Deployment

Forget the idea that modernization has to mean rewriting everything. The real work happens in the in-between, where REST meets SOAP, where sidecars live beside WAR files, and where code changes are political before they're technical. Especially in high-stakes, compliance-bound environments like healthcare, government, and labor systems, modernization doesn’t look like a revolution. It looks like a careful negotiation. When Modernization Isn't Optional (Yet Also Isn't a Rewrite) Enterprise Java applications aren’t always a clean slate. Many developers work with monoliths that began over a decade ago, coded with Hibernate DAOs, JSP-driven UIs, and SOAP-based services stitched together with barely documented business logic. These aren’t museum pieces. They still power critical systems. The real tension arises when modern demands enter the mix: JSON interfaces, cloud triggers, and client-side rendering. Teams are often asked to retrofit REST endpoints into XML-heavy ecosystems or to integrate AWS services without disturbing a decades-old session flow. Modernization, in this context, means layering, not leaping. You’re not rewriting the system; you’re weaving new threads into a fabric that’s still holding the enterprise together. In one modernization project for a public-sector system, even a minor update to session tracking caused a chain reaction that disabled inter-agency authentication. This underscored the principle that modernization efforts must be staged, tested in sandboxed environments, and introduced gradually through abstraction layers, not pushed as big-bang releases. In a real-world healthcare analytics project for a government client, we had to preserve decades-old XML-based report generation logic while enabling modern REST-based data access for third-party dashboards. The solution wasn't to replace, but to extend: we introduced Spring Boot services that intercepted requests, repackaged outputs into JSON, and served them in parallel without touching the legacy data model. It worked, not because we transformed everything, but because we layered just enough to adapt what mattered. The Balancing Act: Spring Boot Meets Legacy WAR Files One common strategy is selective decomposition: peeling off slices of the monolith into Spring Boot services. But this isn’t a clean cut. Existing WAR deployments require coexistence strategies, especially when the primary app is deployed on traditional servlet containers like WebLogic or Tomcat. Spring Boot’s embedded Tomcat can conflict with the parent app server’s lifecycle, and developers often have to carefully configure ports, context paths, and threading models to avoid resource contention. Session management is another complication. Many legacy systems rely on stateful sessions, and introducing token-based authentication in Spring Boot may create a mismatch in how user identity is tracked across services. Teams must decide whether to retain centralized session management or introduce statelessness at the edge and deal with session bridging internally. Configuration strategies also diverge; legacy apps may rely on property files and hardcoded XML, whereas Spring Boot encourages externalized, environment-driven configs. The architectural result often looks transitional. A legacy monolith continues to function as the core engine, but adjacent REST services are spun off using Spring Boot. These services are fronted by an API gateway or reverse proxy, and internal SOAP calls may be wrapped in REST adapters. Anti-corruption layers come into play to keep new services loosely coupled. Done right, this architecture buys time and stability while enabling incremental modernization. A similar approach was applied in a case involving a pension management platform, where we deployed Spring Boot services alongside a WAR-deployed core app still running on WebLogic. The goal wasn’t to replace workflows overnight, but to carefully intercept and extend specific endpoints, such as payment triggers and beneficiary lookups, through newer RESTful interfaces. This allowed independent deployments and cloud-based enhancements without risking the system’s compliance-grade uptime. RESTful Interfaces at War With XML Contracts The move to REST isn’t just about switching verbs or moving from POST to PUT. Older systems may still speak WSDL, and dropping XML isn’t always viable due to integrations with legacy clients, vendors, or compliance protocols. Some government-facing systems still require signed XML payloads validated against DTDs or XSDs. To maintain compatibility, many teams dual-maintain both REST and SOAP formats. JAXB plays a key role in binding Java objects to XML. At the same time, libraries like XStream allow flexible serialization control for hybrid payloads that must meet backward compatibility while providing modern interfaces. Developers often annotate classes with JAXB and then use custom message converters or serialization logic to support both XML and JSON APIs. Here’s an example of a JAXB-bound class with a fallback handler that captures unknown elements: Java @XmlRootElement(name = "User") public class User { private String name; private String email; @XmlElement public String getName() { return name; } @XmlElement public String getEmail() { return email; } @XmlAnyElement public List<Object> getUnknowns() { return unknownFields; } } This setup allows parallel API exposure without breaking legacy consumers, offering a pathway to decouple the front-facing API strategy from backend contracts. From Server Rooms to the Cloud: Incremental AWS Integration You don’t forklift a Java app to the cloud. You sneak it in, one event, one interface, one file upload at a time. One practical method is integrating AWS services alongside existing workflows, without replacing core components. A common pattern involves leveraging S3 buckets for file storage. A legacy Java backend can upload user files or reports to a bucket, which then triggers a Lambda function through an S3 event notification. The Lambda function might perform transformations, enrich data, or send alerts to other services using SNS or SQS. The Spring Cloud AWS toolkit simplifies this integration. Developers use the spring-cloud-starter-aws library to configure S3 clients, IAM roles, and event subscriptions. IAM roles can be applied at the EC2 instance level or dynamically assumed using STS tokens. Within the codebase, these roles provide controlled access to buckets and queues. Diagrammatically, this looks like: This is exactly what played out in a logistics project where we needed to track shipment uploads in real-time. Rather than rewrite the core Java backend, we allowed it to upload files to an S3 bucket. An AWS Lambda picked up the event, transformed the metadata, and forwarded it to a tracking dashboard, all without changing a single servlet in the monolith. It was cloud-native augmentation, not cloud-for-cloud’s-sake. This approach decouples cloud adoption from full-stack reengineering, allowing legacy systems to coexist with modern event-driven flows. It also acts as a proving ground for broader cloud-native transitions without disrupting production workloads. A future extension could involve migrating only certain data processing steps to ECS or EKS, based on what proves stable during these early Lambda integrations. CI/CD and Monitoring for a Codebase You Didn’t Write Legacy code often resists modernization, especially when coverage is low and configuration is arcane. CI/CD pipelines offer a lifeline by enforcing baseline quality checks and deployment discipline. A typical setup involves Jenkins pipelines that trigger on Git push events. The pipeline stages include Maven builds, code linting using PMD and Checkstyle, and static analysis using SonarQube. In large codebases, teams also use Jacoco to track incremental coverage improvements, even if full coverage is impractical. Jenkins shared libraries help enforce common pipeline stages across services. Here’s a Jenkinsfile snippet from one such setup: Groovy pipeline { agent any stages { stage('Build') { steps { sh 'mvn clean install -DskipTests' } } stage('Code Quality') { steps { sh 'mvn pmd:check checkstyle:check sonar:sonar' } } } } Monitoring requires similar compromise. Tools like AppDynamics or New Relic must be configured with custom JVM arguments during startup, often injected through shell scripts or container entrypoints. With EJBs or SOAP layers, automatic instrumentation may fail, requiring explicit trace annotations or manual config. Even if the system is too fragile for full APM adoption, lightweight metrics and error tracking can surface regressions early, making the system more observable without requiring invasive rewrites. Modernization Isn’t Technical First, It’s Strategic Rushing modernity is the fastest way to break an enterprise system. What makes modernization sustainable isn’t tooling; it’s judgment. Whether we’re layering Spring Boot next to a WAR-deployed monolith, adapting REST interfaces over legacy XML, or routing data through S3 buckets instead of adding endpoints, the same principle applies: progress happens at the edges first. Modernization doesn’t begin with code. It begins with the courage to leave working systems untouched, and the clarity to change only what will move the system forward. That’s not just modernization. That’s stewardship.

Scaling microservices for holiday peak traffic is crucial to prevent downtime and ensure a seamless user experience. This guide explores Azure DevOps automation, CI/CD pipelines, and cost-optimization strategies to handle high-demand traffic seamlessly. Manual scaling quickly becomes a bottleneck as organizations deploy dozens, sometimes hundreds, of microservices powered by distinct backend services like Cosmos DB, Event Hubs, App Configuration, and Traffic Manager. Multiple teams juggling these components risk costly delays and errors at the worst possible moments. This is where automation comes in: a game-changing solution that transforms complex, error-prone processes into streamlined, efficient operations. In this article, you’ll explore how automated pipelines can not only safeguard your systems during peak traffic but also optimize costs and boost overall performance in this Microservice world. The Challenge in a Microservices World Imagine a project with over 100 microservices, each maintained by different engineering teams. Every service may have its backend components, for example, as shown below: Cosmos DB: Used for storing data with low-latency access and high throughput.Event Hubs: Ingests telemetry and log data from distributed services.App Configuration: Centrally manages application settings and feature flags.Traffic Manager: Routes user traffic to healthy endpoints during failures. Manual Scaling Is Inefficient Coordinating these tasks manually is cumbersome,  especially when production issues arise. With multiple teams, interacting and collaborating on each microservice’s scaling and configuration can be overwhelming. This is where CI/CD pipelines and Infrastructure-as-Code (IaC) automation become crucial. Automation not only reduces human error but also provides a unified approach for rapid, reliable scaling and updates. Figure 1: A system overview showing how the Web App (Presentation Layer) interacts with microservices (Business Logic Layer), which use Cosmos DB, Event Hubs, and App Configuration (Data Layer). The Integration & Traffic Management layer, including Traffic Manager and Azure DevOps CI/CD, handles traffic routing, deployments, and Slack notifications. Understanding Each Component AKS (Azure Kubernetes Service) AKS is a managed Kubernetes service that simplifies deploying, scaling, and managing containerized applications. In a microservices environment, each service can be deployed as a container within AKS, with independent scaling rules and resource allocation. This flexibility enables you to adjust the number of pods based on real-time demand, ensuring that each service has the computing resources it needs. Cosmos DB Azure Cosmos DB is a globally distributed, multi-model NoSQL database service that delivers low latency and high throughput. In a microservices architecture, each service may have its own Cosmos DB instance to handle specific data workloads. Automation scripts can dynamically adjust throughput to meet changing demand, ensuring your service remains responsive even during peak loads. Event Hubs Azure Event Hubs is a high-throughput data streaming service designed to ingest millions of events per second. It’s particularly useful in microservices for collecting logs, telemetry, and real-time analytics data. By automating the scaling of Event Hubs, you ensure that your data ingestion pipeline never becomes a bottleneck,  even when the number of events spikes during high-traffic periods. App Configuration Azure App Configuration is a centralized service that stores configuration settings and feature flags for your applications. In a microservices ecosystem, different services often need unique settings or dynamic feature toggles. Instead of hard-coding these values or updating configurations manually, App Configuration provides a single source of truth that can be updated on the fly. During peak traffic, a microservice can instantly disable resource-heavy features without redeployment. Traffic Manager Azure Traffic Manager is a DNS-based load-balancing solution that directs user traffic based on endpoint health and performance. For microservices, it ensures that requests are automatically rerouted from failing or overloaded endpoints to healthy ones, minimizing downtime and ensuring a seamless user experience, especially during high-stress scenarios like holiday peak traffic. The Traffic Manager ensures disaster recovery by rerouting traffic from a failed region (e.g., East US) to a healthy backup (e.g., West US) in under 30 seconds, thereby minimizing downtime. Figure 2: High-level view of user traffic flowing through Azure Traffic Manager to an AKS cluster with containerized microservices, which interact with Cosmos DB, Event Hubs, and App Configuration for data, logging, and real-time updates. Automating the Process With CI/CD Pipelines Leveraging Azure DevOps CI/CD pipelines is the backbone of this automation. Here’s how each part fits into the overall process: Continuous integration (CI): Every code commit triggers a CI pipeline that builds and tests your application. This immediate feedback loop ensures that only validated changes move forward.Continuous delivery (CD): Once the CI pipeline produces an artifact, the release pipeline deploys it to production. This deployment stage automatically scales resources (like Cosmos DB and Event Hubs), updates configurations, and manages traffic routing. Dynamic variables, secure service connections, and agent configurations are all set up to interact seamlessly with AKS, Cosmos DB, and other services.Service connections and Slack notifications: Secure service connections (using a service account or App Registration) enable your pipeline to interact with AKS and other resources. Integration with Slack provides real-time notifications on pipeline runs, scaling updates, and configuration changes, keeping your teams informed. Figure 3: Component Diagram — A high-level architectural overview showing Azure DevOps, AKS, Cosmos DB, Event Hubs, App Configuration, Traffic Manager, and Slack interconnected. Core Automation Commands and Validation Below are the essential commands or code for each component, along with validation commands that confirm each update was successful. 