Handling software architecture complexity
Mayank Vaish
Digital Transformation Architect | Generative AI Solution Architect | Sr. Enterprise Architect
Handling software architecture complexity is a critical challenge in software development. Here are some strategies to effectively manage and mitigate software architecture complexity:
1.???? Modularization and Decoupling:
a)???? Divide and break down the system into more manageable, smaller, modular modules and components with clear interfaces. Each module needs to have a distinct role and communicate with other modules using standardized interfaces.
b)??? Use the principle of separation of concerns to isolate different functionalities.
c)???? Apply the Single Responsibility Principle (SRP) to ensure each module has a single, well-defined purpose.
d)??? Use design patterns that promote loose coupling between modules, such as the Singleton pattern or Dependency Injection. Changes in one module have less of an effect on other modules due to loose coupling.??????
2.???? Abstraction and Encapsulation:
a)???? Hide intricate implementation details in interfaces and abstractions. By using encapsulation, you may manage complexity within specific components without making it visible to the entire system.
b)??? Use abstraction layers to separate high-level architecture from low-level details.
c)???? Develop classes and objects that contain behavior and data using the concepts of object-oriented programming.
d)??? Apply the Dependency Inversion Principle (DIP) to reduce coupling between modules.
3.???? Layered Architecture:
a)???? Organize the system into layers, each responsible for a specific aspect of functionality. For example, you might have presentation, business logic, and data access layers.
b)??? Enforce clear boundaries and communication rules between layers to prevent unnecessary dependencies and complexity.
4.???? Use of Design Patterns:
a)???? Apply design patterns like the Model-View-Controller (MVC), Observer, or Strategy pattern to solve recurring architectural problems. Design patterns provide proven solutions to common challenges and improve code maintainability.
b)??? Familiarize yourself with common design patterns like Singleton, Factory, Observer, and MVC.
c)???? Use Cloud Design patterns to solve cloud native and architectural problems. Please see below some of the design patterns.
1)??? Availability Patterns:
A.??? High Availability: Ensure application availability by distributing workloads across multiple Azure regions. Use Azure Traffic Manager or HAProxy for DNS-based routing to healthy regions.
a.???? Redundancy: Redundancy involves having multiple instances of critical components, such as servers, databases, or load balancers, to ensure that if one fails, another can take over. This can be done at various levels, including:
??????????????????????????????????????????????????????????????????????????????????????????? i.???? Server Redundancy: Deploying multiple servers with load balancers to distribute traffic and handle failover.
????????????????????????????????????????????????????????????????????????????????????????? ii.???? Database Redundancy: Using technologies like database replication, clustering, or sharding to maintain multiple copies of the database.
??????????????????????????????????????????????????????????????????????????????????????? iii.???? Data Center Redundancy: Running your application in multiple geographically dispersed data centers or cloud regions to withstand regional outages.
???????????????????????????????????????????????????????????????????????????????????????? iv.???? Deployment Stamp Redundancy: to have multiple global deployment stamps to work as active-active disaster recovery.
b.???? Load Balancing: Load balancers distribute incoming traffic across multiple servers or instances, ensuring that no single server is overwhelmed. Load balancers can also detect and route traffic away from unhealthy servers, improving fault tolerance.
c.???? Failover Mechanisms: Implementing failover mechanisms allows for automatic switching to a backup or secondary system in the event of a primary system failure. This can be done for databases, application servers, and other critical components.
d.???? Distributed Systems: Designing your application as a distributed system, where different components can run independently and communicate over a network, can improve availability. Services can be scaled independently and distributed across multiple servers.
e.???? Replication: Replicating data and services across multiple servers or locations can provide redundancy and fault tolerance. This is commonly used for databases, file systems, and caching layers.
f.????? Stateless Architecture: Stateless applications don't store session or user-specific data on the server, which allows for easier scaling and failover. All necessary information is passed with each request, reducing dependencies on specific server instances.
g.???? Health Checks and Monitoring: Implementing continuous health checks and monitoring for all components of your system helps identify issues early and trigger automated responses, such as failover or scaling.
