Cloud Native Architecture Patterns and Design

Introduction to Cloud Native Application

Before reading about Cloud Native Applications, we must be aware of the term pattern, so in layman, the term pattern is the art of figuring out a good solution to a recurring problem in a conceptual form, such that this solution can then applied to existing use cases.

The role of this pattern is to make the foundation for reactive, a sync-communication between components in the cloud native. These patterns deal with the boundaries, i.e., boundary means where the system interacts with the external system, like humans or other systems. These patterns deal with the control flow for inter-component collaboration between boundary components in cloud native.

Foundation Patterns
Boundary Pattern
Control Patterns

Now in this blog, we are going to explore the Foundation Patterns required for creating an isolated bounded component, eliminating synchronous inter-component communication, and using asynchronous inter-component communication, replication, and eventual consistency for our foundation.

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Why do we need Isolated Components?

A cloud native system must recover quickly from a human error (which occur during development or deployment) to do this, we need to isolate the component from each other so that one component is not going to affect the functionality of another also, during ratification we only have to address that particular fault component.

What is the significance of Asynchronous Communication?

Through this mechanism, calling services to post their request or data and continue with other sets of work (apart from the requested things). It eliminates the need to wait for a response resulting in decoupling the execution of two or more services.

The benefit of Synchronous communication:

Balancing the Capacity of service is better.
Less risk of cascading failure.
Better Decoupling.

What are the types of Cloud Native Architecture Patterns?

There are various types of Cloud Native Patterns that are defined below:

Cloud Native Databases Per-Component

As the name describes here, we will maximize each component's usability by providing each component with its dedicated Cloud Native database to ensure proper bulkhead. Database type may vary according to Microservices.

Why do we need Cloud Native Databases per Component?

To support modern high performance, horizontally scalable, and to achieve global scalability, and also we know some particular technology can work better for a certain type of database, so why not provide them their type of database?

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Event Streaming

It is a messge-driven, subscribe and publish mechanism which maximizes fully managed streaming services to establish asynchronous communication between all inter-components, by which upstream components transfer processing to downstream components by publishing domain events that are consumed downstream.

Note: An event-based system must be an append-only, distributed, shared database.

Why should we focus on Event Streaming?

To achieve elastic, responsive, resilient, and message-driven system efficiently, and in today’s world where data is so big event streaming changes the order of the whole analytics procedure: it can Store queries/analysis parallel. Two most famous tools for event streaming are Kafka and MapR Streams.

What is Event Sourcing?

This is an architectural pattern that ensures that all the changes to an application state stored as a sequence of events or stream. Through this, we not only store or query the events but also we can use the event log to reconstruct past states. There are basically two approaches to event sourcing:

Event-First Variant

In this, the event-first command does not write any data to the database. Instead of writing it, it wraps the data in a domain event and posts it to only one stream.

Database-First Variant

In this approach, a database-first command will write the data to only one cloud native database. We can follow any of the above approaches as per our requirement. But primarily benefits of this solution is to maximize or maintain the asynchronous mechanisms of value-added cloud services to accurately chain together atomic operations to achieve eventual consistency in near real-time.

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Data Lake

It is generally a huge collection of all the data a particular company or any organization collects about its customers, operations, transactions and more; the collected data can be structured or unstructured, i.e., its primary goal is to receive the data regardless of source or structure of data. Benefits of Data Lake over Data warehouses

1. Data Lakes Retain All Data
2. Data Lakes Support All Data Types
3. Data Lakes Support, All Users
4. Data Lakes Adapt Easily to Changes
5. Data Lakes Provide Faster Insights

How can we use Data Lake?

The use and the demand for Data Lake are increasing day-by-day at the enterprise level, and why not as it collects a huge number of data, on which we can operate whatever we want. Some of Data lake uses are:

1. Storage: store raw data regardless of their type.
2. Analytics: It can be used by data scientists, data developers, and business analysts to access data with their choice of analytic tools and frameworks.
3. Visualization: Analyzing is not enough we need to visualize the data for better understanding.
4. Machine Learning: We can use the available data and train our machine to perform a specific task.

Stream Circuit Breaker

It verifies that the performance of Microservices is stable, by continuous checking for any failures and providing an alternate service or error message.

Why we need Stream Circuit Breaker?

Actually, on the Internet, various software systems try to make remote calls to another software running in different processes, generally on different machines across a network. Sometime during the remote call, calls may fail or hang without any response or messages, which may result in cascading failures across multiple systems.

What should we do to overcome this problem?

There come to the stream circuit breaker through which we can solve this problem. We wrap a protected function call in a circuit breaker object, which continuously checks for failures.

Different States of Circuit Breaker:

Open: When it returns an error for calls without executing the function.
Closed: The circuit breaker remains in the closed state when everything is normal.
Half-Open: After a timeout period, the circuit switches to this state to test if the underlying issues still exist.

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Trilateral API

As we are building a reactive, cloud native system composed of bounded isolated components which mainly works on event streaming for inter-component communication, thus it requires a different way of approach. So we need to publish multiple interfaces for each element:

Asynchronous API for publishing events like the state of the component changes.
Synchronous API for processing commands and queries
An asynchronous API for consuming the events emitted by other components.

Building Scalable Cloud Native Applications

Cloud native applications are deployed using Kubernetes, an open-source platform designed for automating deployment, scaling, and management of containerized applications. Originally developed at Google, Kubernetes has become the operating system for deploying cloud native applications. To learn more about cloud native application design, look into the below steps: