What is high availability in cloud computing?

High availability (HA) in cloud computing means ensuring that services and applications are always up and running, even if something goes wrong. It involves having backup systems, automatically switching to those backups if a problem occurs, and spreading resources across different locations to prevent downtime. This ensures that users can always access the services without interruptions.

What is high availability architecture?

High availability architecture is a design approach that ensures a system or application is always available and accessible to users, even in the event of hardware or software failure, network outages, or other disruptions. HA architecture aims to minimize downtime and ensure the system can recover quickly from failures, reducing the impact on users and the business.

Some common techniques used in HA architecture include:

• • Clustering: Grouping multiple servers or nodes to provide redundancy and failover capabilities
  • Load Balancing: Distributing incoming traffic across multiple nodes to ensure that no single node is overwhelmed and becomes a single point of failure
  • Replication: Duplicating data or services across multiple nodes to ensure they remain available even if one node fails
  • Redundancy: Implementing duplicate components or systems to ensure there is always a backup available in case of a failure
  • Fault Tolerance: Designing systems to continue operating even when one or more components fail
  • Auto-scaling: Automatically adding or removing nodes to match changing workload demands, ensuring that the system can handle increased traffic or demand
  • Disaster Recovery: Implementing plans and procedures to recover from catastrophic failures or disasters that affect the entire system

Why is high availability important?

High availability ensures critical systems, applications, and services are always accessible and available to users, customers, and businesses. Here are some reasons why HA is important:

• • Revenue Protection: Downtime can result in significant revenue loss, especially for e-commerce, financial, and other online businesses. HA ensures that systems remain available, minimizing the risk of lost sales and revenue.
  • Customer Satisfaction: Users expect 24/7 access to services and applications. HA ensures that customers can access what they need when they need it, improving overall customer satisfaction and loyalty.
  • Business Continuity: HA ensures businesses run smoothly even if something breaks. This is key for companies heavily dependent on technology.
  • Brand Reputation: Frequent downtime or outages can damage a company’s reputation and erode customer trust. HA helps maintain a positive brand image by ensuring that services are always available.
  • Improved Productivity: HA ensures employees have the tools necessary to do their jobs, preventing roadblocks and allowing them to maximize productivity.

How does high availability work?

To illustrate how high availability works, let’s imagine a scenario involving a busy e-commerce website that must be available 24/7.

This particular website operates on multiple servers, so if one server fails, others immediately take over, keeping the site running smoothly. These servers are spread across different data centers in various locations, so if one data center experiences a problem, the website still remains operational.

In this scenario, automated failover systems detect server issues and quickly switch users to backup servers without manual intervention. Load balancers distribute traffic evenly across all servers, preventing any single server from overloading.

By using these methods—server redundancy, geographic distribution, automated failover, and load balancing—the busy e-commerce website stays reliable and accessible, providing a seamless experience for users and ensuring they can access their favorite products at all times.

High availability vs. disaster recovery

High availability and disaster recovery are related but distinct concepts in IT and business continuity planning. Here’s a table of the differences between HA and DR:

Characteristic	High Availability	Disaster Recovery
Focus	Ensure continuous operation of a specific system or application	Ensure restoration of critical business operations and systems after a disaster
Goal	Minimize downtime and ensure continuous operation	Restore business operations and systems as quickly as possible with minimal data loss
Techniques	Redundancy, load balancing, failover, replication, clustering	Data backup and restore, system replication, cloud-based recovery, crisis management planning
Scope	Specific system or application	The entire organization and its critical operations
Timeframe	Measured in minutes or hours	Measured in days, weeks, or months
Objective	Ensure always-on operation	Ensure business continuity and minimize the impact of a disaster
Trigger	Hardware or software failure, network outage, or other disruptions	Natural disasters, cyberattacks, major system failures, or other catastrophic events

HA ensures the continuous operation of a specific system or application, while DA is about restoring critical business operations and systems after a catastrophic event.

High availability concepts

A high availability architecture relies on several key concepts to keep systems operational with minimal downtime. The concepts include:

Redundancy: Using multiple instances of critical components so that if one fails, others can take over

Failover: Automatically switching to backup systems when a primary component fails to ensure continuous service

Load Balancing: Distributing traffic evenly across servers to prevent any single one from overloading

Geographic Distribution: Spreading resources across different locations to protect against localized failures like natural disasters

Automatic Scaling: Adjusting the number of resources based on current demand to handle traffic spikes and optimize performance

Monitoring and Alerts: Continuously tracking system health and sending alerts for quick issue resolution

Data Backup and Replication: Regularly backing up and replicating data to prevent loss and ensure availability

Health Checks and Self-healing: Regularly testing systems and automatically fixing issues to minimize manual intervention

These concepts work together to maintain reliable and continuous service.

