Previously, our load testing process heavily relied on manual intervention using JMeter, where we manually modified the article and Token ID in the JMeter suite and exported results in CSV format which were then transferred to Excel sheets for analysis during each release. Despite providing us with basic insights like average response time and API throughput, this approach lacked the efficiency and scalability necessary for conducting comprehensive load testing. Furthermore, the time-consuming process of comparing post and pre-deployment results in Excel graphs became a daunting task and hindered report maintenance.

Performance Optimization – Parameterized Azure Load Testing

To overcome these limitations, we embraced the Azure Load Test for our Document360 product, where we harnessed the capabilities of Azure virtual machine infrastructure. By configuring JMeter within a virtual user environment, we unlocked a world of possibilities for load testing. Parameters like the number of threads, ramp-up period, and loop count ensured accurate simulation of user behavior, while response time metrics and error thresholds brought crucial performance insights to the forefront.

With Azure Load Test, we have the flexibility to configure our load testing setup based on specific modules. This enables us to perform testing on various load testing patterns (such as Stress, Spike, ramp up, Soak, and more) to meet specific application requirements. Whether we need to simulate high loads, stress test critical components, or analyze performance under different scenarios, Azure Load Test’s module-based configuration empowers us to tackle diverse testing challenges with ease.

Scalability and Cost Efficiency

Azure’s virtual machines proved to be the perfect ally for load testing. Their scalability allowed us to adjust resources on the fly, without the need for hardware changes. With pay-as-you-go pricing, we only paid for the duration of the load testing, eliminating the need for dedicated hardware investments. Resource isolation ensured reliable results, while environment replication guaranteed consistency across different setups. Rapid provisioning, simplified setup, and test repeatability streamlined the testing process, saving valuable time and effort. Centralized management tools provided easy control, monitoring, and troubleshooting, simplifying the management of our load tests.

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Real-Time Insights and Optimization

With Azure Load Test’s client-side and server-side dashboards, we unlocked real-time monitoring of API performance. Each API’s performance could be tracked and analyzed, allowing us to make data-driven decisions. Comparing test results became effortless, enabling us to identify deviations and improvements across different test runs. By passing parameters like bearer tokens within Azure itself, we simulated real-world scenarios accurately, ensuring our load tests reflected actual usage patterns. The seamless integration with JMeter allowed us to upload comprehensive JMeter suites, empowering us to analyze performance metrics and optimize our application’s performance effectively.

Benchmarking and Comparison

Response time metrics allow for benchmarking and performance comparison across different releases, environments, or competing applications. By establishing baseline response time metrics, we can track the application’s performance over time, assess the impact of changes or optimizations, and make data-driven decisions for future improvements.

Re-Running Tests for Continuous Improvement

But the power of Azure Load Test didn’t stop there. When changes were made in the same pre-release deployment, we could easily re-run the same test, validating the effectiveness of our fixes and improvements. This iterative testing approach paved the way for continuous improvement, ensuring our application met performance expectations.

Insights and Recommendations from Azure

Azure Load Test took our performance testing to the next level by providing insightful recommendations for improving our application’s resiliency. No more digging through logs or struggling to identify bottlenecks. Azure itself suggested solutions, allowing us to optimize our application effortlessly. The resiliency report provided valuable insights into the application’s ability to handle unexpected failures and recover gracefully.

With Azure Load Test’s result dashboard, effortlessly share comprehensive test results via a convenient link. Stakeholders can access and review detailed monitoring information with ease. Furthermore, you can download the test result logs and input files from each test run for further analysis and investigation. Here, we share a sample report of the Constant Load Pattern to provide an overview of the recent release. Where the response time and throughput for the Virtual User (VUser) configuration remained the same at 600ms with no deviation. This suggests that the release did not introduce any significant performance issues.

Conclusion

In this blog, we have demonstrated how we have improved our load testing in Document360 by leveraging Azure load testing. We have also added more scalability, cost efficiency, benchmarking capabilities, and real-time insights into our application load testing. 

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The post Revolutionizing Load Testing with Azure Load Test appeared first on Document360.

By default, all resources in Virtual Network can communicate outbound to the internet. However, we can use public IP addresses or a public load balancer for an inbound internet connection.

Components of Virtual Networking

Address Space:

Address Space is required to define the custom private address space (RFC 1918) range while creating a VNet resource. Microsoft Azure recommends below address spaces for private IP address communication:

• 10.0.0.0 – 10.255.255.255
• 172.16.0.0 – 172.31.255.255
• 192.168.0.0 – 192.168.255.255

Note: Make sure VNet address spaces cannot overlap each other.