h.???? Auto-Scaling: Use auto-scaling mechanisms to automatically adjust the number of application instances based on traffic or resource utilization. This helps maintain performance during traffic spikes.
i.????? Caching: Caching frequently accessed data can reduce the load on backend systems and improve response times. However, it's important to design cache expiration and update strategies to ensure data consistency.
j.????? Content Delivery Networks (CDNs): CDNs distribute content to multiple edge locations, reducing latency and improving availability by serving content from the nearest location to the user.
k.???? Database Load Balancing: Use database load balancers to distribute read and write queries among database replicas, improving database availability and performance.
l.????? Rolling Deployments: When updating your software, use rolling deployments to gradually update instances rather than taking the entire system offline. This ensures uninterrupted service.
m.?? Backup and Restore: Regularly back up data and have a well-defined plan for restoring services in case of data corruption or catastrophic failures.
n.???? Disaster Recovery Planning: Develop a disaster recovery plan that includes backup data centers or cloud regions, ensuring business continuity in case of major disasters.
o.???? Geographic Redundancy: Distribute your system across different geographic regions to reduce the impact of regional failures or natural disasters.
p.???? Service Level Agreements (SLAs): Establish SLAs with cloud providers or third-party services to ensure they meet your availability requirements.
q.???? Testing and Chaos Engineering: Regularly test your system for resilience using techniques like chaos engineering to proactively identify and address potential weaknesses. Azure Chaos Studio
The choice of which HA strategies to implement depends on your specific application requirements, budget, and infrastructure. Often, a combination of these approaches is used to achieve the desired level of availability for a software system.
B.??? Fault Tolerance: Build applications that can gracefully handle failures by implementing retry mechanisms, using Azure Availability Sets, or deploying VMs in different fault domains.
a.???? Redundancy: Redundancy involves having multiple instances of critical components, such as servers or services, so that if one fails, another can take over. This can be done at various levels:
??????????????????????????????????????????????????????????????????????????????????????????? i.???? Server Redundancy: Deploy multiple servers with load balancing to distribute traffic and handle failovers. Azure Availability Sets or deploying VMs in different fault domains.
????????????????????????????????????????????????????????????????????????????????????????? ii.???? Service Redundancy: Run multiple instances of a service and use load balancers or service discovery to route traffic to healthy instances.
??????????????????????????????????????????????????????????????????????????????????????? iii.???? Data Redundancy: Use replication and backup strategies to ensure data availability.
???????????????????????????????????????????????????????????????????????????????????????? iv.???? Deployment Stamp Redundancy: to have multiple global deployment stamps to work as active-active disaster recovery.
b.???? Failover Mechanisms: Implement failover mechanisms that automatically switch to a backup or secondary system when a fault is detected in the primary system. This is common for databases, application servers, and other critical components.
c.???? Graceful Degradation: Design your system to gracefully degrade its functionality when certain components or services fail, rather than crashing completely. This might involve serving a limited set of features or providing read-only access.
d.???? Circuit Breakers: Implement circuit breakers to prevent requests from being sent to a failing service or component. When a fault is detected, the circuit breaker temporarily stops sending requests, allowing the system to recover.
e.???? Retry Strategies: Use retry mechanisms for transient failures. If a request fails, the system can automatically retry the operation for a specified number of times before giving up.
f.????? Timeouts: Set timeouts for requests to prevent them from blocking indefinitely. If a component is taking too long to respond, the system can consider it a fault and take appropriate action.
g.???? Isolation and Microservices: Divide your system into smaller, isolated services (microservices) that can fail independently. If one microservice fails, it doesn't necessarily affect the entire system.
h.???? Message Queues: Use message queues to decouple components and ensure that messages are not lost, even if a component goes down temporarily. This helps maintain data integrity and processing continuity.
i.????? Stateless Architecture: Stateless applications don't store session-specific data on the server, making it easier to recover from failures by routing requests to other healthy instances.
j.????? Monitoring and Alerts: Implement robust monitoring and alerting systems that can quickly detect faults and notify administrators or automated systems for intervention.
k.???? Self-Healing Systems: Build systems that can automatically detect and recover from common faults without manual intervention. This might involve auto-scaling, restarting services, or reconfiguring components.
