Performance

Surprise! This is a bonus blog post for the AI for Web Devs series I recently wrapped up. If you haven’t read that series yet, I’d encourage you to check it out. This post will look at the existing project architecture and ways we can improve it for both application developers and the end user. I’ll be discussing some general concepts, and using specific Akamai products in my examples. Basic Application Architecture The existing application is pretty basic. A user submits two opponents, then the application streams back an AI-generated response of who would win in a fight. The architecture is also simple: The client sends a request to a server. The server constructs a prompt and forwards the prompt to OpenAI. OpenAI returns a streaming response to the server. The server makes any necessary adjustments and forwards the streaming response to the client. I used Akamai’s cloud computing services (formerly Linode) but this would be the same for any hosting service. Fig. 1. Cloud application architecture Technically this works fine, but there are a couple of problems, particularly when users make duplicate requests. It could be faster and more cost-effective to store responses on our server and only go to OpenAI for unique requests. This assumes we don’t need every single request to be non-deterministic (the same input produces a different output). Let’s assume it’s OK for the same input to produce the same output. After all, a prediction for who would win in a fight wouldn’t likely change. Add Database Architecture If we want to store responses from OpenAI, a practical place to put them is in some sort of database that allows for quick and easy lookup using the two opponents. This way, when a request is made, we can check the database first: The client sends a request to a server. The server checks for an existing entry in the database that matches the user’s input. If a previous record exists, the server responds with that data, and the request is complete. Skip the following steps. If not, the server follows from step three in the previous flow. Before closing the response, the server stores the OpenAI results in the database. Fig.2. Application architecture with database With this setup, any duplicate requests will be handled by the database. By making some of the OpenAI requests optional, we can potentially reduce the amount of latency users experience, plus save money by reducing the number of API requests. This is a good start, especially if the server and the database exist in the same region. It would make for much quicker response times than going to OpenAI’s servers. However, as our application becomes more popular, we may start getting users from all over the world. Faster database lookups are great, but what happens if the bottleneck is the latency from the time spent in flight? We can address that concern by moving things closer to the user. Bring in Edge Compute If you’re not already familiar with the term “edge”, this part might be confusing, but I’ll try to explain it simply. Edge refers to content being as close to the user as possible. For some people, that could mean IoT devices or cellphone towers, but in the case of the web, the canonical example is a Content Delivery Network (CDN). I’ll spare you the details, but a CDN is a network of globally distributed computers that can respond to user requests from the nearest node in the network (something I’ve written about in the past). While traditionally they were designed for static assets, in recent years, they started supporting edge computing (also something I’ve written about in the past). With edge computing, we can move a lot of our backend logic super close to the user, and it doesn’t stop at computing. Most edge compute providers also offer some sort of eventually consistent key-value store in the same edge nodes. How could that impact our application? The client sends a request to our backend. The edge compute network routes the request to the nearest edge node. The edge node checks for an existing entry in the key-value store that matches the user’s input. If a previous record exists, the edge node responds with that data, and the request is complete. Skip the following steps. If not, the edge node forwards the request to the origin server, which passes it along to OpenAI and yadda yadda yadda. Before closing the response, the server stores the OpenAI results in the edge key-value store. Fig.3. Application architecture with Edge compute The origin server may not be strictly necessary here, but I think it’s more likely to be there. For the sake of data, compute, and logic flow, this is mostly the same as the previous architecture. The main difference is that the previously stored results now exist super close to users and can be returned almost immediately. (Note: although the data is being cached at the edge, the response is still dynamically constructed. If you don’t need dynamic responses, it may be simpler to use a CDN in front of the origin server and set the correct HTTP headers to cache the response. There are a lot of nuances here, and I could say more but…well, I’m tired and don’t want to. Feel free to reach out if you have any questions.) Now we’re cooking! Any duplicate requests will be responded to almost immediately, while also saving us unnecessary API requests. This sorts out the architecture for the text responses, but we also have AI-generated images. Cache Those Images The last thing we’ll consider today is images. When dealing with images, we need to think about delivery and storage. I’m sure that the folks at OpenAI have their solutions, but some organizations want to own the entire infrastructure for security, compliance, or reliability reasons. Some may even run their image generation services instead of using OpenAI. In the current workflow, the user makes a request that ultimately makes its way to OpenAI. OpenAI generates the image but doesn’t return it. Instead, they return a JSON response with the URL for the image, hosted on OpenAI’s infrastructure. With this response, an <img> tag can be added to the page using the URL, which kicks off another request for the actual image. If we want to host the image on our infrastructure, we need a place to store it. We could write the images onto the origin server’s disk, but that could quickly use up the disk space, and we’d have to upgrade our servers, which can be costly. Object storage is a much cheaper solution (I’ve also written about this). Instead of using the OpenAI URL for the image, we could upload it to our object storage instance and use that URL instead. That solves the storage question, but object storage buckets are generally deployed to a single region. This echoes the problem we had with storing text in a database. A single region may be far away from users, which could cause a lot of latency. Having introduced the edge already, it would be pretty trivial to add CDN features for just the static assets (frankly, every site should have a CDN). Once configured, the CDN will pull images from object storage on the initial request and cache them for any future requests from visitors in the same region. Here’s how our flow for images would look: The client sends a request to generate an image based on their opponents Edge compute checks if the image data for that request already exists. If so, it returns the URL. The image is added to the page with the URL and the browser requests the image. If the image has been previously cached in the CDN, the browser loads it almost immediately. This is the end of the flow. If the image has not been previously cached, the CDN will pull the image from the object storage location, cache a copy of it for future requests, and return the image to the client. This is another end of the flow. If the image data is not in the edge key-value store, the request to generate the image goes to the server and on to OpenAI, which generates the image and returns the URL information. The server starts a task to save the image in the object storage bucket, stores the image data in the edge key-value store, and returns the image data to edge compute. With the new image data, the client creates the image which creates a new request and continues from step five above. Fig.4. Architecture diagram showing a client connecting to an edge node This last architecture is, admittedly, a little bit more complex, but if your application is going to handle serious traffic, it’s worth considering. Voilà Right on! With all those changes in place, we have created AI-generated text and images for unique requests and serve cached content from the edge for duplicate requests. The result is faster response times and a much better user experience (in addition to fewer API calls). I kept these architecture diagrams applicable across various databases, edge compute, object storage, and CDN providers on purpose. I like my content to be broadly applicable. But it’s worth mentioning that integrating the edge is about more than just performance. There are a lot of really cool security features you can enable as well. For example, on Akamai’s network, you can have access to things like web application firewalls (WAF), distributed denial of service (DDoS) protection, intelligent bot detection, and more. That’s all beyond the scope of today’s post, though. So for now, I’ll leave you with a big “thank you” for reading. I hope you learned something. As always, feel free to reach out at any time with comments, questions, or concerns. Thank you so much for reading. If you liked this article, and want to support me, the best ways to do so are to share it and follow me on Twitter.

