Performance

This is an article from DZone's 2023 Observability and Application Performance Trend Report.For more: Read the Report AIOps applies AI to IT operations, enabling agility, early issue detection, and proactive resolution to maintain service quality. AIOps integrates DataOps and MLOps, enhancing efficiency, collaboration, and transparency. It aligns with DevOps for application lifecycle management and automation, optimizing decisions throughout DataOps, MLOps, and DevOps. Observability for IT operations is a transformative approach that provides real-time insights, proactive issue detection, and comprehensive performance analysis, ensuring the reliability and availability of modern IT systems. Why AIOps Is Fundamental to Modern IT Operations AIOps streamlines operations by automating problem detection and resolution, leading to increased IT staff efficiency, outage prevention, improved user experiences, and optimized utilization of cloud technologies. The major contributions of AIOps are shared in Table 1: CONTRIBUTIONS OF AIOPS Key Functions Function Explanations Event correlation Uses rules and logic to filter and group event data, prioritizing service issues based on KPIs and business metrics. Anomaly detection Identifies normal and abnormal behavior patterns, monitoring multiple services to predict and mitigate potential issues. Automated incident management Aims to automate all standardized, high-volume, error-sensitive, audit-critical, repetitive, multi-person, and time-sensitive tasks. Meanwhile, it preserves human involvement in low ROI and customer support-related activities. Performance optimization Analyzes large datasets employing AI and ML, proactively ensuring service levels and identifying issue root causes. Enhanced collaboration Fosters collaboration between IT teams, such as DevOps, by providing a unified platform for monitoring, analysis, and incident response. Table 1 How Does AIOps Work? AIOps involves the collection and analysis of vast volumes of data generated within IT environments, such as network performance metrics, application logs, and system alerts. AIOps uses these insights to detect patterns and anomalies, providing early warnings for potential issues. By integrating with other DevOps practices, such as DataOps and MLOps, it streamlines processes, enhances efficiency, and ensures a proactive approach to problem resolution. AIOps is a crucial tool for modern IT operations, offering the agility and intelligence required to maintain service quality in complex and dynamic digital environments. Figure 1: How AIOps works Popular AIOps Platforms and Key Features Leading AIOps platforms are revolutionizing IT operations by seamlessly combining AI and observability, enhancing system reliability, and optimizing performance across diverse industries. The following tools are just a few of many options: Prometheus acts as an efficient AIOps platform by capturing time-series data, monitoring IT environments, and providing anomaly alerts. OpenNMS automatically discovers, maps, and monitors complex IT environments, including networks, applications, and systems. Shinken enables users to monitor and troubleshoot complex IT environments, including networks and applications. The key features of the platforms and the role they play in AIOps are shared in Table 2: KEY FEATURES OF AIOPS PLATFORMS AND THE CORRESPONDING TASKS Features Tasks Visibility Provides insight into the entire IT environment, allowing for comprehensive monitoring and analysis. Monitoring and management Monitors the performance of IT systems and manages alerts and incidents. Performance Measures and analyzes system performance metrics to ensure optimal operation. Functionality Ensures that the AIOps platform offers a range of functionalities to meet various IT needs. Issue resolution Utilizes AI-driven insights to address and resolve IT issues more effectively. Analysis Analyzes data and events to identify patterns, anomalies, and trends, aiding in proactive decision-making. Table 2 Observability's Role in IT Operations Observability plays a pivotal role in IT operations by offering the means to monitor, analyze, and understand the intricacies of complex IT systems. It enables continuous tracking of system performance, early issue detection, and root cause analysis. Observability data empowers IT teams to optimize performance, allocate resources efficiently, and ensure a reliable user experience. It supports proactive incident management, compliance monitoring, and data-driven decision-making. In a collaborative DevOps environment, observability fosters transparency and enables teams to work cohesively toward system reliability and efficiency. Data sources like logs, metrics, and traces play a crucial role in observability by providing diverse and comprehensive insights into the behavior and performance of IT systems. ROLES OF DATA SOURCES Logs Metrics Traces Event tracking Root cause analysis Anomaly detection Compliance and auditing Performance monitoring Threshold alerts Capacity planning End-to-end visibility Latency analysis Dependency mapping Table 3 Challenges of Observability Observability is fraught with multiple technical challenges. Accidental invisibility takes place where critical system components or behaviors are not being monitored, leading to blind spots in observability. The challenge of insufficient source data can