l.????? Rolling Updates: When deploying updates, use rolling updates to gradually replace instances, minimizing the risk of widespread failures due to bugs or issues in new versions.
m.?? Data Validation and Error Handling: Implement robust data validation and error handling to prevent faulty data or input from causing system failures.
n.???? Chaos Engineering: Proactively test your system's fault tolerance by introducing controlled failures and observing how the system responds. This helps identify weaknesses and improve fault tolerance.
o.???? Geographic Redundancy: Distribute your system across multiple geographic regions or data centers to mitigate the impact of regional failures.
p.???? Backup and Restore: Regularly back up critical data and have well-defined procedures for restoring services in case of data corruption or catastrophic failures.
q.???? Disaster Recovery Planning: Develop a comprehensive disaster recovery plan that includes data backup and recovery strategies, as well as the ability to failover to alternate locations or providers.
The specific fault tolerance strategies you employ will depend on the nature of your application, its critical components, and the level of resilience required. A combination of these strategies is often used to create a robust fault-tolerant architecture.
Azure provides a range of solutions and services that can be used to implement fault tolerance and enhance the resilience of your applications and infrastructure. Here are some Azure solutions for handling fault tolerance:
Azure provides a range of solutions and services that can be used to implement fault tolerance and enhance the resilience of your applications and infrastructure. Here are some Azure solutions for handling fault tolerance:
a)???? Azure Availability Zones: Azure Availability Zones are physically separate datacenters within an Azure region. Deploying your resources across multiple availability zones ensures high availability and fault tolerance. In the event of a failure in one zone, traffic is automatically routed to a healthy zone.
b)??? Azure Virtual Machine Scale Sets: Virtual Machine Scale Sets allow you to automatically distribute and load balance incoming traffic across multiple VM instances. If a VM instance fails, Azure can automatically replace it, maintaining application availability.
c)???? Azure Traffic Manager: Azure Traffic Manager is a DNS-based global load balancer that can be used to distribute traffic across multiple Azure regions or data centers. It can automatically redirect traffic away from unhealthy endpoints.
d)??? Azure Application Gateway with Autoscaling: Azure Application Gateway, when combined with autoscaling, automatically adjusts the number of gateway instances based on traffic load. It helps maintain application availability and responsiveness.
e)???? Azure Load Balancer: Azure Load Balancer can distribute incoming traffic across multiple VMs within a Virtual Network (VNet) or across multiple availability zones. It provides fault tolerance by rerouting traffic in the event of a failure. Or you can use HAProxy as well.
f)???? Azure SQL Database Geo-Replication: Azure SQL Database allows you to create read-only secondary databases in different Azure regions. This provides automatic failover capabilities in the event of a regional outage.
g)??? Azure CosmosDB: Azure Cosmos DB is a globally distributed, multi-model database service that provides high availability and fault tolerance out of the box. However, there are several features and strategies you can use within Azure Cosmos DB to enhance its fault tolerance capabilities even further.
1.???? Multiple Azure Regions: Deploy your Azure Cosmos DB account in multiple Azure regions. Azure Cosmos DB offers multi-region writes, allowing you to write data to multiple regions simultaneously. This ensures data availability and fault tolerance even if an entire Azure region becomes unavailable.
2.???? Automatic Failover: Azure Cosmos DB automatically handles failovers between regions in the event of an outage. It will seamlessly redirect traffic to a healthy region with minimal downtime.
3.???? Global Distribution: Use Azure Cosmos DB's global distribution feature to distribute your data to multiple regions, reducing latency and ensuring data availability to users in different geographic locations.
4.???? Read Regions: You can configure read regions in Azure Cosmos DB to enable low-latency read operations from the nearest region. This not only improves performance but also ensures fault tolerance, as read requests can be automatically rerouted to healthy regions in case of failures.
5.???? Manual Failover: Azure Cosmos DB allows you to perform manual failovers to promote a secondary region to a primary region in case of regional outages or planned maintenance.
6.???? Consistency Levels: Azure Cosmos DB offers different consistency levels, including strong, bounded staleness, session, and eventual consistency. You can choose the consistency level that best fits your application's fault tolerance requirements.
