Distributed design patterns involve the replication of data, applications, and services across different servers or locations. This approach increases application resilience, balances load between disparate resources, enhances scalability, and increases overall system availability.

Table of Contents

AWS Services Involved in Distributed Design Patterns

  • Amazon S3
  • Amazon DynamoDB
  • Amazon EC2
  • AWS Lambda
  • Amazon API Gateway
  • AWS Elastic Beanstalk
  • Amazon RDS (Relational Database Service)
  • Amazon SQS (Simple Queue Service)

Common Distributed Design Patterns

In the context of AWS, these are commonly used distributed design patterns:


Sharding involves partitioning a database into smaller, manageable chunks called shards. It helps improve query response time by reducing the data set size that the database needs to search in response to a query.

An excellent example of sharding on AWS is to use Amazon’s DynamoDB. AWS allows automatic sharding of tables based on the partition key chosen at the table’s creation time.


Replication involves maintaining copies of data or applications across multiple locations. This pattern provides high availability and survivability. On AWS, replication can be implemented via Amazon RDS, which provides Multi-AZ deployment feature to create a standby copy of your database in another Availability Zone for failover support.

Pattern AWS Service Benefit
Sharding DynamoDB Increased query speed
Replication RDS High Availability

Event-Driven Architecture

Event-Driven Architecture (EDA) invokes functions based on events. AWS provides several services to implement EDA, such as AWS Lambda, Step Functions, and Amazon Simple Notification Service (SNS). When an event occurs, AWS publishes a message to a topic using Amazon SNS, which is then delivered to all subscribed endpoints.


Microservices compose an application as a suite of small, loosely coupled services. Each service is independently deployable. Amazon’s container services, such as ECS and EKS or serverless platforms like AWS Lambda, can be used for developing and deploying microservices architecture.

Serverless Architecture

Serverless architecture allows developers to build and run applications and services without thinking about servers. Services like AWS Lambda lets you run your code without provisioning or managing servers.

To summarize, choosing the right distributed design pattern is key to building scalable and resilient applications on AWS. While the patterns described here represent a small subset of the possible design patterns, they provide a fundamental understanding of the concepts and principles involved. This knowledge, when combined with hands-on AWS experience, will go a long way in mastering distributed design patterns’ implementation, a crucial part of the AWS Certified Solutions Architect – Associate exam.

Practice Test

True or False: Distributed Design Patterns in AWS primarily deal with replicating data across multiple regions.

  • True
  • False

Answer: True.

Explanation: One of the key benefits of Distributed Design Patterns is in providing replication and synchronization services across multiple regions or zones, thereby enhancing accessibility and data integrity.

Which AWS service performs in-memory caching in a distributed environment?

  • A. Amazon S3
  • B. Amazon EBS
  • C. Amazon DynamoDB
  • D. Amazon ElastiCache

Answer: D. Amazon ElastiCache.

Explanation: Amazon ElastiCache provides a fully managed, scalable, and secured in-memory data store and cache, perfect for use in a distributed environment.

True or False: The Sharding pattern is suitable for distributing data across multiple database instances.

  • True
  • False

Answer: True.

Explanation: Sharding is a type of database partitioning that divides large databases into smaller, faster, and more easily managed parts called data shards.

Which of the following services can be used in AWS for event-driven distributed system implementation?

  • A. Amazon SNS
  • B. Amazon SQS
  • C. AWS Lambda
  • D. All of the above

Answer: D. All of the above.

Explanation: All these services can work together to form a serverless, event-driven system – SNS for Pub/Sub messaging, SQS for decoupling services, and Lambda for executing code in response to events.

True or False: DynamoDB supports cross-region replication.

  • True
  • False

Answer: True.

Explanation: DynamoDB Global Tables provide automated cross-region replication allowing read and write traffic to be localized to minimize latency.

Amazon CloudFront accomplishes which type of distribution?

  • A. Data distribution
  • B. Compute distribution
  • C. Content distribution
  • D. Log distribution

Answer: C. Content distribution

Explanation: Amazon CloudFront is a content delivery network (CDN) service that accelerates delivery of websites, APIs, and other web assets.