1. Kubernetes Pod Autoscaling (HPA) Core Commands Shell # Update HPA settings: kubectl patch hpa <deploymentName> -n <namespace> - patch '{"spec": {"minReplicas": <min>, "maxReplicas": <max>}' # Validate update: kubectl get hpa <deploymentName> -n <namespace> -o=jsonpath='{.spec.minReplicas}{"-"}{.spec.maxReplicas}{"\n"}' #Expected Output: 3–10 Bash Script for AKS Autoscaling Here’s a shell script for the CI/CD pipeline. This is an example that can be adapted for other automation tasks using technologies such as Terraform, Python, Java, and others. Shell #!/bin/bash # File: scaling-pipeline-details.sh # Input file format: namespace:deploymentname:min:max echo "Logging all application HPA pod count before update" kubectl get hpa --all-namespaces -o=jsonpath='{range .items[*]}{.metadata.namespace}{":"}{.metadata.name}{":"}{.spec.minReplicas}{":"}{.spec.maxReplicas}{"\n"}{end}' cd $(System.DefaultWorkingDIrectory)$(working_dir) INPUT=$(inputfile) OLDIFS=$IFS IFS=':' [ ! -f $INPUT ] && { echo "$INPUT file not found"; exit 99; } while read namespace deploymentname min max do echo "Namespace: $namespace - Deployment: $deploymentname - min: $min - max: $max" cp $(template) "patch-template-hpa-sample-temp.json" sed -i "s/<<min>>/$min/g" "patch-template-hpa-sample-temp.json" sed -i "s/<<max>>/$max/g" "patch-template-hpa-sample-temp.json" echo "kubectl patch hpa $deploymentname --patch $(cat patch-template-hpa-sample-temp.json) -n $namespace" kubectl get hpa $deploymentname -n $namespace -o=jsonpath='{.metadata.namespace}{":"}{.metadata.name}{":"}{.spec.minReplicas}{":"}{.spec.maxReplicas}{"%0D%0A"}' >> /app/pipeline/log/hpa_before_update_$(datetime).properties #Main command to patch the scaling configuration kubectl patch hpa $deploymentname --patch "$(cat patch-template-hpa-sample-temp.json)" -n $namespace #Main command to validate the scaling configuration kubectl get hpa $deploymentname -n $namespace -o=jsonpath='{.metadata.namespace}{":"}{.metadata.name}{":"}{.spec.minReplicas}{":"}{.spec.maxReplicas}{"%0D%0A"}' >> /app/pipeline/log/hpa_after_update_$(datetime).properties rm -f "patch-template-hpa-sample-temp.json" "patch-template-hpa-sample-temp.json".bak done < $INPUT IFS=$OLDIFS tempVar=$(cat /app/pipeline/log/hpa_before_update_$(datetime).properties) curl -k --location --request GET "https://slack.com/api/chat.postMessage?token=$(slack_token)&channel=$(slack_channel)&text=------HPA+POD+Count+Before+update%3A------%0D%0ANamespace%3AHPA-Name%3AMinReplicas%3AMaxReplicas%0D%0A${tempVar}&username=<username>&icon_emoji=<emoji>" tempVar=$(cat /app/pipeline/log/hpa_after_update_$(datetime).properties) #below line is optional for slack notification. curl -k --location --request GET "https://slack.com/api/chat.postMessage?token=$(slack_token)&channel=$(slack_channel)&text=------HPA+POD+Count+After+update%3A------%0D%0ANamespace%3AHPA-Name%3AMinReplicas%3AMaxReplicas%0D%0A${tempVar}&username=<username>&icon_emoji=<emoji>" Create file: patch-template-hpa-sample.json JSON {"spec": { "maxReplicas": <<max>>,"minReplicas": <<min>>} 2. Cosmos DB Scaling Core Commands This can be enhanced further in the CI/CD pipeline with different technologies like a shell, Python, Java, etc. Shell # For SQL Database: az cosmosdb sql database throughput update -g <resourceGroup> -a <accountName> -n <databaseName> --max-throughput <newValue> # Validate update: az cosmosdb sql database throughput show -g <resourceGroup> -a <accountName> -n <databaseName> --query resource.autoscaleSettings.maxThroughput -o tsv #Expected Output: 4000 #Input file format: resourceGroup:accountName:databaseName:maxThroughput:dbType:containerName Terraform Code for Cosmos DB Scaling Shell # Terraform configuration for Cosmos DB account with autoscale settings. resource "azurerm_cosmosdb_account" "example" { name = "example-cosmosdb-account" location = azurerm_resource_group.example.location resource_group_name = azurerm_resource_group.example.name offer_type = "Standard" kind = "GlobalDocumentDB" enable_automatic_failover = true consistency_policy { consistency_level = "Session" } } resource "azurerm_cosmosdb_sql_database" "example" { name = "example-database" resource_group_name = azurerm_resource_group.example.name account_name = azurerm_cosmosdb_account.example.name } resource "azurerm_cosmosdb_sql_container" "example" { name = "example-container" resource_group_name = azurerm_resource_group.example.name account_name = azurerm_cosmosdb_account.example.name database_name = azurerm_cosmosdb_sql_database.example.name partition_key_path = "/partitionKey" autoscale_settings { max_throughput = 4000 } } 3. Event Hubs Scaling Core Commands This can be enhanced further in the CI/CD pipeline with different technologies like a shell, Python, Java, etc. Shell # Update capacity: az eventhubs namespace update -g <resourceGroup> -n <namespace> --capacity <newCapacity> --query sku.capacity -o tsv # Validate update: az eventhubs namespace show -g <resourceGroup> -n <namespace> --query sku.capacity -o tsv #Expected Output: 6 4. Dynamic App Configuration Updates Core Commands This can be enhanced further in the CI/CD pipeline with different technologies like a shell, Python, Java, etc. Shell # Export current configuration: az appconfig kv export -n <appconfig_name> --label <label> -d file --path backup.properties --format properties -y # Import new configuration: az appconfig kv import -n <appconfig_name> --label <label> -s file --path <input_file> --format properties -y # Validate update: az appconfig kv export -n <appconfig_name> --label <label> -d file --path afterupdate.properties --format properties -y #Input file format: Key-value pairs in standard properties format (e.g., key=value). 5. Traffic Management and Disaster Recovery (Traffic Switch) Core Commands This can be enhanced further in the CI/CD pipeline with different technologies like a shell, Python, Java, etc. Shell # Update endpoint status: az network traffic-manager endpoint update --endpoint-status <newStatus> --name <endpointName> --profile-name <profileName> --resource-group <resourceGroup> --type <type> --query endpointStatus -o tsv # Validate update: az network traffic-manager endpoint show --name <endpointName> --profile-name <profileName> --resource-group <resourceGroup> --type <type> --query endpointStatus -o tsv #Expected Output: Enabled #Input file format: profileName:resourceGroup:type:status:endPointName Terraform Code for Traffic Manager (Traffic Switch) JSON resource "azurerm_traffic_manager_profile" "example" { name = "example-tm-profile" resource_group_name = azurerm_resource_group.example.name location = azurerm_resource_group.example.location profile_status = "Enabled" traffic_routing_method = "Priority" dns_config { relative_name = "exampletm" ttl = 30 } monitor_config { protocol = "HTTP" port = 80 path = "/" } } resource "azurerm_traffic_manager_endpoint" "primary" { name = "primaryEndpoint" profile_name = azurerm_traffic_manager_profile.example.name resource_group_name = azurerm_resource_group.example.name type = "externalEndpoints" target = "primary.example.com" priority = 1 } resource "azurerm_traffic_manager_endpoint" "secondary" { name = "secondaryEndpoint" profile_name = azurerm_traffic_manager_profile.example.name resource_group_name = azurerm_resource_group.example.name type = "externalEndpoints" target = "secondary.example.com" priority = 2 } Explanation: These Terraform configurations enable autoscaling and efficient resource allocation for Cosmos DB and Traffic Manager. By leveraging IaC, you ensure consistency and optimize costs by provisioning resources dynamically based on demand. How to Reduce Azure Costs With Auto-Scaling Automation improves operational efficiency and plays a key role in cost optimization. In a microservices ecosystem with hundreds of services, even a small reduction in over-provisioned resources can lead to substantial savings over time. By dynamically scaling resources based on demand, you pay only for what you need. By dynamically adjusting resource usage, businesses can significantly reduce cloud costs. Here are concrete examples: Cosmos DB Autoscaling: For instance, if running 4000 RU/s costs $1,000 per month, reducing it to 1000 RU/s during off-peak hours could lower the bill to $400 monthly, leading to $7,200 in annual savings.AKS Autoscaler: Automatically removing unused nodes ensures you only pay for active compute resources, cutting infrastructure costs by 30%. Visualizing the Process: Sequence Diagram To further clarify the workflow, consider including a Sequence Diagram. This diagram outlines the step-by-step process, from code commit to scaling, configuration updates, and notifications, illustrating how automation interconnects these components. For example, the diagram shows: Developer: Commits code, triggering the CI pipeline.CI pipeline: Builds, tests, and publishes the artifact.CD pipeline: Deploys the artifact to AKS, adjusts Cosmos DB throughput, scales Event Hubs, updates App Configuration, and manages Traffic Manager endpoints.Slack: Sends real-time notifications on each step. Such a diagram visually reinforces the process and helps teams quickly understand the overall workflow. Figure 4: Sequence Diagram — A step-by-step flow illustrating the process from code commit through CI/CD pipelines to resource scaling and Slack notifications. Conclusion Automation is no longer a luxury — it’s the cornerstone of resilient and scalable cloud architectures. In this article, I demonstrated how Azure resources such as Cosmos DB, Event Hubs, App Configuration, Traffic Manager, and AKS can be orchestrated with automation using bash shell scripts, Terraform configurations, Azure CLI commands, and Azure DevOps CI/CD pipelines. These examples illustrate one powerful approach to automating microservices operations during peak traffic. While I showcased the Azure ecosystem, the underlying principles of automation are universal. Similar techniques can be applied to other cloud platforms. Whether you’re using AWS with CloudFormation and CodePipeline or Google Cloud with Deployment Manager and Cloud Build, you can design CI/CD workflows that meet your unique needs. Embrace automation to unlock your infrastructure’s full potential, ensuring your applications not only survive high-demand periods but also thrive under pressure. If you found this guide helpful, subscribe to my Medium blog for more insights on cloud automation. Comment below on your experience with scaling applications or share this with colleagues who might benefit! Your feedback is invaluable and helps shape future content, so let’s keep the conversation going. Happy scaling, and may your holiday traffic be ever in your favor! Further Reading and References Azure Kubernetes Service (AKS) Documentation: Guidance on deploying, managing, and scaling containerized applications using Kubernetes.Azure Cosmos DB Documentation: Dive deep into configuring and scaling your Cosmos DB instances.Azure Event Hubs Documentation: Explore high-throughput data streaming, event ingestion, and telemetry.Azure App Configuration Documentation: Best practices for managing application settings and feature flags in a centralized service.Azure Traffic Manager Documentation: Techniques for DNS-based load balancing and proactive endpoint monitoring.Terraform for Azure: Learn how to leverage Infrastructure as Code (IaC) with Terraform to automate resource provisioning and scaling.Azure DevOps Documentation: Understand CI/CD pipelines, automated deployments, and integrations with Azure services.

