A system architecture is the conceptual model that defines the structure, behavior, and more views of a system. An architecture description is a formal description and representation of a system, organized in a way that supports reasoning about the structures and behaviors of the system.
Many third-party applications, in view of software architecture best practices, make avail software packages as downloadable plug-ins or versions. It is to this particular type, that the Microkernel Architecture is most suited as a result of which it is also called the plug-in architecture pattern.
With this style, enterprise application development services can add pluggable features to an erstwhile version of the software providing for extensibility. The architecture is formulated of two components, with one part dedicated to the core system and the other to the plug-ins. Minimalism is followed while designing the core of the architecture, that stores just the right proportion of components to render the system effective.
The most relatable example of the Microkernel Architecture would be any internet browser. You download a version of the application, that is essentially a software, and depending upon the missing functionalities, download and add plug-ins. Enterprise software development services rely on this pattern for designing large scale, complex applications as well. An example of such a business application could be a software for processing insurance claims.
Benefits
This design has proven its worth as one being highly flexible. Operational possibilities arising from the capability of plug-ins make reacting to such changes in near real-time critical to sustenance. Such changes can be dealt with in isolation with the core system regaining its stable state, for the most part, therefore requiring less developmental updates over time.
An enterprise software development company could face a downtime issue at the time of deployment but that can be minimized or altogether avoided by adding plug-in modules to the core dynamically.
A software development company could test plug-in prototypes in isolation and see for performance issues without affecting the core of the architecture.
Microkernel Architecture is most appreciated for maintaining high-performance applications as the software can be customized to include only those capabilities that are needed the most.
Potential Drawbacks
Apps such as those conceptualized by enterprise mobile app development services, have a non-negotiable scope to scale. However, the Microkernel Architecture is grounded on designs of the product and naturally suited to apps that are smaller in size.
An enterprise app development company could find the Microkernel pattern rather hard to execute due to the vast number of plug-ins compatible with the core. This calls for drawing out governance contracts, updating plug-in regitaries and so many formalities that the implementation becomes a challenge.
Ideal For
Microkernel Architecture is best suited for workflow applications in addition to those that need job scheduling. As pointed above, like a web browser, any application that you want to release with just the right amount of specs but want to leave room that can be filled in by installing additional plug-ins can be built with this design pattern.
CQRS has been around for a long time, but if you’re not familiar with it, it’s new to you. Take a look at a quick introduction to what it is and how it works.
CQRS, Command Query Responsibility Segregation, is a method for optimizing writes to databases (queries) and reads from them (commands). Nowadays, many companies work with one large database. But these databases weren’t originally built to scale. When they were planned in the 1990s, there wasn’t so much data that needed to be consumed quickly.
In the age of Big Data, many databases can’t handle the growing number of complex reads and writes, resulting in errors, bottlenecks, and slow customer service. This is something DevOps engineers need to find a solution for.
Take a ride-sharing service like Uber or Lyft (just as an example; this is not an explanation of how these companies work). Traditionally (before CQRS), the client app queries the service database for drivers, whose profiles they see on their screens. At the same time, drivers can send commands to the service database to update their profile. The service database needs to be able to crosscheck queries for drivers, user locations, and driver commands about their profile and send the cache to client apps and drivers. These kinds of queries can put a strain on the database.
CQRS to the rescue. CQRS dictates the segregation of complex queries and commands. Instead of all queries and commands going to the database by the same code, the queries and data manipulation routines are simplified by adding a process in between for the data processing. This reduces stress on the database by enabling easier reading and writing cache data from the database.
A manifestation of such a segregation would be:
Pushing the commands into a robust and scalable queue platform like Apache Kafka and storing the raw commands in a data warehouse software like AWS Redshift or HP Vertica.
Creating an aggregated cache table where data is queried from and displayed to the users.
The magic happens in the streaming part. Advanced calculations based on Google’s MapReduce technology enable quick and advanced distributed calculations across many machines. As a result, large amounts of data are quickly processed, and the right data gets to the right users, quickly and on time.
CQRS can be used by any service that is based on fast-growing data, whether user data (i.e. user profiles) or machine data (i.e. monitoring metrics). If you want to learn more about CQRS, check out this article.
There is something going on within the front-end community recently. Server-side rendering is getting more and more traction thanks to React and its built-in server-side hydration feature. But it’s not the only solution to deliver a fast experience to the user with a super fast time-to-first-byte (TTFB) score: Pre-rendering is also a pretty good strategy. What’s the difference between these solutions and a fully client-rendered application?
Client-rendered Application
Since frameworks like Angular, Ember.js, and Backbone exists, front-end developers have tended to render everything client-side. Thanks to Google and its ability to “read” JavaScript, it works pretty well, and it’s even SEO friendly.
With a client-side rendering solution, you redirect the request to a single HTML file and the server will deliver it without any content (or with a loading screen) until you fetch all the JavaScript and let the browser compile everything before rendering the content.
Under a good and reliable internet connection, it’s pretty fast and works well. But it can be a lot better, and it doesn’t have to be difficult to make it that way. That’s what we will see in the following sections.
Server-side Rendering (SSR)
An SSR solution is something we used to do a lot, many years ago, but tend to forget in favor of a client-side rendering solution.
With old server-side rendering solutions, you built a web page—with PHP for example—the server compiled everything, included the data, and delivered a fully populated HTML page to the client. It was fast and effective.
But… every time you navigated to another route, the server had to do the work all over again: Get the PHP file, compile it, and deliver the HTML, with all the CSS and JS delaying the page load to a few hundred ms or even whole seconds.
What if you could do the first page load with the SSR solution, and then use a framework to do dynamic routing with AJAX, fetching only the necessary data?
This is why SSR is getting more and more traction within the community because React popularized this problem with an easy-to-use solution: The RenderToString method.
This new kind of web application is called a universal app or an isomorphic app. There’s still some controversy over the exact meanings of these terms and the relationship between them, but many people use them interchangeably.
Anyway, the advantage of this solution is being able to develop an app server-side and client-side with the same code and deliver a really fast experience to the user with custom data. The disadvantage is that you need to run a server.
SSR is used to fetch data and pre-populate a page with custom content, leveraging the server’s reliable internet connection. That is, the server’s own internet connection is better than that of a user with lie-fi), so it’s able to prefetch and amalgamate data before delivering it to the user.
With the pre-populated data, using an SSR app can also fix an issue that client-rendered apps have with social sharing and the OpenGraph system. For example, if you have only one index.html file to deliver to the client, they will only have one type of metadata—most likely your homepage metadata. This won’t be contextualized when you want to share a different route, so none of your routes will be shown on other sites with their proper user content (description and preview picture) that users would want to share with the world.
Pre-rendering
The mandatory server for a universal app can be a deterrent for some and may be overkill for a small application. This is why pre-rendering can be a really nice alternative.
