Có cần tài liệu thiết kế (design document) trong phát triển phần mềm Agile?

Online Retailing Triumph #1: Explore Modern Offshore with SupremeTech & Classmethod

Unlock the Power of Partnership in Customer Service in Online Retailing with SupremeTech, a journey through excellence, collaboration, and innovation. SupremeTech's commitment to continuous improvement led to vital projects, thanks to their partnership with Classmethod. They excel in managing data, ensuring security, and tailoring solutions to client needs. Their ongoing support and dedicated client teams ensure sustained success. Together we prove that the Modern Offshore model is a winning formula for offshore development. Let's explore how we did that in the series of Online Retailing Triumph. Online Retailing Triumph - Episode 2 Online Retailing Triumph - Episode 3 Tackling Data Challenges in Customer Service Digitalization When discussing Customer Service Digitalization, individuals often focus on optimizing the user experience with every click of a button. However, the cornerstone of this digital transformation lies hidden behind each user interface - Data. In the customer service industry, safeguarding customer data is of paramount importance. This is precisely where SupremeTech initiated a collaboration, partnering with a major coffee chain in Japan and Classmethod. This strategic partnership commenced with the critical task of managing a substantial segment of their extensive system. Without effective data management, the system remains devoid of valuable information for analytics. It is this expertise that led to SupremeTech's selection to handle this pivotal case, marking the beginning of a promising partnership. Identifying the Key Challenges Now, with a few stepIn the realm of customer service digitalization, recognizing and addressing challenges is paramount to success. One of the most pivotal challenges faced by businesses is effectively managing data. This is where the power of partnership comes into play, as exemplified by the collaborative efforts of SupremeTech and Classmethod. Collaborating with multiple vendors in one project SupremeTech's journey in overcoming these challenges begins with a crucial partnership role. Our company and Classmethod work as one of the multiple vendors within a complex project of a big brand of coffee chain. This collaboration laid the foundation for tackling the intricate data management needs of the customer service industry. Offshore development, in this context, plays a pivotal role in efficiently executing the project. It leverages a global approach to resource allocation, enabling us to harness a diverse pool of talent, cost advantages, and time zone differences to meet the project's multifaceted demands. As we work cohesively with Classmethod and other vendors, offshore development ensures a dynamic and comprehensive solution to tackle the intricacies of data management in the customer service industry. Dealing with Unpredictable Daily Data Imports A significant hurdle in this endeavor was dealing with the unpredictable nature of daily data imports. SupremeTech, in partnership with Classmethod, needed to develop solutions that could adapt to the ever-changing influx of data, ensuring seamless operations and data security. Collaborating seamlessly alongside Classmethod, our joint team conducted research and maintained a robust line of communication. Every update was consistently shared and cross-verified with the customer, ensuring optimal outcomes. Extracting Data from Store Sources Furthermore, recognizing the paramount importance of data security in the business sphere, SupremeTech unwaveringly prioritized safeguarding every feature and piece of information. The process of extracting data from diverse store sources demands a delicate balance of precision and security, and SupremeTech excels in this critical domain. Here's a closer look at how they manage this intricate task. SupremeTech's approach begins with meticulous research and analysis. They recognize that the data extracted holds the key to unlocking invaluable insights for data analytics. Thus, they leave no stone unturned in understanding the nuances of each client's store sources. One of the standout qualities of SupremeTech is their commitment to maintaining the utmost secrecy and precision during data extraction. Recognizing that data is often a company's most closely guarded asset, they implement stringent security protocols to ensure that data remains confidential and protected throughout the extraction process. SupremeTech's Successful Challenge Conquest SupremeTech's journey in conquering challenges in data management is a testament to its commitment to excellence and its invaluable partnership with Classmethod. From Research to Specialized Data Management Expertise Our success story begins with thorough research, leading to the development of specialized expertise in data management. Supreme Tech's dedication to staying at the forefront of industry knowledge has empowered them to provide innovative solutions. Careful and Methodical Problem Solving with Partner In partnership with Classmethod, SupremeTech approaches challenges with a careful and methodical problem-solving approach. Their synergy ensures that complex issues are addressed comprehensively and efficiently. SupremeTech Customized Solutions for Client-Centric Success SupremeTech's expertise shines through in its ability to tailor solutions that seamlessly align with each client's unique objectives and intricate system requirements. These customer-centric solutions are the cornerstone of their partnership-driven approach. Ongoing Support and Follow-Up Beyond the initial implementation, Supremetech's commitment to its clients is an unwavering promise of continued support and follow-up, ensuring that the journey toward sustained success remains smooth and fruitful. SupremeTech understands that the digital landscape is dynamic, and business needs can change rapidly. Therefore, they