Digital product development with distributed systems is currently used more than it was a few years ago. The reason is the supply of demand.

According to Gartner, over 75% of organizations will be running containerized applications in production by 2022, highlighting the growing importance of distributed systems. But implementing a distributed system in your product is a tough challenge.

This article explores proven best practices for building distributed systems to address this challenge, focusing on resilience, scalability, data management, and performance optimization.

What Are Distributed Systems?

Distributed systems are computer systems composed of multiple interconnected computers or nodes that work together to achieve a common goal. These systems distribute the network's processing, storage, and communication tasks, allowing for parallel and cooperative computation.

They enable sharing of resources, data, and services among the nodes, providing increased scalability, fault tolerance, and performance.

To be specific, AWS uses distributed storage systems like Amazon S3 and distributed computing frameworks to handle massive amounts of data and provide computing power on demand.

How to Design a Distributed System for Digital Product Development

Following are the distributed systems best practices for digital product development:

  1. Clearly outline the goals and functionality expected from the system, ensuring a common understanding among the team. This step helps align everyone's efforts and sets a clear direction for development.
  2. Select a suitable architectural pattern that matches the requirements, such as client-server, microservices, or event-driven. This ensures the system is structured to promote scalability, flexibility, and maintainability.
  3. Break down the system into smaller, manageable components to distribute the workload and facilitate parallel development. This enables teams to work independently on different system parts and promotes modularity.
  4. Establish robust protocols for communication between the system components to ensure reliable data transfer and interoperability. This step fosters seamless collaboration and integration between different parts of the system.
  5. Devise an efficient data storage strategy, considering data volume, access patterns, and data consistency requirements. This ensures optimal storage and retrieval performance, as well as data integrity.
  6. Implement mechanisms to handle failures and recover from disruptions gracefully. This step enhances the system's resilience, minimizing the impact of failures and providing uninterrupted service to users.
  7. Incorporate security features and protocols to safeguard the system from unauthorized access, data breaches, and other potential threats. This step protects sensitive data, maintains user trust, and preserves the system's integrity.
  8. Fine-tune the system to achieve optimal performance by optimizing algorithms, resource utilization, and response times. This step enhances user experience, reduces latency, and increases system efficiency.
  9. Conduct comprehensive testing to verify the system's functionality, performance, and reliability. Evaluation helps identify and address issues or shortcomings before the system is deployed, ensuring a high-quality product.
  10. Continuously monitor the system's performance, collect metrics, and proactively address emerging issues. Regular maintenance guarantees smooth operation, scalability, and the ability to adapt to evolving requirements.

Best Practices for Building Scalable Distributed Systems

Following are distributed systems' best practices to follow:

Design for horizontal scalability: Build your system to handle increasing loads by adding more machines rather than relying on vertical scaling, which has limitations.

Use a distributed file system: Implement a distributed file system like Hadoop Distributed File System (HDFS) or Amazon S3 for efficient data storage and retrieval across multiple nodes.

Employ a distributed caching layer: Utilize distributed caching systems like Memcached or Redis to reduce the load on your backend services and improve performance.

Partition data intelligently: Divide your data into smaller, manageable partitions to distribute the workload evenly across multiple nodes and enable parallel processing.

Implement asynchronous communication: Use message queues or publish-subscribe systems like Apache Kafka to enable asynchronous communication between different components, decoupling them and improving fault tolerance.

Leverage load balancing: Utilize load balancers to evenly distribute incoming traffic across multiple servers, preventing any single node from becoming a bottleneck.

Design fault-tolerant systems: Build redundancy into your system by replicating critical components and data across multiple nodes, ensuring that failures do not cause complete system downtime.

Key Benefits of Distributed Systems

Here are some vital advantages of using distributed systems:

Extensibility and Modularity

Distributed systems are often built using modular architectures, making extending and adding new components or functionalities easier. New nodes or services can be added to the system without significant disruptions, allowing for seamless growth and expansion.

Load Balancing

Distributed systems can distribute the workload evenly across nodes, ensuring that resources are utilized optimally. In addition, load-balancing algorithms help efficiently distribute tasks, preventing individual nodes from becoming overwhelmed while others remain underutilized.

Netflix is a popular streaming service that delivers video content to millions of users worldwide. It uses a distributed CDN to store and serve its video content from multiple edge locations. It also uses distributed databases and caching systems to optimize data retrieval and improve performance.

Data Consistency and Availability

Distributed systems employ replication techniques to ensure data consistency and availability. By replicating data across multiple nodes, organizations can provide better data availability and reduce the risk of data loss.

Additionally, distributed systems can employ mechanisms to maintain data consistency, ensuring that all nodes have the most up-to-date and synchronized data.

Uber is a transportation network company that connects riders with drivers through a mobile app. The app communicates with various distributed components, including the user interface, real-time location tracking, ride dispatching, and payment processing.

Challenges Involved in Building Distributed Systems

Mind these 5 common challenges in designing distributed systems:

1. Concurrency and Coordination

In a distributed system, multiple components or nodes work concurrently, and coordinating their activities becomes complex. Therefore, managing concurrent access to shared resources, ensuring consistency, and avoiding race conditions require careful design and synchronization mechanisms.

2. Communication and Latency

In a distributed system, nodes communicate with each other over a network, which introduces latency and potential communication failures. Therefore, developers must handle message passing, data serialization, and network failures and ensure reliable communication between nodes.

3. Fault Tolerance and Reliability

Distributed systems are prone to failures due to network issues, hardware failures, software bugs, or even malicious attacks. Therefore, building fault-tolerant systems that can handle failures gracefully, recover from them, and maintain reliability is a significant challenge.

5. Scalability and Performance

Distributed systems must scale to handle increasing workloads and accommodate growing user bases. Achieving scalability and high performance requires careful design decisions, load balancing, efficient resource allocation, and minimizing bottlenecks.

Key Principles and Strategies for Building Distributed Systems

To successfully build distributed systems, you may follow the strategies listed below:

  1. Embrace decentralization to reduce single points of failure.
  2. Design for fault tolerance through replication, redundancy, and error detection.
  3. Ensure scalability by distributing workload and adding resources horizontally.
  4. Choose an appropriate consistency model for maintaining data consistency.
  5. Implement concurrency control mechanisms to handle shared resource access.
  6. Use efficient communication protocols for seamless interaction between components.
  7. Partition and replicate data strategically for improved performance and fault tolerance.
  8. Implement comprehensive monitoring and observability for debugging and optimization.
  9. Employ load-balancing techniques to distribute workload evenly across nodes.
  10. Finally, prioritize security through authentication, encryption, and secure communication.

As the demand for sophisticated digital products continues to rise, mastering the art of building distributed systems is paramount. By embracing the best practices explored in this article, organizations can enhance system resilience, scalability, data management, and performance optimization.

Businesses can position themselves at the forefront of innovation with the power of distributed systems, delivering exceptional products and services in today's dynamic digital landscape.