something we want to strive for to ensure our services are independently releasable.
We are, of course, free to mix and match. Some technologies will fit more naturally into one style or another. We do, however, need to appreciate some of the different technical implementation details that will further help us make the right call.
To start with, let’s look at two technologies that fit well when we are considering request/response: remote procedure call (RPC) and REpresentational State Transfer (REST).
Remote procedure call refers to the technique of making a local call and having it execute on a remote service somewhere. There are a number of different types of RPC technology out there. Some of this technology relies on having an interface definition (SOAP, Thrift, protocol buffers). The use of a separate interface definition can make it easier to generate client and server stubs for different technology stacks, so, for example, I could have a Java server exposing a SOAP interface, and a .NET client generated from the Web Service Definition Language (WSDL) definition of the interface. Other technology, like Java RMI, calls for a tighter coupling between the client and server, requiring that both use the same underlying technology but avoid the need for a shared interface definition. All these technologies, however, have the same, core characteristic in that they make a local call look like a remote call.
Many of these technologies are binary in nature, like Java RMI, Thrift, or protocol buffers, while SOAP uses XML for its message formats. Some implementations are tied to a specific networking protocol (like SOAP, which makes nominal use of HTTP), whereas others might allow you to use different types of networking protocols, which themselves can provide additional features. For example, TCP offers guarantees about delivery, whereas UDP doesn’t but has a much lower overhead. This can allow you to use different networking technology for different use cases.
Those RPC implementations that allow you to generate client and server stubs help you get started very, very fast. I can be sending content over a network boundary in no time at all. This is often one of the main selling points of RPC: its ease of use. The fact that I can just make a normal method call and theoretically ignore the rest is a huge boon.
Some RPC implementations, though, do come with some downsides that can cause issues. These issues aren’t always apparent initially, but nonetheless they can be severe enough to outweigh the benefits of being so easy to get up and running quickly.
Some RPC mechanisms, like Java RMI, are heavily tied to a specific platform, which can limit which technology can be used in the client and server. Thrift and protocol buffers have an impressive amount of support for alternative languages, which can reduce this downside somewhat, but be aware that sometimes RPC technology comes with restrictions on interoperability.
In a way, this technology coupling can be a form of exposing internal technical implementation details. For example, the use of RMI ties not only the client to the JVM, but the server too.
The core idea of RPC is to hide the complexity of a remote call. Many implementations of RPC, though, hide too much. The drive in some forms of RPC to make remote method calls look like local method calls hides the fact that these two things are very different. I can make large numbers of local, in-process calls without worrying overly about the performance. With RPC, though, the cost of marshalling and un-marshalling payloads can be significant, not to mention the time taken to send things over the network. This means you need to think differently about API design for remote interfaces versus local interfaces. Just taking a local API and trying to make it a service boundary without any more thought is likely to get you in trouble. In some of the worst examples, developers may be using remote calls without knowing it if the abstraction is overly opaque.
You need to think about the network itself. Famously, the first of the fallacies of distributed computing is “The network is reliable”. Networks aren’t reliable. They can and will fail, even if your client and the server you are speaking to are fine. They can fail fast, they can fail slow, and they can even malform your packets. You should assume that your networks are plagued with malevolent entities ready to unleash their ire on a whim. Therefore, the failure modes you can expect are different. A failure could be caused by the remote server returning an error, or by you making a bad call. Can you tell the difference, and if so, can you do anything about it? And what do you do when the remote server just starts responding slowly? We’ll cover this topic when we talk about resiliency in Chapter 11.
Some of the most popular implementations of RPC can lead to some nasty forms of brittleness, Java’s RMI being a very good example. Let’s consider a very simple Java interface that we have decided to make a remote API for our customer service. Example 4-1 declares the methods we are going to expose remotely. Java RMI then generates the client and server stubs for our method.
In this interface,
findCustomer takes the first name, surname, and email address. What happens if we decide to allow the
Customer object to also be created with just an email address? We could add a new method at this point pretty easily, like so:
The problem is that now we need to regenerate the client stubs too. Clients that want to consume the new method need the new stubs, and depending on the nature of the changes to the specification, consumers that don’t need the new method may also need to have their stubs upgraded too. This is manageable, of course, but to a point. The reality is that changes like this are fairly common. RPC endpoints often end up having a large number of methods for different ways of creating or interacting with objects. This is due in part to the fact that we are still thinking of these remote calls as local ones.
There is another sort of brittleness, though. Let’s take a look at what our
Customer object looks like:
Now, what if it turns out that although we expose the
age field in our
Customer objects, none of our consumers ever use it? We decide we want to remove this field. But if the server implementation removes
age from its definition of this type, and we don’t do the same to all the consumers, then even though they never used the field, the code associated with deserializing the
Customer object on the consumer side will break. To roll out this change, I would have to deploy both a new server and clients at the same time. This is a key challenge with any RPC mechanism that promotes the use of binary stub generation: you don’t get to separate client and server deployments. If you use this technology, lock-step releases may be in your future.
Similar problems occur if I want to restructure the
Customer object even if I didn’t remove fields—for example, if I wanted to encapsulate
surname into a new
naming type to make it easier to manage. I could, of course, fix this by passing around dictionary types as the parameters of my calls, but at that point, I lose many of the benefits of the generated stubs because I’ll still have to manually match and extract the fields I want.
In practice, objects used as part of binary serialization across the wire can be thought of as expand-only types. This brittleness results in the types being exposed over the wire and becoming a mass of fields, some of which are no longer used but can’t be safely removed.
Despite its shortcomings, I wouldn’t go so far as to call RPC terrible. Some of the common implementations that I have encountered can lead to the sorts of problems I have outlined here. Due to the challenges of using RMI, I would certainly give that technology a wide berth. Many operations fall quite nicely into the RPC-based model, and more modern mechanisms like protocol buffers or Thrift mitigate some of sins of the past by avoiding the need for lock-step releases of client and server code.
Just be aware of some of the potential pitfalls associated with RPC if you’re going to pick this model. Don’t abstract your remote calls to the point where the network is completely hidden, and ensure that you can evolve the server interface without having to insist on lock-step upgrades for clients. Finding the right balance for your client code is important, for example. Make sure your clients aren’t oblivious to the fact that a network call is going to be made. Client libraries are often used in the context of RPC, and if not structured right they can be problematic. We’ll talk more about them shortly.
REpresentational State Transfer (REST) is an architectural style inspired by the Web. There are many principles and constraints behind the REST style, but we are going to focus on those that really help us when we face integration challenges in a microservices world, and when we’re looking for an alternative style to RPC for our service interfaces.