7.???? Backup and Restore: Regularly back up your Azure Cosmos DB data using Azure Backup. This ensures that you have a reliable way to restore your data in case of accidental deletions or data corruption.
8.???? Cosmos DB Change Feed: Utilize the Cosmos DB Change Feed to capture and process changes to your data in near real-time. It can be used to maintain synchronized copies of your data for fault tolerance and disaster recovery purposes.
9.???? Monitoring and Alerts: Set up monitoring and alerts for your Azure Cosmos DB account using Azure Monitor and Azure Monitor Logs to detect and respond to issues promptly.
10.? Retry and Circuit Breaker Patterns: Implement retry and circuit breaker patterns in your application code to handle transient failures when communicating with Azure Cosmos DB. This helps your application remain responsive during temporary issues.
11.? Data Partitioning: Use proper data partitioning strategies to distribute data evenly across logical partitions to avoid hotspots and improve fault tolerance in case of regional failures.
12.? Resource Locks: Apply resource locks in Azure to prevent accidental modifications or deletions of your Azure Cosmos DB resources.
13.? Security Best Practices: Implement security best practices, including role-based access control (RBAC), firewall rules, and Virtual Network Service Endpoints, to protect your Azure Cosmos DB resources from unauthorized access and potential vulnerabilities.
h)??? Azure Blob Storage Redundancy Options: Azure Blob Storage offers redundancy options like Locally Redundant Storage (LRS), Geo-Redundant Storage (GRS), and Zone-Redundant Storage (ZRS) to ensure data availability in the face of failures or outages.
i)????? Azure Site Recovery: Azure Site Recovery provides disaster recovery as a service (DRaaS) and allows you to replicate virtual machines and physical servers to Azure for failover and recovery in case of outages.
j)????? Azure Functions and Logic Apps: Azure Functions and Logic Apps are serverless computing services that automatically scale and handle failures, ensuring the execution of critical tasks even if some resources encounter issues.
k)??? Azure Kubernetes Service (AKS): AKS supports the deployment of containerized applications with built-in fault tolerance mechanisms. Kubernetes can automatically replace failed containers or nodes.
l)????? Azure Redis Cache with Redis Cluster: Azure Redis Cache can be configured with Redis Cluster, providing high availability and data replication across multiple Redis instances.
m)?? Azure Application Service Environments (ASE): Azure ASE is a premium service offering of Azure App Service that runs your web apps in a fully isolated, highly scalable environment. It provides fault tolerance and high availability for your web applications.
n)??? Azure Functions Premium Plan: The Premium Plan for Azure Functions offers features like virtual network integration and automatic scale-out based on workload, enhancing fault tolerance for serverless applications.
o)??? Azure Storage Account Failover: Azure Storage Accounts offer the option to perform manual failover to a secondary region in case of a region-wide failure.
p)??? Azure Logic App Integration Service Environment (ISE): Azure Logic App ISE provides a highly isolated and fault-tolerant environment for running your Logic Apps.
These Azure solutions can be combined and customized to meet your specific fault tolerance requirements for your applications and services. The choice of solution(s) will depend on factors like the type of application, data, geographic considerations, and recovery time objectives (RTOs) and recovery point objectives (RPOs) for your workload.
Fault tolerance is a critical aspect of application design to ensure that a system can continue to operate reliably in the presence of failures or errors. There are several design patterns and strategies that can be employed to handle fault tolerance in application design:
1)??? Retry Pattern: Retry patterns involve automatically retrying a failed operation to mitigate transient failures. You can implement various retry strategies, such as exponential backoff or fixed interval retries, to improve the chances of successful operation completion.
2)??? Circuit Breaker Pattern: The circuit breaker pattern monitors the state of a service or component and temporarily blocks requests if it detects a certain threshold of failures. This prevents continuous attempts to access a failing service and allows it time to recover.
3)??? Timeout Pattern: Setting timeouts for operations helps prevent them from blocking indefinitely. If an operation exceeds its predefined timeout, it can be terminated, and alternative actions can be taken, such as retrying the operation or providing a fallback response.