True or False: Data Replication is a strategy where same data is stored on multiple storage devices.

  • True
  • False

Answer: True.

Explanation: Data Replication is a strategy that ensures high availability and reliability by copying data from a database on one server to a database on another server.

Amongst these patterns, which one is not recognized as a standard distributed computing design pattern?

  • A. MapReduce
  • B. Peer-to-peer
  • C. Client-server
  • D. Data Pooling

Answer: D. Data Pooling

Explanation: The first three options are standard patterns, but “Data Pooling” is not recognized as such.

True or False: Amazon Kinesis is used for real-time processing of streaming data at massive scale.

  • True
  • False

Answer: True.

Explanation: Amazon Kinesis can collect and process large streams of data records in real time, allows for timely insights and reactive decision making.

What is the main use of AWS Elastic Beanstalk?

  • A. Deployment of applications
  • B. Storing logs
  • C. Data replication
  • D. Content distribution

Answer: A. Deployment of applications

Explanation: AWS Elastic Beanstalk is an orchestration service offered that abstracts away infrastructure so you can focus on app development.

Interview Questions

What is the primary benefit of using a Sharding design pattern in a distributed system?

Sharding design pattern allows for horizontal database partitioning, which helps in spreading load and reducing the response time across several nodes. It potentially increases read and write capacities which can lead to better performance.

What pattern would you use to buffer and consistently manage the request spikes to your services?

The Throttling pattern would be the best pattern to use. It enables a system to continue functioning in the case of an overuse by limiting the number of requests.

How does the Event-Driven architecture pattern support the decoupling of services in distributed systems?

It provides for asynchronous, non-blocking communication between services. Services communicate by producing and listening to events which help lessen direct dependencies between them enhancing decoupling.

What is the main advantage of the Microservices architecture?

It allows individual components of the application to be developed, deployed, and scaled independently which can greatly enhance fault isolation and allow for continuous integration and delivery.

How does the Strangler Pattern work in AWS?

The Strangler Pattern allows for gradual replacement or augmentation of a system’s functionalities. In AWS, this would involve routing requests to the new system and running the two systems side by side until the old system is phased out.

Explain the functioning of Circuit Breaker in distributed systems?

The Circuit Breaker design pattern involves detecting the number of failed requests and if they exceed a set limit, it “trips” the circuit and all requests fail immediately until the circuit is reset.

What design pattern would be best to use for real-time event processing in distributed systems?

Stream processing design pattern is suitable for live/real-time event processing.

When would you implement scatter/gather pattern in a distributed system?

Scatter/gather pattern is used to parallelize computation task and recombine the results. It helps in decreasing latency in distributing processing tasks.

How can using a CQRS design pattern in a distributed system benefit read-heavy applications?

Command Query Responsibility Segregation (CQRS) separates the read operations from the write operations, thus optimizing them independently. In read-heavy applications, it allows load to be efficiently distributed across databases or systems.

What considerations should be made while using the Queue-Based Load Leveling Pattern?

Considerations when using the Queue-Based Load Leveling Pattern should include the maximum throughput of the system, the size and durability of the queue, message visibility, and potential for message duplication.

What AWS service falls into the Aggregator Microservices design pattern?

AWS Lambda falls into the Aggregator Microservices design pattern as it provides a single point of access to sub-microservices in an application.

How does the Asynchronous messaging pattern allow two applications to integrate and communicate?

It allows two applications to integrate and communicate where the sender posts messages to a message queue and the recipient reads messages *at its own pace* from the queue.

What design pattern would you use for computing large data systems?

The MapReduce design pattern would be the best to use for large data computing as it enables processing and generating large datasets with a parallel, distributed algorithm.

What is the major advantage of applying Bulkhead segregation AWS instances?

Bulkhead segregation allows for fault isolation at the level of instances. If a service in a segregated part fails, it won’t impact the services running on other instances.

Why is the Compensating Transaction pattern useful in distributed architectures?

It is useful for undoing changes when a part of a business operation fails. It provides a way to revert or compensate the completed transactions if a subsequent transaction fails.

Leave a Reply

Your email address will not be published. Required fields are marked *