Most important is the concept of resources. You can think of a resource as a thing that the service itself knows about, like a
Customer. The server creates different representations of this
Customer on request. How a resource is shown externally is completely decoupled from how it is stored internally. A client might ask for a JSON representation of a
Customer, for example, even if it is stored in a completely different format. Once a client has a representation of this
Customer, it can then make requests to change it, and the server may or may not comply with them.
There are many different styles of REST, and I touch only briefly on them here. I strongly recommend you take a look at the Richardson Maturity Model, where the different styles of REST are compared.
REST itself doesn’t really talk about underlying protocols, although it is most commonly used over HTTP. I have seen implementations of REST using very different protocols before, such as serial or USB, although this can require a lot of work. Some of the features that HTTP gives us as part of the specification, such as verbs, make implementing REST over HTTP easier, whereas with other protocols you’ll have to handle these features yourself.
HTTP itself defines some useful capabilities that play very well with the REST style. For example, the HTTP verbs (e.g., GET, POST, and PUT) already have well-understood meanings in the HTTP specification as to how they should work with resources. The REST architectural style actually tells us that methods should behave the same way on all resources, and the HTTP specification happens to define a bunch of methods we can use. GET retrieves a resource in an idempotent way, and POST creates a new resource. This means we can avoid lots of different
editCustomer methods. Instead, we can simply POST a customer representation to request that the server create a new resource, and initiate a GET request to retrieve a representation of a resource. Conceptually, there is one endpoint in the form of a
Customer resource in these cases, and the operations we can carry out upon it are baked into the HTTP protocol.
HTTP also brings a large ecosystem of supporting tools and technology. We get to use HTTP caching proxies like Varnish and load balancers like mod_proxy, and many monitoring tools already have lots of support for HTTP out of the box. These building blocks allow us to handle large volumes of HTTP traffic and route them smartly, in a fairly transparent way. We also get to use all the available security controls with HTTP to secure our communications. From basic auth to client certs, the HTTP ecosystem gives us lots of tools to make the security process easier, and we’ll explore that topic more in Chapter 9. That said, to get these benefits, you have to use HTTP well. Use it badly, and it can be as insecure and hard to scale as any other technology out there. Use it right, though, and you get a lot of help.
Note that HTTP can be used to implement RPC too. SOAP, for example, gets routed over HTTP, but unfortunately uses very little of the specification. Verbs are ignored, as are simple things like HTTP error codes. All too often, it seems, the existing, well-understood standards and technology are ignored in favor of new standards that can only be implemented using brand-new technology—conveniently provided by the same companies that help design the new standards in the first place!
Another principle introduced in REST that can help us avoid the coupling between client and server is the concept of hypermedia as the engine of application state (often abbreviated as HATEOAS, and boy, did it need an abbreviation). This is fairly dense wording and a fairly interesting concept, so let’s break it down a bit.
Hypermedia is a concept whereby a piece of content contains links to various other pieces of content in a variety of formats (e.g., text, images, sounds). This should be pretty familiar to you, as it’s what the average web page does: you follow links, which are a form of hypermedia controls, to see related content. The idea behind HATEOAS is that clients should perform interactions (potentially leading to state transitions) with the server via these links to other resources. It doesn’t need to know where exactly customers live on the server by knowing which URI to hit; instead, the client looks for and navigates links to find what it needs.
This is a bit of an odd concept, so let’s first step back and consider how people interact with a web page, which we’ve already established is rich with hypermedia controls.
Think of the Amazon.com shopping site. The location of the shopping cart has changed over time. The graphic has changed. The link has changed. But as humans we are smart enough to still see a shopping cart, know what it is, and interact with it. We have an understanding of what a shopping cart means, even if the exact form and underlying control used to represent it has changed. We know that if we want to view the cart, this is the control we want to interact with. This is why web pages can change incrementally over time. As long as these implicit contracts between the customer and the website are still met, changes don’t need to be breaking changes.
With hypermedia controls, we are trying to achieve the same level of smarts for our electronic consumers. Let’s look at a hypermedia control that we might have for MusicCorp. We’ve accessed a resource representing a catalog entry for a given album in Example 4-2. Along with information about the album, we see a number of hypermedia controls.
In this document, we have two hypermedia controls. The client reading such a document needs to know that a control with a relation of
artist is where it needs to navigate to get information about the artist, and that
instantpurchase is part of the protocol used to purchase the album. The client has to understand the semantics of the API in much the same way as a human being needs to understand that on a shopping website the cart is where the items to be purchased will be.
As a client, I don’t need to know which URI scheme to access to buy the album, I just need to access the resource, find the buy control, and navigate to that. The buy control could change location, the URI could change, or the site could even send me to another service altogether, and as a client I wouldn’t care. This gives us a huge amount of decoupling between the client and server.
Using these controls to decouple the client and server yields significant benefits over time that greatly offset the small increase in the time it takes to get these protocols up and running. By following the links, the client gets to progressively discover the API, which can be a really handy capability when we are implementing new clients.
One of the downsides is that this navigation of controls can be quite chatty, as the client needs to follow links to find the operation it wants to perform. Ultimately, this is a trade-off. I would suggest you start with having your clients navigate these controls first, then optimize later if necessary. Remember that we have a large amount of help out of the box by using HTTP, which we discussed earlier. The evils of premature optimization have been well documented before, so I don’t need to expand upon them here. Also note that a lot of these approaches were developed to create distributed hypertext systems, and not all of them fit! Sometimes you’ll find yourself just wanting good old-fashioned RPC.
Personally, I am a fan of using links to allow consumers to navigate API endpoints. The benefits of progressive discovery of the API and reduced coupling can be significant. However, it is clear that not everyone is sold, as I don’t see it being used anywhere near as much as I would like. I think a large part of this is that there is some initial upfront work required, but the rewards often come later.
The use of standard textual formats gives clients a lot of flexibility as to how they consume resources, and REST over HTTP lets us use a variety of formats. The examples I have given so far used XML, but at this stage, JSON is a much more popular content type for services that work over HTTP.
The fact that JSON is a much simpler format means that consumption is also easier. Some proponents also cite its relative compactness when compared to XML as another winning factor, although this isn’t often a real-world issue.
JSON does have some downsides, though. XML defines the
link control we used earlier as a hypermedia control. The JSON standard doesn’t define anything similar, so in-house styles are frequently used to shoe-horn this concept in. The Hypertext Application Language (HAL) attempts to fix this by defining some common standards for hyperlinking for JSON (and XML too, although arguably XML needs less help). If you follow the HAL standard, you can use tools like the web-based HAL browser for exploring hypermedia controls, which can make the task of creating a client much easier.
We aren’t limited to these two formats, of course. We can send pretty much anything over HTTP if we want, even binary. I am seeing more and more people just using HTML as a format instead of XML. For some interfaces, the HTML can do double duty as a UI and an API, although there are pitfalls to be avoided here, as the interactions of a human and a computer are quite different! But it is certainly an attractive idea. There are lots of HTML parsers out there, after all.