I discovered this solution with Preact and its own CLI that allows you to compile all pre-selected routes so you can store a fully populated HTML file to a static server. This lets you deliver a super-fast experience to the user, thanks to the Preact/React hydration function, without the need for Node.js.
The catch is, because this isn’t SSR, you don’t have user-specific data to show at this point—it’s just a static (and somewhat generic) file sent directly on the first request, as-is. So if you have user-specific data, here is where you can integrate a beautifully designed skeleton to show the user their data is coming, to avoid some frustration on their part:
There is another catch: In order for this technique to work, you still need to have a proxy or something to redirect the user to the right file.
Why?
With a single-page application, you need to redirect all requests to the root file, and then the framework redirects the user with its built-in routing system. So the first page load is always the same root file.
In order for a pre-rendering solution to work, you need to tell your proxy that some routes need specific files and not always the root index.html file.
For example, say you have four routes (/, /about, /jobs, and blog) and all of them have different layouts. You need four different HTML files to deliver the skeleton to the user that will then let React/Preact/etc. rehydrate it with data. So if you redirect all those routes to the root index.html file, the page will have an unpleasant, glitchy feel during loading, whereby the user will see the skeleton of the wrong page until it finishes loading and replaces the layout. For example, the user might see a homepage skeleton with only one column, when they had asked for a different page with a Pinterest-like gallery.
The solution is to tell your proxy that each of those four routes needs a specific file:
https://my-website.com → Redirect to the root index.html file
https://my-website.com/about → Redirect to the /about/index.html file
https://my-website.com/jobs → Redirect to the /jobs/index.html file
https://my-website.com/blog → Redirect to the /blog/index.html file
This is why this solution can be useful for small applications—you can see how painful it would be if you had a few hundred pages.
Strictly speaking, it’s not mandatory to do it this way—you could just use a static file directly. For example, https://my-website.com/about/ will work without any redirection because it will automatically search for an index.html inside its directory. But you need this proxy if you have param urls—https://my-website.com/profile/guillaume will need to redirect the request to /profile/index.html with its own layout, because profile/guillaume/index.html doesn’t exist and will trigger a 404 error.
In short, there are three basic views at play with the rendering strategies described above: A loading screen, a skeleton, and the full page once it’s finally rendered.
Depending on the strategy, sometimes we use all three of these views, and sometimes we jump straight to a fully-rendered page. Only in one use case are we forced to use a different approach:
Method
Landing (e.g. /)
Static (e.g. /about)
Fixed Dynamic (e.g. /news)
Parameterized Dynamic (e.g. /users/:user-id)
Client-rendered
Loading → Full
Loading → Full
Loading → Skeleton → Full
Loading → Skeleton → Full
Pre-rendered
Full
Full
Skeleton → Full
HTTP 404 (page not found)
Pre-rendered With Proxy
Full
Full
Skeleton → Full
Skeleton → Full
SSR
Full
Full
Full
Full
Client-only Rendering is Often Not Enough
Client-rendered applications are something we should avoid now because we can do better for the user. And doing better, in this case, is as easy as the pre-rendering solution. It’s definitely an improvement over client-only rendering and easier to implement than a fully server-side-rendered application.
An SSR/universal application can be really powerful if you have a large application with a lot of different pages. It allows your content to be focused and relevant when talking to a social crawler. This is also true for search engine robots, which now take your site’s performance into account when ranking it.
Stay tuned for a follow-up tutorial, where I will walk through the transformation of an SPA into pre-rendered and SSR versions, and compare their performance.
Ever wondered how large enterprise scale systems are designed? Before major software development starts, we have to choose a suitable architecture that will provide us with the desired functionality and quality attributes. Hence, we should understand different architectures, before applying them to our design.
What is an Architectural Pattern?
According to Wikipedia,
An architectural pattern is a general, reusable solution to a commonly occurring problem in software architecture within a given context. Architectural patterns are similar to software design pattern but have a broader scope.
In this article, I will be briefly explaining the following 10 common architectural patterns with their usage, pros and cons.
Layered pattern
Client-server pattern
Master-slave pattern
Pipe-filter pattern
Broker pattern
Peer-to-peer pattern
Event-bus pattern
Model-view-controller pattern
Blackboard pattern
Interpreter pattern
1. Layered pattern
This pattern can be used to structure programs that can be decomposed into groups of subtasks, each of which is at a particular level of abstraction. Each layer provides services to the next higher layer.
The most commonly found 4 layers of a general information system are as follows.
Presentation layer (also known as UI layer)
Application layer (also known as service layer)
Business logic layer (also known as domain layer)
Data access layer (also known as persistence layer)
Usage
General desktop applications.
E commerce web applications.
Layered pattern
2. Client-server pattern
This pattern consists of two parties; a server and multiple clients. The server component will provide services to multiple client components. Clients request services from the server and the server provides relevant services to those clients. Furthermore, the server continues to listen to client requests.
Usage
Online applications such as email, document sharing and banking.
Client-server pattern
3. Master-slave pattern
This pattern consists of two parties; master and slaves. The master component distributes the work among identical slave components, and computes a final result from the results which the slaves return.
Usage
In database replication, the master database is regarded as the authoritative source, and the slave databases are synchronized to it.
Peripherals connected to a bus in a computer system (master and slave drives).
Master-slave pattern
4. Pipe-filter pattern
This pattern can be used to structure systems which produce and process a stream of data. Each processing step is enclosed within a filter component. Data to be processed is passed through pipes. These pipes can be used for buffering or for synchronization purposes.
Usage
Compilers. The consecutive filters perform lexical analysis, parsing, semantic analysis, and code generation.
Workflows in bioinformatics.
Pipe-filter pattern
5. Broker pattern
This pattern is used to structure distributed systems with decoupled components. These components can interact with each other by remote service invocations. A broker component is responsible for the coordination of communication among components.
Servers publish their capabilities (services and characteristics) to a broker. Clients request a service from the broker, and the broker then redirects the client to a suitable service from its registry.
In this pattern, individual components are known as peers. Peers may function both as a client, requesting services from other peers, and as a server, providing services to other peers. A peer may act as a client or as a server or as both, and it can change its role dynamically with time.
This pattern primarily deals with events and has 4 major components; event source, event listener, channel and event bus. Sources publish messages to particular channels on an event bus. Listeners subscribe to particular channels. Listeners are notified of messages that are published to a channel to which they have subscribed before.
Usage
Android development
Notification services
Event-bus pattern
8. Model-view-controller pattern
This pattern, also known as MVC pattern, divides an interactive application in to 3 parts as,
model — contains the core functionality and data
view — displays the information to the user (more than one view may be defined)
controller — handles the input from the user
This is done to separate internal representations of information from the ways information is presented to, and accepted from, the user. It decouples components and allows efficient code reuse.
Usage
Architecture for World Wide Web applications in major programming languages.