By Austin Gil CORE

The ability to monitor, analyze, and enhance the performance of applications has become a critical facet in maintaining a seamless user experience and meeting the ever-growing demands of today's digital world. As businesses increasingly rely on complex and distributed systems, the need to gain insights into the performance of applications has become paramount. Delve into the intricacies of Application Performance Monitoring and know about its significance in ensuring the application’s reliability, availability, and overall efficiency. From understanding the core components of APM to exploring its benefits, we aim to explain in detail the concept of APM, here. In this blog, we’ll talk about the importance, functionalities, and pivotal role that application performance monitoring plays in the success of digital initiatives. What Is APM? Application Performance Monitoring (APM) is a comprehensive approach to ensure the optimal functioning of software applications in real-time. It involves collecting, analyzing, and interpreting various metrics & key performance indicators (KPIs) to provide insights into the performance, responsiveness, and overall user experience of an application. In a rapidly evolving digital landscape, where user expectations are high, APM plays a crucial role in maintaining and improving the performance of applications. It goes beyond traditional monitoring by identifying potential issues and offering actionable insights for continuous improvement. Also, to get in-depth insights, it's important to understand in detail what APM tools are and identify popular tools used for implementing this approach. Key Components of APM Application performance monitoring plays a vital role in ensuring a positive user experience, identifying and resolving issues, and ultimately supporting the overall success of an organization. The key components of APM encompass various tools, processes, and strategies that collectively contribute to the efficient functioning of applications. Listed below are the key components of APM: Performance MetricsAPM tools monitor and measure various performance metrics such as response time, latency, throughput, and error rates. These metrics provide a holistic view of how well an application is performing. User Experience MonitoringAPM tools assess the end-user experience by tracking user interactions and load times. This perspective is vital in ensuring that applications meet or exceed user expectations. But, it’s important to know what is APM tools and how each tool is beneficial. Code-level VisibilityAPM testing offers in-depth visibility into the application's code, allowing developers to identify and rectify issues at the source. This includes tracing transactions, analyzing dependencies, and pinpointing bottlenecks. Resource UtilizationMonitoring resource utilization, including CPU, memory, and network usage, helps optimize the application's efficiency and ensure that it operates within acceptable performance thresholds. Error and Log AnalysisAPM tools capture and analyze error rates, exceptions, and logs, providing insights into potential issues and allowing for proactive resolution before they impact users. Scalability AssessmentAPM testing helps assess an application's scalability by monitoring its performance under different loads. This aids capacity planning and ensures the application can handle increasing workloads without degradation. Benefits of Application Performance Monitoring Application Performance Monitoring (APM) offers many benefits that are indispensable in today's technology-driven landscape. Let’s read in detail what is application performance monitoring used for and what are its benefits. Here's a closer look at some of the key advantages: Proactive Issue Resolution: APM enables teams to identify and address potential performance issues before they impact end-users, minimizing downtime and disruptions. Enhanced User Satisfaction: By continuously monitoring and optimizing performance, APM contributes to a positive user experience, fostering customer satisfaction and loyalty. Efficient Resource Allocation: APM tools provide insights into resource utilization, helping organizations optimize infrastructure, reduce costs, and maximize efficiency. Faster Troubleshooting: The detailed visibility offered by APM tools accelerates the troubleshooting process, allowing teams to quickly identify and resolve issues, minimizing the mean time to resolution (MTTR). Data-Driven Decision Making: APM generates valuable data and analytics that inform strategic decision-making, allowing organizations to align development efforts with business objectives. Continuous Improvement: APM is not just about monitoring; it's about leveraging insights for continuous improvement. By addressing performance bottlenecks and refining code, applications can evolve to meet changing demands. Application Performance Monitoring is a proactive and holistic approach to ensuring that software applications deliver exceptional performance, reliability, and a seamless user experience. By embracing APM, organizations can stay ahead in the competitive business space and meet the ever-growing expectations of users and stakeholders. Best Practices for Implementing APM The implementation of APM involves the integration of various tools, which may be supplemented by some processes and best practices to guarantee that your applications work at an optimal performance level. Here are some best practices for implementing APM. Here are some best practices for implementing APM: Select the Right Tools: Select an APM tool that will fit your needs and budget and will integrate with your stack. Think of essential requirements, including supported platforms, programming languages, integrations, scalability, and ease of use. Monitor Key Metrics: Identify performance metrics that are critical to the performance of the system, which include response time, throughput, error rates, CPU, memory usage, and network latency. Tracking these parameters will help to determine the bottlenecks line up and correctly adjust system sources. Distributed Tracing: By the implementation of distributed tracing, one can view the request flow across microservices and distributed systems, which will help to identify the bottlenecks. Distributed tracing helps to determine the causes of congestion, dependencies, and the process of communication between services. Set Baselines and Alerts: Once you set performance thresholds for your applications and create alerts to inform you when performance metrics start to deviate from norms, it will be more convenient for you to take countermeasures before the deviations become critical issues. Perform corrective or remedial actions to resolve performance anomalies before they affect the usage of the system. Anomaly Detection: Leverage anomaly detection techniques to automatically highlight the performance metrics that do not conform to the normal trends. Machine learning concepts can expose deviations from normal patterns and forecast what the problems might be. Continuous Monitoring: Set up a performance tracking system, which monitors metrics in real-time and cumulative. Create a schedule to review the work and processing of the produced data as per trends, patterns and spots of improvement. Final Wrap-Up It’s evident, by now, that APM is not merely a technical necessity but a strategic imperative for businesses navigating the intricate landscape of the digital era. As applications evolve to become the backbone of modern enterprises, ensuring their optimal performance is not just about avoiding downtime. It’s more about delivering unparalleled user experiences, fortifying security postures, and fostering a resilient and future-ready infrastructure. Here, we've delved into the core components of APM, read about what is application monitoring performance used for, and explored its benefits. Using APM tools, teams can proactively address issues, optimize performance, and align technology efforts with overarching business objectives. The benefits of APM extend far beyond the IT department, resonating throughout the entire organizational structure. It empowers decision-makers with actionable insights, allowing for informed choices that drive efficiency, cost-effectiveness, and user satisfaction. It transforms the way businesses perceive and manage their digital assets, instigating a culture of continuous improvement and adaptability.

Optimizing complex MySQL queries is crucial when dealing with large datasets, such as fetching data from a database containing one million records or more. Poorly optimized queries can lead to slow response times and increased load on the database server, negatively impacting user experience and system performance. This article explores strategies to optimize complex MySQL queries for efficient data retrieval from large datasets, ensuring quick and reliable access to information. Understanding the Challenge When executing a query on a large dataset, MySQL must sift through a vast number of records to find the relevant data. This process can be time-consuming and resource-intensive, especially if the query is complex or if the database design does not support efficient data retrieval. Optimization techniques can significantly reduce the query execution time, making the database more responsive and scalable. Indexing: The First Line of Defense Indexes are critical for improving query performance. They work by creating an internal structure that allows MySQL to quickly locate the data without scanning the entire table. Use Indexes Wisely: Create indexes on columns that are frequently used in WHERE clauses, JOIN conditions, or as part of an ORDER BY or GROUP BY. However, be judicious with indexing, as too many indexes can slow down write operations. Index Type Matters: Depending on the query and data characteristics, consider using different types of indexes, such as B-tree (default), Hash, FULLTEXT, or Spatial indexes. Optimizing Query Structure The way a query is structured can have a significant impact on its performance. Avoid SELECT: Instead of selecting all columns with `SELECT *,` specify only the columns you need. This reduces the amount of data MySQL has to process and transfer. Use JOINs Efficiently: Ensure that JOINs are done on indexed columns and that you're using the most efficient type of JOIN for your specific case, whether it be INNER JOIN, LEFT JOIN, etc. Subqueries vs. JOINs: Sometimes, rewriting subqueries as JOINs can improve performance, as MySQL might be able to optimize JOINs better in some scenarios. Leveraging MySQL Query Optimizations MySQL offers built-in optimizations that can be leveraged to improve query performance. Query Caching: While query caching is deprecated in MySQL 8.0, for earlier versions, it can significantly improve performance by storing the result set of a query in memory for quick retrieval on subsequent executions. Partitioning: For extremely large tables, partitioning can help by breaking down a table into smaller, more manageable pieces, allowing queries to search only a fraction of the data. Analyzing and Fine-Tuning Queries MySQL provides tools to analyze query performance, which can offer insights into potential optimizations. EXPLAIN Plan: Use the `EXPLAIN` statement to get a detailed breakdown of how MySQL executes your query. This can help identify bottlenecks, such as full table scans or inefficient JOIN operations. Optimize Data Types: Use appropriate data types for your columns. Smaller data types consume less disk space, memory, and CPU cycles. For example, use INT instead of BIGINT if the values do not exceed the INT range. Practical Example Consider a table `orders` with over one million records, and you need to fetch recent orders for a specific user. An unoptimized query might look like this: MySQL SELECT * FROM orders WHERE user_id = 12345 ORDER BY order_date DESC LIMIT 10; Optimization Steps 1. Add an Index: Ensure there are indexes on `user_id` and `order_date.` This allows MySQL to quickly locate orders for a specific user and sort them by date. MySQL CREATE INDEX idx_user_id ON orders(user_id); CREATE INDEX idx_order_date ON orders(order_date); 2. Optimize the SELECT Clause: Specify only the columns you need instead of using `SELECT *.` 3. Review JOINs and Subqueries: If your query involves JOINs or subqueries, ensure they are optimized based on the analysis provided by the `EXPLAIN` plan. Following these optimization steps can drastically reduce the execution time of your query, improving both the performance of your database and the experience of your users. Conclusion Optimizing complex MySQL queries for large datasets is an essential skill for developers and database administrators. By applying indexing, optimizing query structures, leveraging MySQL's built-in optimizations, and using analysis tools to fine-tune queries, significant performance improvements can be achieved. Regularly reviewing and optimizing your database queries ensures that your applications remain fast, efficient, and scalable, even as your dataset grows.