Early-career developers often struggle with secure coding practices. GitHub Copilot, an AI pair programmer, can assist in writing safer code when used wisely. However, guidance is key; a 2021 study found that approximately 40% of Copilot’s generated code had security vulnerabilities when developers weren’t careful. GitHub responded by upgrading Copilot’s AI model and adding a real-time vulnerability filter to block common insecure patterns. In this post, we’ll explore how Copilot can help you write more secure code through clear prompts and examples. Guiding Copilot to Write Secure Code Copilot learns from billions of lines of code, including both good and bad practices. To get secure suggestions, you must steer it in the right direction. The GitHub documentation notes that many vulnerabilities (like SQL injection, cross-site scripting, etc.) can be avoided with practices such as parameterized queries, input validation, and avoiding hard-coded secrets. You encourage safer code outputs by explicitly asking Copilot for these practices in your prompts (e.g., via comments or function names). Always review Copilot’s suggestions — think of them as helpers that still need your verification. Let’s walk through five examples where Copilot assists in writing more secure code. Each example highlights a common security issue and how a well-crafted prompt yields a secure solution. Example 1: Avoid Hard-Coding Secrets (Use Environment Variables) Hard-coding API keys or passwords in code is a serious risk — it could expose secrets if your code is leaked. Copilot’s vulnerability filter is designed to catch this by flagging hard-coded credentials. For example, if you write a comment indicating you want to use an environment variable for an API key, Copilot will suggest doing exactly that: Python # Use environment variable for API key api_key = os.getenv('SERVICE_API_KEY') if not api_key: raise RuntimeError("API key not found in environment") # ... proceed to use api_key ... Why This Helps Instead of inserting a secret directly in code, the suggestion uses an environment variable. This way, the sensitive key isn’t stored in your source code, reducing the chance of accidental exposure. Avoiding hard-coded sensitive data is a core secure coding practice. With Copilot’s guidance, you easily implement this best practice. (Feeling confident with secrets? Next, let’s tackle database queries). Example 2: Prevent SQL Injection With Parameterized Queries SQL injection is one of the most common web vulnerabilities. It happens when user input is concatenated into SQL statements without proper handling, allowing attackers to inject malicious SQL. GitHub Copilot can help prevent this by suggesting parameterized queries instead of raw string building. For instance, if you prompt Copilot to insert data using a parameterized query, you might get code like: Python # Insert user input into database with a parameterized query sql = "INSERT INTO orders (email, product_name) VALUES (?, ?)" cursor.execute(sql, (user_email, product_name)) Why This Helps Using "?" placeholders (or $1, $2 in some languages) with cursor.execute ensures that the database driver handles the user data safely. This prevents malicious input from altering the SQL command, thwarting injection attacks. In contrast, an insecure approach would build the query as a string (e.g., "... VALUES ('" + user_email + "')"), which is vulnerable. Copilot’s suggestion follows the secure pattern, as GitHub’s own guides emphasize, parameterized queries neutralize SQL injection risk. (We’ve secured our database writes. Now, how about securing user-facing outputs?) Example 3: Sanitize Outputs to Prevent XSS Cross-site scripting (XSS) occurs when an application injects user input into web pages without sanitization, potentially letting attackers run scripts in the user’s browser. Copilot can assist by proposing safer output methods. Suppose we have JavaScript code that displays a user’s name on a page. Instead of using a vulnerable property, we can ask Copilot to ensure it’s safe. For example: JavaScript // Display user-provided name safely on the page const nameElement = document.getElementById('name-display'); nameElement.textContent = `Showing results for "${name}"`; Why This Helps Here, Copilot suggested using textContent instead of setting HTML directly. The textContent property will automatically escape any malicious content in the name, preventing it from being interpreted as HTML or script. An insecure version using innerHTML might have been exploited by a name like "<script>...</script>". In fact, Copilot is capable of detecting this XSS issue and recommending textContent as a fix. By following such suggestions, you ensure that user input is sanitized before rendering. (With front-end display secured, let’s wrap up what we’ve learned.) Example 4: Safe DOM Update to Prevent XSS Copilot Prompt In a JavaScript file, we write a function outline with a comment describing our goal: JavaScript // Safely display user-provided comment on the page without XSS vulnerabilities function renderComment(commentText) { // TODO: add commentText to the DOM safely } Copilot Suggestion GitHub Copilot autocompletes the function with a secure implementation: JavaScript function renderComment(commentText) { const commentDiv = document.createElement('div'); commentDiv.textContent = commentText; // assign text safely, not as HTML document.getElementById('comments').appendChild(commentDiv); } Explanation In this suggestion, Copilot chose textContent instead of innerHTML when inserting user input into the DOM. This is more secure because textContent treats the content strictly as text, meaning any HTML tags or scripts in commentText are not executed but shown as plain text. By avoiding innerHTML, the code prevents cross-site scripting (XSS) attacks, where malicious scripts could otherwise be injected and run on your page. The Copilot prompt included “without XSS,” so it knew to avoid insecure patterns. This way, even a beginner can automatically get a safer solution for rendering user input. Example 5: Enforcing HTTPS in an Express App Copilot Prompt In a Node.js/Express application, we add a comment indicating the need for HTTPS enforcement: JavaScript // Express middleware to redirect HTTP requests to HTTPS for security Copilot Suggestion Copilot generates a middleware snippet that forces secure connections: JavaScript app.enable('trust proxy'); // enable if behind a proxy (e.g., Heroku) app.use((req, res, next) => { if (!req.secure) { // Redirect to the same URL but with HTTPS return res.redirect('https://' + req.headers.host + req.url); } next(); }); Explanation Copilot recognized the security intent and suggested a common Express pattern for HTTPS enforcement. The code checks req.secure, a boolean provided by Express that is false for HTTP requests and true for HTTPS. If the request isn’t secure, it automatically redirects the client to the HTTPS version of the URL. This ensures all data transfer is encrypted via SSL/TLS, protecting sensitive information in transit. The inclusion of app.enable('trust proxy') is a bonus best practice Copilot added for scenarios (like cloud hosting) where a proxy might handle SSL — it makes the req.secure check reliable. For a beginner, this generated snippet not only saves time but also instills a security best practice (always using HTTPS) without having to know the exact code upfront. Conclusion: Copilot as a Security Co-Pilot GitHub Copilot can be a valuable ally in writing secure code when guided thoughtfully. We saw how clear prompts (like “use env var” or “parameterized query”) lead to security-conscious code suggestions. By leveraging Copilot’s knowledge of best practices, even a novice developer can implement defenses against common vulnerabilities. That said, Copilot is a generalist, not a specialist — it doesn’t replace dedicated security tools or your own vigilance. Always review Copilot’s output and use additional measures like code scanning and dependency checks for a comprehensive security strategy. The good news is that Copilot’s real-time checks (for hard-coded secrets, injection patterns, etc.), combined with your oversight, create a strong feedback loop for safer coding. In summary, GitHub Copilot can help you write more secure code faster, from catching injection risks to suggesting safer libraries. With the right guidance and verification, it transforms from an auto-completion tool into a pseudo-security coach. Use it to amplify your secure coding practices — and remember, the best results come when you pair Copilot’s power with your own careful eye. Guided thoughtfully, Copilot becomes not just an AI assistant, but a reliable partner in building robust, secure software

Abstract: A point of sale (POS) transaction system is a crucial component of retail and hospitality businesses, as it handles sales, payments, inventory management, and customer data. These systems need to process transactions efficiently, ensure data integrity, and be highly available, particularly in high-traffic environments like retail stores or restaurants. Amazon Aurora is an ideal database solution for POS systems due to its high availability, scalability, and the ability to handle large volumes of data with low-latency performance. This article will walk through a real-world example of implementing AWS Aurora for a POS transaction system and show how to optimize its use in this context. System Requirements for POS Transaction System Before diving into the implementation, let’s first define the system requirements and constraints: Transaction Consistency: Transactions must be processed atomically, ensuring accurate data during each sale (e.g., inventory updates, customer record updates).High Availability: The POS system must be available 24/7, even in the event of server or network failures.Scalability: The system should scale to handle peak loads, such as high transaction volumes during holidays or sales.Low Latency: Transactions need to be processed quickly with minimal delays.Security: Sensitive customer data, such as credit card information, must be securely handled. High-Level Architecture for POS System Using AWS Aurora Architecture uses an Amazon Aurora MySQL-compatible database to handle transactional data for the POS system. Here is the overall architecture: Frontend: POS terminals (could be mobile apps, web apps, or dedicated devices) interact with a backend API.Backend API: The backend (possibly running on AWS Lambda or EC2) processes the business logic, interacts with the Aurora database, and handles requests from the POS terminals.Aurora Database: The Aurora instance stores: Transactions: Records of individual sales, including item details, quantity, customer information, and payment status.Inventory: Real-time inventory data that is updated as sales are processed.Customer: Customer profiles, purchase history, loyalty points, etc. Example Components: Amazon Aurora (MySQL-compatible)Amazon API Gateway for POS terminal communicationAWS Lambda to run backend logic (e.g., transaction processing)Amazon S3 for storing transaction receipts and logs (optional)AWS CloudWatch for monitoringAmazon IAM for securing access to resources Setting Up Aurora for the POS System Create an Aurora Cluster Login to AWS Console: Go to the RDS Console.Click Create Database and choose Amazon Aurora as the engine. Database Engine: Select MySQL-compatible Aurora (for this example, as it's often used in transactional systems). Choose Instance Type: For a production environment, choose an instance class like db.r5.large or db.r5.xlarge based on your transaction load. Storage Configuration: Use Aurora Auto-Scaling Storage to automatically scale up as your database grows.Set the backup retention to a suitable duration (e.g., 7-30 days). High Availability: Enable Multi-AZ Deployment for fault tolerance and automatic failover to ensure high availability. Set Up Network: Choose a VPC where your Aurora cluster will reside.Ensure the proper security groups and network access control lists (NACLs) are set for secure connections. Security: Enable Encryption at Rest using AWS KMS (Key Management Service).Enable SSL/TLS for secure data transmission. Finish Setup: Review the settings and launch the Aurora instance. Set Up Database Schema Next, design your database schema based on the POS system’s needs. Here's an example schema: transactions