This pattern is useful for problems for which no deterministic solution strategies are known. The blackboard pattern consists of 3 main components.
blackboard — a structured global memory containing objects from the solution space
knowledge source — specialized modules with their own representation
control component — selects, configures and executes modules.
All the components have access to the blackboard. Components may produce new data objects that are added to the blackboard. Components look for particular kinds of data on the blackboard, and may find these by pattern matching with the existing knowledge source.
Usage
Speech recognition
Vehicle identification and tracking
Protein structure identification
Sonar signals interpretation.
Blackboard pattern
10. Interpreter pattern
This pattern is used for designing a component that interprets programs written in a dedicated language. It mainly specifies how to evaluate lines of programs, known as sentences or expressions written in a particular language. The basic idea is to have a class for each symbol of the language.
Usage
Database query languages such as SQL.
Languages used to describe communication protocols.
Interpreter pattern
Comparison of Architectural Patterns
The table given below summarizes the pros and cons of each architectural pattern.
Comparison of Architectural Patterns
Hope you found this article useful. I would love to hear your thoughts. 😇
One of the most common, and efficient, models deployed by enterprises is the Layered Architecture, also called the n-tiered pattern. It packs similar components together in a horizontal manner and is self-independent. What does that mean?
It implies that the layers of the model are interconnected to each other but not interdependent. Similar components stay at the same level allowing the layers to be separated inadvertently based on the nature of the code. It is this isolation, that lends the software layers an independent nature.
Consider an instance, wherein you’d want to switch from an Oracle database to an SQL. This shift may cause you to upend the database layer but will not have a domino effect on any other layer.
Evidently, it serves a challenge for an enterprise software architect to create layers that separate from each other. Nevertheless, since the roles of each layer are clearly distinct, it accredits this software development architecture the following qualities:
It is easily maintainable as enterprise software developers with limited, or should we say pertinent, knowledge can be assigned to operate on a single layer.
You can test changes in layers separately from each other.
Upgraded versions of the software can be implemented effortlessly.
The flow of code is top-down, meaning it enters the presentation layer first and trickles down to the bottom-most layer that is the database layer. Each layer has a designated task based on the nature of the components it preserves. These could be checking the consistency of values within the code or reformatting the code altogether.
Refactoring – a key – way to lower frontend maintenance cost is a software development process by which developers change the internal shape and size of the code. They do it without affecting its external attributes can also be carried out in an n-tiered model.
This software development architecture can be customized to add layers to the presentation, business, persistence, and database levels. Such a model is called a Hybrid Layered architecture.
Benefits
Amongst the various types of software architecture, the layered variant suits enterprises that don’t want to go overboard with experimenting and want to stick to the traditional software architecture design patterns.
Testing components become relatively easier as inter-dependencies are negligible in this format of software development engineering.
Considering many software frameworks were built with the backdrop of an n-tiered structure, applications built with them, as a result, happen to be in the layered format as well.
Potential Drawbacks
Larger applications tend to be resource-intensive if based on this format, therefore for such projects, it is advised to overlook the layered pattern.
Although the layers are independent, yet the entire version of the software is installed as a single unit. Therefore, even if you update a single layer, you would have to re-install the entire apparatus all over again.
Such systems are not scalable due to the coupling between the layers.
Ideal For
The Layered architecture pattern suits the niche of LOB i.e. Line of Business Applications. These are applications that are essential to the functioning of the business itself. For instance, the accounts department of an organization needs software such as QuickBooks, Xero, Sage or Wave Accounting for keeping financial data.
Similarly, the marketing team would demand a customer relationship management software slash tool to help them cope with the volume of interactions. In short, applications that do more than just CRUD (create, read, update, and delete) operations are suited to the layered architecture pattern.
Quality Assurance practice was relatively simple when we built the Monolith systems with traditional waterfall development models. The Quality Assurance (QA) teams would start the GUI layer’s validation process after waiting for months for the product development. To enhance the testing process, we would have to spend a lot of efforts and $$$ (commercial tools) in automating the GUI via various tools like Microfocus UFT, Selenium, Test Complete, Coded UI, Ranorex etc., and most often these tests are complex to maintain and scale. Thus, most QA teams would have to restrict their automated tests to smoke and partial regression, ending in inadequate test coverage.
With modern technology, the new era tech companies, including Varo, have widely adopted Microservices-based architecture combined with the Agile/ Dev-Ops development model. This opens up a lot of opportunities for Quality Assurance practice, and in my opinion, this was the origin of the transformation from Quality Assurance to “Quality Engineering.”
The Common Pitfall
While automated testing gives us a massive benefit with the three R’s (Repeatable → Run any number of times, Reliable → Run with confidence, Reusable → Develop, and share), it also comes with maintenance costs. I like to quote Grady Boosh’s — “A fool with a tool is still a fool.” Targeting inappropriate areas would not provide us the desired benefit. We should consider several factors to choose the right candidate for automation. Few to name are the lifespan of the product, the volatility of the requirements, the complexity of the tests, business criticality, and technical feasibility.
It’s well known that the cost of a bug increases toward the right of software lifecycle. So it is necessary to implement several walls of defenses to arrest these software bugs as early as possible (Shift-Left Testing paradigm). By implementing an agile development model with a fail-fast mindset, we have taken care of our first wall of defense. But to move faster in this shorter development cycle, we must build robust automated test suites to take care of the rolled out features and make room for testing the new features.
The 3 Layer Architecture
The Varo architecture comprises three essential layers.
The Frontend layer (Web, iOS, Mobile apps) — User experience
The Orchestration layer (GraphQl) — Makes multiple microservices calls and returns the decision and data to the frontend apps
The Microservice layer (gRPC, Kafka, Postgres) — Core business layer
While understanding the microservice architecture for testing, there were several questions posed.
Which layer to test?
What to test in these layers?
Does testing frontend automatically validate downstream service?
Does testing multiple layers introduce redundancies?
We will try to answer these by analyzing the table below, which provides an overview of what these layers mean for quality engineering.
After inferring the table, we have loosely adopted the Testing Pyramid pattern to invest in automated testing as:
Full feature/ functional validations on Microservices layer
Business process validations on Orchestration layer
E2E validations on Frontend layer
The diagram below best represents our test strategy for each layer.
Note: Though we have automated white-box tests such as unit-test and integration-test, we exclude those in this discussion.
Use Case
Let’s take the example below for illustration to understand best how this Pyramid works.
The user is presented with a form to submit. The form accepts three inputs — Field A to get the user identifier, Field B a drop-down value, and Field C accepts an Integer value (based on a defined range).
Once the user clicks on the Submit button, the GraphQL API calls Microservice A to get what type of customer. Then it calls the next Microservice B to validate the acceptable range of values for Field C (which depends on the values from Field A and Field B).