Java's automatic memory management is one of its most notable features, providing developers with the convenience of not having to manually manage memory allocation and deallocation. However, there may be cases where a developer wants to create a custom Java automatic memory management system to address specific requirements or constraints. In this guide, we will provide a granular step-by-step process for designing and implementing a custom Java automatic memory management system. Step 1: Understand Java's Memory Model Before creating a custom memory management system, it is crucial to understand Java's memory model, which consists of the heap and the stack. The heap stores objects, while the stack holds local variables and method call information. Your custom memory management system should be designed to work within this memory model. Step 2: Design a Custom Memory Allocator A custom memory allocator is responsible for reserving memory for new objects. When designing your memory allocator, consider the following: Allocation strategies: Choose between fixed-size blocks, variable-size blocks, or a combination of both. Memory alignment: Ensure that memory is correctly aligned based on the underlying hardware and JVM requirements. Fragmentation: Consider strategies to minimize fragmentation, such as allocating objects of similar sizes together or using a segregated free list. Step 3: Implement Reference Tracking To manage object lifecycles, you need a mechanism to track object references. You can implement reference tracking using reference counting or a tracing mechanism. In reference counting, each object maintains a counter of the number of references to it, whereas in tracing, the memory manager periodically scans the memory to identify live objects. Step 4: Choose a Garbage Collection Algorithm Select a garbage collection algorithm that suits your application's requirements. Some common algorithms include: Mark and Sweep: Marks live objects and then sweeps dead objects to reclaim memory. Mark and Compact: Similar to mark and sweep, but also compacts live objects to reduce fragmentation. Copying: Divides the heap into two areas and moves live objects from one area to the other, leaving behind a contiguous block of free memory. Step 5: Implement Root Object Identification Identify root objects that serve as the starting points for tracing live objects. Root objects typically include global variables, thread stacks, and other application-specific roots. Maintain a set of root objects for your custom memory management system. Step 6: Implement a Marking Algorithm Design and implement a marking algorithm that identifies live objects by traversing object references starting from the root objects. Common algorithms for marking include depth-first search (DFS) and breadth-first search (BFS). Step 7: Implement a Sweeping Algorithm Design and implement a sweeping algorithm that reclaims memory occupied by dead objects (those not marked as live). This can be done by iterating through the entire memory space and freeing unmarked objects or maintaining a list of dead objects during the marking phase and releasing them afterward. Step 8: Implement Compaction (Optional) If your memory model is prone to fragmentation, you may need to implement a compaction algorithm that defragments memory by moving live objects closer together and creating a contiguous block of free memory. Step 9: Integrate With Your Application Integrate your custom memory management system with your Java application by replacing the default memory management system and ensuring that object references are properly managed throughout the application code. Step 10: Monitor and Optimize Monitor the performance and behavior of your custom memory management system to identify any issues or areas for improvement. Fine-tune its parameters, such as heap size, allocation strategies, and collection frequency, to optimize its performance for your specific application requirements. Example Here's an example of a basic mark and sweep garbage collector in Java: Java import java.util.ArrayList; import java.util.List; class CustomObject { boolean marked = false; List<CustomObject> references = new ArrayList<>(); } class MemoryManager { List<CustomObject> heap = new ArrayList<>(); List<CustomObject> roots = new ArrayList<>(); CustomObject allocateObject() { CustomObject obj = new CustomObject(); heap.add(obj); return obj; } void addRoot(CustomObject obj) { roots.add(obj); } void removeRoot(CustomObject obj) { roots.remove(obj); } void mark(CustomObject obj) { if (!obj.marked) { obj.marked = true; for (CustomObject ref : obj.references) { mark(ref); } } } void sweep() { List<CustomObject> newHeap = new ArrayList<>(); for (CustomObject obj : heap) { if (obj.marked) { obj.marked = false; newHeap.add(obj); } } heap = newHeap; } void collectGarbage() { // Mark phase for (CustomObject root : roots) { mark(root); } // Sweep phase sweep(); } } Conclusion In conclusion, implementing a custom automatic memory management system in Java is a complex and advanced task that requires a deep understanding of the JVM internals. The provided example demonstrates a simplified mark and sweep garbage collector for a hypothetical language or runtime environment, which serves as a starting point for understanding the principles of garbage collection.

By Arun Pandey CORE

Amazon Web Services (AWS) offers a range of tools to help users manage their resources effectively, ensuring they are secure, well-performing, and cost-optimized. One such tool is AWS Trusted Advisor, an application that inspects your AWS environment and provides real-time recommendations in various categories, including cost optimization. While many AWS customers are familiar with the essential cost-saving tips Trusted Advisor provides, a wealth of more profound insights and advanced strategies can be leveraged for even more significant savings. This blog will explore some of these advanced tactics to help you maximize your AWS investment. Understanding AWS Trusted Advisor Before delving into the advanced cost optimization strategies, let’s quickly review what AWS Trusted Advisor does. It analyzes your AWS environment using a set of checks and provides recommendations to help you follow AWS best practices. What Trusted Advisor Offers Trusted Advisor offers recommendations across five categories: Cost optimization: Identifying underutilized resources and opportunities to reduce your spend. Performance: Suggestions to improve the speed and responsiveness of your applications. Security: Highlight potential security gaps and provide best practices for securing your AWS resources. Fault tolerance: Ensuring your application is resilient and has appropriate backup measures. Service limits: Check if you’re close to exceeding your service limits. Within these categories, the focus of this blog is to dive into cost optimization and explore how to go beyond the essential advice. Going Beyond Basic Cost-Saving Measures While Trusted Advisor provides straightforward advice, such as shutting down idle instances or deleting unattached EBS volumes, many other opportunities for cost optimization can be explored. Utilize Cost Allocation Tags Implementing and Managing Tags Cost allocation tags can transform how you track your AWS spend. By tagging resources, these tags allow you to assign costs to specific projects, departments, or environments. Once implemented, you can run detailed reports that provide insights into where your money is going, allowing for more targeted cost-saving strategies. Advanced Tagging Strategies Go beyond just tagging by environment or project. Implement more granular tags, such as cost centers, specific users, or types of usage (e.g., development, testing, production). This detailed tagging enables precise tracking and accountability, leading to more sophisticated budgeting and forecasting. Right-Sizing Resources Analyzing Usage Patterns Trusted Advisor will point out underutilized resources, but it's up to you to analyze usage patterns over time to determine the right size for your resources. Use AWS CloudWatch to track metrics and usage over extended periods to make informed decisions about sizing. Adopting Elasticity Consider implementing auto-scaling or using serverless architectures like AWS Lambda, where you only pay for what you utilize. These services can automatically adjust to your application’s needs, ensuring you are not settling for unused capacity. Reserved and Spot Instances Strategic Purchasing Purchasing Reserved Instances (RIs) or using Spot Instances for specific workloads can offer significant savings over on-demand pricing. However, the trick lies in identifying which workloads suit these purchasing options. For instance, workloads with predictable usage patterns are ideal candidates for RIs. Spot Instance Best Practices Spot Instances can be purchased at a significant discount, but they come with the risk of being outbid. Use them for stateless, fault-tolerant applications or workloads that can tolerate interruptions, such as batch processing jobs. Storage Optimization Cleaning up Redundant Data Regularly review and delete old snapshots and unused volumes. Trusted Advisors can point out unattached volumes, but only you can decide when a snapshot is no longer necessary. Intelligent Tiering Use S3 Intelligent Tiering for data with unknown or changing access patterns. It automatically moves your data to the most cost-effective access tier without performance impact or operational overhead. Use of AWS Budgets and Cost Explorer Budgets for Cost Control AWS Budgets can set custom cost and usage budgets that alert you when you're about to exceed your budgeted amount. This proactive measure can prevent unexpected costs. Deep Dive With Cost Explorer AWS Cost Explorer allows for a more detailed analysis of your spending patterns. You can visualize AWS spending and usage trends and pinpoint areas for potential savings. Leverage Automation for Cost Savings Automation Scripts and Tools Write automation scripts to start and stop instances, create and delete snapshots, and manage other resources based on usage patterns. Use AWS Lambda functions triggered by CloudWatch Events to automate these tasks. Infrastructure as Code (IaC) Use IaC tools such as AWS CloudFormation or Terraform to manage infrastructure, ensuring that only the required resources are provisioned and any unused resources are de-provisioned automatically. Continuous Optimization: A Cost-Saving Philosophy Embrace a Culture of Cost Awareness Foster an organizational culture where cost-efficiency is a priority. Encourage teams to monitor and optimize their use of AWS resources continuously. Regular Review of Trusted Advisor Recommendations Make it a practice to review and act upon Trusted Advisor recommendations regularly. Continuous improvement is critical to maintaining cost efficiency. FAQs How Does AWS Trusted Advisor Differ From AWS Cost Explorer? AWS Trusted Advisor and AWS Cost Explorer serve complementary functions in managing AWS costs. Trusted Advisor provides real-time guidance across various categories, including cost optimization, by offering specific recommendations on reducing costs and improving efficiency. It focuses on resource usage and service configurations to identify opportunities for savings. On the other hand, AWS Cost Explorer is a tool specifically designed for visualizing and analyzing your AWS spend. It allows you to view historical data, forecast future costs, and understand your cost drivers at a granular level. Cost Explorer gives you the data analysis capability to make informed decisions about your AWS spending. Is AWS Trusted Advisor Free, or Does It Come With Additional Costs? AWS Trusted Advisor offers a set of basic checks available to all AWS users at no extra charge. These include several cost optimization, best practices, and service limit checks. However, you need a subscription to AWS Business or Enterprise Support plans to access detailed checks and recommendations across cost optimization, security, fault tolerance, and performance. These plans provide a more comprehensive analysis and benefit larger or more complex AWS environments. Conclusion Cost optimization on AWS is an ongoing process, not a one-time setup. By leveraging the advanced strategies provided by AWS Trusted Advisor and complementing them with your own continuous review and optimization efforts, you can significantly reduce your AWS bill while maintaining high performance and reliability.