High availability requirements and best practices

To achieve high availability, you need to implement strategies and best practices that ensure your systems are resilient, reliable, and able to operate continuously, even in the event of failures or disruptions. This implementation involves a combination of redundancy, geographic distribution, automation, and regular monitoring. Here are the key steps to building a highly available architecture that minimizes downtime and maintains consistent service availability.

How to achieve high availability 

To achieve HA, focus on a few core strategies to ensure your systems are always operational:

• • Use Redundant Resources: Deploy multiple instances of servers, databases, and critical components to prevent single points of failure. Doing this ensures that if one part fails, another can take over immediately.
  • Distribute Across Multiple Locations: Spread your resources across different data centers or geographic regions to protect against localized failures, such as power outages or natural disasters.
  • Implement Automated Failover and Load Balancing: Set up automatic failover systems to switch to backup resources in case of failure and use load balancers to distribute traffic across servers, maintaining performance and availability evenly.
  • Monitor Continuously: Use monitoring tools to detect issues early and set up alerts for any potential problems so that you can resolve them quickly.
  • Regular Backups and Testing: Back up critical data regularly and test your HA setup to ensure that failover mechanisms and recovery processes work effectively.

By focusing on these key areas, you can build a reliable, highly available cloud infrastructure that minimizes downtime and provides consistent service to your users.

How to measure availability

Measuring availability involves calculating the percentage of time a system, service, or application is operational and accessible to users over a given period. Availability is typically expressed as a percentage, indicating how often the system is up and running.

Measuring availability

• • Understand the Formula for Availability
    You can calculate availability using this formula:
    

• • Uptime: The total time the system is operational and available
  • Downtime: The total time the system is unavailable or not functioning as expected
  • Define the Measurement Period
    Choose a specific period to measure availability, such as an hour, day, month, or year. This period helps you understand the system’s performance over time and identify patterns or trends in availability.
    
  • Monitor and Record Uptime and Downtime
    Use monitoring tools and software to track and log the system’s uptime and downtime continuously. These tools can automatically detect outages, performance issues, and any incidents causing downtime.
  • Calculate Downtime
    Determine the total downtime during the chosen period. Downtime includes both planned (e.g., maintenance) and unplanned outages. Unplanned downtime is often the focus for availability metrics, but you can also calculate separate metrics for each.
  • Compute Availability Percentage
    Plug the values of uptime and downtime into the availability formula to calculate the percentage. For example, if a system is down for 30 minutes in a month (43,200 minutes), this is what your formula would look like:

• • Determine Availability Target
    Compare the calculated availability with your target or service level agreement (SLA). A common target for HA systems is “five nines,” or 99.999% availability, which translates to less than 5.26 minutes of downtime annually.

Common availability metrics

• • Three Nines (99.9%): Less than 8.76 hours of downtime annually
  • Four Nines (99.99%): Less than 52.56 minutes of downtime annually
  • Five Nines (99.999%): Less than 5.26 minutes of downtime annually

Measuring availability involves calculating the uptime percentage using a simple formula and monitoring the system continuously. By tracking this metric, you can assess how well your system meets its availability targets and identify areas for improvement.

Key takeaways and resources

To ensure high availability, focus on redundancy by using backup instances for critical components and automating failover to minimize downtime. Implement load balancing to distribute traffic and spread resources across multiple locations to protect against localized failures. Use automatic scaling to handle demand fluctuations, continuously monitor system health, and regularly back up and replicate data. Additionally, test failover processes and incorporate self-healing mechanisms to address issues promptly.

Resources

You can gain more knowledge about this topic by reading these articles:

• • Always-Available Real-Time Data With High Availability In Couchbase Analytics
  • Deploying Couchbase for High-Availability in Google Cloud Platform – Part 1
  • Deploying Couchbase for High-Availability in Google Cloud Platform – Part 2
  • Distributed Databases: An Overview
  • Cross Data Center Replication (XDCR) – Couchbase Products

The post High Availability Architecture: Requirements & Best Practices appeared first on The Couchbase Blog.