provide dedicated client support teams, equipped with deep expertise and a thorough understanding of the client's specific objectives. This personalized approach ensures that clients have a reliable partner to turn to for assistance, guidance, and troubleshooting. Maintaining Strong Client Partnerships - Classmethod  SupremeTech's dedication to maintaining strong client partnerships, exemplified by its collaboration with Classmethod (CM), underscores its commitment to fostering enduring relationships built on trust and reliability. Committed to Continuous Improvement SupremeTech's unwavering commitment to continuous improvement has earned them a series of significant projects with CM. Their dedication to staying ahead in industry trends and emerging technologies ensures they offer cutting-edge solutions. This commitment has solidified their partnership with CM, demonstrating the tangible benefits of their innovative approach.  In essence, SupremeTech's journey of continuous improvement not only keeps them competitive but also strengthens partnerships and attracts vital projects, underlining their dedication to excellence. Development systems and technologies Below are the resources and technologies we use to develop the services: Details of entrustment: Implementation, Testing, Migration, Maintenance & Operation Platform: Web Development language: PHP, Javascript Let SupremeTech help you to start the system now! Start to invest for your start now with the help of SupremeTech. Our expertise and solutions will empower you to request! Don't wait, let's take the first step towards digitizing your customer service now!

A Virtual Office for Remote and Hybrid Workers

Today.ly is a virtual office service that provides all of the benefits of a physical office for professionals working from the comfort of their homes. Engaging Alternative for Remote Work The debate between working remotely and working in the office rages on. We won't get into the pros and cons of each work style. However, we believe that working remotely in Today.ly's office simulation can make remote work more fun and interactive (and potentially more attractive to die-hard in-office workers). Sure, the days of everyone being cooped up for months on end are over. For some companies and industries, working from home is no longer a necessity. Today.ly allows teams to work remotely without completely losing the serendipitous moments that physical offices provide.  Working Together as Partners SupremeTech started developing Today.ly after being approached by our current business partner in Singapore. They came to us with the idea of creating a virtual office space during the peak hours of remote work. Our partner relied on us to leverage our expertise in R&D to create and develop an MVP. One of the main reasons for choosing SupremeTech as their partner was the proximity of Vietnam to Singapore. However, they also recognized our ability to deliver a quality product quickly and efficiently.  Communication and Cross-border Collaboration As with most offshore development projects, the communication element can make or break a partnership. SupremeTech works with many companies from all over the world. Our expertise is mainly in the Japanese market. There were some initial challenges in getting used to communicating with a Singaporean partner, but our team overcame these challenges and worked smoothly and successfully with all members of the project. We used Today.ly to organize calls and meetings with our foreign partners because we felt that the product would help create cross-border collaboration. Developing Something More than A Video Call Tool The business idea was born during the pandemic, as were many online learning and work tools. However, we did not build Today.ly with a focus on online calls. We developed Today.ly to mimic more of the spontaneous happenings of the office. It was designed to function like an online office while allowing employees to enjoy the convenience and comfort of their home. We wanted to provide a virtual office that could strengthen the bonds of employees. This is not a video calling service; this is a virtual office that increases collaboration and teamwork amongst team members, even when they are physically separated.  But that was actually one of the main challenges of this project: how can we build something that takes advantage of the benefits of office work, but isn't redundant or completely useless in the market? The answer was to create an online space for communication, not just a communication tool. Nearly all companies use meetings to interact and discuss ongoing projects. For remote meetings, this means using a video call to communicate. There's nothing wrong with a regular video call; in fact, in some cases, it's probably more useful than any other method. Even if you think an email or message would suffice, meetings are inevitable.  Virtual Offices Doing More for Your Business! Sometimes a video call or DM just isn't enough; there's a technological barrier between us and the other participants. Today.ly simulates a meeting room in a way that feels more personal than talking heads in a square. It emulates the physical space. There's crystal clear audio, multi-screen sharing, and even a meeting table and chairs. Nothing can replace face-to-face communication, but Today.ly allows companies to mimic certain aspects while users are sitting in their own living rooms. It will always be a challenge to replicate the old-fashioned face-to-face dialogue, but Today.ly users have found greater motivation and connection within their teams.  Today.ly was initially for companies and industries that support working from home. The features were designed to make working online an interactive experience. While many companies have already returned to traditional office life, Today.ly is still a valuable tool for hybrid work environments and provides all businesses with an exciting alternative to traditional working environments.