By Raghava Dittakavi CORE

Automating AWS Load Balancers is essential for managing cloud infrastructure efficiently. This article delves into the importance of automation using the AWS Load Balancer controller and Ingress template. Whether you're new or experienced, grasping these configurations is vital to streamlining Load Balancer settings on Amazon Web Services, ensuring a smoother and more effective setup. A high-level illustration of AWS Application Load Balancer with Kubernetes cluster A load balancer acts as clients' main point of contact, distributing incoming traffic across multiple targets, like EC2 instances, in various Availability Zones. This enhances application availability. Listeners, configured with protocols and ports, check for client connection requests. Rules set for each listener dictate how the load balancer routes requests to registered targets based on conditions. Prioritized rules include actions to be performed. A default rule is necessary for each listener, with the option to define additional rules for enhanced control. Ingress Template Ingress Templates are pivotal in AWS Load Balancer management, simplifying the configuration process for enhanced efficiency. These templates define rules that dictate how traffic is directed to services. They are vital for ensuring optimal resource utilization and maintaining security. With Ingress Templates, you can easily specify routing policies, manage backend services, and implement health checks. For example, you can create rules for directing traffic to specific products or AWS accounts. This section explores the necessity of Ingress Templates in AWS and provides sample rules, illustrating their importance in load balancer configuration. AWS Load Balancer Controller AWS Load Balancer Controller is a crucial component for managing Application Load Balancers (ALB) efficiently in the AWS environment. It acts as a bridge between Kubernetes clusters and AWS services, simplifying the deployment and management of ALBs directly through Kubernetes manifests. This controller is essential for automating load balancer configuration, ensuring seamless integration of Kubernetes workloads with AWS infrastructure. By using the AWS Load balancer Controller, users can enhance scalability, reduce manual intervention, and optimize the performance of applications running on Kubernetes clusters within the AWS ecosystem. Creating an Ingress Template Crafting an Ingress Template for AWS Load Balancers involves several key components to ensure effective configuration. Rules: Define routing rules specifying how traffic is directed based on paths or hosts. Backend Services: Specify backend services to handle the traffic, including service names and ports. Health Checks: Implement health checks to ensure the availability and reliability of backend services. We'll walk through each component, detailing their significance and providing examples to create a comprehensive Ingress Template for AWS Load Balancers. This step-by-step approach ensures a well-structured and functional configuration tailored to your specific application needs. YAML apiVersion: networking.k8s.io/v1 kind: Ingress metadata: name: sample-ingress annotations: kubernetes.io/ingress.class: "alb" alb.ingress.kubernetes.io/scheme: "internet-facing or internal" alb.ingress.kubernetes.io/certificate-arn: "arn:aws:acm:your-region:your-account-id:certificate/your-acm-cert-arn" spec: rules: - host: "*" http: paths: - path: /* pathType: Prefix backend: service: name: default-service port: number: 80 - path: /products pathType: Prefix backend: service: name: products-service port: number: 80 - path: /accounts pathType: Prefix backend: service: name: accounts-service port: number: 80 metadata: Specifies the name of the Ingress and includes annotations for AWS-specific settings. kubernetes.io/ingress.class: "alb": Specifies the Ingress class to be used, indicating that the AWS ALB Ingress Controller should manage the Ingress. alb.ingress.kubernetes.io/scheme: "internet-facing" or "internal": Determines whether the ALB should be internet-facing or internal.Options: "internet-facing": The ALB is accessible from the internet. "internal": The ALB is internal and not accessible from the internet alb.ingress.kubernetes.io/certificate-arn: "arn:aws:acm:your-region:your-account-id: certificate/your-acm-cert-arn": Specifies the ARN (Amazon Resource Name) of the ACM (AWS Certificate Manager) certificate to be associated with the ALB. spec.rules: Defines routing rules based on the host. The /* rule directs traffic to the default service, while /products and /accounts have specific rules for products and accounts services. pathType: Specifies the type of matching for the path. backend.service.name and backend. service.port: Specifies the backend services for each rule. AWS Load Balancer Controller AWS Load Balancer Controller is a controller to help manage Elastic Load Balancers for a Kubernetes cluster. It satisfies Kubernetes Ingress resources by provisioning Application Load Balancers. For more information about the AWS Load Balancer, refer to the AWS Load Balancer Controller. YAML apiVersion: apps/v1 kind: Deployment metadata: labels: app.kubernetes.io/component: controller app.kubernetes.io/name: aws-load-balancer-controller name: aws-load-balancer-controller namespace: alb-ingress spec: replicas: 1 selector: matchLabels: app.kubernetes.io/component: controller app.kubernetes.io/name: aws-load-balancer-controller template: metadata: labels: app.kubernetes.io/component: controller app.kubernetes.io/name: aws-load-balancer-controller spec: containers: - args: - --cluster-name=@@env: <<your EKS cluster name>> - --ingress-class=alb image: public.ecr.aws/eks/aws-load-balancer-controller:v2.5.2 livenessProbe: failureThreshold: 2 httpGet: path: /healthz port: 61779 scheme: HTTP initialDelaySeconds: 30 timeoutSeconds: 10 name: controller ports: - containerPort: 9443 name: webhook-server protocol: TCP resources: limits: cpu: 200m memory: 700Mi requests: cpu: 100m memory: 300Mi securityContext: allowPrivilegeEscalation: false readOnlyRootFilesystem: true runAsNonRoot: true volumeMounts: - mountPath: /tmp/k8s-webhook-server/serving-certs name: cert readOnly: true priorityClassName: system-cluster-critical securityContext: fsGroup: 1337 serviceAccountName: lineplanner-alb-ingress-controller terminationGracePeriodSeconds: 10 volumes: - name: cert secret: defaultMode: 420 secretName: aws-load-balancer-webhook-tls --- apiVersion: v1 kind: Service metadata: labels: app.kubernetes.io/name: aws-load-balancer-controller name: aws-load-balancer-webhook-service namespace: alb-ingress spec: ports: - port: 443 targetPort: 9443 selector: app.kubernetes.io/component: controller app.kubernetes.io/name: aws-load-balancer-controller Apply the AWS Load Balancer and Ingress template YAML files using the 'kubectl apply' command, as specified in the snippet below. Shell kubectl apply -f ingress-file.yaml kubectl apply -f aws-alb-controller.yaml Check the deployment status and monitor events to ensure successful configuration. Shell # To verify AWS Load Balancer controller deployment status kubectl get pods -n abl-ingress # To verify ingress deployment status kubectl get ingress kubectl describe ingress <<your-ingress-name>> Confirm the creation and configuration of the AWS Load Balancer through AWS Console or CLI. Shell aws elbv2 describe-load-balancers --names <<your-load-balancer-name>> Conclusion This article highlighted the pivotal role of automating AWS Load Balancers using AWS Controller and Ingress Templates. The seamless orchestration provided by AWS Controller streamlines configuration, promoting efficiency and scalability. Ingress Templates play a crucial role in defining rules, backend services, and health checks, simplifying load balancer management. The benefits include enhanced resource utilization, reliability, and a more straightforward deployment process. By leveraging these tools, users can optimize their AWS infrastructure, ensuring a robust and responsive application environment. Embrace automation for a future-ready, resilient cloud architecture that adapts to evolving business needs.