Subnet:

Subnet plays a vital role in segmenting the virtual network into multiple networks to isolate the traffic and provides better security. A subnet can be defined as a range of IP addresses within the virtual network address space range. An IP defined in a subnet can communicate with other IP addresses configured within the VNet by default. However, we can customize this behavior using the Network Security Group.

NSG:

Network Security Groups (NSGs) are used in Azure Virtual Network (VNet) to provide an additional layer of network security by filtering inbound and outbound traffic based on specific protocols, ports, or IP addresses. NSGs help to segment the network traffic, monitor network activity, and customize security rules to meet business needs.

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How does “Route All” impacts VNet?

In Azure Virtual Network (VNet), “Route all ” is a network routing option that directs all traffic in the VNet to the next hop. This means that any traffic originating from resources within the VNet will be sent to the specified next hop, which can be any configured Azure resources (Ex: Azure VM, Load Balancer, Application Gateway, or VPN Gateway).

The impact of using “Route all” in Azure VNet can be significant and should be carefully considered. below are some of the key considerations while designing the networking with route all options:

Network Performance:

“Route all” can affect network performance, as all traffic in the VNet is directed to the specified next hop. If the next hop is not properly sized or configured, it can become a bottleneck and cause performance issues.

Network Security:

It is possible to have an impact on network security. Since all traffic is directed to the next hop, it can be intercepted or redirected if the next hop is compromised. It’s important to ensure that the next hop is properly secured and monitored.

Routing Complexity:

It can make routing more complex, primarily if multiple next-hops are used for different types of traffic. This can make it harder to troubleshoot the network issues and can lead to possible routing conflicts if not properly configured.

Cost:

It can also have cost implications, as the next hop may be an Azure resource that incurs additional charges. It’s important to consider the cost of using “Route all” and to ensure that it is properly sized and configured to minimize costs.

How it helps Document360?

Isolation:

Document360 uses a dedicated Virtual Network for the services hosted in the cloud for better isolation and sophisticated network topologies.

Security:

VNet provides a secure environment and network for all our applications. Moreover, it provides reliable and excellent network connectivity. It provides an extra layer of security for our resources, as they are not directly exposed to potential threats.

Customization:

It gives us full control over network configuration to monitor the incoming/outgoing traffic and create the rules to allow or deny the traffic.

Scalability:

VNet is highly scalable; it allows us to easily add or remove resources based on business demands. We can also use Azure VNet peering to connect multiple VNets which enables us to scale out the network architecture without sacrificing performance.

SNAT Port Exhaustion

Azure Web Apps or virtual machines may need to have outbound connectivity to the internet for certain use cases. This process uses source network address translation (SNAT) to translate the web application’s private addresses to the load balancer’s public IP address. Generally, Azure App Service web application needs to connect with different external applications/endpoints, such as Redis, Search Server endpoints SQL/NoSQL Databases, and call another RESTful service.

Every instance of Azure App service is initially pre-allocated with 128 SNAT ports. The SNAT port limit affects when every connection to the same destination IP and destination port uses a SNAT port.

SNAT port exhaustion occurs when the server runs out of available ports to use for translation.

Cost:

Azure VNet is a paid service, so it’s important to consider the cost implications of your network design. You can use Azure Cost Management and Billing to monitor your Azure usage and optimize your costs.

Conclusion

Azure Virtual Network is a powerful service that provides a secure and scalable way to connect your resources in the cloud. By carefully designing your network topology and considering factors such as IP addressing and security, you can create a customized network environment that meets your business needs. With Azure VNet, you can take full advantage of the cloud’s flexibility and agility while maintaining the security and control that your business requires.

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The post How Document360 Leverages Azure Virtual Network for Isolation and Security? appeared first on Document360.

To address these challenges, we’ve recently launched a new feature that lets users import their API definitions directly into your platform, giving them clear prominence in the API’s endpoints, parameters, and response codes. This feature has already generated positive user feedback, but you want to take it a step further and make it even more robust and handy to use.

To that end, we’ve introduced a command-line tool called d360, which allows developers to import or resync their API definitions directly from their CI/CD pipelines. This tool automates the documentation process, ensuring that the documentation is always up-to-date and in sync with the API code. We’ve also taken care to ensure that the tool is flexible, customizable, and integrates seamlessly with other development tools and services.