Validations:
1. Feature Validations
✓ Positive behavior (Smoke, Functional, System Integration)
Validating behavior with a valid set of data combinations
Validating database
Validating integration — Impact on upstream/ downstream systems
✓ Negative behavior
Validating with invalid data (for example: Invalid authorization, Disqualified data)
2. Fluent Validations
✓ Evaluating field definitions — such as
Mandatory field (not empty/not null)
Invalid data types (for example: Int → negative value, String → Junk values with special characters or UUID → Invalid UUID formats)
Let’s look at how the “feature validations” can be written for the above use case by applying one of the test case authoring techniques — Boundary Value Analysis.
To test the scenario above, it would require 54 different combinations of feature validations, and below is the rationale to pick the right candidate for each layer.
Microservice Layer: This is the layer delivered first, enabling us to invest in automated testing as early as possible (Shift-Left). And the scope for our automation would be 100% of all the above scenarios.
Orchestration Layer: This layer translates the information from the microservice to frontend layers; we try to select at least two tests (1 positive & 1 negative) for each scenario. The whole objective is to ensure the integration is working as expected.
Frontend Layer: In this layer, we focus on E2E validations, which means these validations would be a part of the complete user journey. But we would ensure that we have at least one or more positive and negative scenarios embedded in those E2E tests. Business priority (frequently used data by the real-time users) helps us to select the best scenario for our E2E validations.
Conclusion
There are always going to be sets of redundant tests across these layers. But that is the trade-off we had to take to ensure that we have correct quality gates on each of these layers. The pros of this approach are that we achieve safe and faster deployments to Production by enabling quicker testing cycles, better test coverage, and risk-free decisions. In addition, having these functional test suites spread across the layers helps us to isolate the failures in respective areas, thus saving us time to troubleshoot an issue.
However, often, not one size fits all. The decision has to be made based on understanding how the software architecture is built and the supporting infrastructure to facilitate the testing efforts. One of the critical success factors for this implementation is building a good quality engineering team with the right skills and proper tools. But that is another story — Coming soon “Quality Engineering: Redefined.”
To solve such problems, a preferred approach is to take advantage of a centralized logging service that aggregate logs from each service instance.
Users can search through these logs from one centralized spot and configure alerts when certain messages appear.
Standard tools are available and widely used by various enterprises.
ELK Stack is the most frequently used solution, where logging daemon, Logstash, collects and aggregate logs which can be searched via a Kibana dashboard indexed by Elasticsearch.
Microservices are defined as a self-regulating, and independent codebase that can be written and maintained even by a small team of developers. Microservices Architecture consists of such loosely coupled services with each service responsible for the execution of its associated business logic.
The services are separated from each other based on the nature of their domains and belong to a mini-microservice pool. Enterprise mobile app developers leverage the capabilities of this architecture especially for complex applications.
Microservices Architecture allows developers to release versions of software thanks to sophisticated automation of software building, testing, and deployment – something that acts as a prime differentiation point between Microservices and Monolithic architecture.
Benefits
Since the services are bifurcated into pools, the architecture design pattern makes the system highly fault-tolerant. In other words, the whole software won’t collapse on its head even if some microservices cease to function.
An enterprise mobile app development company working on such an architecture for clients can deploy multiple programming languages to build different microservices for their specific purpose. Therefore the technology stack can be kept updated with the latest upgrades in computing.
This architecture is a perfect fit for applications that need to scale. Since the services are already independent of each other, they can scale individually rather than overloading the entire system with the need to expand.
Services can be integrated into any application depending upon the scope of work.
Potential Drawbacks
Since each service is unique in its ability to contribute to the whole codebase, it could be challenging for an enterprise mobile application development company to interlink all and operate so many distinctive services seamlessly.
Developers must define a standard protocol for all services to adhere to. It is important to do so, as the decentralized approach towards coding microservices in multiple languages can pose serious issues while debugging.
Each microservice with its limited environment is responsible to maintain the integrity of the data. It is up to the architects of such a system to come up with a universally consistent data integrity protocol, wherever possible.
You definitely need the best of breed professionals to design such a system for you as the technology stack keeps changing.
Ideal For
Use Microservices Architecture for apps in which a specific segment will be used heavily than the others and would need a sporadic burst of scaling. Instead of a standalone application you may also deploy this for a service that provides functionality to other applications of the system.
Almost every website or web-application uses routing. Discovering a website by changing its URL is a very powerful feature that comes standard with the web. How all of this is handled can vary a lot between different websites and web-applications.
All websites and web-applications, whether they use server-side or client-side routing, are accessed from a server. How a website or web-application responds to different URLs is commonly handled server-side, although with the rising popularity of JavaScript frameworks, other ways have been found to manage routing.
Routing
Routing is the mechanism by which requests are connected to some code. It is essentially the way you navigate through a website or web-application. By clicking on a link, the URL changes which provides the user with some new data or a new webpage.
Server-side
When browsing, the adjustment of a URL can make a lot of things happen. This will happen regularly by clicking on a link, which in turn will request a new page from the server. This is what we call a server-side route. A whole new document is served to the user.
A server-side request causes the whole page to refresh. This is because a new GET request is sent to the server which responds with a new document, completely discarding the old page altogether.
Pros
A server-side route will only request the data that’s needed. No more, no less.
Because server-side routing has been the standard for a long time, search engines are optimised for webpages that come from the server.
Cons
Every request results in a full-page refresh. That means that unnecessary data is being requested. A header and a footer of a webpage often stays the same. This isn’t something you would want to request from the server again.
It can take a while for the page to be rendered. However, this is only the case when the document to be rendered is very large or when you have slow internet speed.
Client-side
A client-side route happens when the route is handled internally by the JavaScript that is loaded on the page. When a user clicks on a link, the URL changes but the request to the server is prevented. The adjustment to the URL will result in a changed state of the application. The changed state will ultimately result in a different view of the webpage. This could be the rendering of a new component, or even a request to a server for some data that the application will turn into some HTML elements.
It is important to note that the whole page won’t refresh when using client-side routing. There are just some elements inside the application that will change.
Pros
Because less data is processed, routing between views is generally faster.
Smooth transitions and animations between views are easier to implement.
Cons
The whole website or web-application needs to be loaded on the first request. That’s why the initial loading time usually takes longer.
Because the whole website or web-application is loaded initially, there is a possibility that there is data downloaded for views you won’t even come across.
It requires more setup work or even a library. Because server-side is the standard, extra code must be written to make client-side routing possible.
Search engine crawling is less optimised. Google is making good progress on crawling single-paged-apps, but it isn’t nearly as efficient as server-side routed websites.
Summary
There is no best method to manage your routing. Server-side and client-side routing both have their advantages and weaknesses. It is important to make your decision based on the needs of your website or web-application, or heck, even combine the two.
Event sourcing, eventual consistency, microservices, CQRS… These are quickly becoming household names in mainstream application development. But do you know what makes them tick? What are the basic building blocks required to assemble complex, business-centric applications from fine-grained services without turning the lot into a big ball of mud?