Are you looking at your organization's efforts to enter or expand into the cloud-native landscape and feeling a bit daunted by the vast expanse of information surrounding cloud-native observability? When you're moving so fast with Agile practices across your DevOps, SREs, and platform engineering teams, it's no wonder this can seem a bit confusing. Unfortunately, the choices being made have a great impact on both your business, your budgets, and the ultimate success of your cloud-native initiatives that hasty decisions upfront lead to big headaches very quickly down the road. In the previous introduction, we looked at the problem facing everyone with cloud-native observability. It was the first article in this series. In this article, you'll find the first pitfall discussion that's another common mistake organizations make. By sharing common pitfalls in this series, the hope is that we can learn from them. After laying the groundwork in the previous article, it's time to tackle the first pitfall, where we need to look at how to control the costs and the broken cost models we encounter with cloud-native observability. O11y Costs Broken One of the biggest topics of the last year has been how broken the cost models are for cloud-native observability. I previously wrote about why cloud-native observability needs phases, detailing how the second generation of observability tooling suffers from this broken model. "The second generation consisted of application performance monitoring (APM) with the infrastructure using virtual machines and later cloud platforms. These second-generation monitoring tools have been unable to keep up with the data volume and massive scale that cloud-native architectures..." They store all of our cloud-native observability data and charge for this, and as our business finds success, scaling data volumes means expensive observability tooling, degraded visualization performance, and slow data queries (rules, alerts, dashboards, etc.). Organizations would not care how much data is being stored or what it costs if they had better outcomes, happier customers, higher levels of availability, faster remediation of issues, and, above all, more revenue. Unfortunately, as pointed out on TheNewStack, "It's remarkable how common this situation is, where an organization is paying more for their observability data than they do for their production infrastructure." The issue quickly resolves itself around the answer to the question, "Do we need to store all our observability data?" The quick and dirty answer is, of course, not! There has been almost no incentive for any tooling vendors to provide insights into the data we are ingesting for what is actually being used and what is not. It turns out that when you do take a good look at the data coming in and are able to filter all your data at ingestion for what is not touched by any user, not ad-hoc queried, not part of any dashboard, not part of any rule, and not used for any alerts. It turns out to make quite a difference in data costs. In the example above, we designed a dashboard for a service status overview, initially while ingesting over 280K data points. With the ability to inspect and clarify that a lot of these data points were not used in the organization, the same ingestion flow was reduced to just 390 single data points being stored. The cost reduction here depends on your vendor pricing, but with the effect shown here, it's obviously going to be a dramatic cost control tool. It's important to understand that we need to ingest what we can collect, but we really only want to store what we are actually going to use for queries, rules, alerts, and visualizations. Below is an architectural view of how we are assisted by having control plane functionality and tooling between our data ingestion and data storage. Any data we are not storing can later be passed through to storage should a future project require it. Finally, without standards and ownership of the cost-controlling processes in an organization, there is little hope of controlling costs. To this end, the FinOps role has become critical to many organizations, and the entire field started a community in 2019 known as the FinOps Foundation. It's very important that cloud-native observability vendors join these efforts moving forward, and this should be a point of interest when evaluating new tooling. Today, 90% of the Fortune 50 now have FinOps teams. The road to cloud-native success has many pitfalls, and understanding how to avoid the pillars and focusing instead on solutions for the phases of observability will save much wasted time and energy. Coming Up Next Another pitfall is when organizations focus on The Pillars in their observability solutions. In the next article in this series, I'll share why this is a pitfall and how we can avoid it wreaking havoc on our cloud-native observability efforts.