Personally, though, I am still a fan of XML. Some of the tool support is better. For example, if I want to extract only certain parts of the payload (a technique we’ll discuss more in “Versioning”) I can use XPATH, which is a well-understood standard with lots of tool support, or even CSS selectors, which many find even easier. With JSON, I have JSONPATH, but this is not widely supported. I find it odd that people pick JSON because it is nice and lightweight, then try and push concepts into it like hypermedia controls that already exist in XML. I accept, though, that I am probably in the minority here and that JSON is the format of choice for most people!
As REST has become more popular, so too have the frameworks that help us create RESTFul web services. However, some of these tools trade off too much in terms of short-term gain for long-term pain; in trying to get you going fast, they can encourage some bad behaviors. For example, some frameworks actually make it very easy to simply take database representations of objects, deserialize them into in-process objects, and then directly expose these externally. I remember at a conference seeing this demonstrated using Spring Boot and cited as a major advantage. The inherent coupling that this setup promotes will in most cases cause far more pain than the effort required to properly decouple these concepts.
There is a more general problem at play here. How we decide to store our data, and how we expose it to our consumers, can easily dominate our thinking. One pattern I saw used effectively by one of our teams was to delay the implementation of proper persistence for the microservice, until the interface had stabilized enough. For an interim period, entities were just persisted in a file on local disk, which is obviously not a suitable long-term solution. This ensured that how the consumers wanted to use the service drove the design and implementation decisions. The rationale given, which was borne out in the results, was that it is too easy for the way we store domain entities in a backing store to overtly influence the models we send over the wire to collaborators. One of the downsides with this approach is that we are deferring the work required to wire up our data store. I think for new service boundaries, however, this is an acceptable trade-off.
In terms of ease of consumption, you cannot easily generate a client stub for your REST over HTTP application protocol like you can with RPC. Sure, the fact that HTTP is being used means that you get to take advantage of all the excellent HTTP client libraries out there, but if you want to implement and use hypermedia controls as a client you are pretty much on your own. Personally, I think client libraries could do much better at this than they do, and they are certainly better now than in the past, but I have seen this apparent increased complexity result in people backsliding into smuggling RPC over HTTP or building shared client libraries. Shared code between client and server can be very dangerous, as we’ll discuss in “DRY and the Perils of Code Reuse in a Microservice World”.
A more minor point is that some web server frameworks don’t actually support all the HTTP verbs well. That means that it might be easy for you to create a handler for GET or POST requests, but you may have to jump through hoops to get PUT or DELETE requests to work. Proper REST frameworks like Jersey don’t have this problem, and you can normally work around this, but if you are locked into certain framework choices this might limit what style of REST you can use.
Performance may also be an issue. REST over HTTP payloads can actually be more compact than SOAP because it supports alternative formats like JSON or even binary, but it will still be nowhere near as lean a binary protocol as Thrift might be. The overhead of HTTP for each request may also be a concern for low-latency requirements.
HTTP, while it can be suited well to large volumes of traffic, isn’t great for low-latency communications when compared to alternative protocols that are built on top of Transmission Control Protocol (TCP) or other networking technology. Despite the name, WebSockets, for example, has very little to do with the Web. After the initial HTTP handshake, it’s just a TCP connection between client and server, but it can be a much more efficient way for you to stream data for a browser. If this is something you’re interested in, note that you aren’t really using much of HTTP, let alone anything to do with REST.
For server-to-server communications, if extremely low latency or small message size is important, HTTP communications in general may not be a good idea. You may need to pick different underlying protocols, like User Datagram Protocol (UDP), to achieve the performance you want, and many RPC frameworks will quite happily run on top of networking protocols other than TCP.
Consumption of the payloads themselves requires more work than is provided by some RPC implementations that support advanced serialization and deserialization mechanisms. These can become a coupling point in their own right between client and server, as implementing tolerant readers is a nontrivial activity (we’ll discuss this shortly), but from the point of view of getting up and running, they can be very attractive.
Despite these disadvantages, REST over HTTP is a sensible default choice for service-to-service interactions. If you want to know more, I recommend REST in Practice (O’Reilly), which covers the topic of REST over HTTP in depth.
There are two main parts we need to consider here: a way for our microservices to emit events, and a way for our consumers to find out those events have happened.
Traditionally, message brokers like RabbitMQ try to handle both problems. Producers use an API to publish an event to the broker. The broker handles subscriptions, allowing consumers to be informed when an event arrives. These brokers can even handle the state of consumers, for example by helping keep track of what messages they have seen before. These systems are normally designed to be scalable and resilient, but that doesn’t come for free. It can add complexity to the development process, because it is another system you may need to run to develop and test your services. Additional machines and expertise may also be required to keep this infrastructure up and running. But once it does, it can be an incredibly effective way to implement loosely coupled, event-driven architectures. In general, I’m a fan.
Do be wary, though, about the world of middleware, of which the message broker is just a small part. Queues in and of themselves are perfectly sensible, useful things. However, vendors tend to want to package lots of software with them, which can lead to more and more smarts being pushed into the middleware, as evidenced by things like the Enterprise Service Bus. Make sure you know what you’re getting: keep your middleware dumb, and keep the smarts in the endpoints.
Another approach is to try to use HTTP as a way of propagating events. ATOM is a REST-compliant specification that defines semantics (among other things) for publishing feeds of resources. Many client libraries exist that allow us to create and consume these feeds. So our customer service could just publish an event to such a feed when our customer service changes. Our consumers just poll the feed, looking for changes. On one hand, the fact that we can reuse the existing ATOM specification and any associated libraries is useful, and we know that HTTP handles scale very well. However, HTTP is not good at low latency (where some message brokers excel), and we still need to deal with the fact that the consumers need to keep track of what messages they have seen and manage their own polling schedule.
I have seen people spend an age implementing more and more of the behaviors that you get out of the box with an appropriate message broker to make ATOM work for some use cases. For example, the Competing Consumer pattern describes a method whereby you bring up multiple worker instances to compete for messages, which works well for scaling up the number of workers to handle a list of independent jobs. However, we want to avoid the case where two or more workers see the same message, as we’ll end up doing the same task more than we need to. With a message broker, a standard queue will handle this. With ATOM, we now need to manage our own shared state among all the workers to try to reduce the chances of reproducing effort.
If you already have a good, resilient message broker available to you, consider using it to handle publishing and subscribing to events. But if you don’t already have one, give ATOM a look, but be aware of the sunk-cost fallacy. If you find yourself wanting more and more of the support that a message broker gives you, at a certain point you might want to change your approach.