4)??? Bulkhead Pattern: The bulkhead pattern involves segregating different parts of a system or service to limit the impact of a failure in one area on the rest of the system. It can be applied to thread pools, database connections, or microservices.
5)??? Fallback Pattern: In the fallback pattern, you provide an alternative or default behavior in case an operation fails. This ensures that even if a critical component fails, the application can continue to function with reduced functionality.
6)??? Retry and Circuit Breaker Combination: Combining the retry and circuit breaker patterns can provide robust fault tolerance. You can retry an operation when the circuit is closed (healthy state) and immediately trip the circuit (open state) when the operation continues to fail.
7)??? Queue-Based Load Leveling: In this pattern, incoming requests are placed in a queue before being processed. This can help absorb bursts of traffic and smooth out variations in load, improving the overall resilience of the system.
8)??? Idempotent Operations: Designing operations to be idempotent means that they can be safely retried without causing unintended side effects. This is particularly useful in systems where retries are common.
9)??? Stateless Services: Stateless services store no session or user-specific data on the server, making it easier to recover from failures by routing requests to other healthy instances.
10) Microservices and Containerization: Implementing microservices architecture with containerization allows for isolated and independently scalable components. If one microservice fails, it doesn't necessarily impact the entire system.
11) Chaos Engineering: Chaos engineering is a practice that involves intentionally injecting failures and disruptions into a system to test its resilience. It helps identify weaknesses in fault tolerance strategies.
12) Geographic Redundancy and Disaster Recovery: Designing your application to run across multiple geographic regions or data centers can ensure that it remains available even in the event of regional outages or natural disasters.
13) Health Checks and Monitoring: Implement continuous health checks and monitoring to detect failures and automatically trigger fault tolerance mechanisms, such as failing over to backup resources or restarting failed components.
14) Rolling Deployments and Canary Releases: When updating your application, use rolling deployments and canary releases to gradually introduce changes and monitor their impact on system stability.
15) Backup and Restore: Regularly back up data and have well-defined plans for restoring services in case of data corruption or catastrophic failures.
The choice of fault tolerance patterns and strategies depends on the specific requirements and constraints of your application. Often, a combination of these patterns is used to achieve the desired level of fault tolerance in complex systems.
C.??? Load Balancing: Distribute incoming traffic evenly across multiple instances of an application to improve performance and fault tolerance. Azure Load Balancer and Application Gateway are commonly used for this pattern. Load balancing is a critical component of software architecture that helps distribute incoming network traffic or workloads across multiple servers or resources to ensure optimal utilization, performance, and reliability. There are several ways to handle load balancing in software architecture:
a.???? Hardware Load Balancers: These are dedicated physical appliances designed specifically for load balancing. They provide high performance and often include advanced features like SSL offloading and traffic inspection. Examples include F5 BIG-IP and Citrix ADC.
b.???? Software Load Balancers: Software load balancers run on standard servers or virtual machines and offer load balancing capabilities through software. Popular software load balancers include:
??????????????????????????????????????????????????????????????????????????????????????????? i.???? Nginx: An open-source, high-performance HTTP and reverse proxy server that can also be used as a software load balancer.
????????????????????????????????????????????????????????????????????????????????????????? ii.???? HAProxy: An open-source TCP/HTTP load balancer known for its speed and efficiency.
??????????????????????????????????????????????????????????????????????????????????????? iii.???? Envoy: A modern, cloud-native proxy and load balancer developed by Lyft and now part of the CNCF (Cloud Native Computing Foundation).
c.???? Load Balancing as a Service (LBaaS): Some cloud providers offer load balancing as a managed service. For example, Amazon Web Services (AWS) provides Elastic Load Balancing (ELB), Google Cloud offers Google Cloud Load Balancing, and Microsoft Azure has Azure Load Balancer. These services can automatically distribute traffic to backend instances and scale as needed.
d.???? DNS Load Balancing: DNS-based load balancing involves configuring DNS records to resolve to multiple IP addresses. When a client looks up the DNS record, it receives one of the IP addresses in a round-robin or weighted manner, distributing traffic across multiple servers. DNS load balancing is simple but lacks fine-grained control over traffic distribution.