table: Stores each sale, including items, quantity, price, customer, and payment details.inventory table: Tracks item stock levels.customers table: Stores customer profiles, loyalty points, and history. Example SQL for creating the tables: SQL -- Transactions Table CREATE TABLE transactions ( transaction_id INT AUTO_INCREMENT PRIMARY KEY, customer_id INT, transaction_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP, total_amount DECIMAL(10, 2), payment_status ENUM('pending', 'completed', 'failed') NOT NULL, FOREIGN KEY (customer_id) REFERENCES customers(customer_id) ); -- Inventory Table CREATE TABLE inventory ( item_id INT AUTO_INCREMENT PRIMARY KEY, item_name VARCHAR(255) NOT NULL, stock_quantity INT NOT NULL, price DECIMAL(10, 2) NOT NULL ); -- Customers Table CREATE TABLE customers ( customer_id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255) NOT NULL, email VARCHAR(255) UNIQUE, loyalty_points INT DEFAULT 0 ); To handle varying workloads: Use Read Replicas in Aurora for offloading read-heavy operations (e.g., reporting or inventory lookups).Use Aurora Auto-Scaling for storage, and monitor the database with CloudWatch metrics to scale your infrastructure. Implementing the POS Transaction Flow with Aurora Let’s walk through a simplified transaction flow of the POS system that integrates with the Aurora database. POS Terminal Sends Transaction Request When a customer makes a purchase, the POS terminal sends a request to the backend API with the following information: Customer IDList of items purchased (item ID, quantity)Payment method (credit card, cash, etc.) Backend API Processes Transaction The backend API (e.g., running on AWS Lambda or EC2) processes the transaction as follows: Begin Transaction: Start a database transaction to ensure atomicity.Lock relevant rows in the inventory table to prevent overselling. Update Inventory: Check the inventory table to verify that the items are in stock.Decrement the stock quantity for each purchased item. Create Transaction Record: Insert a record into the transactions table with transaction details (customer ID, total amount, payment status).Handle Payment: Integrate with a payment gateway (e.g., Stripe, PayPal) to process the payment.If the payment is successful, update the transaction record with a completed payment status. If failed, mark it as failed. Commit the Transaction: If all operations succeed, commit the transaction to Aurora, ensuring the changes are saved.If any step fails, roll back the transaction to maintain data consistency. Send Receipt: Generate a receipt (PDF, JSON) and optionally store it in Amazon S3.Return the receipt details to the POS terminal for customer review. Example transaction flow in pseudo-code: Python def process_transaction(transaction_data): try: # Start a transaction db.begin_transaction() # Update inventory for item in transaction_data['items']: update_inventory(item['item_id'], item['quantity']) # Insert transaction record transaction_id = insert_transaction(transaction_data) # Process payment payment_status = process_payment(transaction_data['payment_method']) if payment_status == 'completed': # Update transaction to completed db.execute("UPDATE transactions SET payment_status='completed' WHERE transaction_id = %s", (transaction_id,)) else: # Update transaction to failed db.execute("UPDATE transactions SET payment_status='failed' WHERE transaction_id = %s", (transaction_id,)) # Commit the transaction db.commit() # Send receipt to S3 (optional) receipt_url = generate_receipt(transaction_id) return receipt_url except Exception as e: # Rollback transaction in case of error db.rollback() raise e c) Performance Monitoring and Alerts Amazon CloudWatch can be used to monitor the performance of the Aurora database (e.g., CPU usage, read/write latency, disk I/O).Set up CloudWatch Alarms to notify administrators of any potential issues, such as high transaction latency or replication lag in Aurora replicas. Security and Data Protection To ensure the security of sensitive customer and transaction data: Encryption: Ensure that Aurora encryption is enabled for both data at rest (using AWS KMS) and in transit (using SSL/TLS).Access Control: Use AWS IAM roles to grant the necessary permissions to the backend API and Lambda functions to interact with Aurora securely.Data Masking: Mask or limit access to sensitive data, such as credit card information, when storing or processing it.Audit Logs: Enable Aurora audit logging and CloudTrail to track access and changes to the database. Scaling for High Traffic (e.g., Black Friday Sales) Use Aurora Read Replicas: Distribute read traffic to replicas to ensure that the primary instance handles only write operations.Auto Scaling: Aurora automatically adjusts storage, but also monitors for spikes in transaction volume and adjusts the instance size as necessary.Aurora Serverless (Optional): For scenarios where traffic is highly variable (e.g., seasonal sales), use Aurora Serverless to scale up during peak times and down during off-peak hours. Conclusion Amazon Aurora provides a powerful, scalable, and highly available database solution for implementing a Point of Sale (POS) system. By leveraging Aurora’s managed services, performance optimizations, and automatic failover, you can ensure that your POS system remains responsive, even during high transaction volumes. The seamless integration with other AWS services, such as Lambda, S3, and CloudWatch, makes it easy to build a robust and secure transaction processing system that meets the demands of modern retail and hospitality businesses.

Azure is expanding their Postgres portfolio, now offering a range of managed Postgres services to suit the needs of developers and teams of all sizes. In this post, we’ll walk through the main Postgres options available on the Azure Marketplace, so you can find the one that works best for your application. Why Deploy Postgres Through the Azure Marketplace? If your company runs on Azure infra, using the Azure Marketplace to set up your database makes things simple and efficient. Here’s why: Unified billing. You can manage your database costs right alongside the rest of your Azure services, which most likely simplifies the internal process of approval for your team. Built-in security. You can use your Microsoft credentials to access the database. Integrated workflows. You can use Azure CLI and SDKs to manage your database, integrating it with other Azure tools without issues. Managed Postgres Services in Azure Now that you’re set in the Azure Marketplace, let’s take a look at your options. Azure Database for PostgreSQL Azure Database for PostgreSQL was the first managed Postgres service introduced by Azure. Launched in 2017, it was Azure's initial step into offering managed Postgres services to developers looking to ease the main management tasks associated with running Postgres in production, getting help with automated backups, high availability, and basic scaling capabilities. Azure Database for PostgreSQL offers two primary flavors, Single Server and Flexible Server. Single Server was the first implementation of the service; over time, Azure recognized the growing demand for more flexible and advanced features, which led to the introduction of Flexible Server. This new flavor aimed to address some of the limitations of Single Server by offering more control, better scaling options, and features like zone-redundant high availability and VNet integration. Flexible Server has since become the recommended option as Azure transitions away from Single Server, which is set to retire in March 2025. For this reason, let’s take a closer look at the Flexible Server configuration, since it will very soon be the only flavor of Azure Database for PostgreSQL: Flexible Server: Feature Overview As we were saying, Flexible Server is the most recent iteration of Azure Database for PostgreSQL, built to provide greater control over database management and configuration. Feature highlights include: Multi-AZ deploymentsCustomizable maintenance windowsVNet integration for enhanced securityThe ability to manually stop and start servers to optimize costsThe possibility to autoscale compute using Azure Automation Tasks Flexible Server: Storage In a flexible server, users provision storage based on their anticipated needs and get billed for it at the end of the month (storage is billed by GiB/month). Generally speaking, this is how things work: When setting up your PostgreSQL Flexible Server, you'll start with at least 32 GiB of storage.You can manually increase the storage capacity as your data requirements grow. Once expanded, the storage size cannot be reduced. To decrease storage, you'd need to create a new server with the desired size and migrate your data. Flexible Server offers a “storage autogrow” feature that automatically scales up storage when usage reaches a certain threshold. This helps prevent disruptions due to insufficient storage. Note that autogrow only scales storage up; it doesn't reduce storage if usage decreases. At the end of the month, your receive charges are based on the provisioned storage size, not actual usage. Flexible Server: Compute Flexible Server offers several compute scalability options to suit different workloads. Compute resources are provisioned in virtual cores (vCores) and organized into pricing tiers, each with specific capabilities and performance characteristics. Here's a breakdown: Burstable. For intermittent and light workloads, such as development or testing environments. They’re billed via a credit-based system: CPU credits accumulate during idle times and are consumed during bursts of activity. General purpose. Designed for most business applications requiring balanced compute and memory resources, e.g., web applications. The memory-to-vCore ratio is 5 GiB per vCore. Memory optimized. For high-performance workloads with low latency and high memory requirements, e.g. analytical applications. Memory-to-vCore ratio is 10 GiB per vCore. How scaling works: Manual scaling (standard). Compute resources can be manually scaled up or down using the Azure portal, CLI, or API. While scaling up typically does not cause downtime, scaling down may result in brief downtime (up to a few minutes) as active connections are re-established. Both scaling actions can cause a temporary performance impact during the reconfiguration process.Autoscaling (custom). Flexible Server does not natively support automatic scaling for compute resources, but users can implement autoscaling through Azure Automation Tasks. Neon Serverless Postgres Neon provides a serverless Postgres solution designed to help teams ship modern applications faster. As an Azure Native Integration, developers can provision and manage Neon organizations directly within the Azure portal. Unlike traditional managed Postgres services, Neon natively separates compute and storage layers and implements an innovative storage design that supports database branching. This architecture is tailored for medium-sized teams who want a Postgres experience that adapts dynamically to their application's demands and workflows while minimizing costs. Neon: Feature Overview Neon Serverless Postgres' features are aimed at simplifying database management for teams deploying serverless applications: Databases provisioned instantly (under a second) Automatically scales compute and storage resources based on usageDevelopers can create isolated branches of their database for testing or development Supports up to 10,000 concurrent connections Neon: Storage Neon’s storage architecture separates storage from compute and keeps a record of Postgres' WAL, supporting features like database branching and point-in-time restores via copy-on-write. Data is stored using a combination of object storage and SSDs, distributed across multiple availability zones to enhance reliability and availability. Storage is billed based on a subscription model that includes a predefined volume within the monthly fee. Any usage beyond this allocation is charged based on actual consumption, calculated hourly. Storage is not billed by allocation but by usage (it "scales down"). Neon: Compute Neon automatically adjusts compute resources based on real-time workload demands. It scales up during peak activity to handle increased traffic and scales down to zero when the database is idle for more than 5 minutes. Neon’s autoscaling