Do you have text that you want to process automatically? Maybe you want to pull out key products or topics of conversation? Maybe you want to get the sentiment? The possibilities are many with this week's topic: NLP with spaCy and Python. Our guest, Vincent D. Warmerdam, has worked on spaCy and other tools at Explosion AI and he's here to give us his tips and tricks for working with text from Python.

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I’m flying to Washington DC today with my oldest son to look at colleges. It was a vacation day from work (mostly), but I still did some reading worth sharing.

[article] Techniques to Tackle Technical Debt with Dustin Thostenson. Read this transcript (or listen to the podcast itself) for insights into identifying, prioritizing, and fixing tech debt.

[article] Paying down tech debt: further learnings. More on tech debt! This looks at incremental and big-bang approaches.

[blog] Debunking Kafka Top 5 Use Cases. Derek’s had enough of people throwing too many use cases at their Kafka infrastructure.

[article] Google’s NotebookLM evolves: What IT leaders need to know about its enterprise applications. This is the most interesting AI tech from the past month, and that’s saying something. And it embeds a tweet from me, so you KNOW it’s good.

[blog] Innovating at the speed of light: A CosMc’s story. You don’t have to be a small company to be nimble. McDonald’s folks share how they went from concept to reality in a short time.

[article] AWS sees customers repatriating workloads as cloud wars heat up. I think what you’re seeing is folks who lifted-and-shifted having buyers remorse. And a few cloud vendors cleaned up on those early migrations, but now have unsettled tenants.

[blog] Cost management for AI/ML platforms with Google Kubernetes Engine. Use clouds the right way, and repatriation will seem silly. I like when clouds make it easier to use them the right way.

[blog] Google is a Leader in the 2024 Gartner® Magic Quadrant for Container Management. Speaking of GKE, it’s still among the best ways to do Kubernetes in the cloud. I use all the options from the major hyperscalers, and don’t think it’s as close as represented here!

[blog] Pulumi Google Cloud Provider Version 8.0.0. If you’re using Pulumi already, or just looking to get rolling with a good infrastructure automation strategy, take a look at what’s new.

[article] Pfizer’s Future of Development. How does a big company truly change how they deliver software? I like the focus areas here, and the outcomes are excellent.

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The Wall Street Journal reported that Qualcomm has approached Intel about a complete takeover of the company in recent days.

The post Report: Qualcomm Made a New Bid to Purchase Intel appeared first on Thurrott.com.

As the cloud becomes essential for modern software development, integrating cloud solutions into your development process can significantly boost productivity. Microsoft Azure offers a comprehensive suite of services and tools to help developers create, deploy, and manage cloud applications. Using Azure extensions for Visual Studio Code is one of the simplest ways to utilize Azure’s features. This blog post will discuss using the Azure Tools extension pack for Visual Studio Code and the best extensions for various development roles.

 

 

Why Use Azure Tools for Visual Studio Code?

By integrating Azure Tools directly into VS Code, developers can deploy applications to Azure quickly and manage cloud infrastructure without leaving their coding environment. The Azure Tools extension pack offers a powerful set of tools for developers ranging from frontend specialists to DevOps engineers.

Let’s take a deeper dive into the available extensions and help you choose the right ones for your specific needs.

 

Azure Tools for Every Developer Type

The Azure Tools extension pack consists of various extensions tailored to specific development tasks. Whether you’re a fullstack developer, DevOps engineer, or data engineer, there’s an extension that fits your workflow.

 

Fullstack Developers

Fullstack developers work on both frontend and backend aspects of applications. With Azure, you can deploy an entire stack to the cloud effortlessly.

• Azure Container Apps: Allows you to create or manage containerized applications, enabling you to handle different parts of your app stack, such as frontend and backend.
• Azure Functions: Create serverless functions to handle backend tasks and seamlessly integrate with frontend apps.
• Azure App Service: Deploy complete web solutions, including dynamic backend services, APIs, and server-side logic.

When to Use: If you're building a web app with both frontend and backend components, use these extensions to deploy and manage the entire stack on Azure.

 

Backend Developers

Backend developers often build microservices architectures and manage backend APIs and databases. Azure offers tools that simplify scaling, containerization, and serverless backend logic.

• Azure Container Apps: Use this to manage server-side logic, APIs, and microservices efficiently with containerized solutions.
• Azure Functions: Provides the ability to create scalable, event-driven backend services.
• Azure App Service: Enables deploying RESTful APIs and other backend components.