In terms of what we actually send over these asynchronous protocols, the same considerations apply as with synchronous communication. If you are currently happy with encoding requests and responses using JSON, stick with it.
Some of this asynchronous stuff seems fun, right? Event-driven architectures seem to lead to significantly more decoupled, scalable systems. And they can. But these programming styles do lead to an increase in complexity. This isn’t just the complexity required to manage publishing and subscribing to messages as we just discussed, but also in the other problems we might face. For example, when considering long-running async request/response, we have to think about what to do when the response comes back. Does it come back to the same node that initiated the request? If so, what if that node is down? If not, do I need to store information somewhere so I can react accordingly? Short-lived async can be easier to manage if you’ve got the right APIs, but even so, it is a different way of thinking for programmers who are accustomed to intra-process synchronous message calls.
Time for a cautionary tale. Back in 2006, I was working on building a pricing system for a bank. We would look at market events, and work out which items in a portfolio needed to be repriced. Once we determined the list of things to work through, we put these all onto a message queue. We were making use of a grid to create a pool of pricing workers, allowing us to scale up and down the pricing farm on request. These workers used the competing consumer pattern, each one gobbling messages as fast as possible until there was nothing left to process.
The system was up and running, and we were feeling rather smug. One day, though, just after we pushed a release out, we hit a nasty problem. Our workers kept dying. And dying. And dying.
Eventually, we tracked down the problem. A bug had crept in whereby a certain type of pricing request would cause a worker to crash. We were using a transacted queue: as the worker died, its lock on the request timed out, and the pricing request was put back on the queue—only for another worker to pick it up and die. This was a classic example of what Martin Fowler calls a catastrophic failover.
Aside from the bug itself, we’d failed to specify a maximum retry limit for the job on the queue. We fixed the bug itself, and also configured a maximum retry. But we also realized we needed a way to view, and potentially replay, these bad messages. We ended up having to implement a message hospital (or dead letter queue), where messages got sent if they failed. We also created a UI to view those messages and retry them if needed. These sorts of problems aren’t immediately obvious if you are only familiar with synchronous point-to-point communication.
The associated complexity with event-driven architectures and asynchronous programming in general leads me to believe that you should be cautious in how eagerly you start adopting these ideas. Ensure you have good monitoring in place, and strongly consider the use of correlation IDs, which allow you to trace requests across process boundaries, as we’ll cover in depth in Chapter 8.
I also strongly recommend Enterprise Integration Patterns (Addison-Wesley), which contains a lot more detail on the different programming patterns that you may need to consider in this space.
Whether you choose to become a REST ninja, or stick with an RPC-based mechanism like SOAP, the core concept of the service as a state machine is powerful. We’ve spoken before (probably ad nauseum by this point) about our services being fashioned around bounded contexts. Our customer microservice owns all logic associated with behavior in this context.
When a consumer wants to change a customer, it sends an appropriate request to the customer service. The customer service, based on its logic, gets to decide if it accepts that request or not. Our customer service controls all lifecycle events associated with the customer itself. We want to avoid dumb, anemic services that are little more than CRUD wrappers. If the decision about what changes are allowed to be made to a customer leak out of the customer service itself, we are losing cohesion.
Having the lifecycle of key domain concepts explicitly modeled like this is pretty powerful. Not only do we have one place to deal with collisions of state (e.g., someone trying to update a customer that has already been removed), but we also have a place to attach behavior based on those state changes.
I still think that REST over HTTP makes for a much more sensible integration technology than many others, but whatever you pick, keep this idea in mind.
Reactive extensions, often shortened to Rx, are a mechanism to compose the results of multiple calls together and run operations on them. The calls themselves could be blocking or nonblocking calls. At its heart, Rx inverts traditional flows. Rather than asking for some data, then performing operations on it, you observe the outcome of an operation (or set of operations) and react when something changes. Some implementations of Rx allow you to perform functions on these observables, such as RxJava, which allows traditional functions like
filter to be used.
The various Rx implementations have found a very happy home in distributed systems. They allow us to abstract out the details of how calls are made, and reason about things more easily. I observe the result of a call to a downstream service. I don’t care if it was a blocking or nonblocking call, I just wait for the response and react. The beauty is that I can compose multiple calls together, making handling concurrent calls to downstream services much easier.
As you find yourself making more service calls, especailly when making multiple calls to perform a single operation, take a look at the reactive extensions for your chosen technology stack. You may be surprised how much simpler your life can become.
One of the acronyms we developers hear a lot is DRY: don’t repeat yourself. Though its definition is sometimes simplified as trying to avoid duplicating code, DRY more accurately means that we want to avoid duplicating our system behavior and knowledge. This is very sensible advice in general. Having lots of lines of code that do the same thing makes your codebase larger than needed, and therefore harder to reason about. When you want to change behavior, and that behavior is duplicated in many parts of your system, it is easy to forget everywhere you need to make a change, which can lead to bugs. So using DRY as a mantra, in general, makes sense.
DRY is what leads us to create code that can be reused. We pull duplicated code into abstractions that we can then call from multiple places. Perhaps we go as far as making a shared library that we can use everywhere! This approach, however, can be deceptively dangerous in a microservice architecture.
One of the things we want to avoid at all costs is overly coupling a microservice and consumers such that any small change to the microservice itself can cause unnecessary changes to the consumer. Sometimes, however, the use of shared code can create this very coupling. For example, at one client we had a library of common domain objects that represented the core entities in use in our system. This library was used by all the services we had. But when a change was made to one of them, all services had to be updated. Our system communicated via message queues, which also had to be drained of their now invalid contents, and woe betide you if you forgot.
If your use of shared code ever leaks outside your service boundary, you have introduced a potential form of coupling. Using common code like logging libraries is fine, as they are internal concepts that are invisible to the outside world. RealEstate.com.au makes use of a tailored service template to help bootstrap new service creation. Rather than make this code shared, the company copies it for every new service to ensure that coupling doesn’t leak in.
My general rule of thumb: don’t violate DRY within a microservice, but be relaxed about violating DRY across all services. The evils of too much coupling between services are far worse than the problems caused by code duplication. There is one specific use case worth exploring further, though.
I’ve spoken to more than one team who has insisted that creating client libraries for your services is an essential part of creating services in the first place. The argument is that this makes it easy to use your service, and avoids the duplication of code required to consume the service itself.
The problem, of course, is that if the same people create both the server API and the client API, there is the danger that logic that should exist on the server starts leaking into the client. I should know: I’ve done this myself. The more logic that creeps into the client library, the more cohesion starts to break down, and you find yourself having to change multiple clients to roll out fixes to your server. You also limit technology choices, especially if you mandate that the client library has to be used.