algorithm monitors CPU usage, working set size, and memory utilization to dynamically adjust resources with precision. After scaling to zero, databases experience a cold start latency of around 500 ms. Autoscaling occurs without downtime or disruptions to active connections. In terms of billing, Neon measures compute consumption in compute hours, which factor in both CPU and memory usage as well as the duration the database is active.Monthly subscriptions include a certain number of compute hours within the base fee.After the included hours are consumed, additional compute usage is billed based on true usage, with charges calculated hourly. Azure Cosmos DB for PostgreSQL Azure Cosmos DB for PostgreSQL is Azure’s answer for developers who need a distributed Postgres database service capable of handling large-scale, high-performance applications. The service is based on a distributed, “shared-nothing” architecture that allows the cluster to scale by adding more nodes, each with its own storage and compute resources: A coordinator node manages metadata and orchestrates query planning across the cluster.Worker nodes store the actual data and execute queries in parallel. CosmosDB: Feature Overview Azure Cosmos DB for PostgreSQL is built to handle large, high-performance workloads by distributing data across a cluster of nodes. Key features include: Automatic sharding of data across worker nodes while the coordinator node manages query planning and execution, across multiple Azure regions Add or remove nodes to scale compute and storage independentlyBuilt-in replication and fault tolerance ensure consistent uptime and reliability. CosmosDB: Storage Azure Cosmos DB for PostgreSQL uses a distributed storage system where each node has its own allocated storage capacity: Storage must be provisioned for each node (coordinator and workers) based on anticipated needs. The minimum per node is typically 128 GiB, with the ability to increase as requirements grow.Storage in Cosmos DB does not “auto-grow” dynamically. You’ll need to manually increase storage when nearing capacity.Charges are based on the total provisioned storage across all nodes (not usage-based). CosmosDB: Compute When setting up a Cosmos DB for a PostgreSQL cluster, users provision compute resources separately for the coordinator node and worker nodes. The coordinator node requires enough compute power to efficiently handle query coordination for distributed data; in the case of the worker nodes, they determine the horizontal scaling capacity of the database, each one of them having its own provisioned compute resources. So there are two dimensions of scaling in CosmosDB: Vertical scaling. E.g., increasing memory for the coordinator to handle larger query planning or scaling up worker nodes to support more intensive queries. This is a manual operation and may involve downtime.Horizontal scaling. This involves the addition or removal of worker nodes, a process that’s also handled manually. When adding nodes, data is re-sharded and redistributed across the cluster, a process that can temporarily impact performance. In terms of compute billing, compute costs are billed for the provisioned resources on both coordinator and worker nodes (not usage-based). It’s worth noticing that horizontal scaling directly affects the number of worker nodes, increasing or decreasing costs accordingly. EDB Postgres AI Cloud Service The EDB Postgres AI Cloud Service is an offering from EnterpriseDB (EDB) that combines Postgres features with Oracle compatibility, making it a good choice for enterprises looking to migrate from Oracle to Postgres. EDB Postgres: Feature Overview This service offers features aimed at enterprises looking for a Postgres service, with an emphasis on machine learning and AI workloads. It provides support for Oracle database syntax, functions, and proceduresIncludes built-in replication, clustering, and failover options Features robust encryption, role-based access control, and audit loggingIt offers optimizations for running machine learning and AI workloads EDB Postgres: Storage In EDB Postgres AI Cloud Service, users allocate storage based on their expected workload requirements. Storage size can be manually increased, but it cannot shrink once provisioned. At the end of the month, charges are based on provisioned storage, with additional costs for backup storage that exceeds the default free tier. EDB Postgres: Compute In the EDB Postgres AI Cloud Service, users can manually adjust compute resources, such as virtual CPUs (vCPUs) and memory, for individual instances through the EDB Cloud Console. This process is designed to minimize downtime, though some operations may require brief interruptions. Currently, the service does not support automatic scaling of compute resources; all scaling operations must be performed manually. Pick Your Flavor of Postgres in the Azure Marketplace Here’s a recap to help guide your decision: Azure Database for PostgreSQL Flexible Server is a robust managed Postgres option, especially attractive if you have experienced DBAs in your team. Neon Serverless Postgres requires less manual oversight and a lower price to entry, what makes it a great option for smaller teams and startups. Azure Cosmos DB for PostgreSQL is the best choice for applications that demand distribution across multiple regions and horizontal scalability. EDB Postgres AI Cloud Service has great supporting features if you're migrating from Oracle to Postgres. Each option has its strengths and trade-offs. The best way to decide is to explore them in the Azure Marketplace and choose the one that fits your application’s requirements. Go ahead and create your first Postgres database.

Recent advancements in cloud computing and serverless architectures have already changed the way applications are created. Among the most widely acknowledged services for serverless computing is AWS Lambda, which enables the execution of code without managing any servers. On the other hand, a large-scale application generally contains many dependencies and maintainable configurations. This article examines how AWS Lambda layers and CloudFormation can be used to develop scalable, efficient, and maintainable serverless systems. What are AWS Lambda Layers? Lambda Layers solve common issues faced in serverless development, such as management of common dependencies among functions, deployable package optimizations, and developer productivity. The advantages of Lambda layers are best seen in large applications built out of multiple functions that depend on internally developed utility libraries and commonly used third-party libraries. Key advantages of using AWS Lambda layers can be summarized thus: 1. The ability to leverage existing competencies Lambda layers promote compliance with the Don't Repeat Yourself (DRY) principle. Rather than having redundant dependencies or common logic as part of a given Lambda function, such elements are grouped together and encapsulated in a single layer to allow sharing across many functions. This benefits the elimination of duplicate code and promotes the consistent application of common libraries or logic. For example, an organization using the requests library for HTTP calls in 10 different Lambda functions can create a layer with that library and attach it to all relevant functions, rather than packaging requests in each one. 2. Small payload sizes AWS Lambda has packaging size limits: maximum compressed size of 50 MB and a maximum unpacked size of 250 MB. Many dependencies, especially those relevant to data science like NumPy, Pandas, and SciPy, add to the larger sizes of packages. By using layers on such dependencies, it becomes easier to have a clean function package. Now that the package is used to purely focus on the core business logic. 3. Improved Evaluation and Maintenance Lambda layers eliminate the need to update each function when changes are required to a common dependency. Only the layer has to be updated, then a new version is published, and then linked to the dependent functions. 4. Better version control An pdate at the layer level causes a fresh version to be generated. This facilitates the selection of the particular version of the layer used by any specified function and hence offers full control of the deployment life cycle. Besides ensuring backward compatibility, the versioning model also makes it easier to implement test protocols as a precursor to mass deployments in all functions. 5. Custom Execution Contexts and Extra Functionality Lambda layers make it possible to integrate personalized runtime environments, thus enabling developers to implement programming languages that the Lambda runtime doesn't support natively. Secondly, additional features like a custom Ruby or PHP runtime are enabled by these layers. Thirdly, Datadog and New Relic type monitoring solutions can also be integrated in order to increase overall observability. 6. Isolation and Security Segregation of confidential configuration files, software development toolkits (SDKs), or tools into separate layers makes it easier for teams to manage their governance in terms of managing access. These layers are shareable across multiple accounts using the AWS Resource Access Manager (RAM) and restricted further using IAM permissions. Lambda Layers with CloudFormation to Optimize AWS Lambda layers integrate with CloudFormation for seamless deployment and infrastructure maintenance. These are the steps towards optimizing serverless computing: Step 1: Create a Lambda layer Prepare the dependencies: Create a folder structure with all the dependencies within:: Shell mkdir python pip install requests -t python/ Zip the layer content: Shell zip -r layer.zip python/ Upload the layer to AWS: Shell aws lambda publish-layer-version --layer-name MyLayer --zip-file fileb://layer.zip --compatible-runtimes python3.8 Save the layer ARN: Make a note of the ARN that AWS returns so it can be used with CloudFormation.. Step 2: Define the Lambda layer in CloudFormation Plain Text Resources: MyLayer: Type: AWS::Lambda::LayerVersion Properties: LayerName: MyLayer CompatibleRuntimes: - python3.8 Content: S3Bucket: my-layer-bucket S3Key: layer.zip Step 3: Using the Layer with a Lambda function Plain Text Resources: MyFunction: Type: AWS::Lambda::Function Properties: FunctionName: MyFunction Runtime: python3.8 Handler: app.handler Code: S3Bucket: my-code-bucket S3Key: my-code.zip Layers: - !Ref MyLayer Role: !GetAtt MyLambdaExecutionRole.Arn Step 4: Automate the deployment using CloudFormation Shell aws cloudformation deploy --template-file template.yaml --stack-name MyServerlessApp Use Case: API Backend Optimization Scenario: A serverless API has been developed where a number of Lambda functions depend on common dependencies like requests and boto3. Problem: Repeated dependencies increase package size and slow deployment. Solution: Put the common dependencies into a Lambda layer.Use CloudFormation for declaring layers and functions.Refer to one shared layer from multiple functions and avoid redundancy. Best Practices of Lambda Layers with CloudFormation Version layers appropriately: Semantic versioning will allow for the tracking of updates.Optimize layer content: Include only the necessary dependencies, keeping in mind the size.Leverage S3 for large files: Store larger layers in S3 and reference them in CloudFormation.Test compatibility: Test the compatibility of the layers with AWS Lambda runtimes.Monitor usage: Monitoring through AWS CloudWatch will be helpful in observing the performance and requests per layer. Benefits of Combining Lambda Layers and CloudFormation Efficiency: Providing smooth development and deployment processes.Scalability: Easily replicate architectures across environments.Cost Savings: Minimize storage and duplication of dependencies.Maintainability: Simplify updates and version management. Conclusion AWS Lambda layers and CloudFormation are indeed very powerful solutions that can be used to optimize serverless architecture. Decoupling dependencies with automated infrastructure provisioning will enable developers to build scalable, efficient, and maintainable applications. Be it small project development or enterprise system management, integration will certainly reduce the overhead while letting innovations begin. Transform your serverless workflow by combining Lambda layers and CloudFormation today.