This article examines a fundamental building block — event streaming. Leading the charge will be Apache Kafka — the de facto standard in event streaming platforms, which we’ll observe through Kafdrop — a feature-packed web UI.
A Brief Intro
Event streaming platforms reside in the broader class of Message-oriented Middleware (MoM) and are similar to traditional message queues and topics but offer stronger temporal guarantees and typically order-of-magnitude performance gains due to log-structured immutability. In simple terms, write operations are mostly limited to sequential appends, which make them fast. Really fast.
Whereas messages in a traditional Message Queue (MQ) tend to be arbitrarily ordered and generally independent of one another, events (or records) in a stream tend to be strongly ordered, often chronologically or causally. Also, a stream persists its records, whereas an MQ will discard a message as soon as it has been read.
For this reason, event streaming tends to be a better fit for Event-Driven Architectures, encompassing event sourcing, eventual consistency, and CQRS concepts. (Of course, there are FIFO message queues too, but the differences between FIFO queues and fully-fledged event streaming platforms are quite substantial, not limited to ordering alone.)
Event streaming platforms are a comparatively recent paradigm within the broader MoM domain. There are only a handful of mainstream implementations available, compared to hundreds of MQ-style brokers, some going back to the 1980s (e.g. Tuxedo). Compared to established standards such as AMQP, MQTT, XMPP, and JMS, there are no equivalent standards in the streaming space.
Event streaming platforms are an active area of continuous research and experimentation. In spite of this, streaming platforms aren’t just a niche concept or an academic idea with a few esoteric use cases; they can be applied effectively to a broad range of messaging and eventing scenarios, routinely displacing their more traditional counterparts.
The diagram below offers a brief overview of the Kafka component architecture. While the intention isn’t to indoctrinate you with all of Kafka’s inner workings, some appreciation of its design will go a long way in explaining the key concepts that we will cover shortly.
Kafka is a distributed system comprising several key components:
Broker nodes: Responsible for the bulk of I/O operations and durable persistence within the cluster. Brokers accommodate the append-only log files that comprise the topic partitions hosted by the cluster. Partitions can be replicated across multiple brokers for both horizontal scalability and increased durability — these are called replicas. A broker node may act as the leader for certain replicas, while being a follower for others. A single broker node will also be elected as the cluster controller — responsible for the internal management of partition states. This includes the arbitration of the leader-follower roles for any given partition.
ZooKeeper nodes: Under the hood, Kafka needs a way to manage the overall controller status in the cluster. Should the controller drop out for whatever reason, there is a protocol in place to elect another controller from the set of remaining brokers. The actual mechanics of controller election, heart-beating, and so forth, are largely implemented in ZooKeeper. ZooKeeper also acts as a configuration repository of sorts, maintaining cluster metadata, leader-follower states, quotas, user information, ACLs, and other housekeeping items. Due to the underlying gossiping and consensus protocol, the number of ZooKeeper nodes must be odd.
Producers: These are client applications responsible for appending records to Kafka topics. Because of the log-structured nature of Kafka and the ability to share topics across multiple consumer ecosystems, only producers are able to modify the data in the underlying log files. The actual I/O is performed by the broker nodes on behalf of the producer clients. Any number of producers may publish to the same topic, selecting the partitions used to persist the records.
Consumers: These are client applications that read from topics. Any number of consumers may read from the same topic; however, depending on the configuration and grouping of consumers, there are rules governing the distribution of records among the consumers.
Topics, Partitions, Records, and Offsets
A partition is a totally ordered sequence of records and is fundamental to Kafka. A record has an ID — a 64-bit integer offset and a millisecond-precise timestamp. Also, it may have a key and a value; both are byte arrays and both are optional. The term “totally ordered” simply means that for any given producer, records will be written in the order they were emitted by the application. If record P was published before Q, then P will precede Q in the partition. (Assuming P and Q share a partition.)
Furthermore, they will be read in the same order by all consumers; P will always be read before Q, for every possible consumer. This ordering guarantee is vital in a large majority of use cases. Published records will generally correspond to some real-life events, and preserving the timeline of these events is often essential.
Note: Kafka uses the term “record,” where others might use “message” or “event.” In this article, we will use the terms interchangeably, depending on the context. Similarly, you might see the term “stream” as a generic substitute for “topic.”
There is no recognized ordering across producers. If two (or more) producers emit records simultaneously, those records may materialize in arbitrary order. That said, this order will still be observed uniformly across all consumers.
A record’s offset uniquely identifies it in the partition. The offset is a strictly monotonically-increasing integer in a sparse address space, meaning that each successive offset is always higher than its predecessor and there may be varying gaps between neighboring offsets. Gaps might legitimately appear if compaction is enabled or as a result of transactions; we don’t need to delve into the details at this stage. Suffice it to say that offsets need not be contiguous.
Your application shouldn’t attempt to literally interpret an offset or guess what the next offset might be. It may, however, infer the relative order of any record pair based on their offsets, sort the records by their offset, and so forth.
The diagram below shows what a partition looks like on the inside.1
start of partition
2
+--------+-----------------+
3
|0..00000|First record |
4
+--------+-----------------+
5
|0..00001|Second record |
6
+--------+-----------------+
7
|0..00002|Third record |
8
+--------+-----------------+
9
|0..00003|Fourth record |
10
+--------+-----------------+
11
|0..00007|Fifth record |
12
+--------+-----------------+
13
|0..00008|Sixth record |
14
+--------+-----------------+
15
|0..00010|Seventh record |
16
+--------+-----------------+
17
...
18
+--------+-----------------+
19
|0..56789|Last record |
20
+--------+-----------------+
21
end of partition
The beginning offset, also called the low-water mark, is the first message that will be presented to a consumer. Due to Kafka’s bounded retention, this is not necessarily the first message that was published. Records may be pruned on the basis of time and/or partition size. When this occurs, the low-water mark will appear to advance, and records earlier than the low-water mark will be truncated.
Conversely, the high-water mark is the offset immediately following the last record in the partition, also known as the end offset. It is the offset that will be assigned to the next record that will be published. It is not the offset of the last record.
A topic is a logical composition of partitions. A topic may have one or more partitions, and a partition must be a part of exactly one topic. Topics are fundamental to Kafka, allowing for both parallelism and load balancing.
Earlier, we said that partitions exhibit total order. Because partitions within a topic are mutually independent, the topic is said to exhibit partial order. In simple terms, this means that certain records may be ordered in relation to one another while being unordered with respect to certain other records. The concepts of total and partial order, while sounding somewhat academic, are hugely important in the construction of performant event streaming pipelines. They enables us to process records in parallel where we can, while maintaining order where we must. We’ll explore the concepts of record order, consumer parallelism, and topic sizing in a short while.