result in incomplete or inadequate observability, limiting the ability to gain insights into system performance. Dealing with multiple information formats poses difficulties in aggregating and analyzing data from various sources, making it harder to maintain a unified view of the system. Popular Observability Platforms and Key Features Observability platforms offer a set of key capabilities essential for monitoring, analyzing, and optimizing complex IT systems. OpenObserve provides scheduled and real-time alerts and reduces operational costs. Vector allows users to collect and transform logs, metrics, and traces. The Elastic Stack — comprising Elasticsearch, Kibana, Beats, and Logstash — can search, analyze, and visualize data in real time. The capabilities of observability platforms include real-time data collection from various sources such as logs, metrics, and traces, providing a comprehensive view of system behavior. They enable proactive issue detection, incident management, root cause analysis, system reliability aid, and performance optimization. Observability platforms often incorporate machine learning for anomaly detection and predictive analysis. They offer customizable dashboards and reporting for in-depth insights and data-driven decision-making. These platforms foster collaboration among IT teams by providing a unified space for developers and operations to work together, fostering a culture of transparency and accountability. Leveraging AIOps and Observability for Enhanced Performance Analytics Synergizing AIOps and observability represents a cutting-edge strategy to elevate performance analytics in IT operations, enabling data-driven insights, proactive issue resolution, and optimized system performance. Observability Use Cases Best Supported by AIOps Elevating cloud-native and hybrid cloud observability with AIOps: AIOps transcends the boundaries between cloud-native and hybrid cloud environments, offering comprehensive monitoring, anomaly detection, and seamless incident automation. It adapts to the dynamic nature of cloud-native systems while optimizing on-premises and hybrid cloud operations. This duality makes AIOps a versatile tool for modern enterprises, ensuring a consistent and data-driven approach to observability, regardless of the infrastructure's intricacies. Seamless collaboration of dev and ops teams with AIOps: AIOps facilitates the convergence of dev and ops teams in observability efforts. By offering a unified space for data analysis, real-time monitoring, and incident management, AIOps fosters transparency and collaboration. It enables dev and ops teams to work cohesively, ensuring the reliability and performance of IT systems. Challenges To Adopting AIOps and Observability The three major challenges to adopting AIOps and observability are data complexity, integration complexity, and data security. Handling the vast and diverse data generated by modern IT environments can be overwhelming. Organizations need to manage, store, and analyze this data efficiently. Integrating AIOps and observability tools with existing systems and processes can be complex and time-consuming, potentially causing disruptions if not executed properly. The increased visibility into IT systems also raises concerns about data security and privacy. Ensuring the protection of sensitive information is crucial. Impacts and Benefits of Combining AIOps and Observability Across Sectors The impacts and benefits of integrating AIOps and observability transcend industries, enhancing reliability, efficiency, and performance across diverse sectors. It helps in improved incident response by using machine learning to detect patterns and trends, enabling proactive issue resolution, and minimizing downtime. Predictive analytics anticipates capacity needs and optimizes resource allocation in advance, which ensures uninterrupted operations. Full-stack observability leverages data from various sources — including metrics, events, logs, and traces (MELT) — to gain comprehensive insights into system performance, supporting timely issue identification and resolution. MELT capabilities are the key drivers where metrics help pinpoint issues, events automate alert prioritization, logs aid in root cause analysis, and traces assist in locating problems within the system. All contribute to improved operational efficiency. APPLICATION SCENARIOS OF COMBINING AIOPS AND OBSERVABILITY Industry Sectors Key Contributions Finance Enhance fraud detection, minimize downtime, and ensure compliance with regulatory requirements, thus safeguarding financial operations. Healthcare Improve patient outcomes by guaranteeing the availability and performance of critical healthcare systems and applications, contributing to better patient care. Retail Optimize supply chain operations, boost customer experiences, and maintain online and in-store operational efficiency. Manufacturing Enhance the reliability and efficiency of manufacturing processes through predictive maintenance and performance optimization. Telecommunications Support network performance to ensure reliable connectivity and minimal service disruptions. E-commerce Real-time insights into website performance, leading to seamless shopping experiences and improved conversion rates. Table 4 The application scenarios of combining AIOps and observability span diverse industries, showcasing their transformative