e.???? Round Robin Load Balancing: This basic algorithm distributes incoming requests sequentially to a list of servers. While simple, it may not take into account server health or varying workloads, leading to uneven distribution.
f.????? Least Connections Load Balancing: Servers with the fewest active connections receive new requests. This approach aims to evenly distribute the load based on the current server's capacity.
g.???? Weighted Round Robin or Weighted Least Connections: Assign different weights to servers based on their capacity or performance. Servers with higher weights receive more traffic, allowing for more precise load balancing.
h.???? IP Hash Load Balancing: The client's IP address is used to determine which server should handle the request. This ensures that requests from the same client are consistently routed to the same server, which can be useful for session-based applications.
i.????? Session Affinity (Sticky Sessions): Some applications require that all requests from the same client be directed to the same server. Session affinity mechanisms, like cookie-based or IP-based affinity, ensure that a client's requests are routed to the same backend server for the duration of their session.
j.????? Content-Based Routing: This approach directs traffic based on the content of the request. For example, a load balancer might route requests for images to one server and requests for videos to another.
k.???? Health Checks: Load balancers can continuously monitor the health of backend servers. Unhealthy servers can be temporarily taken out of rotation, preventing them from receiving traffic until they recover.
l.????? Global Server Load Balancing (GSLB): GSLB distributes traffic across multiple data centers or cloud regions, taking into account factors like geographical proximity and server health. This is particularly useful for achieving high availability and disaster recovery.
m.?? Auto-Scaling: Configure auto-scaling policies that dynamically add or remove servers based on traffic or resource utilization, ensuring optimal performance during traffic spikes.
n.???? Content Delivery Networks (CDNs): CDNs use a distributed network of edge servers to cache and deliver content closer to end-users. While not a traditional load balancer, CDNs can help offload traffic from origin servers and improve content delivery performance.
Load balancing is a critical component of software architecture that helps distribute incoming network traffic or workloads across multiple servers or resources to ensure optimal utilization, performance, and reliability. There are several ways to handle load balancing in software architecture:
Azure provides several load balancing solutions to help distribute network traffic and ensure high availability and performance for your applications. Here are some of the primary load balancing solutions available in Azure:
a)???? Azure Load Balancer:
a.???? Azure Public Load Balancer: Used for distributing incoming internet traffic to virtual machines (VMs) inside Azure. It supports both inbound and outbound load balancing and can be used for applications hosted in Azure.
b.???? Azure Internal Load Balancer: Distributes network traffic to VMs that are part of a Virtual Network (VNet) in Azure. It's primarily used for internal or backend services.
c.???? Azure Application Gateway: A layer 7 load balancer that is designed for web applications. It provides features like SSL termination, URL-based routing, and web application firewall (WAF) capabilities.
b)??? Azure Traffic Manager: Azure Traffic Manager is a DNS-based global load balancer that directs client traffic to the most appropriate endpoint based on routing rules and the proximity of the user to the endpoints. It's ideal for distributing traffic across multiple Azure regions or data centers.
c)???? Azure Front Door: Azure Front Door is a global, scalable entry point that uses the Microsoft global network to route traffic to your applications. It provides features like dynamic site acceleration, SSL offload, and threat protection.
d)??? Azure Application Gateway WAF (Web Application Firewall): This is an extension of Azure Application Gateway that includes Web Application Firewall capabilities to protect web applications from common web vulnerabilities and attacks.
e)???? Azure CDN (Content Delivery Network): Azure CDN accelerates the delivery of high-bandwidth content to users by caching it at strategically placed locations. It improves the performance and availability of web applications by serving content from the nearest edge location to the user.
f)???? Azure Firewall: While primarily a security service, Azure Firewall can also be used to control and route traffic between VNets and subnets, effectively acting as a network-based load balancer for securing traffic flows.
g)??? Azure Kubernetes Service (AKS) Load Balancing: If you're running containerized applications on Azure Kubernetes Service, AKS provides built-in load balancing for your microservices through Kubernetes' native load balancing mechanisms.
h)??? Azure Bastion: Azure Bastion is a secure and remote desktop protocol (RDP) and SSH access gateway. While not a traditional load balancer, it can help manage secure remote access to VMs in Azure.