A model for client libraries I like is the one for Amazon Web Services (AWS). The underlying SOAP or REST web service calls can be made directly, but everyone ends up using just one of the various software development kits (SDKs) that exist, which provide abstractions over the underlying API. These SDKs, though, are written by the community or AWS people other than those who work on the API itself. This degree of separation seems to work, and avoids some of the pitfalls of client libraries. Part of the reason this works so well is that the client is in charge of when the upgrade happens. If you go down the path of client libraries yourself, make sure this is the case.
Netflix in particular places special emphasis on the client library, but I worry that people view that purely through the lens of avoiding code duplication. In fact, the client libraries used by Netflix are as much (if not more) about ensuring reliability and scalability of their systems. The Netflix client libraries handle service discovery, failure modes, logging, and other aspects that aren’t actually about the nature of the service itself. Without these shared clients, it would be hard to ensure that each piece of client/server communications behaved well at the massive scale at which Netflix operates. Their use at Netflix has certainly made it easy to get up and running and increased productivity while also ensuring the system behaves well. However, according to at least one person at Netflix, over time this has led to a degree of coupling between client and server that has been problematic.
If the client library approach is something you’re thinking about, it can be important to separate out client code to handle the underlying transport protocol, which can deal with things like service discovery and failure, from things related to the destination service itself. Decide whether or not you are going to insist on the client library being used, or if you’ll allow people using different technology stacks to make calls to the underlying API. And finally, make sure that the clients are in charge of when to upgrade their client libraries: we need to ensure we maintain the ability to release our services independently of each other!
One consideration I want to touch on is how we pass around information about our domain entities. We need to embrace the idea that a microservice will encompass the lifecycle of our core domain entities, like the
Customer. We’ve already talked about the importance of the logic associated with changing this
Customer being held in the customer service, and that if we want to change it we have to issue a request to the customer service. But it also follows that we should consider the customer service as being the source of truth for
When we retrieve a given
Customer resource from the customer service, we get to see what that resource looked like when we made the request. It is possible that after we requested that
Customer resource, something else has changed it. What we have in effect is a memory of what the
Customer resource once looked like. The longer we hold on to this memory, the higher the chance that this memory will be false. Of course, if we avoid requesting data more than we need to, our systems can become much more efficient.
Sometimes this memory is good enough. Other times you need to know if it has changed. So whether you decide to pass around a memory of what an entity once looked like, make sure you also include a reference to the original resource so that the new state can be retrieved.
Let’s consider the example where we ask the email service to send an email when an order has been shipped. Now we could send in the request to the email service with the customer’s email address, name, and order details. However, if the email service is actually queuing up these requests, or pulling them from a queue, things could change in the meantime. It might make more sense to just send a URI for the
Order resources, and let the email server go look them up when it is time to send the email.
A great counterpoint to this emerges when we consider event-based collaboration. With events, we’re saying this happened, but we need to know what happened. If we’re receiving updates due to a
Customer resource changing, for example, it could be valuable to us to know what the
Customer looked like when the event occurred. As long as we also get a reference to the entity itself so we can look up its current state, then we can get the best of both worlds.
There are other trade-offs to be made here, of course, when we’re accessing by reference. If we always go to the customer service to look at the information associated with a given
Customer, the load on the customer service can be too great. If we provide additional information when the resource is retrieved, letting us know at what time the resource was in the given state and perhaps how long we can consider this information to be fresh, then we can do a lot with caching to reduce load. HTTP gives us much of this support out of the box with a wide variety of cache controls, some of which we’ll discuss in more detail in Chapter 11.
Another problem is that some of our services might not need to know about the whole
Customer resource, and by insisting that they go look it up we are potentially increasing coupling. It could be argued, for example, that our email service should be more dumb, and that we should just send it the email address and name of the customer. There isn’t a hard-and-fast rule here, but be very wary of passing around data in requests when you don’t know its freshness.
In every single talk I have ever done about microservices, I get asked how do you do versioning? People have the legitimate concern that eventually they will have to make a change to the interface of a service, and they want to understand how to manage that. Let’s break down the problem a bit and look at the various steps we can take to handle it.
The best way to reduce the impact of making breaking changes is to avoid making them in the first place. You can achieve much of this by picking the right integration technology, as we’ve discussed throughout this chapter. Database integration is a great example of technology that can make it very hard to avoid breaking changes. REST, on the other hand, helps because changes to internal implementation detail are less likely to result in a change to the service interface.
Another key to deferring a breaking change is to encourage good behavior in your clients, and avoid them binding too tightly to your services in the first place. Let’s consider our email service, whose job it is to send out emails to our customers from time to time. It gets asked to send an order shipped email to customer with the ID 1234. It goes off and retrieves the customer with that ID, and gets back something like the response shown in Example 4-3.
Now to send the email, we need only the
telephoneNumber. We want to simply pull out those fields we care about, and ignore the rest. Some binding technology, especially that used by strongly typed languages, can attempt to bind all fields whether the consumer wants them or not. What happens if we realize that no one is using the
telephoneNumber and we decide to remove it? This could cause consumers to break needlessly.
Likewise, what if we wanted to restructure our
Customer object to support more details, perhaps adding some further structure as in Example 4-4? The data our email service wants is still there, and still with the same name, but if our code makes very explicit assumptions as to where the
lastname fields will be stored, then it could break again. In this instance, we could instead use XPath to pull out the fields we care about, allowing us to be ambivalent about where the fields are, as long as we can find them. This pattern—of implementing a reader able to ignore changes we don’t care about—is what Martin Fowler calls a Tolerant Reader.
<fullname>Sam "Magpiebrain" Newman
The example of a client trying to be as flexible as possible in consuming a service demonstrates Postel’s Law (otherwise known as the robustness principle), which states: “Be conservative in what you do, be liberal in what you accept from others.” The original context for this piece of wisdom was the interaction of devices over networks, where you should expect all sorts of odd things to happen. In the context of our request/response interaction, it can lead us to try our best to allow the service being consumed to change without requiring us to change.
It’s crucial to make sure we pick up changes that will break consumers as soon as possible, because even if we choose the best possible technology, breaks can still happen. I am strongly in favor of using consumer-driven contracts, which we’ll cover in Chapter 7, to help spot these problems early on. If you’re supporting multiple different client libraries, running tests using each library you support against the latest service is another technique that can help. Once you realize you are going to break a consumer, you have the choice to either try to avoid the break altogether or else embrace it and start having the right conversations with the people looking after the consuming services.
Wouldn’t it be great if as a client you could look just at the version number of a service and know if you can integrate with it? Semantic versioning is a specification that allows just that. With semantic versioning, each version number is in the form
MAJOR.MINOR.PATCH. When the
MAJOR number increments, it means that backward incompatible changes have been made. When
MINOR increments, new functionality has been added that should be backward compatible. Finally, a change to
PATCH states that bug fixes have been made to existing functionality.