Example: Publishing Messages
Let’s put some of this theory into practice. We are going to spin up a pair of Docker containers — one for Kafka and another for Kafdrop. Rather than launching them individually, we’ll use Docker Compose.
Create a docker-compose.yaml file in a directory of your choice, containing the following:1
Note: We’re using the obsidiandynamics/kafka image for convenience because it neatly bundles Kafka and ZooKeeper into a single image. If you wanted to, you could replace this with images from Confluent or Wurstmeister, but then you’d have to wire it all up properly. The obsidiandynamics/kafka image does all this for you, so it’s highly recommended for beginners (and lazy pros).
Then, start it with docker-compose up. Once it boots, navigate to localhost:9000 in your browser. You should see the Kafdrop landing screen.
You should see our single-broker cluster. It’s a promising start, but there are no topics. Not a problem; let’s create a topic and publish some messages using Kafka’s command-line tools. Conveniently, we already have a Kafka image running as part of our docker-compose stack, so we can shell into it to use the built-in CLI tools.1
docker exec -it kafka-kafdrop_kafka_1 bash
This gets you into a Bash shell. The tools are in the /opt/kafka/bin directory, so let’s cd into it:1
cd /opt/kafka/bin
Create a topic named streams-intro with three partitions:1
Note:kafka-topics uses the --bootstrap-server argument to configure the Kafka broker list, while kafka-console-producer uses the --broker-list argument for the same purpose. Also, --property arguments are largely undocumented; be prepared to Google your way around.
Records are separated by newlines. The key and the value parts are delimited by colons, as indicated by the key.separator property. For the sake of an example, type in the following (a copy-paste will do):1
foo:first message
2
foo:second message
3
bar:first message
4
foo:third message
5
bar:second message
Press CTRL+D when done. Then, switch back to Kafdrop and click on the streams-intro topic. You’ll see an overview of the topic, along with a detailed breakdown of the underlying partitions:
Let’s pause for a moment and dissect what’s been done. We created a topic with three partitions. We then published five records using two unique keys — foo and bar. Kafka uses keys to map records to partitions, such that all records with the same key will always appear on the same partition. Handy, but also important because it lets the publisher dictate the precise order of records. We’ll discuss key hashing and partition assignments in more detail later; in the meanwhile, sit back and enjoy the ride.
Looking at the partitions table, partition #0 has the first and last offsets at zero and two respectively. Partition #2 has them at zero and three, while partition #1 appears to blank. Clicking on #0 in the Kafdrop web UI sends us to a topic viewer:
We can see the two records published under the bar key. Note, they are completely unrelated to the foo records. Other than being collated within the same topic, there is nothing that binds records across partitions.
Note: In case you were wondering, the arrow to the left of the message lets you expand and pretty-print JSON-encoded messages. As our examples didn’t use JSON, there’s nothing to pretty-print.
It can be said without exaggeration that Kafka’s built-in tooling is an abomination. There is no consistency in the naming of command arguments and the simple act of publishing keyed messages requires you to jump through hoops — passing in obscure, undocumented properties. The usability of the built-in tools is a well-known heartache within the Kafka community. This is a real shame. It’s like buying a Ferrari, only to have it delivered with plastic hub caps. Fortunately, there are alternatives — both commercial and open source — that can fill the glaring gaps in tooling and observability.
Consumers and Consumer Groups
So far we have learned that producers emit records into the stream; these records are organized into nicely ordered partitions. Kafka’s pub-sub topology adheres to a flexible multipoint-to-multipoint model, meaning that there may be any number of producers and consumers simultaneously interacting with a stream. Depending on the actual solution context, stream topologies may also be point-to-multipoint, multipoint-to-point, and point-to-point. It’s about time we looked at how records are consumed.
A consumer is a process or a thread that attaches to a Kafka cluster via a client library. (One is available for most languages.) A consumer generally, but not necessarily, operates as part of an encompassing consumer group. The group is specified by the group.id property. Consumer groups are effectively a load-balancing mechanism within Kafka — distributing partition assignments approximately evenly among the individual consumer instances within the group.
When the first consumer in a group joins the topic, it will receive all partitions in that topic. When a second consumer subsequently joins, it will get approximately half of the partitions, relieving the first consumer of half of its prior load. The process runs in reverse when consumers leave (by disconnecting or timing out) — the remaining consumers will absorb a greater number of partitions.
So, a consumer siphons records from a topic, pulling from the share of partitions that have been assigned to it by Kafka, alongside the other consumers in its group. As far as load-balancing goes, this should be fairly straightforward. But here’s the kicker — the act of consuming a record does not remove it. This might seem contradictory at first, especially if you associate the act of consuming with depletion. (If anything, a consumer should have been called a ‘reader’, but let’s not dwell on the choice of terminology.)
The simple fact is, consumers have absolutely no impact on topics and their partitions. A topic is an append-only ledger that may only be mutated by the producer, or by Kafka itself (as part of compaction or cleanup). Consumers are “cheap,” so you can have quite a number of them tail the logs without stressing the cluster. This is yet another point of distinction between an event stream and a traditional message queue, and it’s a crucial one.
A consumer internally maintains an offset that points to the next record in a partition, advancing the offset for every successive read. When a consumer first subscribes to a topic, it may elect to start at either the head-end or the tail-end of the topic. This behavior is controlled by setting the auto.offset.reset property to one of latest, earliest or none. In the latter case, an exception will be thrown if no previous offset exists for the consumer group.
Consumers retain their offset state vector locally. Since consumers across different consumer groups do not interfere, there may be any number of them reading concurrently from the same topic. Consumers run at their own pace — a slow or backlogged consumer has no impact on its peers.
To illustrate this concept, consider a scenario involving a topic with two partitions. Two consumer groups, A and B, are subscribed to the topic. Each group has three instances, the consumers being named A1, A2, A3, B1, B2, and B3. The diagram below illustrates how the two groups might share the topic and how the consumers advance through the records independently of one another.1
Look carefully and you’ll notice something is missing. Consumers A3 and B1 aren’t there. That’s because Kafka guarantees that a partition may only be assigned to at most one consumer within its consumer group. (We say ‘at most’ to cover the case when all consumers are offline.) Because there are three consumers in each group, but only two partitions, one consumer will remain idle — waiting for another consumer in its respective group to depart before being assigned a partition.
In this manner, consumer groups are not only a load-balancing mechanism, but also a fence-like exclusivity control, used to build highly performant pipelines without sacrificing safety, particularly when there is a requirement that a record may only be handled by one thread or process at any given time.
Consumer groups are also used to ensure availability. By periodically pulling records from a topic, the consumer implicitly signals to the cluster that it’s in a ‘healthy’ state, thereby extending the lease over its partition assignment. However, should the consumer fail to read again within the allowable deadline, it will be deemed faulty and its partitions will be reassigned — apportioned among the remaining ‘healthy’ consumers within its group. This deadline is controlled by the max.poll.interval.ms consumer client property, set to five minutes by default.