By Eric D. Schabell CORE

Cloud computing has transformed the way businesses operate. It has allowed organizations to quickly scale up or down, improving agility and reducing costs. However, while the cloud offers many benefits, it can also be easy to overspend on cloud resources if not managed properly. In the world of cloud computing, Amazon Web Services (AWS) has become a leading provider of infrastructure-as-a-service (IaaS) solutions. With AWS, businesses can leverage scalable, flexible, and cost-effective computing resources to support their operations. However, as the complexity and scale of AWS deployments grow, so do the associated costs. To manage these costs, AWS cost optimization has become a critical aspect of AWS management. In this blog, we will discuss how to maximize your cloud investments through AWS cost optimization services. Importance of Cost Optimization in AWS Businesses may face difficulties managing their AWS costs as they deploy and scale their AWS workloads. Therefore, cost optimization is essential for businesses that want to avoid overspending and maximize their ROI. The cloud computing model allows businesses to pay only for the resources and services they use. However, if not managed correctly, this can result in unnecessary or inefficient resource usage, leading to unnecessary costs. Businesses can reduce cloud costs and increase return on investment through effective cost optimization. A few reasons why cost optimization in AWS is essential are: Cost reduction: This can be achieved by monitoring and optimizing usage patterns, choosing the right pricing models, and selecting the appropriate instance types. Resource optimization: By identifying unused or underutilized resources, organizations can reduce their AWS spending and ensure that resources are used optimally. Better budgeting and forecasting: By monitoring and analyzing their AWS usage data, companies can better budget and forecast their AWS spending. This allows them to plan for future growth and avoid unexpected bills. Improved performance: Businesses can ensure that their applications run successfully and efficiently by choosing the right instance types and optimizing resource usage. Businesses can reinvest their savings from cost optimization in other aspects of their operations, such as innovation, R&D, and employee training. By leveraging AWS cost optimization tools and strategies, businesses can optimize their cloud computing usage, achieve increased efficiency, and remain competitive in today’s fast-paced digital marketplace. Overview of AWS Cost Optimization Tools AWS Cost Explorer AWS Cost Explorer is a web-based tool that provides insights into your AWS costs. It allows you to view and analyze your AWS usage and costs over time and provides recommendations for cost optimization. AWS Trusted Advisor Trusted Advisor provides automated suggestions to improve your AWS setup in several areas, including cost reduction, security and performance. AWS Budgets Budgets is a tool that allows you to set custom cost and usage budgets for your AWS resources. It sends alerts when you exceed your budget, helping you to avoid unexpected costs. AWS Cost Anomaly Detection This tool uses machine learning algorithms to detect anomalies in your AWS usage and spending. It alerts you when it identifies unusual spending patterns, enabling you to investigate and take corrective action. AWS Compute Optimizer AWS Compute Optimizer analyzes your EC2 instances and recommends optimal instance types and sizes based on your usage patterns. This helps you reduce costs by ensuring that you are only using the resources you need. Amazon S3 Amazon S3 provides features like transfer acceleration and direct connect. This can help optimize data transfer costs by reducing the amount of data that needs to be transferred over the internet. AWS Reserved Instance (RI) Reports RI Reports optimize RI usage by providing usage insights and cost reduction opportunities. AWS Savings Plans Commit to usage level for discounts with Savings Plans. This also allows you to significantly reduce costs compared to on-demand pricing. AWS CloudWatch Monitor AWS resources and applications in real-time as well as streamline your infrastructure and application maintenance with AWS CloudWatch. Leverage these tools and implement cost optimization best practices. This will reduce your AWS expenses and improve the efficiency of your AWS environment. Best Practices for AWS Cost Optimization With the right optimization strategies in place, businesses can analyze and adjust their AWS usage to match their actual needs. This allows companies to reduce wasted capacity and select the most cost-effective pricing models and options for their workloads. Use the Right-Sized Resources Use resources that are appropriately sized for the workload. Oversized resources can lead to unnecessary costs, while undersized resources can lead to performance issues. Regularly monitor usage and adjust resources accordingly. Use Spot Instances Spot Instances are a cost-effective option for non-critical workloads. They provide compute capacity at significantly lower costs compared to On-Demand Instances. However, you must be able to handle interruptions, as these instances can be reclaimed by AWS. Use Reserved Instances (RIs) and Savings Plans RI and Savings Plans offer significant discounts on compute resources. Analyze your workload patterns to identify which instances are likely to be used for an extended period and commit to RI or Savings Plan. Use Auto Scaling Automatically add or remove instances based on demand. This ensures that you only pay for resources when they are needed. It can help you reduce costs by scaling down when demand is low. Use AWS Cost Management Tools AWS provides a range of cost management tools, including Cost Explorer, Trusted Advisor, and Budgets. These tools help you monitor and optimize your AWS usage and costs. Use a Serverless Architecture Serverless architectures allow you to pay only for the resources you consume and scale automatically based on demand. This helps reduce costs by eliminating the need to manage and pay for idle resources. Use CloudFront and CDN Use Amazon CloudFront and CDN to reduce bandwidth usage and increase the performance of your application. These services can help reduce your data transfer costs and increase the speed of your application. Use AWS Cost Allocation Tags Use AWS Cost Allocation Tags to categorize your resources based on purpose, owner, or environment. This will help you identify cost drivers, optimize resource usage, and allocate costs effectively. Things To Consider for Better Cost Optimization Analyze resource usage patterns and adjust capacity to match actual needs, which can help reduce costs and improve performance. Choose the most cost-effective pricing model based on workload needs and usage patterns. AWS resources can automatically adjust capacity in response to changes in demand. This helps organizations scale resources up or down as needed without over-provisioning or underutilizing capacity. Choose the best storage options according to data usage patterns and access needs. Review storage usage regularly to remove unnecessary or redundant data. Establish processes and tools for monitoring and controlling costs, setting budgets, and cost optimization goals. Select the most cost-effective pricing models as well as options for different resources and services. Leverage the Benefits of AWS Cost Optimization AWS cost optimization aims to reduce unnecessary costs. At the same time, it enables businesses to get the most out of their computing resources. The components of this service include: Cost-effective resources Matching supply and demand Optimizing over time Cost-aware architecture Managing expenditure AWS cost optimization is essential for businesses that want to reduce their AWS expenses without sacrificing performance, security, or reliability. Businesses can optimize their AWS usage by following best practices and leveraging AWS cost optimization tools and services. This will improve their return on investment and free up resources to invest in other areas of their operations. As a leading AWS Cloud consulting services provider, our team of AWS-certified experts, including certified solution architects, certified cloud practitioners, and certified developers, is dedicated to helping you achieve optimal AWS cost efficiency. Following industry best practices, we offer top-notch AWS solutions with a primary focus on cost optimization. Our experienced AWS consultants are committed to continually monitoring your AWS usage and making necessary adjustments to ensure ongoing cost optimization. We adopt a comprehensive approach to AWS cloud cost optimization, which includes analyzing current usage, identifying areas for optimization, implementing cost optimization strategies, and continuously monitoring and refining our approach over time. Contact our AWS experts to get a better understanding of how you can leverage AWS cost optimization services.