By Srinivas Chippagiri CORE

TL; DR: The End of “Good Enough Agile” “Good Enough Agile” is ending as AI automates mere ceremonial tasks and Product Operating Models demand outcome-focused teams. Agile professionals must evolve from process facilitators to strategic product thinkers or risk obsolescence as organizations adopt AI-native approaches that embody Agile values without ritual overhead. The Perfect Storm Coming After Good Enough Agile For two decades, many of us have participated in, or at least witnessed, a prolonged performance of “Agile-as-theater.” Now, the curtain is falling. Mechanical Scrum, stand-ups — or Daily Scrum, if you prefer that term — without tangible purpose, estimation rituals that pretend to forecast the future, Jira-as-performance-art; we’ve normalized Agile as a checklist. Useful, perhaps, if you blinked hard enough and never dared ask about the return on investment. That era is ending, not with a dramatic bang, but with a slow, irrevocable drift toward irrelevance for those who don’t adapt. What’s forcing this change? Two converging forces that aren’t just disruptive but existential threats to “Good Enough Agile:” Artificial intelligence and the Product Operating Model. Let’s be brutally honest: If your primary Agile practice revolves around facilitating meetings, meticulously documenting progress, and shepherding tickets from “To Do” to “Done,” you are now officially redundant. AI can, and already does, perform these tasks. It’s faster and cheaper and doesn’t need a “Servant Leader” to guide a Retrospective summary and its follow-up communication. Mechanical Agile: Already Obsolete The uncomfortable truth is that most Agile implementations never graduated beyond the delivery phase. Strategy? That was deemed someone else’s problem. Discovery? Often skipped, outsourced, or diluted into a Product Backlog of unvalidated ideas. Empowerment remained a popular keynote topic, not an operational reality. Agile teams became efficient delivery machines: tactical, often fast, but fundamentally disconnected from actual business and customer outcomes. That’s not agility; that’s a feature factory wearing a lanyard that says “Scrum.” The 2020 Scrum Guide states, “The Scrum Team is responsible for all product-related activities from stakeholder collaboration, verification, maintenance, operation, experimentation, research, and development…”. Yet, in practice, how many Scrum Teams are truly empowered to this extent? Most are boxed into building what someone else, somewhere else, decided. And AI is going to eat that box. Consider what generative AI achieves today: Summarizes Sprint Reviews and Retrospectives,Clusters customer feedback into actionable themes,Highlights potential blockers by scanning Jira, Slack, and Confluence,Prepares release notes and offers data-informed team improvement suggestions. If your role primarily focuses on these facilitation, coordination, or status-tracking aspects, you’re no longer competing with other humans. You’re competing with code and tokens. AI doesn’t need psychological safety or emotional labor. It needs inputs and patterns. It doesn’t coach, it completes. Product Operating Models: The New Baseline for Value Creation If AI relentlessly attacks the how of mechanical Agile, Product Operating Models fundamentally redefine the why and what. The Product Operating Model, as championed by Marty Cagan, isn’t just a new practice; it’s a shift in how effective companies build, deliver, and iterate on value. It demands teams solve real customer problems aligned with tangible business outcomes, not just dutifully executing on stakeholder wish lists or pre-defined feature roadmaps. This model requires: Empowered teams assigned meaningful problems to solve, not just features to build. They are accountable for outcomes.Decision-making that spans product strategy, discovery, and delivery, with teams deeply involved in determining what is valuable, usable, feasible, and viable.A culture of trust over control, principles over rigid processes, innovation over mere predictability, and learning over fear of failure. It’s not that the Product Model dismisses Agile principles. Instead, it subsumes them. Think of it as an evolved organism that has internalized Agile’s most useful DNA, like continuous delivery and cross-functional collaboration, and discarded the empty rituals. This shift exposes how shallow many Agile adoptions are. Recent survey data highlights that 12% identify a lack of product vision as leading to “Feature Factory” waste, while another 33% pointed to a leadership gap, not necessarily micromanagement, but a disconnect between professing Agile values and actually empowering teams to achieve outcomes. Poor Agile implementation was cited by 10%, showing that process obsession often hurts more than it helps, and 12% highlighted cultural resistance, where psychological safety and a learning environment are absent. Old Agile vs. New Reality Here’s what the paradigm shift demands: Stand-ups vs. Outcomes: Are you syncing or solving?Estimates vs. Telemetry: Are you gambling with guesses or learning in real time?Belief vs. Evidence: Does your Product Backlog reflect strategy — or stakeholder fantasy?Mechanical Rituals vs. Market Results: Is your Agile a safety blanket or a value engine? This is not a theoretical debate. It’s a fork in the road. The Agile Industrial Complex Is on Notice Agile didn’t die because it wasn’t valuable. It’s struggling because when agility became a product, it lost its edge. The monetization of the Agile Manifesto. The transformation theater. The armies of consultants selling templates for self-organization. The playbook peddlers. Organizations wanted change but settled for frameworks instead. They got stand-ups instead of strategy. They got rituals instead of results. The Agile industrial complex mistook adoption for impact. It sold belief over evidence. And the reckoning is here. “But Our Agile Transformation Is Working!” I know what you’re thinking. Perhaps your teams genuinely feel empowered. Maybe your Retrospectives drive real change. Your Product Owner might truly represent customer needs rather than stakeholder demands. Congratulations! If that’s your reality, you’re already practicing what I’m advocating for. You’ve transcended mechanical Agile and built something that actually works. You’re not the target of this critique; you’re proof that it’s possible. But here’s the uncomfortable question: Are you sure you’re not confusing efficient delivery with effective outcomes? Many teams that feel “empowered” are still fundamentally executing someone else’s strategy, with more autonomy in building features. The test is simple: Can your team pivot the entire product direction based on what you’re learning? Or do you need permission? Acknowledging the Loss If you’re feeling defensive or unsettled right now, that’s completely understandable. Many of us have invested years mastering practices that felt meaningful and valuable. We’ve built our professional identities around frameworks that promise humanizing work and unleashing creativity. The events that once felt revolutionary now risk becoming ritual. The frameworks that once liberated teams have calcified into the process. This isn’t your failure; it’s a natural evolution that happens to every successful practice. Letting go of what once worked doesn’t diminish its past value or your expertise in applying it. It takes courage to evolve beyond what made you successful. (And I do include myself here, believe me.) What Happens Next: The Rise of Post-Agile Organizations Product-led organizations that fully embrace AI and outcome-driven Product Models will likely skip past traditional, ceremonial Agile entirely. They will: Use real-time telemetry (or data) to understand what users do, not just guess what they might wantLeverage AI to generate tests, documentation, and even first-pass UIs in minutes, not SprintsFocus on learning velocity — how quickly they can validate hypotheses and adapt, not just delivery velocityReallocate human intellect to the highest-leverage work: deep customer insight, ethical considerations, strategic judgment, and genuine invention These organizations won’t be hiring legions of Agile Coaches. They’ll seek Product Strategists and Product Coaches who understand the full value creation lifecycle. They won’t need Scrum Masters to run meetings. They’ll have empowered, cross-functional teams with live telemetry dashboards and a clear mandate to ship value, not just track velocity. And they will outcompete traditional organizations decisively. A Vision of What’s Possible Imagine working on a team where AI handles the administrative overhead, where real-time data tells you immediately if you’re solving the right problem, and where psychological safety comes from shared accountability for outcomes rather than adherence to process. Picture teams that spend their energy on deep customer research, ethical considerations, and creative problem-solving rather than estimation poker and Sprint Planning. Envision organizations where “empowerment” isn’t a buzzword but an operational reality: Teams that can pivot strategies based on evidence, not just tactics based on backlog priorities. This isn’t about losing the human element of work. It’s about elevating it. When AI handles coordination and data analysis, humans become free to do what we do best: Understand nuanced problems, navigate complex stakeholder dynamics, and create innovative solutions. This future isn’t dystopian; it’s energizing. But only for those willing to evolve toward it. Scrum Can Survive — By Going Back to Its Roots and Becoming Invisible There’s still a place for Scrum, but only if it’s stripped back to its original, minimalist intent: a lightweight framework enabling a small, autonomous team to inspect, adapt, and solve complex problems while delivering valuable increments. It should be the near-invisible scaffolding that supports the team’s functionality, not the focus of their work. The second you start optimizing your Scrum process instead of your product and its outcomes, you’ve already lost the plot. How to Stay Relevant: A Survival Guide This article isn’t about fear-mongering; it’s about a clear-eyed assessment of a fundamental shift. (And I have been struggling to formulate it for months.) If you’re sensing this transition is real and inevitable, here’s how to navigate it: 1. Become Radically Product-Literate Stop facilitating. Start understanding. Learn product strategy. Immerse in discovery. Study customer behavior. Know how the business makes money and how your work contributes to it. If AI can do a significant part of your current job, immediately pivot your development towards uniquely human strengths: Coaching for critical thinking, systems thinking, complex problem framing, and outcome-oriented product strategy. 2. Shift from Output to Outcome Obsession Shipping fast is not valuable in and of itself. Don’t be satisfied with being a world-class delivery facilitator. Insist on understanding and contributing to why anything is being built. Push for access to the strategic context and the discovery process; your value multiplies when you connect delivery excellence to strategic intent. 3. Partner with AI, Don’t Compete AI is not your enemy. It’s your amplifier. Automate coordination. Use LLMs for sense-making. Audit your rituals mercilessly: If a meeting or artifact doesn’t directly drive a measurable, valuable outcome, kill it. Free yourself to do the one thing AI can’t: Frame the right problems and align humans to solve them. Conclusion: This Isn’t a Fad. It’s Evolution. We are not just weathering the storm but witnessing evolution in real-time. You are living through a paradigm shift defining the next two decades of product development. “Agile” isn’t “broken” simply because of poor adoption or choosing the “wrong” framework. It’s transforming because the world has changed technologically, strategically, and economically, and our practices must also change. There’s a delicious irony here: A practice built on rapid learning and continuous adaptation has become remarkably bad at eating its own dog food. We’ve spent years teaching organizations to inspect, adapt, and embrace change over following a plan. Yet, when faced with the most significant technological and strategic shifts in decades, much of the Agile community has chosen to double down on familiar practices rather than inspect and adapt them. The very principles we’ve evangelized, I refer to empiricism, experimentation, and pivoting based on evidence, should have prepared us for this moment. Instead, we’ve often responded like any other established industry: Defending the status quo, questioning the data, and hoping the disruption will somehow pass us by. We’re entering an era of AI-native, outcome-obsessed, telemetry-driven organizations. They don’t need Agile frameworks. They embody the values. The fundamental question is no longer about doing Agile right but being effective in a world increasingly shaped by intelligent automation and a relentless focus on demonstrable product outcomes. Are you ready to help shape what comes next? The future belongs to those who can bridge the gap between Agile’s foundational values and tomorrow’s technological reality. The question isn’t whether change is coming — it’s whether you’ll lead it or be swept along. What will you choose to build?