To see how useful semantic versioning can be, let’s look at a simple use case. Our helpdesk application is built to work against version 1.2.0 of the customer service. If a new feature is added, causing the customer service to change to 1.3.0, our helpdesk application should see no change in behavior and shouldn’t be expected to make any changes. We couldn’t guarantee that we could work against version 1.1.0 of the customer service, though, as we may rely on functionality added in the 1.2.0 release. We could also expect to have to make changes to our application if a new 2.0.0 release of the customer service comes out.
You may decide to have a semantic version for the service, or even for an individual endpoint on a service if you are coexisting them as detailed in the next section.
This versioning scheme allows us to pack a lot of information and expectations into just three fields. The full specification outlines in very simple terms the expectations clients can have of changes to these numbers, and can simplify the process of communicating about whether changes should impact consumers. Unfortunately, I haven’t see this approach used enough in the context of distributed systems.
If we’ve done all we can to avoid introducing a breaking interface change, our next job is to limit the impact. The thing we want to avoid is forcing consumers to upgrade in lock-step with us, as we always want to maintain the ability to release microservices independently of each other. One approach I have used successfully to handle this is to coexist both the old and new interfaces in the same running service. So if we want to release a breaking change, we deploy a new version of the service that exposes both the old and new versions of the endpoint.
This allows us to get the new microservice out as soon as possible, along with the new interface, but give time for consumers to move over. Once all of the consumers are no longer using the old endpoint, you can remove it along with any associated code, as shown in Figure 4-5.
When I last used this approach, we had gotten ourselves into a bit of a mess with the number of consumers we had and the number of breaking changes we had made. This meant that we were actually coexisting three different versions of the endpoint. This is not something I’d recommend! Keeping all the code around and the associated testing required to ensure they all worked was absolutely an additional burden. To make this more manageable, we internally transformed all requests to the V1 endpoint to a V2 request, and then V2 requests to the V3 endpoint. This meant we could clearly delineate what code was going to be retired when the old endpoint(s) died.
This is in effect an example of the expand and contract pattern, which allows us to phase breaking changes in. We expand the capabilities we offer, supporting both old and new ways of doing something. Once the old consumers do things in the new way, we contract our API, removing the old functionality.
If you are going to coexist endpoints, you need a way for callers to route their requests accordingly. For systems making use of HTTP, I have seen this done with both version numbers in request headers and also in the URI itself—for example, /v1/customer/ or /v2/customer/. I’m torn as to which approach makes the most sense. On the one hand, I like URIs being opaque to discourage clients from hardcoding URI templates, but on the other hand, this approach does make things very obvious and can simplify request routing.
For RPC, things can be a little trickier. I have handled this with protocol buffers by putting my methods in different namespaces—for example,
v2.createCustomer—but when you are trying to support different versions of the same types being sent over the network, this can become really painful.
Another versioning solution often cited is to have different versions of the service live at once, and for older consumers to route their traffic to the older version, with newer versions seeing the new one, as shown in Figure 4-6. This is the approach used sparingly by Netflix in situations where the cost of changing older consumers is too high, especially in rare cases where legacy devices are still tied to older versions of the API. Personally, I am not a fan of this idea, and understand why Netflix uses it rarely. First, if I need to fix an internal bug in my service, I now have to fix and deploy two different sets of services. This would probably mean I have to branch the codebase for my service, and this is always problematic. Second, it means I need smarts to handle directing consumers to the right microservice. This behavior inevitably ends up sitting in middleware somewhere or a bunch of
nginx scripts, making it harder to reason about the behavior of the system. Finally, consider any persistent state our service might manage. Customers created by either version of the service need to be stored and made visible to all services, no matter which version was used to create the data in the first place. This can be an additional source of complexity.
Coexisting concurrent service versions for a short period of time can make perfect sense, especially when you’re doing things like blue/green deployments or canary releases (we’ll be discussing these patterns more in Chapter 7). In these situations, we may be coexisting versions only for a few minutes or perhaps hours, and normally will have only two different versions of the service present at the same time. The longer it takes for you to get consumers upgraded to the newer version and released, the more you should look to coexist different endpoints in the same microservice rather than coexist entirely different versions. I remain unconvinced that this work is worthwhile for the average project.
So far, we haven’t really touched on the world of the user interface. A few of you out there might just be providing a cold, hard, clinical API to your customers, but many of us find ourselves wanting to create beautiful, functional user interfaces that will delight our customers. But we really do need to think about them in the context of integration. The user interface, after all, is where we’ll be pulling all these microservices together into something that makes sense to our customers.
In the past, when I first started computing, we were mostly talking about big, fat clients that ran on our desktops. I spent many hours with Motif and then Swing trying to make my software as nice to use as possible. Often these systems were just for the creation and manipulation of local files, but many of them had a server-side component. My first job at ThoughtWorks involved creating a Swing-based electronic point-of-sale system that was just part of a large number of moving parts, most of which were on the server.
Over the last couple of years, organizations have started to move away from thinking that web or mobile should be treated differently; they are instead thinking about digital more holistically. What is the best way for our customers to use the services we offer? And what does that do to our system architecture? The understanding that we cannot predict exactly how a customer might end up interacting with our company has driven adoption of more granular APIs, like those delivered by microservices. By combining the capabilities our services expose in different ways, we can curate different experiences for our customers on their desktop application, mobile device, wearable device, or even in physical form if they visit our brick-and-mortar store.
So think of user interfaces as compositional layers—places where we weave together the various strands of the capabilities we offer. So with that in mind, how do we pull all these strands together?
Constraints are the different forms in which our users interact with our system. On a desktop web application, for example, we consider constraints such as what browser visitors are using, or their resolution. But mobile has brought a whole host of new constraints. The way our mobile applications communicate with the server can have an impact. It isn’t just about pure bandwidth concerns, where the limitations of mobile networks can play a part. Different sorts of interactions can drain battery life, leading to some cross customers.
The nature of interactions changes, too. I can’t easily right-click on a tablet. On a mobile phone, I may want to design my interface to be used mostly one-handed, with most operations being controlled by a thumb. Elsewhere, I might allow people to interact with services via SMS in places where bandwidth is at a premium—the use of SMS as an interface is huge in the global south, for example.
So, although our core services—our core offering—might be the same, we need a way to adapt them for the different constraints that exist for each type of interface. When we look at different styles of user interface composition, we need to ensure that they address this challenge. Let’s look at a few models of user interfaces to see how this might be achieved.
There are a couple of downsides with this approach. First, we have little ability to tailor the responses for different sorts of devices. For example, when I retrieve a customer record, do I need to pull back all the same data for a mobile shop as I do for a helpdesk application? One solution to this approach is to allow consumers to specify what fields to pull back when they make a request, but this assumes that each service supports this form of interaction.
Another key question: who creates the user interface? The people who look after the services are removed from how their services are surfaced to the users—for example, if another team is creating the UI, we could be drifting back into the bad old days of layered architecture where making even small changes requires change requests to multiple teams.
This communication could also be fairly chatty. Opening lots of calls directly to services can be quite intensive for mobile devices, and could be a very inefficient use of a customer’s mobile plan! Having an API gateway can help here, as you could expose calls that aggregate multiple underlying calls, although that itself can have some downsides that we’ll explore shortly.
Rather than having our UI make API calls and map everything back to UI controls, we could have our services provide parts of the UI directly, and then just pull these fragments in to create a UI, as in Figure 4-8. Imagine, for example, that the recommendation service provides a recommendation widget that is combined with other controls or UI fragments to create an overall UI. It might get rendered as a box on a web page along with other content.
A variation of this approach that can work well is to assemble a series of coarser-grained parts of a UI. So rather than creating small widgets, you are assembling entire panes of a thick client application, or perhaps a set of pages for a website.
These coarser-grained fragments are served up from server-side apps that are in turn making the appropriate API calls. This model works best when the fragments align well to team ownership. For example, perhaps the team that looks after order management in the music shop serves up all the pages associated with order management.
You still need some sort of assembly layer to pull these parts together. This could be as simple as some server-side templating, or, where each set of pages comes from a different app, perhaps you’ll need some smart URI routing.
One of the key advantages of this approach is that the same team that makes changes to the services can also be in charge of making changes to those parts of the UI. It allows us to get changes out faster. But there are some problems with this approach.
First, ensuring consistency of the user experience is something we need to address. Users want to have a seamless experience, not to feel that different parts of the interface work in different ways, or present a different design language. There are techniques to avoid this problem, though, such as living style guides, where assets like HTML components, CSS, and images can be shared to help give some level of consistency.
Another probem is harder to deal with. What happens with native applications or thick clients? We can’t serve up UI components. We could use a hybrid approach and use native applications to serve up HTML components, but this approach has been shown time and again to have downsides. So if you need a native experience, we will have to fall back to an approach where the frontend application makes API calls and handles the UI itself. But even if we consider web-only UIs, we still may want very different treatments for different types of devices. Building responsive components can help, of course.
There is one key problem with this approach that I’m not sure can be solved. Sometimes the capabilities offered by a service do not fit neatly into a widget or a page. Sure, I might want to surface recommendations in a box on a page on our website, but what if I want to weave in dynamic recommendations elsewhere? When I search, I want the type ahead to automatically trigger fresh recommendations, for example. The more cross-cutting a form of interaction is, the less likely this model will fit and the more likely it is that we’ll fall back to just making API calls.
A common solution to the problem of chatty interfaces with backend services, or the need to vary content for different types of devices, is to have a server-side aggregation endpoint, or API gateway. This can marshal multiple backend calls, vary and aggregate content if needed for different devices, and serve it up, as we see in Figure 4-9. I’ve seen this approach lead to disaster when these server-side endpoints become thick layers with too much behavior. They end up getting managed by separate teams, and being another place where logic has to change whenever some functionality changes.
The problem that can occur is that normally we’ll have one giant layer for all our services. This leads to everything being thrown in together, and suddenly we start to lose isolation of our various user interfaces, limiting our ability to release them independently. A model I prefer and that I’ve seen work well is to restrict the use of these backends to one specific user interface or application, as we see in Figure 4-10.
This pattern is sometimes referred to as backends for frontends (BFFs). It allows the team focusing on any given UI to also handle its own server-side components. You can see these backends as parts of the user interface that happen to be embedded in the server. Some types of UI may need a minimal server-side footprint, while others may need a lot more. If you need an API authentication and authorization layer, this can sit between our BFFs and our UIs. We’ll explore this more in Chapter 9.
The danger with this approach is the same as with any aggregating layer; it can take on logic it shouldn’t. The business logic for the various capabilities these backends use should stay in the services themselves. These BFFs should only contain behavior specific to delivering a particular user experience.
Many of the aforementioned options don’t need to be one-size-fits-all. I could see an organization adopting the approach of fragment-based assembly to create a website, but using a backends-for-frontends approach when it comes to its mobile application. The key point is that we need to retain cohesion of the underlying capabilities that we offer our users. We need to ensure that logic associated with ordering music or changing customer details lives inside those services that handle those operations, and doesn’t get smeared all over our system. Avoiding the trap of putting too much behavior into any intermediate layers is a tricky balancing act.
We’ve looked at approaches to breaking apart existing systems that are under our control, but what about when we can’t change the things we talk to? For many valid reasons, the organizations we work for buy commercial off-the-shelf software (COTS) or make use of software as a service (SaaS) offerings over which we have little control. So how do we integrate with them sensibly?
If you’re reading this book, you probably work at an organization that writes code. You might write software for your own internal purposes or for an external client, or both. Nonetheless, even if you are an organization with the ability to create a significant amount of custom software, you’ll still use software products provided by external parties, be they commercial or open source. Why is this?
First, your organization almost certainly has a greater demand for software than can be satisfied internally. Think of all the products you use, from office productivity tools like Excel to operating systems to payroll systems. Creating all of those for your own use would be a mammoth undertaking. Second, and most important, it wouldn’t be cost effective! The cost for you to build your own email system, for example, is likely to dwarf the cost of using an existing combination of mail server and client, even if you go for commercial options.
My clients often struggle with the question “Should I build, or should I buy?” In general, the advice I and my colleagues give when having this conversation with the average enterprise organization boils down to “Build if it is unique to what you do, and can be considered a strategic asset; buy if your use of the tool isn’t that special.”
For example, the average organization would not consider its payroll system to be a strategic asset. People on the whole get paid the same the world over. Likewise, most organizations tend to buy content management systems (CMSes) off the shelf, as their use of such a tool isn’t considered something that is key to their business. On the other hand, I was involved early on in rebuilding the Guardian’s website, and there the decision was made to build a bespoke content management system, as it was core to the newspaper’s business.
So the notion that we will occasionally encounter commercial, third-party software is sensible, and to be welcomed. However, many of us end up cursing some of these systems. Why is that?
One challenge associated with integrating with and extending the capabilities of COTS products like CMS or SaaS tools is that typically many of the technical decisions have been made for you. How do you integrate with the tool? That’s a vendor decision. Which programming language can you use to extend the tool? Up to the vendor. Can you store the configuration for the tool in version control, and rebuild from scratch, so as to enable continuous integration of customizations? It depends on choices the vendor makes.