Monitoring application and website performance has become critical to delivering a smooth digital experience to users. With users' attention spans dwindling at an ever-increasing rate, even minor hiccups in performance can cause users to abandon an app or website. This directly impacts key business metrics like customer conversions, engagement, and revenue. To proactively identify and fix performance problems, modern DevOps teams rely heavily on monitoring solutions. Two of the most common techniques for monitoring website and application performance are Real User Monitoring (RUM) and Synthetic Monitoring. RUM focuses on gathering data from actual interactions, while Synthetic Monitoring simulates user journeys for testing. This article provides an in-depth exploration of RUM and Synthetic Monitoring, including: How each methodology works The advantages and use cases Key differences between the two approaches When to use each technique How RUM and Synthetic Monitoring can work together The Growing Importance of Performance Monitoring Digital experiences have become the key customer touchpoints for most businesses today. Whether it is a mobile app, web application, or marketing website — the quality of the user experience directly impacts success. However, with the growing complexity of modern web architectures, performance problems can easily slip in. Issues may arise from the app code, web server, network, APIs, databases, CDNs, and countless other sources. Without comprehensive monitoring, these problems remain invisible. Performance issues severely impact both customer experiences and business outcomes: High latency leads to sluggish response times, hurting engagement Error spikes break journeys, increase abandonment Crashes or downtime block customers entirely To avoid losing customers and revenue, DevOps teams are prioritizing user-centric performance monitoring across both production systems and lower environments. Approaches like Real User Monitoring and Synthetic Monitoring help uncover the real impact of performance on customers. Real User Monitoring: Monitoring Actual User Experiences Real User Monitoring (RUM) tracks the experiences of real-world users as they interact with a web or mobile application. It helps understand exactly how an app is performing for end users in the real world. Source Key Benefits of Real User Monitoring Accurate Real-World Insights Visualize real user flows, behavior, and activity on the live site Segment visitors by location, browser, device type, etc. Analyze peak site usage periods and models. RUM data reflects the true uncontrolled diversity of real user environments - the long tail beyond synthetic testing. Uncovering UX Issues and Friction Pinpoint usability struggles leading to confusion among users Identify confusing page layouts or site navigability issues Optimize UX flows demonstrating excessive abandonment Improve form completion and conversion funnel success Human insights expose true experience barriers and friction points. User Behavior Analytics Source Which site areas attract the most user attention and which the least? Diagnose ineffective page layouts driving away visitors Analyze visitor attributes for key personas and audience targeting Identify navigability barriers confusing users Analytics empower understanding your audience and keeping them engaged. Production Performance Monitoring Waterfall analysis of page load times and request metrics JavaScript error rates and front-end performance Endpoint response times and backend throughput Infrastructure capacity and memory utilization RUM provides DevOps teams with visibility into how an application performs for genuine users across diverse environments and scenarios. However, RUM data can vary substantially depending on the user's device, browser, location, network, etc. It also relies on having enough real user sessions across various scenarios. Synthetic Monitoring: Simulating User Journeys Synthetic Monitoring provides an alternative approach to performance monitoring. Rather than passively gathering data from real users, it actively simulates scripted user journeys across the application. Source These scripts replicate critical business scenarios - such as user login, adding items to the cart, and checkout. Synthetic agents situated across the globe then crawl the application to mimic users executing these journeys. Detailed performance metrics are gathered for each step without needing real user traffic. Key Benefits of Synthetic Monitoring Proactive Issue Detection Identify performance regressions across code updates Find problems impacted by infrastructure changes Validate fixes and ensure resolutions stick Establish proactive alerts against issues Continuous synthetic tests enable uncovering issues before users notice. 24/7 Testing Under Controlled Conditions Accurately test continuous integration/deployment pipelines Map performance across geography, network, and environments Scale tests across browsers, devices, and scenarios Support extensive regression testing suites Synthetic scripts test sites around the clock across the software delivery lifecycle. Flexible and Extensive Coverage Codify an extensive breadth of critical user journeys Stretch test edge cases and diverse environments Dynamically adjust test types, frequencies, and sampling Shift testing to lower environments to expand coverage Scripting enables testing flexibility beyond normal usage. Performance Benchmarking and Alerting Establish dynamic performance baselines Continuously validate performance SLAs Trigger alerts on user journey failures or regressions Enforce standards around availability, latency, and reliability Proactive monitoring enables meeting critical performance SLAs. By controlling variables like device profiles, browsers, geo locations, and network conditions, synthetic monitoring can test scenarios that may be infrequent from real users. However, synthetic data is still an approximation of the real user experience. Key Differences Between RUM and Synthetic Monitoring While RUM and synthetic monitoring have some superficial similarities in tracking website performance, they have fundamental differences: Category Real User Monitoring (RUM) Synthetic Monitoring Data Source Real user traffic and interactions Simulated scripts that mimic user flows User Environments Diverse and unpredictable Various devices, browsers, locations, networks Customizable and controlled Consistent browser, geography, network Frequency Continuous, passive data collectionAs real user accesses the application Active test executionsScheduled crawling of user journeys Precision vs Accuracy Accurately reflects unpredictable real user experiences Precise and consistent measurementsControlled test conditions Use Cases Understand user behavior, satisfactionOptimize user experience Technical performance measurementJourney benchmarking, alerting Issue Reproduction Analyze issues currently impacting real users Proactively detect potential issues before impacting users Test Coverage Covers real user flows actually executed Flexibly test a breadth of scenarios beyond real user coverage Analytics Conversion rates, user flows, satisfaction scores Waterfall analysis, performance KPI tracking In a nutshell: RUM provides real user perspectives but with variability across environments Synthetic monitoring offers controlled consistency but is still an estimate of user experience When Should You Use RUM vs. Synthetic Monitoring? RUM and synthetic monitoring are actually complementary approaches, each suited for specific use cases: Use Cases for Real User Monitoring Gaining visibility into real-world analytics and behavior Monitoring live production website performance Analyzing user satisfaction and conversion funnels Debugging performance issues experienced by users Generating aggregated performance metrics across visits Use Cases for Synthetic Monitoring Continuous testing across user scenarios Benchmarking website speed from multiple geographic regions Proactively testing staging/production changes without real users Validating performance SLAs are met for critical user journeys Alerting immediately if user flows fail or regress Using RUM and Synthetic Monitoring Together While Real User Monitoring (RUM) and Synthetic Monitoring take different approaches, they provide complementary visibility into application performance. RUM passively gathers metrics on real user experiences. Synthetic proactively simulates journeys through scripted crawling. Using both together gives development teams the most accurate and comprehensive monitoring data. Some key examples of synergistically leveraging both RUM and synthetic monitoring: Synergy Tactic Real User Monitoring ( RUM) Synthetic Monitoring Outcomes Validating Synthetic Scripts Against RUM Analyze real website traffic - top pages, flows, usage models Configure synthetic scripts that closely reflect observed real-user behavior Replay synthetic tests across sites pre-production to validate performance Ensures synthetic tests, environments, and workloads mirror reality Detecting Gaps Between RUM and Synthetic Establish overall RUM performance benchmarks for key web pages Compare synthetic performance metrics versus RUM standards Tune synthetic tests targeting pages or flows exceeding RUM baselines Comparing RUM and synthetic reveals gaps in test coverage or environment configurations Setting SLAs and Alert Thresholds Establish baseline thresholds for user experience metrics using RUM Define synthetic performance SLAs for priority user journeys Trigger alerts on synthetic SLA violations to prevent regressions SLAs based on real user data help maintain standards as changes roll out Reproducing RUM Issues via Synthetic Pinpoint problematic user flows using RUM session diagnostics Construct matching synthetic journeys for affected paths Iterate test tweaks locally until issues are resolved Synthetic tests can reproduce issues without impacting real users Proactive Blind Spot Identification Analyze RUM data to find rarely exercised app functionality Build focused synthetic scripts testing edge cases Shift expanded testing to lower environments <br> Address defects before reaching real users Targeted synthetic tests expand coverage beyond real user visibility RUM Data Enhances Synthetic Alerting Enrich synthetic alerts with corresponding RUM metrics Add details on real user impact to synthetic notifications Improve context for triaging and prioritizing synthetic failures RUM insights help optimize synthetic alert accuracy Conclusion Real User Monitoring (RUM) and Synthetic Monitoring provide invaluable yet complementary approaches for monitoring website and application performance. RUM provides accuracy by gathering metrics on actual user sessions, exposing real points of friction. Synthetic provides consistency, testing sites around the clock via scripts that simulate user journeys at scale across locations and environments. While RUM reveals issues currently impacting real users, synthetic enables proactively finding potential problems through extensive testing. Using both together gives organizations the best of both worlds — accurately reflecting the real voices of users while also comprehensively safeguarding performance standards. RUM informs on UX inefficiencies and conversion barriers directly from user perspectives, while synthetic flexibly tests at breadth and scale beyond normal traffic levels. For preventative and end-to-end visibility across the technology delivery chain, leveraging both real user data and synthetic crawling provides the most robust web performance monitoring solution. RUM and synthetic testing offer indispensable and synergistic visibility for engineering teams striving to deliver seamless digital experiences.