In this blog, we’ll take a closer look at the challenges of documenting APIs, the benefits of using command-line tools like d360, and how to use d360 in your own development workflow. We’ll provide examples of the commands available in the tool and explain how to set up the tool in your CI/CD pipeline. By the end of this blog, you’ll have a clear understanding of how d360 can help you streamline your documentation process and ensure that your users have the information they need to successfully use your API.

npm install d360 -g

d360 apidocs:resync –userId=<Enter User ID>

                    --apiReferenceId <Enter API Reference Id>

                    --apiKey <Enter API Key>

                    --path <File path or URL>

d360 apidocs 
             --apiKey=c92e71ab-ebdf-4007-89ed-5d47493052cd

             --userId=3340e95e-2b68-4a3f-a8c9-124bcaec9972

             --versionId=d486783f-b833-446e-aa71-615ac51392c3

             --path=https://petstore.swagger.io/v2/swagger.json

d360 apidocs:resync 

                  --apiKey=c92e71ab-ebdf-4007-89ed-5d47493052cd

                  --userId=3340e95e-2b68-4a3f-a8c9-124bcaec9972

                  --apiReferenceId=d486783f-b833-446e-aa71-615ac51392c3

                  --path=https://petstore.swagger.io/v2/swagger.json

Conclusion

To sum up, I had a tremendous experience fostering this command line tool, which offers a compelling and effective method for importing and resyncing API definitions to Document360. Our team will constantly enhance this tool’s experience and stability.

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The post Introducing d360, a command line interface to streamline API documentation appeared first on Document360.

In Document360, we have a portal to create content and a knowledge base site to view published content. Our team performs various types of testing like functional, performance, and security to ensure product quality. There is no debate that if companies want to improve their overall efficiency, the next step is the adoption of Automation in testing.

To ensure maximum efficiency, we decided to automate our functional testing, via various testing tools and frameworks. We compared various solutions before picking the right one based on our requirements.

Conclusion

Our test automation framework uses a hybrid approach that is designed and developed to be robust, easy to maintain, scalable, and very flexible to execute end-to-end testing with all necessary features. Ultimately it’s helping us to reduce total testing time and manual tester effort.

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The post Test Automation Framework: How we built it for Document360 appeared first on Document360.

We were constantly trying to improve our application’s performance, and while closely monitoring the application for any setbacks & improvements, we found out that there’s a significant number of database writes that’s been happening every day through a particular endpoint.

There are 2 sides to Document360, one being the portal where the user will be writing articles, reviewing them, and managing the overall knowledge base. The second part is the knowledge base site where the customers will just consume the contents of the knowledge base.

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Deep diving further

In layperson’s terms, a public-facing knowledgebase site will get more views compared to the portal where the people will write articles and manage the knowledge base. We started monitoring all the endpoints that are being consumed by the customer-facing knowledge base sites and collected the metrics for them.

Being a knowledge base, tracking is one of the most important parts of the application where we show the knowledge base total number of views/reads/likes/dislikes, etc… All this information must be collected when users visit the Knowledge base site and we consolidate & show the metrics on the portal. We noticed that for collecting analytics Earlier we have been used this API (UpdateTrackingInformation) and this is responsible for collecting all the metrics from the Knowledge base site & writing them to the database.

Imagining a basic flow

Whenever a user comes to the Knowledge base site, the user may visit multiple articles, read them, spend time on some articles where it requires more context, or the user may even like/dislike various articles. Now even if we consider a rough estimate that each user sees at least 5 articles and interact with them, that is 5 separate database hits to write the information to our database.

Looking at the average number of hits that have been made to this API is having a huge impact on our database. The average number of hits per day is 145K and when we see it for a week the numbers are very huge 821K. We are sure that we ought to do something to address this problem.

The solution we arrived

Analytics is not the most immediate information required instantly. Whenever 500 users visit the site, we thought that it does not make sense to show analytics instantly in the portal, as nobody in the knowledge base would be looking at analytics for the current date time or today.

We have completely changed our analytics architecture so that the number of DB hits made to the database is drastically lower than earlier. The final architecture that we arrived at is represented in the below diagram.

We started to use queuing mechanism to consolidate & write the results to the database by grouping the results based on the project. For sake of simplicity, let’s consider there are two websites.

• docs.document360.com 
• docs.biztalk360.com 

There may be multiple users visiting the documentation site of the products for various use cases. Based on our new architecture all the information that is collected for Document360 will be grouped into a single set and written to the database in a single BulkWrite operation. In this way, we reduce the maximum number of connections and writes that are being made to the database.