How Agile Development Influences Developer And QA Tool Choices

On the surface, it might seem like the Agile methodology is simply about reimagining waterfall development by keeping an open mind. However, this approach has far-reaching implications for people, tools and processes. That said, let’s dissect the major Agile development characteristics that affect developer and quality assurance (QA) tool choices and talk about some great examples of Agile development tools: Increased Collaboration Agile development pushes for greater synergy amongst teams. It's hard to release higher-quality products faster if many of the people involved aren't openly communicating with each other about challenges. For example, a developer working alone can easily store, manage and reuse code using one machine. However, things get trickier once a project needs more hands and numerous software units are being created. You'll need a space where everyone's work can be converged, with changes indicated coherently. Photo by Tim van der Kuip on Unsplash  This is where tools like GitHub come in. GitHub will enable developers to combine their code into modules and easily perform version control. More importantly, developers can do all this remotely since it's cloud-based. It’s also worth noting that GitHub isn’t the only code hosting/management solution out there. You can always try others like TaraVault, AWS CodeCommit, Bitbucket, SourceForge, and many more. By the way, collaboration doesn’t begin only when the team has been formed. It’s an ethos that can be applied even when building the team and responding to talent departure or unavailability. With tools like Crowdsource.io, you can set up a project folder, post your needs and respond to interested developers. This social approach simplifies recruitment, especially during emergencies. Continuous Testing In continuous testing, teams don’t have to wait for all units to be ready before they start. Instead, they endeavor to test whatever small piece is ready as others are prepared. By doing so, they are less likely to be blindsided by complex problems in the software that delay a release as the team tries to fix them. This approach often means the testing workload will fluctuate as the project progresses, so quality assurance teams will have to be swifter at scaling testing capacity accordingly. One of the best ways to achieve this is by using test automation tools. Photo by freestocks on Unsplash More specifically, you'll need user-friendly test automation tools if teams are to quickly add new automations in response to changes in the testing workload. They should also facilitate reusability and easily plug into your CI/CD pipelines, which is what tools like Leapwork are good at. Additionally, an Agile testing team needs robust tracking and reporting capabilities to excel at continuous testing. This is where tools like JIRA and nTask Issue Tracker shine. They’ll offer features tailored to defect tracking while also helping you analyze QA team performance in real time so you can know how to increase efficiency and remain on schedule. Value-Driven Development You can only offer so much value if you aren't listening and responding to users. So to get better at value-driven development, you must create a smoother path for user feedback to be absorbed into the development lifecycle promptly. Photo by krakenimages on Unsplash One critical piece in this area is the usability testing tools you rely on. They should have functionality that speaks to agility, such as; Recruiting participants remotelyA/B testingIntegration with various tools like prototyping solutions, design tools, productivity tools and more. These are some of the crucial functions needed because: You want to be able to start user feedback collection from anywhere as soon as you get the green light. In that sense, participant recruitment should involve as little bureaucracy as possible and be easier to manage as you conduct more tests down the road. Testers should be able to compare feature variations side-by-side instead of testing one, then trying another later. So through A/B testing, you shorten the time it takes to discover whether a slight tweak in a feature caused a significant improvement in usability. Once you have user opinions and usability metrics, there shouldn't be that many error-prone processes to link the data to the concerned parties and the different tools they use to respond to the feedback. Whoever is going to modify a product should be able to quickly see how their previous contribution fared. The other leaders who have to sign off on additional changes should also be able to view this information simultaneously, so there isn't much sitting and waiting. Tools like UserZoom, Loop11, UsabilityHub, Userlytics and Maze will go a long way in helping you increase the customer value realized with each iteration. The glue that holds everything together While we’ve talked about the Agile QA process and Agile development software, another key aspect is more about the administrative side of an Agile project. When it comes to this area, you'll hear a lot of buzzwords and office terminology thrown around, like modularity, effective communication and visibility. But to be clearer about the tenets of organizational agility, here’s what Agile teams should emphasize during the development life cycle: Teams should be able to zoom in on the smallest of processes as this is how they'll know what's essential and what isn't and also what could be improved. Organizations should give more autonomy to the lower ranks so they can do some things at their discretion. If every small move has to first be run up a long chain of command, all in the name of maintaining control and minimizing risk, you can forget about faster releases. Whatever you practice should be incremental. For example, when figuring out how to communicate better, you don't just set benchmarks and lean back. Instead, you should revisit them to see if they are working and decide whether to maintain, increase, decrease or move sideways. So how exactly do these principles shape tool choices? Well, you’ll need Agile project management tools that offer features like automated notifications, workflow customization, analytics and recommendations. There are plenty of options to try; Agilean, ProofHub, ActiveCollab, MeisterTask, Axosoft, JIRA and DailyScrum. Wrapping Up Ultimately, tools that work for some may not work for you, so it starts with understanding your existing organizational culture and how much you're willing to alter it in pursuit of agility. That’s when you’ll know where tools are applicable and which functionality they must have. To learn more about how to pick the right developer and QA tools for Agile development, contact us for a free consultation.

Best Practices for Building Reliable AWS Lambda Functions

Welcome back to the "Mastering AWS Lambda with Bao" series! The previous episode explored how AWS Lambda connects to the world through AWS Lambda triggers and events. Using S3 and DynamoDB Streams triggers, we demonstrated how Lambda automates workflows by processing events from multiple sources. This example provided a foundation for understanding Lambda’s event-driven architecture. However, building reliable Lambda functions requires more than understanding how triggers work. To create AWS lambda functions that can handle real-world production workloads, you need to focus on optimizing performance, implementing robust error handling, and enforcing strong security practices. These steps optimize your Lambda functions to be scalable, efficient, and secure. In this episode, SupremeTech will explore the best practices for building reliable AWS Lambda functions, covering two essential areas: Optimizing Performance: Reducing latency, managing resources, and improving runtime efficiency.Error Handling and Logging: Capturing meaningful errors, logging effectively with CloudWatch, and setting up retries. Adopting these best practices, you’ll be well-equipped to optimize Lambda functions that thrive in production environments. Let’s dive in! Optimizing Performance Optimize the Lambda function's performance to run efficiently with minimal latency and cost. Let's focus first on Cold Starts, a critical area of concern for most developers. Understanding Cold Starts What Are Cold Starts? A Cold Start occurs when AWS Lambda initializes a new execution environment to handle an incoming request. This happens under the following circumstances: When the Lambda function is invoked for the first time.After a period of inactivity (execution environments are garbage collected after a few minutes of no activity – meaning it will be shut down automatically).When scaling up to handle additional concurrent requests. Cold starts introduce latency because AWS needs to set up a new execution environment from scratch. Steps Involved in a Cold Start: Resource Allocation:AWS provisions a secure and isolated container for the Lambda function.Resources like memory and CPU are allocated based on the function's configuration.Execution Environment Initialization:AWS sets up the sandbox environment, including:The /tmp directory is for temporary storage.Networking configurations, such as Elastic Network Interfaces (ENI), for VPC-based Lambdas.Runtime Initialization:The specified runtime (e.g., Node.js, Python, Java) is initialized.For Node.js, this involves loading the JavaScript engine (V8) and runtime APIs.Dependency Initialization:AWS loads the deployment package (your Lambda code and dependencies).Any initialization code in your function (e.g., database connections, library imports) is executed.Handler Invocation:Once the environment is fully set up, AWS invokes your Lambda function's handler with the input event. Cold Start Latency Cold start latency varies depending on the runtime, deployment package size, and whether the function runs inside a VPC: Node.js and Python: ~200ms–500ms for non-VPC functions.Java or .NET: ~500ms–2s due to heavier runtime initialization.VPC-Based Functions: Add ~500ms–1s due to ENI initialization. Warm Starts In contrast to cold starts, Warm Starts reuse an already-initialized execution environment. AWS keeps environments "warm" for a short time after a function is invoked, allowing subsequent requests to bypass initialization steps. Key Differences: Cold Start: New container setup → High latency.Warm Start: Reused container → Minimal latency (~<100ms). Reducing Cold Starts Cold starts can significantly impact the performance of latency-sensitive applications. Below are some actionable strategies to reduce cold starts, each with good and bad practice examples for clarity. 1. Use Smaller Deployment Packages to optimize lambda function Good Practice: Minimize the size of your deployment package by including only the required dependencies and removing unnecessary files.Use bundlers like Webpack, ESBuild, or Parcel to optimize your package size.Example: const DynamoDB = require('aws-sdk/clients/dynamodb'); // Only loads DynamoDB, not the entire SDK Bad Practice: Bundling the entire AWS SDK or other large libraries without considering modular imports.Example: const AWS = require('aws-sdk'); // Loads the entire SDK, increasing package size Why It Matters: Smaller deployment packages load faster during the initialization phase, reducing cold start latency. 2. Move Heavy Initialization Outside the Handler Good Practice: Place resource-heavy operations, such as database or SDK client initialization, outside the handler function so they are executed only once per container lifecycle – a cold start.Example: const DynamoDB = new AWS.DynamoDB.DocumentClient(); exports.handler = async (event) => {     const data = await DynamoDB.get({ Key: { id: '123' } }).promise();     return data; }; Bad Practice: Reinitializing resources inside the handler for every invocation.Example: exports.handler = async (event) => {     const DynamoDB = new AWS.DynamoDB.DocumentClient(); // Initialized on every call     const data = await DynamoDB.get({ Key: { id: '123' } }).promise();     return data; }; Why It Matters: Reinitializing resources for every invocation increases latency and consumes unnecessary computing power. 3. Enable Provisioned Concurrency1 Good Practice: Use Provisioned Concurrency to pre-initialize a set number of environments, ensuring they are always ready to handle requests.Example:AWS CLI: aws lambda put-provisioned-concurrency-config \ --function-name myFunction \ --provisioned-concurrent-executions 5 AWS Management Console: Why It Matters: Provisioned concurrency ensures a constant pool of pre-initialized environments, eliminating cold starts entirely for latency-sensitive applications. 4. Reduce Dependencies to optimize the lambda function Good Practice: Evaluate your libraries and replace heavy frameworks with lightweight alternatives or native APIs.Example: console.log(new Date().toISOString()); // Native JavaScript API Bad Practice: Using heavy libraries for simple tasks without considering alternatives.Example: const moment = require('moment'); console.log(moment().format()); Why It Matters: Large dependencies increase the deployment package size, leading to slower initialization during cold starts. 5. Avoid Unnecessary VPC Configurations Good Practice: Place Lambda functions outside a VPC unless necessary. If a VPC is required (e.g., to access private resources like RDS), optimize networking using VPC endpoints.Example:Use DynamoDB and S3 directly without placing the Lambda inside a VPC. Bad Practice: Deploying Lambda functions inside a VPC unnecessarily, such as accessing services like DynamoDB or S3, which do not require VPC access.Why It’s Bad: Placing Lambda in a VPC introduces additional latency due to ENI setup during cold starts. Why It Matters: Functions outside a VPC initialize faster because they skip ENI setup. 6. Choose Lightweight Runtimes to optimize lambda function Good Practice: Use lightweight runtimes like Node.js or Python for faster initialization than heavier runtimes like Java or .NET.Why It’s Good: Lightweight runtimes require fewer initialization resources, leading to lower cold start latency. Why It Matters: Heavier runtimes have higher cold start latency due to the complexity of their initialization process. Summary of Best Practices for Cold Starts AspectGood PracticeBad PracticeDeployment PackageUse small packages with only the required dependencies.Bundle unused libraries, increasing the package size.InitializationPerform heavy initialization (e.g., database connections) outside the handler.Initialize resources inside the handler for every request.Provisioned ConcurrencyEnable provisioned concurrency for latency-sensitive applications.Ignore provisioned concurrency for high-traffic functions.DependenciesUse lightweight libraries or native APIs for simple tasks.Use heavy libraries like moment.js without evaluating lightweight alternatives.VPC ConfigurationAvoid unnecessary VPC configurations; use VPC endpoints when required.Place all Lambda functions inside a VPC, even when accessing public AWS services.Runtime SelectionChoose lightweight runtimes like Node.js or Python for faster initialization.Use heavy runtimes like Java or .NET for simple, lightweight workloads. Error Handling and Logging Error handling and logging are critical for optimizing your Lambda functions are reliable and easy to debug. Effective error handling prevents cascading failures in your architecture, while good logging practices help you monitor and troubleshoot issues efficiently. Structured Error Responses Errors in Lambda functions can occur due to various reasons: invalid input, AWS service failures, or unhandled exceptions in the code. Properly structured error handling ensures that these issues are captured, logged, and surfaced effectively to users or downstream services. 1. Define Consistent Error Structures Good Practice: Use a standard error format so all errors are predictable and machine-readable.Example: {   "errorType": "ValidationError",   "message": "Invalid input: 'email' is missing",   "requestId": "12345-abcd" } Bad Practice: Avoid returning vague or unstructured errors that make debugging difficult. { "message": "Something went wrong", "error": true } Why It Matters: Structured errors make debugging easier by providing consistent, machine-readable information. They also improve communication with clients or downstream systems by conveying what went wrong and how it should be handled. 2. Use Custom Error Classes Good Practice: In Node.js, define custom error classes for clarity: class ValidationError extends Error {   constructor(message) {     super(message);     this.name = "ValidationError";     this.statusCode = 400; // Custom property   } } // Throwing a custom error if (!event.body.email) {   throw new ValidationError("Invalid input: 'email' is missing"); } Bad Practice: Use generic errors for everything, making identifying or categorizing issues hard.Example: throw new Error("Error occurred"); Why It Matters: Custom error classes make error handling more precise and help segregate application errors (e.g., validation issues) from system errors (e.g., database failures). 3. Include Contextual Information in Logs Good Practice: Add relevant information like requestId, timestamp, and input data (excluding sensitive information) when logging errors.Example: console.error({     errorType: "ValidationError",     message: "The 'email' field is missing.",     requestId: context.awsRequestId,     input: event.body,     timestamp: new Date().toISOString(), }); Bad Practice: Log errors without any context, making debugging difficult.Example: console.error("Error occurred"); Why It Matters: Contextual information in logs makes it easier to identify what triggered the error and where it happened, improving the debugging experience. Retry Logic Across AWS SDK and Other Services Retrying failed operations is critical when interacting with external services, as temporary failures (e.g., throttling, timeouts, or transient network issues) can disrupt workflows. Whether you’re using AWS SDK, third-party APIs, or internal services, applying retry logic effectively can ensure system reliability while avoiding unnecessary overhead. 1. Use Exponential Backoff and Jitter Good Practice: Apply exponential backoff with jitter to stagger retry attempts. This avoids overwhelming the target service, especially under high load or rate-limiting scenarios.Example (General Implementation): async function retryWithBackoff(fn, retries = 3, delay = 100) {     for (let attempt = 1; attempt <= retries; attempt++) {         try {             return await fn();         } catch (error) {             if (attempt === retries) throw error; // Rethrow after final attempt             const backoff = delay * 2 ** (attempt - 1) + Math.random() * delay; // Add jitter             console.log(`Retrying in ${backoff.toFixed()}ms...`);             await new Promise((res) => setTimeout(res, backoff));         }     } } // Usage Example const result = await retryWithBackoff(() => callThirdPartyAPI()); Bad Practice: Retrying without delays or jitter can lead to cascading failures and amplify the problem. for (let i = 0; i < retries; i++) {     try {         return await callThirdPartyAPI();     } catch (error) {         console.log("Retrying immediately...");     } } Why It Matters: Exponential backoff reduces pressure on the failing service, while jitter randomizes retry times, preventing synchronized retry storms from multiple clients. 2. Leverage Built-In Retry Mechanisms Good Practice: Use the built-in retry logic of libraries, SDKs, or APIs whenever available. These are typically optimized for the specific service.Example (AWS SDK): const DynamoDB = new AWS.DynamoDB.DocumentClient({     maxRetries: 3, // Number of retries     retryDelayOptions: { base: 200 }, // Base delay in ms }); Example (Axios for Third-Party APIs):Use libraries like axios-retry to integrate retry logic for HTTP requests. const axios = require('axios'); const axiosRetry = require('axios-retry'); axiosRetry(axios, {     retries: 3, // Retry 3 times     retryDelay: (retryCount) => retryCount * 200, // Exponential backoff     retryCondition: (error) => error.response.status >= 500, // Retry only for server errors }); const response = await axios.get("https://example.com/api"); Bad Practice: Writing your own retry logic unnecessarily when built-in mechanisms exist, risking suboptimal implementation. Why It Matters: Built-in retry mechanisms are often optimized for the specific service or library, reducing the likelihood of bugs and configuration errors. 3. Configure Service-Specific Retry Limits Good Practice: Set retry limits based on the service's characteristics and criticality.Example (AWS S3 Upload): const s3 = new AWS.S3({ maxRetries: 5, // Allow more retries for critical operations retryDelayOptions: { base: 300 }, // Slightly longer base delay }); Example (Database Queries): async function queryDatabaseWithRetry(queryFn) {     await retryWithBackoff(queryFn, 5, 100); // Retry with custom backoff logic } Bad Practice: Allowing unlimited retries can cause resource exhaustion and increase costs. while (true) {     try {         return await callService();     } catch (error) {         console.log("Retrying...");     } } Why It Matters: Excessive retries can lead to runaway costs or cascading failures across the system. Always define a sensible retry limit. 4. Handle Transient vs. Persistent Failures Good Practice: Retry only transient failures (e.g., timeouts, throttling, 5xx errors) and handle persistent failures (e.g., invalid input, 4xx errors) immediately.Example: const isTransientError = (error) =>     error.code === "ThrottlingException" || error.code === "TimeoutError"; async function callServiceWithRetry() {     await retryWithBackoff(() => {         if (!isTransientError(error)) throw error; // Do not retry persistent errors         return callService();     }); } Bad Practice: Retrying all errors indiscriminately, including persistent failures like ValidationException or 404 Not Found. Why It Matters: Persistent failures are unlikely to succeed with retries and can waste resources unnecessarily. 5. Log Retry Attempts Good Practice: Log each retry attempt with relevant context, such as the retry count and delay. async function retryWithBackoff(fn, retries = 3, delay = 100) {     for (let attempt = 1; attempt <= retries; attempt++) {         try {             return await fn();         } catch (error) {             if (attempt === retries) throw error;             console.log(`Attempt ${attempt} failed. Retrying in ${delay}ms...`);             await new Promise((res) => setTimeout(res, delay));         }     } } Bad Practice: Failing to log retries makes debugging or understanding the retry behavior difficult. Why It Matters: Logs provide valuable insights into system behavior and help diagnose retry-related issues. Summary of Best Practices for Retry logic AspectGood PracticeBad PracticeRetry LogicUse exponential backoff with jitter to stagger retries.Retry immediately without delays, causing retry storms.Built-In MechanismsLeverage AWS SDK retry options or third-party libraries like axios-retry.Write custom retry logic unnecessarily when optimized built-in solutions are available.Retry LimitsDefine a sensible retry limit (e.g., 3–5 retries).Allow unlimited retries, risking resource exhaustion or runaway costs.Transient vs PersistentRetry only transient errors (e.g., timeouts, throttling) and fail fast for persistent errors.Retry all errors indiscriminately, including persistent failures like validation or 404 errors.LoggingLog retry attempts with context (e.g., attempt number, delay,  error) to aid debugging.Fail to log retries, making it hard to trace retry behavior or diagnose problems. Logging Best Practices Logs are essential for debugging and monitoring Lambda functions. However, unstructured or excessive logging can make it harder to find helpful information. 