These Azure load balancing solutions can be used individually or in combination to meet your specific application's requirements for scalability, availability, and performance. The choice of which solution to use depends on factors like the type of application, traffic distribution needs, security requirements, and geographic considerations.
2)??? Data Management Patterns:
A.??? Data Partitioning: Split large datasets into smaller partitions to improve scalability and performance. Azure Cosmos DB provides built-in support for partitioning.
B.??? Caching: Implement caching mechanisms using Azure Cache for Redis or Azure Managed Cache Service to reduce database load and improve response times.
C.??? Data Replication: Replicate data across Azure regions for disaster recovery and improved performance. Azure SQL Database and Cosmos DB offer replication options.
3)??? Compute Patterns:
A.??? Serverless Computing: Implement serverless functions using Azure Functions for event-driven, scalable, and cost-effective compute.
B.??? Containerization: Use Azure Kubernetes Service (AKS) for container orchestration and deployment of containerized applications.
C.??? Auto-scaling: Automatically scale resources based on demand to optimize costs and ensure performance.
4)??? Security Patterns:
A.??? Identity and Access Management: Use Azure Active Directory (Azure AD) for identity and access control to secure applications and APIs.
B.??? Network Security Groups (NSGs): Implement NSGs to control inbound and outbound traffic to network interfaces, VMs, and other Azure resources.
C.??? Encryption: Encrypt data at rest and in transit using Azure Key Vault for key management and Azure Storage Service Encryption.
5)??? Messaging Patterns:
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A.??? Publish-Subscribe: Implement the publish-subscribe pattern using Azure Service Bus or Azure Event Grid for decoupled communication between microservices.
B.??? Message Queues: Use Azure Queue Storage or Azure Service Bus Queues for reliable messaging between components.
C.??? Event Sourcing: Capture and store events in a durable manner for auditing and data replay. Azure Event Hubs can be used for this pattern.
6)??? Monitoring and Management Patterns:
A.??? Application Insights: Integrate Azure Application Insights for monitoring application performance and gaining insights into usage.
B.??? Log Analytics: Use Azure Monitor and Log Analytics to collect and analyze logs and telemetry data for troubleshooting and insights.
C.??? Azure DevOps Integration: Implement CI/CD pipelines and infrastructure as code using Azure DevOps for automated deployment and management.
Pattern
Summary
Category
Create helper services that send network requests on behalf of a consumer service or application.
Implement a fa?ade or adapter layer between a modern application and a legacy system.
Decouple backend processing from a frontend host, where backend processing needs to be asynchronous, but the frontend still needs a clear response.
Create separate backend services to be consumed by specific frontend applications or interfaces.
Isolate elements of an application into pools so that if one fails, the others will continue to function.
Load data on demand into a cache from a data store
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Let each service decide when and how a business operation is processed, instead of depending on a central orchestrator.
Handle faults that might take a variable amount of time to fix when connecting to a remote service or resource.
Split a large message into a claim check and a payload to avoid overwhelming a message bus.
Undo the work performed by a series of steps, which together define an eventually consistent operation.
Enable multiple concurrent consumers to process messages received on the same messaging channel.
Consolidate multiple tasks or operations into a single computational unit
Segregate operations that read data from operations that update data by using separate interfaces.
Deploy multiple independent copies of application components, including data stores.
The great variety of systems and devices on the shop floor can make workload configuration a difficult problem.
Use an append-only store to record the full series of events that describe actions taken on data in a domain.
Move configuration information out of the application deployment package to a centralized location.
Delegate authentication to an external identity provider.
Protect applications and services by using a dedicated host instance that acts as a broker between clients and the application or service, validates and sanitizes requests, and passes requests and data between them.
Use a gateway to aggregate multiple individual requests into a single request.
Offload shared or specialized service functionality to a gateway proxy.
Route requests to multiple services using a single endpoint.
Deploy backend services into a set of geographical nodes, each of which can service any client request in any region.
Implement functional checks in an application that external tools can access through exposed endpoints at regular intervals.
