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Distributed design patterns on AWS

Mastering Distributed Systems: Architectural patterns and granular solutions

Design patterns are strategic solutions to recurrent problems within a given domain. Specifically, distributed design patterns address the challenges inherent in distributed systems. These patterns provide methodologies for efficiently structuring components, managing vast datasets, tackling computational hurdles, mitigating traffic surges, and maintaining system availability, among other concerns. Employing these patterns enables developers to design scalable, robust, and effective architectures adept at handling the complexity of distributed computing systems.


Distributed design patterns often related to architectural patterns, yet they equally cater to more granular issues not encompassing the entire scope of the domain. Initially, I will cover architectural blueprints for distributed systems. Subsequently, we delve into solutions for particular subdomains, such as data management, traffic regulation, and optimization of compute resources.



Microservice architecture


Microservice architecture was previously discussed in the 'Design Principles for Microservices on AWS' post, highlighting key assumptions and related AWS services. In this post, I will briefly mention that this architecture aims to make components loosely coupled. 


Such relationships between components facilitate their development independently by different teams or in various programming languages. It's entirely feasible for some components to be developed in Rust, others in Node.js, and yet others in Python within the same application. 


Another advantage is reliability; in the event of a failure, it's more likely that only a part of the services or a single service will go down, not the entire application. 


Finally, it's easy to scale a distributed system composed of microservices by adding new instances of particular services (horizontal scaling).


The core services for designing a microservice architecture include Amazon SQS, Amazon SNS, AWS Lambda, and Amazon DynamoDB, as well as virtual machines such as EC2.



Event-Driven Architecture


I already delved into event-driven architecture on AWS in greater detail, focusing on its benefits and associated AWS services. Here, I'll briefly mention that this architecture is characterized by the way loosely coupled components communicate with each other, making it prevalent in microservice architectures. Such architectures are reactive, with the primary means of communication being the transmission of events that carry the necessary data. 


Personally, whenever I think of EDA, a Rube Goldberg machine comes to mind, which always triggers a chain reaction of events.


The core services for designing an EDA include Amazon EventBridge, AWS Lambda, AWS Step Functions, Amazon Simple Notification Service (SNS), and Amazon Simple Queue Service (SQS).



Serverless Architecture


This type of architecture is primarily related to how resources are managed; in this case, they are managed by AWS. Additionally, it's common to utilize serverless technologies in conjunction with microservices and event-driven architectures because they are easy to implement, cost-effective (if managed correctly), and inherently offer high availability.


I have discussed serverless architecture on AWS in more detail in previous posts.

The core services for designing a serverless architecture include AWS Lambda, AWS Step Functions, Amazon S3, Amazon API Gateway, and Amazon Cognito.



Load balancing


Load balancing related to a specific aspect of distributed systems rather than to its overarching architecture, setting it apart from the patterns discussed earlier in this article. Nonetheless, it is equally crucial for ensuring the reliable and smooth operation of applications. Load balancing focuses on managing traffic optimally, especially considering unexpected traffic spikes, such as those experienced by e-commerce sites during events like Black Friday. 


It enables the management of auto-scaling groups by adding or removing EC2 instances based on demand, thus making your application more responsive when needed, while keeping cost-effectiveness in mind. 


AWS offers several types of load balancers designed for different use cases, as covered in Load balancing concepts on AWS post. All of these options are part of the Elastic Load Balancing service.





Good architecture, effective communication, and efficient traffic management often aren't sufficient for high-traffic applications. A low-hanging fruit in this context is caching. When correctly implemented, caching can significantly reduce the load on your infrastructure, thereby enhancing your application's performance and reduce cost.


Caching can be implemented using various services, from the dedicated Amazon ElastiCache, which stores data in-memory rather than in databases, to CloudFront, which acts as a CDN for any hosting. Further details can be found in Caching Strategies on AWS


Additionally, there are specific solutions for particular AWS services that help reduce the load on main components by acting as proxies, such as RDS Proxy, which is especially useful for serverless architectures where connection pooling management poses a significant challenge.. More information on this can be found in Proxy Concepts on AWS.



Distributed data processing

Large datasets produced by distributed systems require specific AWS services capable of handling substantial amounts of data processing and analytics. 


For smaller applications, a managed database typically suited for OLTP (Online Transaction Processing) can be efficiently handled by RDS or DynamoDB as a good starting point. 


For larger applications that necessitate OLAP (Online Analytical Processing) capabilities, services such as Kinesis, EMR, and Glue are additionally required. These services provide the necessary infrastructure for real-time data streaming, big data processing, and data integration tasks, respectively, catering to the needs of complex and data-intensive applications.





Distributed systems are inherently complex, and manually managing their construction, testing, and deployment processes can lead to errors, consuming significant time, cost, and resources. Automating these processes using dedicated AWS services, such as CodePipeline, CodeBuild, and CodeCommit, is a more efficient approach. These services integrate seamlessly with CloudFormation, which facilitates Infrastructure as Code (IaC), enhancing automation and resource management.





Challenges with distributed systems

Design interactions in a distributed system to prevent failures

Implementing distributed design patterns on AWS

Three advanced design patterns for high available applications using Amazon CloudFront

What’s the Difference Between OLAP and OLTP?