If you are lucky, how easy—or hard—it is to work with the tool from a development point of view has been considered as part of the tool selection process. But even then, you are effectively ceding some level of control to an outside party. The trick is to bring the integration and customization work back on to your terms.
Many tools that enterprise organizations purchase sell themselves on their ability to be heavily customized just for you. Beware! Often, due to the nature of the tool chain you have access to, the cost of customization can be more expensive than building something bespoke from scratch! If you’ve decided to buy a product but the particular capabilities it provides aren’t that special to you, it might make more sense to change how your organization works rather than embark on complex customization.
Content management systems are a great example of this danger. I have worked with multiple CMSes that by design do not support continuous integration, that have terrible APIs, and for which even a minor-point upgrade in the underlying tool can break any customizations you have made.
Salesforce is especially troublesome in this regard. For many years it has pushed its Force.com platform, which requires the use of a programming language, Apex, that exists only within the Force.com ecosystem!
Another challenge is how you integrate with the tool. As we discussed earlier, thinking carefully about how you integrate between services is important, and ideally you want to standardize on a small number of types of integration. But if one product decides to use a proprietary binary protocol, another some flavor of SOAP, and another XML-RPC, what are you left with? Even worse are the tools that allow you to reach right inside their underlying data stores, leading to all the same coupling issues we discussed earlier.
COTS and SAAS products absolutely have their place, and it isn’t feasible (or sensible) for most of us to build everything from scratch. So how do we resolve these challenges? The key is to move things back on to your own terms.
The core idea here is to do any customizations on a platform you control, and to limit the number of different consumers of the tool itself. To explore this idea in detail, let’s look at a couple of examples.
In my experience, the CMS is one of the most commonly used product that needs to be customized or integrated with. The reason for this is that unless you want a basic static site, the average enterprise organization wants to enrich the functionality of its website with dynamic content like customer records or the latest product offerings. The source of this dynamic content is typically other services inside the organization, which you may have actually built yourself.
The temptation—and often the selling point of the CMS—is that you can customize the CMS to pull in all this special content and display it to the outside world. However, the development environment for the average CMS is terrible.
Let’s look at what the average CMS specializes in, and what we probably bought it for: content creation and content management. Most CMSes are pretty bad even at doing page layout, typically providing drag-and-drop tools that don’t cut the mustard. And even then, you end up needing to have someone who understands HTML and CSS to fine-tune the CMS templates. They tend to be terrible platforms on which to build custom code.
The answer? Front the CMS with your own service that provides the website to the outside world, as shown in Figure 4-11. Treat the CMS as a service whose role is to allow for the creation and retrieval of content. In your own service, you write the code and integrate with services how you want. You have control over scaling the website (many commercial CMSes provide their own proprietary add-ons to handle load), and you can pick the templating system that makes sense.
Most CMSes also provide APIs to allow for content creation, so you also have the ability to front that with your own service façade. For some situations, we’ve even used such a façade to abstract out the APIs for retrieving content.
We’ve used this pattern multiple times across ThoughtWorks in the last few years, and I’ve done this more than once myself. One notable example was a client that was looking to push out a new website for its products. Initially, it wanted to build the entire solution on the CMS, but it had yet to pick one. We instead suggested this approach, and started development of the fronting website. While waiting for the CMS tool to be selected, we faked it by having a web service that just surfaced static content. We ended up going live with the site well before the CMS was selected by using our fake content service in production to surface content to the live site. Later on, we were able to just drop in the eventually selected tool without any change to the fronting application.
Using this approach, we keep the scope of what the CMS does down to a minimum and move customizations onto our own technology stack.
The CRM—or Customer Relationship Management—tool is an often-encountered beast that can instill fear in the heart of even the hardiest architect. This sector, as typified by vendors like Salesforce or SAP, is rife with examples of tools that try to do everything for you. This can lead to the tool itself becoming a single point of failure, and a tangled knot of dependencies. Many implementations of CRM tools I have seen are among the best examples of adhesive (as opposed to cohesive) services.
The scope of such a tool typically starts small, but over time it becomes an increasingly important part of how your organization works. The problem is that the direction and choices made around this now-vital system are often made by the tool vendor itself, not by you.
I was involved recently in an exercise to try to wrest some control back. The organization I was working with realized that although it was using the CRM tool for a lot of things, it wasn’t getting the value of the increasing costs associated with the platform. At the same time, multiple internal systems were using the less-than-ideal CRM APIs for integration. We wanted to move the system architecture toward a place where we had services that modeled our businesses domain, and also lay the groundwork for a potential migration.
The first thing we did was identify the core concepts to our domain that the CRM system currently owned. One of these was the concept of a project—that is, something to which a member of staff could be assigned. Multiple other systems needed project information. What we did was instead create a project service. This service exposed projects as RESTful resources, and the external systems could move their integration points over to the new, easier-to-work-with service. Internally, the project service was just a façade, hiding the detail of the underlying integration. You can see this in Figure 4-12.
The work, which at the time of this writing was still under way, was to identify other domain concepts that the CRM was handling, and create more façades for them. When the time comes for migration away from the underlying CRM, we could then look at each façade in turn to decide if an internal software solution or something off the shelf could fit the bill.
When it comes to legacy or even COTS platforms that aren’t totally under our control, we also have to deal with what happens when we want to remove them or at least move away from them. A useful pattern here is the Strangler Application Pattern. Much like with our example of fronting the CMS system with our own code, with a strangler you capture and intercept calls to the old system. This allows you to decide if you route these calls to existing, legacy code, or direct them to new code you may have written. This allows you to replace functionality over time without requiring a big bang rewrite.
When it comes to microservices, rather than having a single monolithic application intercepting all calls to the existing legacy system, you may instead use a series of microservices to perform this interception. Capturing and redirecting the original calls can become more complex in this situation, and you may require the use of a proxy to do this for you.
We’ve looked at a number of different options around integration, and I’ve shared my thoughts on what choices are most likely to ensure our microservices remain as decoupled as possible from their other collaborators:
Avoid database integration at all costs.
Understand the trade-offs between REST and RPC, but strongly consider REST as a good starting point for request/response integration.
Prefer choreography over orchestration.
Avoid breaking changes and the need to version by understanding Postel’s Law and using tolerant readers.
Think of user interfaces as compositional layers.
We covered quite a lot here, and weren’t able to go into depth on all of these topics. Nonetheless, this should be a good foundation to get you going and point you in the right direction if you want to learn more.
We also spent some time looking at how to work with systems that aren’t completely under our control in the form of COTS products. It turns out that this description can just as easily apply to software we wrote!
Some of the approaches outlined here apply equally well to legacy software, but what if we want to tackle the often-monumental task of bringing these older systems to heel and decomposing them into more usable parts? We’ll discuss that in detail in the next chapter.