Create indexes over the fields in data stores that are frequently referenced by queries.
Coordinate the actions performed by a collection of collaborating task instances in a distributed application by electing one instance as the leader that assumes responsibility for managing the other instances.
Generate prepopulated views over the data in one or more data stores when the data isn't ideally formatted for required query operations.
Break down a task that performs complex processing into a series of separate elements that can be reused.
Prioritize requests sent to services so that requests with a higher priority are received and processed more quickly than those with a lower priority.
Enable an application to announce events to multiple interested consumers asynchronously, without coupling the senders to the receivers.
Use a queue that acts as a buffer between a task and a service that it invokes in order to smooth intermittent heavy loads.
Limiting pattern to help you avoid or minimize throttling errors related to these throttling limits and to help you more accurately predict throughput.
Enable an application to handle anticipated, temporary failures when it tries to connect to a service or network resource by transparently retrying an operation that's previously failed.
Manage data consistency across microservices in distributed transaction scenarios. A saga is a sequence of transactions that updates each service and publishes a message or event to trigger the next transaction step.
Coordinate a set of actions across a distributed set of services and other remote resources.
Process a set of related messages in a defined order, without blocking processing of other groups of messages.
Divide a data store into a set of horizontal partitions or shards.
Deploy components of an application into a separate process or container to provide isolation and encapsulation.
Deploy static content to a cloud-based storage service that can deliver them directly to the client.
Incrementally migrate a legacy system by gradually replacing specific pieces of functionality with new applications and services.
Control the consumption of resources used by an instance of an application, an individual tenant, or an entire service.
Use a token or key that provides clients with restricted direct access to a specific resource or service.
5.???? Documentation and Diagrams:
a)???? Create architecture documentation and diagrams (Current Architecture, Target Architecture, Global Deployment Architecture, Single Deployment Stamp Architecture, Component Architecture, Integration Architecture) that visually represent the system's structure and components. These documents serve as a reference for developers and help them understand the system's design.
b)??? Consider using tools to draw diagram. https://app.diagrams.net/
6.???? Code Reviews and Peer Collaboration:
a)???? Conduct code reviews to ensure that architectural guidelines and best practices are followed. Peer collaboration allows team members to identify and address complexity early in the development process.
b)??? Code reviews of your Terraform Script Should also be done to ensure that Architecture Pattern is being followed.
7.???? Testing and Test Automation:
a)???? Implement thorough testing strategies, including unit testing, integration testing, and system testing. Testing helps identify defects and unexpected interactions that contribute to complexity.
b)??? Automate testing processes to ensure consistent and repeatable validation of system behavior in your multi-stage gated pipelines.
8.???? Refactoring:
a)???? Regularly review and refactor code to simplify complex areas. Refactoring involves restructuring code without changing its external behavior, making it easier to understand and maintain.
b)??? Use automated refactoring tools when available.
9.???? Continuous Learning:
a)???? Stay updated on emerging technologies, architectural trends, and best practices. Attend conferences, participate in online forums, and engage in continuous learning to improve your architectural skills.
10.? Architecture Reviews:
a)???? Conduct architecture reviews with the team to assess the overall system design. Identify areas of concern, potential bottlenecks, and opportunities for improvement.
11.? Prototyping and Proof of Concepts:
a)???? Use prototyping and proof of concepts to validate architectural decisions and mitigate risks associated with complex design choices. Prototypes allow you to experiment with different approaches before committing to a final design.
12.? Scalability and Performance Considerations:
a)???? Anticipate scalability and performance requirements early in the design process. Ensure that the architecture can handle growth and increased load without introducing unnecessary complexity.
b)??? Consider using gRPC and GraphQL along with SPA web-based applications for better performance and scalability, hosted in auto scalable infrastructure.
13.? Feedback Loop:
a)???? Maintain a feedback loop with stakeholders, including end-users and product owners. Their input can help refine the architecture and align it with evolving business needs.
Handling software architecture complexity is an ongoing process that requires a combination of technical skills, collaboration, and a commitment to continuous improvement. It's possible for software architects and development teams to effectively manage complexity and deliver maintainable and scalable software systems by following these strategies and best practices.
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