To use a transportation analogy, a topic is like a highway, while a partition is a lane. A record is the equivalent of a car, and its occupants correspond to the record’s value. Several cars can safely travel on the same highway, providing they keep to their lane. Cars sharing the same line ride in a sequence, forming a queue. Now, suppose each lane leads to an off-ramp, diverting its traffic to some location. If one off-ramp gets banked up, other off-ramps may still flow smoothly.
It’s precisely this highway-lane metaphor that Kafka exploits to achieve its end-to-end throughput, easily reaching millions of records per second on commodity hardware. When creating a topic, one can choose the partition count — the number of lanes, if you will.
The partitions are divided approximately evenly among the individual consumers in a consumer group, with a guarantee that no partition will be assigned to two (or more) consumers at the same time, providing that these consumers are part of the same consumer group. Referring to our analogy, a car will never end up in two off-ramps simultaneously; however, two lanes might conceivably lead to the same off-ramp.
Note: A topic may be resized after creation by increasing the number of partitions. It is not possible, however, to decrease the partition count without recreating the topic.
Records correspond to events, messages, commands, or any other streamable content. Precisely how records are partitioned is left to the discretion of the producer(s). A producer may explicitly assign a partition index when publishing a record, although this approach is rarely used. A much more common approach is to assign a key to a record, as we have done in our earlier example. The key is completely opaque to Kafka. In other words, Kafka doesn’t attempt to interpret the contents of the key, treating it as an array of bytes. These bytes are hashed to derive a partition index, using a consistent hashing technique.
Records sharing the same hash are guaranteed to occupy the same partition. Assuming a topic with multiple partitions, records with a different key will likely end up in different partitions. However, due to hash collisions, records with different hashes may also end up in the same partition. Such is the nature of hashing. If you understand how a hash table works, this is no different.
Producers rarely care which specific partition the records will map to, only that related records end up in the same partition, and that their order is preserved. Similarly, consumers are largely indifferent to their assigned partitions, so long that they receive the records in the same order as they were published, and their partition assignment does not overlap with any other consumer in their group.
Committing Offsets
We already said that consumers maintain an internal state with respect to their partition offsets. At some point, that state must be shared with Kafka, so that when a partition is reassigned, the new consumer can resume processing from where the outgoing consumer left off. Similarly, if the consumers were to disconnect, upon reconnection they would ideally skip over those records that have already been processed.
Persisting the consumer state back to the Kafka cluster is called committing an offset. Typically, a consumer will read a record (or a batch of records) and commit the offset of the last record, plus one. If a new consumer takes over the topic, it will commence processing from the last committed offset, hence the plus-one step is essential. (Otherwise, the last processed record would be handled a second time.)
Fun fact: Kafka employs a recursive approach to managing committed offsets, elegantly utilising itself to persist and track offsets. When an offset is committed, Kafka will publish a binary record on the internal __consumer_offsets topic. The contents of this topic are compacted in the background, creating an efficient event store that progressively reduces to only the last known commit points for any given consumer group.
Controlling the point when an offset is committed provides a great deal of flexibility around delivery guarantees, handing Kafka a yet another trump card. The term ‘delivery’ assumes not just reading a record, but the full processing cycle, complete with any side-effects. One can shift from an at-most-once to an at-least-once delivery model by simply moving the commit operation from a point before the processing of a record is commenced, to a point sometime after the processing is complete. With this model, should the consumer fail midway through processing a record, the record will be re-read the following partition reassignment.
By default, a Kafka consumer will automatically commit offsets every five seconds, regardless of whether the consumer has finished processing the record. Often, this is not what you want, as it may lead to mixed delivery semantics. For example, in the event of consumer failure, some records might be delivered twice, while others might not be delivered at all. To enable manual offset committing, set the enable.auto.commit property to false.
Note: There are a few gotchas like this in Kafka. Pay close attention to the (producer and consumer) client properties in the official Kafka documentation, particularly to the stated defaults. Don’t assume for a moment that the defaults are sensible, insofar as they ought to favour safety over other competing qualities. Kafka defaults tend to be optimised for performance, and will need to be explicitly overridden on the client when safety is a critical objective. Fortunately, setting the properties to insure safety has only a minor impact on performance — Kafka is still a beast. Remember the first rule of optimisation: Don’t do it. Kafka would have been even better, had their creators given this more thought.
Getting offset committing right can be tricky, and routinely catches out beginners. A committed offset implies that the record one below that offset and all prior records have been dealt with by the consumer. When designing at-least-once or exactly-once applications, an offset should only be committed when the application is dealt with with the record in question and all records before it.
In other words, the record has been processed to the point that any actions that would have resulted from the record have been carried out and finalized. This may include calling other APIs, updating a database, committing transactions, persisting the record’s payload, or publishing more records. Stated otherwise, if the consumer were to fail after committing the record, then not ever seeing this record again must not be detrimental to its correctness.
In the at-least-once (and by extension, the exactly-once) scenario, a typical consumer implementation will commit its offset linearly, in tandem with the processing of the records. That is, read a record, commit it (plus-one), read the next, commit it (plus one), and so on. A common tactic is to process a batch of records concurrently (where this makes sense), using a thread pool, and only confirm the last record when the entire batch is done. The commit process in Kafka is very efficient, the client library will send commit requests asynchronously to the cluster using an in-memory queue, without blocking the consumer. The client application can register an optional callback, notifying it when the commit has been acknowledged by the cluster.
The consumer group is a somewhat understated concept that is pivotal to the versatility of an event streaming platform. By simply varying the affinity of consumers with their groups, one can arrive at vastly different distribution topologies — from a topic-like, pub-sub behavior to an MQ-style, point-to-point model. Because records are never truly consumed (the advancing offset only creates the illusion of consumption), one can concurrently superimpose disparate distribution topologies over a single event stream.
Free Consumers
Consumer groups are completely optional; a consumer does not need to be encompassed in a consumer group to pull messages from a topic. A free consumer omits the group.id property. Doing so allows it to operate under relaxed rules, entirely transferring the responsibility for consumer management to the application.
Note: The use of the term ‘free’ to denote a consumer without an encompassing group is not part of the standard Kafka nomenclature. As Kafka lacks a canonical term to describe this, the term ‘free’ was adopted here.
Free consumers do not subscribe to a topic. Instead, the consuming application is responsible for manually assigning a set of topic-partitions to the consumer, individually specifying the starting offset for each topic-partition pair. Free consumers do not commit their offsets to Kafka; it is up to the application to track the progress of such consumers and persist their state as appropriate, using a datastore of their choosing. The concepts of automatic partition assignment, rebalancing, offset persistence, partition exclusivity, consumer heart-beating and failure detection, and other so-called niceties accorded to consumer groups cease to exist in this mode.