Serverless architecture is a way of building and running applications without the need to manage infrastructure. You write your code, and the cloud provider handles the rest - provisioning, scaling, and maintenance. AWS offers various serverless services, with AWS Lambda being one of the most prominent. When we talk about "serverless," it doesn't mean servers are absent. Instead, the responsibility of server maintenance shifts from the user to the provider. This shift brings forth several benefits: Cost-efficiency: With serverless, you only pay for what you use. There's no idle capacity because billing is based on the actual amount of resources consumed by an application. Scalability: Serverless services automatically scale with the application's needs. As the number of requests for an application increases or decreases, the service seamlessly adjusts. Reduced operational overhead: Developers can focus purely on writing code and pushing updates, rather than worrying about server upkeep. Faster time to market: Without the need to manage infrastructure, development cycles are shorter, enabling more rapid deployment and iteration. Importance of Resiliency in Serverless Architecture As heavenly as serverless sounds, it isn't immune to failures. Resiliency is the ability of a system to handle and recover from faults, and it's vital in a serverless environment for a few reasons: Statelessness: Serverless functions are stateless, meaning they do not retain any data between executions. While this aids in scalability, it also means that any failure in the function or a backend service it depends on can lead to data inconsistencies or loss if not properly handled. Third-party services: Serverless architectures often rely on a variety of third-party services. If any of these services experience issues, your application could suffer unless it's designed to cope with such eventualities. Complex orchestration: A serverless application may involve complex interactions between different services. Coordinating these reliably requires a robust approach to error handling and fallback mechanisms. Resiliency is, therefore, not just desirable, but essential. It ensures that your serverless application remains reliable and user-friendly, even when parts of the system go awry. In the subsequent sections, we will examine the circuit breaker pattern, a design pattern that enhances fault tolerance and resilience in distributed systems like those built on AWS serverless technologies. Understanding the Circuit Breaker Pattern Imagine a bustling city where traffic flows smoothly until an accident occurs. In response, traffic lights adapt to reroute cars, preventing a total gridlock. Similarly, in software development, we have the circuit breaker pattern—a mechanism designed to prevent system-wide failures. Its primary purpose is to detect failures and stop the flow of requests to the faulty part, much like a traffic light halts cars to avoid congestion. When a particular service or operation fails to perform correctly, the circuit breaker trips and future calls to that service are blocked or redirected. This pattern is essential because it allows for graceful degradation of functionality rather than complete system failure. It’s akin to having an emergency plan: when things go awry, the pattern ensures that the rest of the application can continue to operate. It provides a recovery period for the failed service, wherein no additional strain is added, allowing for potential self-recovery or giving developers time to address the issue. Relationship Between the Circuit Breaker Pattern and Fault Tolerance in Distributed Systems In the interconnected world of distributed systems where services rely on each other, fault tolerance is the cornerstone of reliability. The circuit breaker pattern plays a pivotal role in this by ensuring that a fault in one service doesn't cascade to others. It's the buffer that absorbs the shock of a failing component. By monitoring the number of recent failures, the pattern decides when to open the "circuit," thus preventing further damage and maintaining system stability. The concept is simple yet powerful: when the failure threshold is reached, the circuit trips, stopping the flow of requests to the troubled service. Subsequent requests are either returned with a pre-defined fallback response or are queued until the service is deemed healthy again. This approach not only protects the system from spiraling into a state of unresponsiveness but also shields users from experiencing repeated errors. Relevance of the Circuit Breaker Pattern in Microservices Architecture Microservices architecture is like a complex ecosystem with numerous species—numerous services interacting with one another. Just as an ecosystem relies on balance to thrive, so does a microservices architecture depend on the resilience of individual services. The circuit breaker pattern is particularly relevant in such environments because it provides the necessary checks and balances to ensure this balance is maintained. Given that microservices are often designed to be loosely coupled and independently deployable, the failure of a single service shouldn’t bring down the entire system. The circuit breaker pattern empowers services to handle failures gracefully, either by retrying operations, redirecting traffic, or providing fallback solutions. This not only improves the user experience during partial outages but also gives developers the confidence to iterate quickly, knowing there's a safety mechanism in place to handle unexpected issues. In modern applications where uptime and user satisfaction are paramount, implementing the circuit breaker pattern can mean the difference between a minor hiccup and a full-blown service interruption. By recognizing its vital role in maintaining the health of a microservices ecosystem, developers can craft more robust and resilient applications that can withstand the inevitable challenges that come with distributed computing. Leveraging AWS Lambda for Resilient Serverless Microservices When we talk about serverless computing, AWS Lambda often stands front and center. But what is AWS Lambda exactly, and why is it such a game-changer for building microservices? In essence, AWS Lambda is a service that lets you run code without provisioning or managing servers. You simply upload your code, and Lambda takes care of everything required to run and scale your code with high availability. It's a powerful tool in the serverless architecture toolbox because it abstracts away the infrastructure management so developers can focus on writing code. Now, let's look at how the circuit breaker pattern fits into this picture. The circuit breaker pattern is all about preventing system overloads and cascading failures. When integrated with AWS Lambda, it monitors the calls to external services and dependencies. If these calls fail repeatedly, the circuit breaker trips and further attempts are temporarily blocked. Subsequent calls may be routed to a fallback mechanism, ensuring the system remains responsive even when a part of it is struggling. For instance, if a Lambda function relies on an external API that becomes unresponsive, applying the circuit breaker pattern can help prevent this single point of failure from affecting the entire system. Best Practices for Utilizing AWS Lambda in Conjunction With the Circuit Breaker Pattern To maximize the benefits of using AWS Lambda with the circuit breaker pattern, consider these best practices: Monitoring and logging: Use Amazon CloudWatch to monitor Lambda function metrics and logs to detect anomalies early. Knowing when your functions are close to tripping a circuit breaker can alert you to potential issues before they escalate. Timeouts and retry logic: Implement timeouts for your Lambda functions, especially when calling external services. In conjunction with retry logic, timeouts can ensure that your system doesn't hang indefinitely, waiting for a response that might never come. Graceful fallbacks: Design your Lambda functions to have fallback logic in case the primary service is unavailable. This could mean serving cached data or a simplified version of your service, allowing your application to remain functional, albeit with reduced capabilities. Decoupling services: Use services like Amazon Simple Queue Service (SQS) or Amazon Simple Notification Service (SNS) to decouple components. This approach helps in maintaining system responsiveness, even when one component fails. Regular testing: Regularly test your circuit breakers by simulating failures. This ensures they work as expected during real outages and helps you refine your incident response strategies. By integrating the circuit breaker pattern into AWS Lambda functions, you create a robust barrier against failures that could otherwise ripple across your serverless microservices. The synergy between AWS Lambda and the circuit breaker pattern lies in their shared goal: to offer a resilient, highly available service that focuses on delivering functionality, irrespective of the inevitable hiccups that occur in distributed systems. While AWS Lambda relieves you from the operational overhead of managing servers, implementing patterns like the circuit breaker is crucial to ensure that this convenience does not come at the cost of reliability. By following these best practices, you can confidently use AWS Lambda to build serverless microservices that aren't just efficient and scalable but also resilient to the unexpected. Implementing the Circuit Breaker Pattern With AWS Step Functions AWS Step Functions provides a way to arrange and coordinate the components of your serverless applications. With AWS Step Functions, you can define workflows as state machines, which can include sequential steps, branching logic, parallel tasks, and even human intervention steps. This service ensures that each function knows its cue and performs at the right moment, contributing to a seamless performance. Now, let's introduce the circuit breaker pattern into this choreography. When a step in your workflow hits a snag, like an API timeout or resource constraint, the circuit breaker steps in. By integrating the circuit breaker pattern into AWS Step Functions, you can specify conditions under which to "trip" the circuit. This prevents further strain on the system and enables it to recover, or redirect the flow to alternative logic that handles the issue. It's much like a dance partner who gracefully improvises a move when the original routine can't be executed due to unforeseen circumstances. To implement this pattern within AWS Step Functions, you can utilize features like Catch and Retry policies in your state machine definitions. These allow you to define error handling behavior for specific errors or provide a backoff rate to avoid overwhelming the system. Additionally, you can set up a fallback state that acts when the circuit is tripped, ensuring that your application remains responsive and reliable. The benefits of using AWS Step Functions to implement the circuit breaker pattern are manifold. First and foremost, it enhances the robustness of your serverless application by preventing failures from escalating. Instead of allowing a single point of failure to cause a domino effect, the circuit breaker isolates issues, giving you time to address them without impacting the entire system. Another advantage is the reduction in cost and improved efficiency. AWS Step Functions allows you to pay per transition of your state machine, which means that by avoiding unnecessary retries and reducing load during outages, you're not just saving your system but also your wallet. Last but not least, the clarity and maintainability of your serverless workflows improve. By defining clear rules and fallbacks, your team can instantly understand the flow and know where to look when something goes awry. This makes debugging faster and enhances the overall development experience. Incorporating the circuit breaker pattern into AWS Step Functions is more than just a technical implementation; it's about creating a choreography where every step is accounted for, and every misstep has a recovery routine. It ensures that your serverless architecture performs gracefully under pressure, maintaining the reliability that users expect and that businesses depend on. Conclusion The landscape of serverless architecture is dynamic and ever-evolving. This article has provided a foundational understanding. In our journey through the intricacies of serverless microservices architecture on AWS, we've encountered a powerful ally in the circuit breaker pattern. This mechanism is crucial for enhancing system resiliency and ensuring that our serverless applications can withstand the unpredictable nature of distributed environments. We began by navigating the concept of serverless architecture on AWS and its myriad benefits, including scalability, cost-efficiency, and operational management simplification. We understood that despite its many advantages, resiliency remains a critical aspect that requires attention. Recognizing this, we explored the circuit breaker pattern, which serves as a safeguard against failures and an enhancer of fault tolerance within our distributed systems. Especially within a microservices architecture, it acts as a sentinel, monitoring for faults and preventing cascading failures. Our exploration took us deeper into the practicalities of implementation with AWS Step Functions and how they orchestrate serverless workflows with finesse. Integrating the circuit breaker pattern within these functions allows error handling to be more robust and reactive. With AWS Lambda, we saw another layer of reliability added to our serverless microservices, where the circuit breaker pattern can be cleverly applied to manage exceptions and maintain service continuity. Investing time and effort into making our serverless applications reliable isn't just about avoiding downtime; it's about building trust with our users and saving costs in the long run. Applications that can gracefully handle issues and maintain operations under duress are the ones that stand out in today's competitive market. By prioritizing reliability through patterns like the circuit breaker, we not only mitigate the impact of individual component failures but also enhance the overall user experience and maintain business continuity. In conclusion, the power of the circuit breaker pattern in a serverless environment cannot be overstated. It is a testament to the idea that with the right strategies in place, even the most seemingly insurmountable challenges can be transformed into opportunities for growth and innovation. As architects, developers, and innovators, our task is to harness these patterns and principles to build resilient, responsive, and reliable serverless systems that can take our applications to new heights.

By Satrajit Basu CORE