1. Mask or Exclude Sensitive Data Good Practice: Avoid logging sensitive information like:User credentialsAPI keys, tokens, or secretsPersonally Identifiable Information (PII)Use tools like AWS Secrets Manager for sensitive data management.Example: Mask sensitive fields before logging: const sanitizedInput = {     ...event,     password: "***", }; console.log(JSON.stringify({     level: "info",     message: "User login attempt logged.",     input: sanitizedInput, })); Bad Practice: Logging sensitive data directly can cause security breaches or compliance violations (e.g., GDPR, HIPAA).Example: console.log(`User logged in with password: ${event.password}`); Why It Matters: Logging sensitive data can expose systems to attackers, breach compliance rules, and compromise user trust. 2.  Set Log Retention Policies Good Practice: Set a retention policy for CloudWatch log groups to prevent excessive log storage costs.AWS allows you to configure retention settings (e.g., 7, 14, or 30 days). Bad Practice: Using the default “Never Expire” retention policy unnecessarily stores logs indefinitely. Why It Matters: Unmanaged logs increase costs and make it harder to find relevant data. Retaining logs only as long as needed reduces costs and keeps logs manageable. 3. Avoid Excessive Logging Good Practice: Log only what is necessary to monitor, troubleshoot, and analyze system behavior.Use info, debug, and error levels to prioritize logs appropriately. console.info("Function started processing..."); console.error("Failed to fetch data from DynamoDB: ", error.message); Bad Practice: Logging every detail (e.g., input payloads, execution steps) unnecessarily increases log volume.Example: console.log(`Received event: ${JSON.stringify(event)}`); // Avoid logging full payloads unnecessarily Why It Matters: Excessive logging clutters log storage, increases costs, and makes it harder to isolate relevant logs. 4. Use Log Levels (Info, Debug, Error) Good Practice: Use different log levels to differentiate between critical and non-critical information.info: For general execution logs (e.g., function start, successful completion).debug: For detailed logs during development or troubleshooting.error: For failure scenarios requiring immediate attention. Bad Practice: Using a single log level (e.g., console.log() everywhere) without prioritization. Why It Matters: Log levels make it easier to filter logs based on severity and focus on critical issues in production. Conclusion In this episode of "Mastering AWS Lambda with Bao", we explored critical best practices for building reliable AWS Lambda functions, focusing on optimizing performance, error handling, and logging. Optimizing Performance: By reducing cold starts, using smaller deployment packages, lightweight runtimes, and optimizing VPC configurations, you can significantly lower latency and optimize Lambda functions. Strategies like moving initialization outside the handler and leveraging Provisioned Concurrency ensure smoother execution for latency-sensitive applications.Error Handling: Implementing structured error responses and custom error classes makes troubleshooting easier and helps differentiate between transient and persistent issues. Handling errors consistently improves system resilience.Retry Logic: Applying exponential backoff with jitter, using built-in retry mechanisms, and setting sensible retry limits optimizes that Lambda functions gracefully handle failures without overwhelming dependent services.Logging: Effective logging with structured formats, contextual information, log levels, and appropriate retention policies enables better visibility, debugging, and cost control. Avoiding sensitive data in logs ensures security and compliance. Following these best practices, you can optimize lambda function performance, reduce operational costs, and build scalable, reliable, and secure serverless applications with AWS Lambda. In the next episode, we’ll dive deeper into "Handling Failures with Dead Letter Queues (DLQs)", exploring how DLQs act as a safety net for capturing failed events and ensuring no data loss occurs in your workflows. Stay tuned! Note: 1. Provisioned Concurrency is not a universal solution. While it eliminates cold starts, it also incurs additional costs since pre-initialized environments are billed regardless of usage. When to Use:Latency-sensitive workloads like APIs or real-time applications where even a slight delay is unacceptable.When Not to Use:Functions with unpredictable or low invocation rates (e.g., batch jobs, infrequent triggers). For such scenarios, on-demand concurrency may be more cost-effective.