By Stefan Wolpers CORE

I am building a fleet of Python FastAPI microservices and aiming to scale them efficiently in a production environment. Each service runs inside a Docker container, which keeps it isolated and portable. I’m orchestrating these containers with Kubernetes, specifically using the lightweight K3s distribution on the Azure cloud. In this article, I share my hands-on experience optimizing this setup for high performance and reliability in production. Containerizing FastAPI Microservices Containerization is a cornerstone of my microservices strategy. I package each FastAPI service with Docker, bundling its code and dependencies into an image. This ensures a modular, conflict-free architecture — each service effectively “lives” in its own environment, preventing dependency clashes and simplifying deployment. For example, a typical Dockerfile for one of my FastAPI microservices looks like: Dockerfile FROM python:3.10-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY . . CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"] This produces a lean image: I start from a slim Python base, install only the required packages, copy in the application code, and launch Uvicorn (the ASGI server for FastAPI). Running each service as a container means I can deploy many instances in parallel without worrying that one service’s dependencies will interfere with another’s. Why K3s on Azure? Kubernetes is my go-to for managing containers at scale, but instead of using a heavyweight managed cluster, I chose K3s. K3s is a CNCF-certified Kubernetes distribution known for its lightweight footprint (a single binary under 100 MB) and minimal resource overhead. By deploying K3s on Azure virtual machines, I get the benefits of Kubernetes (like self-healing, service discovery, and scaling) without the full complexity of a large cluster. K3s’s slim design means the control plane uses less memory and CPU, leaving more room for my FastAPI workloads. Architecture Overview Figure: High-level architecture of the microservices on K3s An Azure load balancer directs traffic through an ingress (Traefik) to various FastAPI service pods running in the K3s cluster. Each service can scale horizontally, and services communicate internally with a shared database for persistence. I have an Azure Load Balancer in front of the cluster that directs external traffic to an ingress controller within K3s. Out of the box, K3s comes with Traefik as the default ingress, which I use to route requests to the appropriate FastAPI service. Each microservice is deployed as a Kubernetes Deployment with multiple replicas (pods). This architecture allows any service to scale independently based on demand. The diagram above illustrates how incoming requests hit the load balancer and then flow through Traefik to the appropriate service pods. Kubernetes Deployments and Health Checks Deploying a FastAPI service on K3s involves writing a Kubernetes manifest. Here’s a snippet of a deployment (with health checks) for one service: YAML spec: replicas: 3 template: spec: containers: - name: service-a image: myregistry/service-a:1.0 livenessProbe: httpGet: { path: /health, port: 80 } initialDelaySeconds: 30 periodSeconds: 15 readinessProbe: httpGet: { path: /health, port: 80 } initialDelaySeconds: 5 periodSeconds: 5 I start with replicas: 3 to run three instances of the service. The livenessProbe and readinessProbe are crucial for a robust system. The liveness probe periodically hits the /health endpoint — if a service fails this check, Kubernetes automatically restarts that container to recover it. The readiness probe ensures a new pod only starts receiving traffic after it reports healthy. These probes prevent sending requests to unready or hung instances, improving overall resilience. Autoscaling and Performance One big advantage of Kubernetes (including K3s) is easy horizontal scaling. I leverage the Horizontal Pod Autoscaler (HPA) to adjust the number of pod replicas based on load. I configure an HPA to keep CPU usage around 70%. If traffic spikes and CPU usage goes above that threshold, Kubernetes spawns additional pods to handle the load, then scales down when the load subsides. FastAPI’s asynchronous model lets each instance handle many requests concurrently, but when that isn’t enough, scaling out with more pods keeps latency low. I also tuned the autoscaling behavior: too aggressive and it would spin up pods unnecessarily; too conservative and it might lag behind traffic bursts. Through trial and error, I achieved smooth scaling during peak hours without over-provisioning. Observability: Logging and Monitoring Running many microservice instances means observability is vital. I set up centralized logging, which allows a unified search across logs when debugging issues. For metrics, I integrated Prometheus to scrape cluster and application metrics (CPU, memory, request latencies, etc.). In Kubernetes, observability revolves around gathering metrics, logs, and traces to understand the system’s internal state. Grafana dashboards provide real-time visibility into performance (e.g., response times, error rates per service). I also use distributed tracing to follow a request’s path through multiple services, helping pinpoint bottlenecks across service boundaries. These observability tools let me quickly detect misbehaving services and fine-tune our system’s performance. Load Balancing and Service Discovery In this K3s-based platform, load balancing happens at multiple levels. The Azure Load Balancer directs traffic to the K3s ingress (Traefik), which routes incoming requests to the correct service based on URL paths. Each microservice also has an internal Kubernetes Service for service discovery. This means services can call each other by name, and Kubernetes will load-balance the requests across the pods. I don’t need to hard-code any addresses; the combination of Traefik ingress and Kubernetes service discovery provides robust load balancing for both external and inter-service traffic. CI/CD and Deployment Workflow To manage deployments at scale, I rely on an automated CI/CD pipeline. My code is hosted on GitHub, and each commit triggers a GitHub Actions workflow. The pipeline builds a Docker image for each microservice (using the Dockerfile shown earlier), tags it with a version, and pushes it to a container registry. After that, the pipeline applies the updated Kubernetes manifests to the K3s cluster using kubectl apply, which performs a rolling update with zero downtime. In this way, going from a git push to running code in production is largely automated. This workflow makes releases consistent and requires minimal manual intervention. Lessons Learned in Production Building and scaling microservices with Docker and K3s has taught me several valuable lessons: Graceful shutdowns: Ensure each FastAPI service handles termination signals. Kubernetes sends a SIGTERM to pods during shutdown; it should finish ongoing requests before exiting.Rollout timing: Tweak readiness probes and pod termination grace periods so new pods receive traffic only when ready, and old pods stay alive until finishing work. This prevents blips in availability during rolling deployments.Monitoring overhead: Balance observability with performance. It’s easy to overload the cluster with too many monitoring agents or excessive metrics scrapes. I learned to monitor what matters and adjust intervals to reduce overhead. Conclusion By using Docker and Kubernetes (K3s) together, I’m running a production-grade microservices platform that scales on demand, remains resilient, and is observable. FastAPI provides speed and flexibility in development, Docker ensures consistency across environments, and K3s on Azure gives me the power of Kubernetes with a leaner footprint. With health checks, autoscaling, ingress routing, and thorough logging in place, the system can handle real-world traffic and recover from issues with minimal manual intervention. This journey has reinforced that careful architecture planning and a focus on observability are key when deploying microservices at scale. This approach has so far delivered a smooth, high-performance experience.

Kubernetes has taken over the world by storm. You could unconditionally say that it has won the container war. Docker has similar offering with Docker Swarm but Kubernetes offers a lot more in the form of resiliency, scale, ecosystem and production readiness. So, what does Kubernetes solve? And why does its demand grow drastically? In order to understand this we need to go back in history. Virtualization Virtualization is the process of creating a virtual representation of your physical hardware. One well-known example is what we call the Virtual Machine (VM). Previous to VMs, a single server was allocated to one customer and most of the time, the server was underutilized. VM's solved this problem because now: One physical machine could host multiple tenant workloads without compromising security.Because the underlying infrastructure was cheap, the cost of ownership was reduced. While this was great, the Internet as we know evolved rapidly. Services that offer e-commerce, movie streaming, and storage are handling billions of transactions daily which leads to the need for dynamic scaling. At the same time around 2006-2010, AWS launched EC2 solving the elastic compute/dynamic scaling problem. You could provision compute with just a mouse click and Amazon would take care of provisioning them. There was still one problem though. In order to run a service, you need dependencies to be available first. For example, let's say you need to run a Java server. For that, you would need: Java Run time Environment (JRE)An agent that monitors the service process and restart it in case failuresApache Tomcat/ JettyMvn/Gradle depending on the dependency manager So, when a VM is launched, all of the required software would have to be installed before it could be used. And when things are required to be set up from scratch, they are error-prone and time-consuming. Containers Containers solved this problem beautifully. With Containers, you specify all the requirements to run the software in an image file. Now, when a new VM is spun up, all you need is Docker. The image when provided to the Docker runtime, spins up a container, and your application is up and running in seconds. What was a multiple hours effort came down to seconds. Below is the example of a Docker Image: Dockerfile # 1. Base image FROM python:3.11-slim # 2. Set the working directory in the container WORKDIR /app # 3. Copy local code to the container COPY . . # 4. Install dependencies RUN pip install --no-cache-dir -r requirements.txt # 5. Expose the application port EXPOSE 5000 # 6. Run the application CMD ["python", "app.py"] While containers had many advantages they came with some caveats too. It is common for containers to fail and when it fails, manual intervention is required to bring them up. Now, if a service is running hundreds or thousands of containers, this will quickly go out of hand. Containers are also lightweight compared to virtual machines (VMs). While a container can start up in just a few seconds, a VM typically takes several minutes. This difference becomes significant when handling spiky or unpredictable traffic. Kubernetes Kubernetes solved both these problems. It took care of spinning up new containers, depending on demand and recycle failed containers automatically. Let's look at some of its core concepts: Cluster A cluster is nothing but the set of machines on which the containerized applications run.Simply put, these can be your VMs or the physical hardware.Node Node is a single VM or physical machine in the cluster.Pod Pod is the smallest deployable unit in the Kubernetes. This is where the container is deployed along with shared storage/network capabilities. While you could deploy multiple containers inside a Pod, the best practices is to just have one container per pod.Control PlaneThe control plane is a key component in the Kubernetes cluster. It is responsible for making sure the current state matches the desired state of the cluster. The desired state is provided to the control plane in the form of a spec file. YAML apiVersion: apps/v1 kind: Deployment metadata: name: books-api-deployment labels: app: books-api spec: replicas: 2 # Desired number of pod replicas selector: matchLabels: app: books-api template: metadata: labels: app: books-api spec: containers: - name: books-api image: myregistry/books-api:v1.0.0 # Replace with your actual image ports: - containerPort: 8080 env: - name: ENVIRONMENT value: "production" - name: DB_HOST valueFrom: configMapKeyRef: name: books-api-config key: db_host KeyDescriptionapiVersionSpecifies the version of the Kubernetes API to use.kindEverything in Kubernetes is Objects. Pod, Deployment, Job, Replica set are some of the examples. Kind specifies the type of Kubernetes object.metadataProvides additional fields that can be added to the deployment like name for the deployment.specResponsible for providing the desired state of the Deployment object. Defines things like ports to expose, volumes to mount, name of the container images.selectorSpecifies which pods belong to this deployment. In this case all pods with name books-api are selected.templatePod details are provided here.replicasSpecifies the desired number of pod replicas.envenv talks Environment variables that need to be available in every Pod. In this case ENVIRONMENT and DB_HOST variables are made available to all the book-api pods. What we have discussed till now is just the bare bones of Kubernetes. In the next set of articles, we will discuss setting up a hello world Kubernetes cluster, storing secrets and setting up observability using OpenTelemetry where we will emit metrics and logs. Similar to package manager for Node, .NET and Java, Helm is the packager manager for Kubernetes. We will discuss it in depth, too.