Free consumers are not observed in the wild as often as their grouped counterparts. There are predominantly two use cases where a free consumer is an appropriate choice. The first, is when you genuinely need full control of the partition assignment scheme and/or you require an alternative place to store consumer offsets. This is very rare.
Needless to say, it’s also very difficult to implement correctly, given the multitude of scenarios one must account for. The second, more commonly seen use case, is when you have a stateless or ephemeral consumer that needs to monitor a topic. For example, you might be interested in tailing a topic to identify specific records, or just as a debugging tool. You might only care about records that were published when your stateless consumer was online, so concerns such as persisting offsets and resuming from the last processed record are completely irrelevant.
A good example of where this is used routinely is the Kafdrop web UI, which we’ve already seen. When you click on a topic to view the messages, Kafdrop creates a free consumer and assigns the requested partition to it, reading the records from the supplied offsets. Navigating to a different topic or partition will reset the consumer, discarding any prior state.
The illustration below outlines the relationship between producers, topics, partitions, consumers, and consumer groups.1
Topics are subdivided into partitions, each forming an independent, totally-ordered sequence within a wider, partially-ordered stream.
Multiple producers are able to publish to a topic, picking a partition at will. This may be accomplished either directly, by specifying a partition index, or indirectly, by way of a record key, which deterministically hashes to a consistent partition index. (In the diagram above, both Producer 1 and Producer 2 publish to the same topic.)
Partitions in a topic can be load-balanced across a population of consumers in a consumer group, allocating partitions approximately evenly among the members of that group. (Consumer 2 and Consumer 3 each get one partition.)
A consumer in a group is not guaranteed a partition assignment. Where the group’s population outnumbers the partitions, some consumers will remain idle until this balance equalizes or tips in favor of the other side. (Consumer 4 remains partition-less.)
Partitions may be manually assigned to free consumers. If necessary, an entire topic may be assigned to a single free consumer — this is done by individually assigning all partitions. (Consumer 1 can be freely assigned any partition.)
Exactly-Once Delivery
When contrasting at-least-once with at-most-once delivery semantics, an often-asked question is: Why can’t we have it exactly once?
Without delving into the academic details, which involve conjectures and impossibility proofs, it is sufficient to say that exactly-once semantics are not possible without collaboration with the consumer application. What does this mean in practice?
Consumers in event streaming applications must be idempotent. In other words, processing the same record repeatedly should have no net effect on the consumer ecosystem. If a record has no additive effects, the consumer is inherently idempotent. (For example, if the consumer simply overwrites an existing database entry with a new one, then the update is naturally idempotent.) Otherwise, the consumer must check whether a record has already been processed, and to what extent, prior to processing a record. The combination of at-least-once delivery and consumer idempotence collectively leads to exactly-once semantics.
Example: A Trading Platform
With all this theory looming over us like Kubrick’s Monolith, it would be inappropriate to conclude without offering the reader a practical scenario.
Let’s say you were looking for specific price patterns in listed stocks, emitting trading signals when a particular pattern is identified. There are a large number of stocks, and understandably you’d like them processed in parallel. However, the time series for any given ticker code must be processed sequentially on a single consumer.
Kafka makes this use case, and others like it, almost trivial to implement. We would create a pair of topics: prices for the raw price data, and orders for any resulting orders. We can be fairly generous with our partition counts, as the nature of the data gives us ample opportunities for parallelism.
At the feed source, we could publish a record for each price on the prices topic, keyed by the ticker code. Kafka’s automatic partition assignment will ensure that every ticker code is handled by (at most) one consumer in its group. The consumer instances are free to scale in and out to match the processing load. Consumer groups should be meaningfully named, ideally reflecting the purpose of the consuming application. A good example might be trading-strategy.abc, for a fictitious trading strategy named ‘ABC’.
Once a price pattern is identified by the consumer, it can publish another message — the order request — on the orders topic. We’ll muster up another consumer group — order-execution — responsible for reading the orders and forwarding them to the broker.
In this simple example, we have created an end-to-end trading pipeline that is entirely event-driven and highly scalable — at least theoretically, assuming there are no other bottlenecks. We can dynamically add more processing nodes to the individual stages to cope with the increased load where it’s called for.
Now, let’s spice things up a bit. Suppose you need several trading strategies operating concurrently, driven by a common data feed. Furthermore, the trading strategies will be developed by different teams; the objective being to decouple these implementations as much as possible, allowing the teams to operate autonomously — develop and deploy at their individual cadence, perhaps even using different programming languages and tool-chains. That said, you’d ideally want to reuse as much of what’s already been written. So, how would we pull this off?
Kafka’s flexible multipoint-to-multipoint pub-sub architecture combines stateful consumption with broadcast semantics. Using distinct consumer groups, Kafka allows disparate applications to share input topics, processing events at their own pace. The second trading strategy would need a dedicated consumer group — trading-strategy.xyz — applying its specific business logic to the common pricing stream, publishing the resulting orders to the same orders topic. In this fashion, Kafka enables you to construct modular event processing pipelines from discrete elements that are readily reusable and composable.
Note: In the days of service buses and traditional ‘enterprisey’ message brokers, before event sourcing entered the mainstream, you would have had to choose between persistent message queues or transient broadcast topics. In our example, you would likely have created multiple FIFO queues, using the fan-out pattern. Because Kafka generalises pub-sub topics and persistent message queues into a unified model, a single source topic can power a diverse range of consumers without incurring duplication.
In Conclusion
Event streaming platforms are a highly effective building block in the construction of modular, loosely-coupled, event-driven applications. Within the world of event streaming, Kafka has solidified its position as the go-to open-source solution that is both amazingly flexible and highly performant. Concurrency and parallelism are at the heart of Kafka’s architecture, forming partially-ordered event streams that can be load-balanced across a scalable consumer ecosystem. A simple reconfiguration of consumers and their encompassing groups can bring about vastly different event distribution and processing semantics; shifting the offset commit point can invert the delivery guarantee from an at-most-once to an at-least-once model.
Of course, Kafka isn’t without its flaws. The tooling is sub-par, to put it mildly; most Kafka practitioners have long abandoned the out-of-the-box CLI utilities in favour of other open-source tools such as Kafdrop, Kafkacat and third-party commercial offerings like Kafka Tool. The breadth of Kafka’s configuration options is overwhelming, with defaults that are riddled with gotchas, ready to shock the unsuspecting first-time user.
All in all, Kafka represents a paradigm shift in how we architect and build complex systems. Its benefits go beyond the superfluous, and they dwarf any of the niggles that are bound to exist in a technology that has undergone such aggressive adoption. Crucially, it paves the way for further progress in its space; Apache Pulsar is a prime example of an alternative platform that has improved on much of Kafka’s shortcomings, yet owes a great deal to its predecessor for laying the cornerstone and bringing the genre to the mainstream.