The new metrics chart after we rolled out our analytics architecture. On average per day, the number of database writes is 2K and for a week the number stands at 12K

Conclusion

The difference is huge, we can see a slight decrease in our overall operational costs including the API servers and our database. So, consider queuing for any such scenarios where data like analytics is not required immediately henceforth the application’s performance can be improved by significantly reducing the overall load on the servers & database.

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The post How we redesigned the analytics data processing in Document360 appeared first on Document360.

How does Crawling and Rendering work?

A Google bot or a computer that has a list of links from our website known as ‘sitemap’ will be available on every site provided by the site owners. When the crawler finds a page, it tries to render the page like what the browser does, but without any user context.

Most of the content is rendered on the server and it is ready to be viewed by the browser on request. Server-side rendering allows for an amazingly fast experience on the site with quick initial load times and less JavaScript to be painted. But still, most applications cannot just rely on complete SSR.

Having JavaScript on the client allows us to do ‘event handling’ and run logic on the client with little to no requests to the server to perform client-side operations.

The Document360 Knowledge Base experience

All knowledge base sites have a few sections in common. These are very standard and will be available on any knowledge base site that you visit.

• Categories tree to navigate between content (left)
• Content part (middle)
• Table of contents (right)
• Search (top)

So far, we have not spoken about the actual problem yet. Recently some of our customers have started complaining that the knowledge base SEO is having an impact and the users are having trouble finding content using search engines. After our initial analysis, we have found out the mechanism in which we load the content on the page was causing an impact on the SEO.

The implementation that we had to fetch the content is done once after the client is initialized and an ‘ajax’ request is made to fetch the contents of the currently selected article and the content is painted on the DOM later. The knowledge base was loaded based on the below steps.

• User opens a URL Eg:  [https://docs.document360.com/docs/february-2023-release-note]
• The overall layout of the page is rendered on the server
• The server returns the overall rendered layout page along with some data that is required for doing some rendering on the client.
• Once the layout is initialized the client sends a request to the server to fetch content for the current article, the network request URL looks like this ‘{subDomain}/load-article/{slug}’
• To give a more exact example, the URL might look like this https://docs.document360.com/docs/february-2023-release-note
  
  Do you notice the problem here? The actual URL where the user is viewing the article is shown in the screenshot below.
  
  
  
  
  
  This is the same URL a google bot will crawl, but it was not able to find the content of the article because google does not know that we load the content via a sub nested route ‘load-article’. So, this is obvious that this is a fundamental problem and has a massive impact on our SEO. This led to a couple more issues
• Duplicate results on Google Search
• Google cached content is empty
  
  
  
  When we viewed the ‘cached’ page of the public site, it was just empty. So, we ventured out on an optimal and strong solution that will improve our SEO score as well as not lead to any more problems in the future.

Here’s how we solved the problem:

We were still convinced that we should not reload the entire page when the user switches between articles from the category tree, so we anyway had to make an ‘ajax’ call. But we need the content to be painted without painting the entire page.

Let us imagine two scenarios:

• A user visits the page and reads the articles by navigating the tree
• A bot or spider crawling the page on the links
  
  

Also Read: How to SEO Your Knowledge Base Articles

Scenario 1: User visits a page

When a user is visiting the page, as mentioned above we should only paint the content on navigating between articles. But the same is not true for case 2, when a bot visits a URL, it just needs to crawl through the content, it does not care or know whether we paint the content again or not.

A better example to understand it better. From a user perspective, when a user browses through articles, we remove the nested route called ‘ load-article’ and just initiate a request to the actual URL with some additional query parameters to inform the server that this request is initiated by a user.





The URL looks like this ‘{subDomain}/release-notes/?partialView=on’. Google will consider a URL with and without parameters as two different URLs. To resolve the problem, we added a canonical tag so Google will ignore the URL with query parameter [More Reference on Google Docs].

A site can have query params to preserve the state etc. So, when this request hits the server, the server responds with only the content. The content block once received will be painted on the client.

Scenario 2: Bot visits a page

When a bot visits the page, we render the entire content of the page on the server. When a bot visits a page, it only knows the URL of the content directly via the sitemap.

Final words

We deployed this solution and we observed it for a couple of weeks internally and found that this approach works better and improved our metrics measured via Google Lighthouse.

Before




After

If you are interested in more in-depth research on the workings of SEO, you can check out the references below.

• [What is a web crawler? | How web spiders work | Cloudflare]  
• [Google Search – Discover how Google Search works]  
• [How Google Search Works for Beginners | Google Search Central | Documentation | Google Developers] 

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The post How we Improved SEO Performance for Document360 appeared first on Document360.