When to Use: When developing microservices, backend logic, or APIs for your application, these tools provide the ability to scale effortlessly without managing infrastructure.

 

DevOps Engineers

If you’re responsible for CI/CD pipelines and managing cloud infrastructure, the Azure Tools extension pack can help automate the entire process.

• Azure Container Apps: Automates the deployment and scaling of containerized applications.
• Azure Functions: Use serverless functions in automated deployment processes, integrating into broader workflows.
• Azure App Service: Integrates directly with code repositories, offering automated deployments and performance monitoring.
• Azure Static Web Apps: Integrates with GitHub Actions or Azure DevOps for seamless automation.

When to Use: Use these extensions when setting up CI/CD pipelines or automating deployment processes for scalable applications.

 

Frontend Developers

Frontend developers specialize in building user interfaces with libraries like React, Angular, or Vue.js. The Azure Tools extension pack streamlines workflows for static websites and single-page applications.

• Azure Static Web Apps: Deploy and preview static websites easily.
• Azure App Service: Use for deploying dynamic frontend apps with backend APIs.
• Azure Storage: Manage static assets and offer data storage solutions.

When to Use: Perfect for deploying and managing static websites or single-page applications, with integrations for automatic deployments from GitHub or Azure DevOps.

 

Enterprise Developers

Working on large-scale apps or building Software as a Service (SaaS)? The Azure Tools extension pack offers extensive capabilities for scaling and monitoring mission-critical applications.

• Azure Container Apps: Scale multi-tenant services in containers.
• Azure Functions: Automate backend processes and workflows.
• Azure App Service: Deploy and monitor large-scale applications with robust scaling options.

When to Use: When building enterprise-level applications or SaaS products, these tools help ensure scalability and reliability for business-critical services.

 

API Developers

If you build APIs for mobile or web applications, Azure has tools that make it easy to create, deploy, and manage APIs.

• Azure Functions: Deploy serverless APIs with easy scaling.
• Azure Container Apps: Use containers for deploying APIs with complex requirements.
• Azure Storage: Manage API data storage with blobs, queues, and tables.

When to Use: For building RESTful or serverless APIs, these extensions streamline the process of deployment and scaling.

 

Data Engineers

Data engineers need tools for processing and analyzing large data streams. The Azure Tools extension pack helps create scalable environments for data pipelines and workflows.

• Azure Container Apps: Deploy data processing pipelines in containers.
• Azure Functions: Trigger ETL tasks and automate workflows based on real-time data changes.

When to Use: When working with large-scale data streams, these tools help create scalable environments for real-time data processing and analysis.

 

How to Get Started

Install Visual Studio Code: If you haven’t already, download and install Visual Studio Code.

Install the Azure Tools extension pack: Open VS Code, navigate to the Extensions view, and search for “Azure Tools.” Install the extension pack from Microsoft.

Navigate to Azure view: Click on the Azure icon in sidebar to open the Azure Resources view. Once you're signed in, the Resources view will display all your existing resources. You can create and manage these services right from VS Code. 

Sign in to Azure: After installation, sign in to your Azure account directly from VS Code by pressing F1 and selecting “Azure: Sign In”. 

Begin Building: After signing in, you can deploy apps, manage containers, and automate deployments, all within VS Code.

 

Conclusion

The Azure Tools extension pack allows developers to use Azure's cloud services without changing environments, making it easier to build web apps, manage backend services, deploy APIs, and process large datasets.

Ready to supercharge your development process? Install the Azure Tools extension pack and start deploying to Azure today!

Gerhard Lazu joins us for Kaizen 16! Our Pipe Dream™️ is becoming a reality, our custom feeds are shipping, our deploys are rolling out faster & our tooling is getting just right.

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Featuring:

• Gerhard Lazu – Mastodon, Twitter, GitHub, Website
• Jerod Santo – Mastodon, Twitter, GitHub, LinkedIn
• Adam Stacoviak – Mastodon, Twitter, GitHub, LinkedIn, Website

Show Notes:

• Discussion #520: 🎧 Kaizen 16
• github.com/thechangelog/pipedream
• 🎬 Why we started building our single-purpose CDN for changelog.com
• Join us in Zulip
• Namespace – Accelerate your developer workflow
• Setup the app locally with just one command: just contribute #521

Something missing or broken? PRs welcome!

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