Author: Gaurav Gaur

Why Go Cloud-Native?

Cloud-native technologies empower us to produce increasingly larger and more complex systems at scale. It is a modern approach to designing, building, and deploying applications that can fully capitalize on the benefits of the cloud. The goal is to allow organizations to innovate swiftly and respond effectively to market demands.

Agility and Flexibility

Organizations often migrate to the cloud for the enhanced agility and the speed it offers. The ability to set up thousands of servers in minutes contrasts sharply with the weeks it typically takes for on-premises operations. Immutable infrastructure provides confidence in configurable and secure deployments and helps reduce time to market.

Hit Pause!

I've been on break for the past few days, completely unplugged from work. It's been a time of reflection, diving into fiction, and sometimes simply sitting and doing nothing. I've wandered through various Christmas markets and taken a few spontaneous day trips to nearby towns, enjoying holiday cheer. Sure, at times, I felt physically tired, but never exhausted. It made me realize how crucial it is to hit pause.

In our hectic day-to-day lives, we constantly find ourselves making decisions. From the moment we wake up — deciding on breakfast, travel to the office or work from home, and so on — to big decisions such as where to attend university, which car to buy, or where to live, each carrying its weight. Internet sources claim that we make roughly whooping 35,000 decisions a day! If we assume an adult sleeps for eight hours, thankfully decision-free, that amounts to more than 2,100 decisions per waking hour or about three decisions every five seconds.

In today's rapidly evolving technology landscape,  it's common for applications to migrate to the cloud to embrace the microservice architecture. While this architectural approach offers scalability, reusability, and adaptability, it also presents a unique challenge: effectively managing communication between these microservices. Successfully coordinating messages among these services is a fundamental aspect of their design. There are two popular methodologies available to tackle this challenge. The first, Service Orchestration, was discussed in my previous article. In this article, we will dig into the second methodology: Choreography. We aim to comprehensively understand the nuances, advantages, and disadvantages that the Choreography methodology brings.

Problem Context

To address the communication challenge, we introduced the concept of an orchestrator, a central authority tasked with coordinating and streamlining the flow of transactions across these autonomous microservices. But here's the question that looms large: Is the Orchestration Pattern a universal solution, or does its effectiveness vary depending on the scenario? Can a central orchestrator virtually manage all the business problems, or are there scenarios where a different approach besides being preferable is also essential?

As organizations migrate to the cloud, they desire to exploit this on-demand infrastructure to scale their applications. But such migrations are usually complex and need established patterns and control points to manage. In my previous blog posts, I covered a few of the proven designs for cloud applications. In this article, I’ll introduce the Orchestration Pattern (also known as the Orchestrator Pattern) to add to the list. This technique allows the creation of scalable, reliable, and fault-tolerant systems. The approach can help us manage the flow and coordination among components of a distributed system, predominantly in a microservices architecture. Let’s dive into a problem statement to see how this pattern works.

Problem Context

Consider a legacy monolith retail e-commerce website. This complex monolith consists of multiple subdomains such as shopping baskets, inventory, payments etc. When a client sends a request, the website performs a sequence of operations to fulfil the request. In this traditional architecture, each operation can be described as a method call. The biggest challenge for the application is scaling with the demand. So, the organisation decided to migrate this application to the cloud. However, the monolithic approach that the application uses is too restricted and would limit scaling even in the cloud. Adopting a lift and shift approach to perform migration would not reap the real benefits of the cloud.

Applications today collect an infinite amount of data. Many applications need to transform this data before applying any meaningful business logic. Tackling this complex data or a similar processor-intensive task without a thought-through strategy can have a high-performance impact. This article introduces a scalability pattern – pipes and filters – that promotes reusability and is appropriate for such scenarios.

Problem Context

Consider a scenario where incoming data triggers a sequence of processing steps, where each step brings data closer to the desired output state. The origin of data is referred to as a data source. Examples of data sources could be home IoT devices, a video feed from roadside cameras, or continuous inventory updates from warehouses. The processing steps during the transformation usually execute a specific operation and are referred to as filters. These processing steps are independent and do not have a side effect; i.e., running a step does not depend on any other steps. Each filtering step reads the data, performs a transforming operation based on local data, and produces an output. Once the data has gone through the filters, it reaches its final processed stage where it is consumed, referred to as data sink.

Any modern website on the internet today receives thousands of hits, if not millions. Without any scalability strategy, the website is either going to crash or significantly degrade in performance—a situation we want to avoid. As a known fact, adding more powerful hardware or scaling vertically will only delay the problem. However, adding multiple servers or scaling horizontally without a well-thought-through approach may not reap the benefits to their full extent.

The recipe for creating a highly scalable system in any domain is to use proven software architecture patterns. Software architecture patterns enable us to create cost-effective systems that can handle billions of requests and petabytes of data. The article describes the most basic and popular scalability pattern known as Load Balancing. The concept of Load Balancing is essential for any developer building a high-volume traffic site in the cloud. The article first introduces the Load balancer, then discusses the type of Load balancers; next is load balancing in the cloud, followed by Open-source options, and finally, a few pointers to choose load balancers.