By Siri Varma Vegiraju CORE

One common issue with growing teams with an extensive codebase is maintaining the code quality. It requires tremendous effort to maintain the code base, and manual code reviews often create a bottleneck during the development process. One standard practice is to get the pull request (PR) reviewed by a senior engineer before merging it into the code base. But developers often get overloaded with reviews as they continue to deliver their regular tasks and might overlook some of the minute details that end up in production. I personally had an experience where a simple missing null check created a cascading error during migration, ultimately leading to corrupted data in production. Human beings tend to make such mistakes, and it is a very common issue. In this article, I would like to share how I implemented an AI-powered code review assistant, which pulls the data from GitHub, uses the locally hosted qwen2.5-coder model on Ollama to review the code, and publishes the comments back to GitHub. You might wonder, “Don’t we have GitHub Copilot?” Yes, we do, but it might not work efficiently with proprietary code, such as code specific to ServiceNow, Salesforce, etc. To address this issue, this project included a manual review system to correct the feedback from the model, which is then used to fine-tune the model. Problem: Code Review Bottlenecks Being part of a development team, you will face these issues: Delay in reviews: As senior developers manage code reviews along with their tasks, there is a significant delay in reviews.Inconsistent standards: Different reviewers focus on different aspects, leading to unpredictable feedback.Context switching: Developers lose productivity when switching between coding and reviewing.Learning curve: Junior developers need detailed guidance but don’t always get comprehensive feedback. All these issues encouraged me to build a solution to provide a consistent one-pass review, while giving human beings the ability to make a final decision. Solution: GitHub + Ollama Integration To start with an MVP, I have built a web application based on Flask that acts as a bridge between GitHub and locally hosted LLM models. Let’s dive into how it works and how you can implement it in your environment. +-------------------+ +-------------------+ +--------------------+ | | | | | | | GitHub +----->+ Flask App +----->+ Ollama API | | Enterprise | | (Reviews Server)| |(Local qwen2.5 Model)| | | | | | | +-------------------+ +------+-----+------+ +--------------------+ | ^ | | +-------v-----+-------+ +--------------------+ | | | | | Comments Database +---->+ Monaco Editor UI | | (JSON/SQL) | | Web Interface | | | | | +---------------------+ +--------------------+ Here are some core components in the application: GitHub integration: Fetches PR files and their diffs through GitHub’s API.Flask application: Processes code for AI consumption and manages the review workflow.Ollama integration: Sends code to locally-hosted model (qwen2.5-coder) and processes the LLM’s feedback.Monaco editor UI: Provides a rich interface for reviewing code and suggestions.Comments database: Stores and tracks review comments (used a simple JSON in this project). Implementation Details Setting Up Ollama Locally To set up Ollama locally, visit ollama.com, download Ollama based on your operating system, and follow the onscreen instructions to install it. Once you are done with the installation, you can choose the model based on your use case from the list of models available on Ollama. In this case, I have selected qwen2.5-coder. Use the command below to interact with the model in the command line. Shell ollama run qwen2.5-coder Setting Up Flask If you already do not have Flask installed, install it using pip install Flask. Once you’ve successfully set up Flask, you can begin building the application by adding routes, templates, and integrating with external services or databases. Let’s look into the app.py, which is the core of the Flask app, where we define all the api routes, business logic, etc. So, to call the model, Ollama provides an API endpoint ‘http://localhost:11434/api/generate’. Refer to the code below to understand how we pass the information to Ollama. Python def call_ollama_model(code): try: response = requests.post(OLLAMA_API_URL, json={ 'model': 'qwen2.5-coder', 'prompt': f""" Please review the following JavaScript code for potential issues, improvements, and suggestions. For each issue, include: 1. The line number 2. The issue type (bug, performance, readability, security) 3. A brief explanation of the problem 4. A suggested fix Here's the code you need to review: {code} Format your response as JSON with the following structure: {{ "comments": [ {{ "line": 42, "type": "security", "message": "Unvalidated user input used in SQL query", "suggestion": "Use parameterized queries with prepared statements" } ] } """, 'stream': False }) if response.status_code == 200: result = response.json() return process_ollama_response(result['response']) else: return f"Error calling Ollama model: {response.status_code}" except Exception as e: return f"Error calling Ollama model: {str(e)}" Function to Fetch GitHub PR Details Python def get_pr_files(repo_owner, repo_name, pull_number): headers = {"Authorization": f"token {GITHUB_TOKEN}"} url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/pulls/{pull_number}/files" response = requests.get(url, headers=headers) if response.status_code == 200: files = response.json() return files else: return None Comment Management System The application stores comments locally and tracks which ones have been synced to GitHub: Python def save_comments_to_file(comment): # Load existing comments if os.path.exists(COMMENTS_FILE): with open(COMMENTS_FILE, 'r') as file: try: all_comments = json.load(file) except json.JSONDecodeError: all_comments = {} else: all_comments = {} # Add or update comment pr_key = f"{comment['repo_owner']}/{comment['repo_name']}/pull/{comment['pull_number']}" if pr_key not in all_comments: all_comments[pr_key] = [] # Generate a unique ID for the comment comment_id = str(uuid.uuid4()) comment['id'] = comment_id comment['synced_to_github'] = False comment['created_at'] = datetime.now().isoformat() all_comments[pr_key].append(comment) # Save back to file with open(COMMENTS_FILE, 'w') as file: json.dump(all_comments, file, indent=4) return comment_id Submitting Comments to GitHub Python def submit_comment_to_github(comment_id): # Load all comments with open(COMMENTS_FILE, 'r') as file: all_comments = json.load(file) # Find the comment by ID for pr_key, comments in all_comments.items(): for comment in comments: if comment['id'] == comment_id and not comment['synced_to_github']: # Extract PR details repo_parts = pr_key.split('/') repo_owner = repo_parts[0] repo_name = repo_parts[1] pull_number = repo_parts[3] # Create GitHub comment headers = {"Authorization": f"token {GITHUB_TOKEN}"} url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/pulls/{pull_number}/comments" payload = { "body": comment['body'], "commit_id": comment['commit_id'], "path": comment['path'], "line": comment['line'] } response = requests.post(url, headers=headers, json=payload) if response.status_code == 201: # Mark as synced comment['synced_to_github'] = True with open(COMMENTS_FILE, 'w') as write_file: json.dump(all_comments, write_file, indent=4) return True return False return False User Interface Implementation The application features a clean interface built around the Monaco editor, making it easy to review code and manage feedback: HTML <!DOCTYPE html> <html> <head> <title>AI Code Review Assistant</title> <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/monaco-editor/0.34.0/min/vs/editor/editor.main.css"> <script src="https://cdnjs.cloudflare.com/ajax/libs/monaco-editor/0.34.0/min/vs/loader.js"></script> </head> <body> <div class="container"> <div class="header"> <h1>AI Code Review Assistant</h1> <form id="prForm"> <input type="text" id="repoOwner" placeholder="Repository Owner"> <input type="text" id="repoName" placeholder="Repository Name"> <input type="number" id="pullNumber" placeholder="PR Number"> <button type="submit">Analyze PR</button> </form> </div> <div class="main"> <div id="editor-container"></div> <div id="comments-container"> <h3>AI Suggestions</h3> <div id="ai-comments"></div> <h3>Add Comment</h3> <form id="commentForm"> <input type="number" id="lineNumber" placeholder="Line Number"> <select id="commentType"> <option value="improvement">Improvement</option> <option value="bug">Bug</option> <option value="security">Security</option> <option value="performance">Performance</option> </select> <textarea id="commentText" placeholder="Comment text"></textarea> <button type="submit">Add Comment</button> </form> <button id="submitToGitHub">Submit All Comments to GitHub</button> <button id="exportComments">Export Comments for Training</button> </div> </div> </div> <script> // Monaco editor initialization code require.config({ paths: { 'vs': 'https://cdnjs.cloudflare.com/ajax/libs/monaco-editor/0.34.0/min/vs' }); require(['vs/editor/editor.main'], function() { window.editor = monaco.editor.create(document.getElementById('editor-container'), { value: '// Code will appear here', language: 'javascript', theme: 'vs-dark', readOnly: true, lineNumbers: 'on', glyphMargin: true }); }); // Rest of the UI JavaScript </script> </body> </html> Fine-Tuning the AI Model As discussed in the introduction, to improve the LLM's performance for our specific codebase, we can fine-tune the model using data collected through the review process. Exporting Training Data The application includes an endpoint to export comments in a format suitable for fine-tuning: Python @app.route('/export_comments', methods=['GET']) def export_comments(): """Export all comments in a format suitable for model fine-tuning.""" if os.path.exists(COMMENTS_FILE): with open(COMMENTS_FILE, 'r') as file: try: all_comments = json.load(file) # Format for fine-tuning training_data = [] for pr_key, comments in all_comments.items(): for comment in comments: if comment.get('human_approved', False): training_example = { "input": comment.get('code_context', ''), "output": comment.get('body', '') } training_data.append(training_example) return jsonify({ "status": "success", "training_examples": len(training_data), "data": training_data }) except json.JSONDecodeError: return jsonify({"status": "error", "message": "Invalid comments file"}) else: return jsonify({"status": "error", "message": "No comments file found"}) Fine-Tuning Using Unsloth Unsloth is an excellent tool for efficiently fine-tuning models. Here's how to use it with your exported data. NOTE: In this application I built, I did not have enough data to fine-tune the model, but if you want to try this, please refer to the below. It uses unsloth/llama-3-8b to fine-tune based on the custom data. Install Unsloth: Shell pip install unsloth Then, create a fine-tuning script (taken from the official tutorial): Python from unsloth import FastLanguageModel import torch import pandas as pd import json # Load the dataset from the exported JSON with open("exported_comments.json", "r") as f: data = json.load(f) training_data = data["data"] # Convert to DataFrame df = pd.DataFrame(training_data) # Initialize the model model, tokenizer = FastLanguageModel.from_pretrained( model_name="unsloth/llama-3-8b", max_seq_length=2048, dtype=torch.bfloat16, load_in_4bit=True, ) # Set up a supervised fine-tuning trainer trainer = FastLanguageModel.get_peft_trainer( model=model, tokenizer=tokenizer, train_dataset=df, formatting_func=lambda example: f"Review this code: {example['input']}\n\nReview: {example['output']}", peft_config=FastLanguageModel.get_peft_config(r=16, lora_alpha=32, target_modules=["q_proj", "k_proj", "v_proj", "o_proj"]), max_seq_length=2048, dataset_text_field="formatting_func", batch_size=1, epochs=3, learning_rate=2e-4, gradient_accumulation_steps=4, warmup_ratio=0.05, logging_steps=1, ) # Train the model trainer.train() # Save the fine-tuned model model_path = "code-review-model" trainer.save_model(model_path) # Export for Ollama FastLanguageModel.export_to_gguf( model_path=model_path, output_path="code-review-model.gguf", quantization_method="q4_k_m" ) Adding Your Fine-Tuned Model to Ollama After fine-tuning, create a Modelfile to import your model into Ollama: Plain Text # code-review-modelfile FROM ./code-review-model.gguf PARAMETER temperature 0.1 PARAMETER top_p 0.7 PARAMETER stop "</review>" SYSTEM """ You are an expert code reviewer specializing in identifying bugs, security issues, performance problems, and code quality improvements. When reviewing code: 1. Focus on substantive issues that impact functionality, security, and performance 2. Provide specific, actionable feedback with line numbers 3. Suggest concrete solutions for each issue identified 4. Format your response consistently for each issue 5. Be concise but thorough """ Finally, create the model in Ollama: Shell ollama create code-reviewer -f code-review-modelfile Now you can update your application to use your custom fine-tuned model. Conclusion AI-powered assistants can help streamline development by automating first-pass reviews while maintaining human oversight for accuracy. Let me know your comments on my project and any suggestions for improving it.