potential in improving system reliability, availability, and performance across the board. Operational Guidance for AIOps Implementation Operational guidance for AIOps implementation offers a strategic roadmap to navigate the complexities of integrating AI into IT operations, ensuring successful deployment and optimization. Figure 2: Steps for implementing AIOps The Future of AIOps in Observability: The Road Ahead AIOps' future in observability promises to be transformative. As IT environments become more complex and dynamic, AIOps will play an increasingly vital role in ensuring system reliability and performance and will continue to evolve, integrating with advanced technologies like cognitive automation, natural language understanding (NLU), large language models (LLMs), and generative AI. APPLICATION SCENARIOS OF COMBINING AIOPS AND OBSERVABILITY Impact Area Role of AIOps Synergy With Cognitive Automation LLM and Generative AI Integration Data collection and analysis Collects and analyzes a wide range of IT data, including performance metrics, logs, and incidents Process unstructured data, such as emails, documents, and images Predict potential issues based on historical data patterns and generate reports Incident management Automatically detects, prioritizes, and responds to IT incidents Extract relevant information from incident reports and suggest or implement appropriate actions Understand its context and generate appropriate responses Root cause analysis Identifies root causes of incidents Access historical documentation and knowledge bases to offer detailed explanations and solutions Provide recommendations by analyzing historical data for resolving issues NLU Uses NLU to process user queries and understand context Engage in natural language conversations with IT staff or end-users, improving user experiences Power chatbots and virtual IT assistants, offering user-friendly interaction and support to answer queries and provide guidance Table 5 Conclusion The fusion of AI/ML with AIOps has ushered in a new era of observability. IT operations are constantly evolving, and so is the capability to monitor, analyze, and optimize performance. In the age of AI/ML-driven observability, our IT operations won't merely survive, but will thrive, underpinned by data-driven insights, predictive analytics, and an unwavering commitment to excellence. References: OpenNMS repositories, GitHub OpenObserve repositories, GitHub OpsPAI/awesome-AIOps, GitHub Precompiled binaries and Docker images for Prometheus components Shinken documentation This is an article from DZone's 2023 Observability and Application Performance Trend Report.For more: Read the Report

By Tuhin Chattopadhyay CORE

This is an article from DZone's 2023 Observability and Application Performance Trend Report.For more: Read the Report In today's digital landscape, the growing importance of monitoring and managing application performance cannot be overstated. With businesses increasingly relying on complex applications and systems to drive their operations, ensuring optimal performance has become a top priority. In essence, efficient application performance management can mean the difference between business success and failure. To better understand and manage these sophisticated systems, two key components have emerged: telemetry and observability. Telemetry, at its core, is a method of gathering and transmitting data from remote or inaccessible areas to equipment for monitoring. In the realm of IT systems, telemetry involves collecting metrics, events, logs, and traces from software applications and infrastructure. This plethora of data is invaluable as it provides insight into system behavior, helping teams identify trends, diagnose problems, and make informed decisions. In simpler terms, think of telemetry as the heartbeat monitor of your application, providing continuous, real-time updates about its health. Observability takes this concept one step further. It's important to note that while it does share some similarities with traditional monitoring, there are distinct differences. Traditional monitoring involves checking predefined metrics or logs for anomalies. Observability, on the other hand, is a more holistic approach. It not only involves gathering data but also understanding the "why" behind system behavior. Observability provides a comprehensive view of your system's internal state based on its external outputs. It helps teams understand the overall health of the system, detect anomalies, and troubleshoot potential issues. Simply put, if telemetry tells you what is happening in your system, observability explains why it's happening. The Emergence of Telemetry and Observability in Application Performance In the early days of information systems, understanding what a system was doing at any given moment was a challenge. However, the advent of telemetry played a significant role in mitigating this issue. Telemetry, derived from Greek roots tele (remote) and metron (measure), is fundamentally about measuring data remotely. This technique has been used extensively in various fields such as meteorology, aerospace, and healthcare, long before its application in information technology. As the complexity of systems grew, so did the need for more nuanced understanding of their behavior. This is where observability — a term borrowed from control theory — entered the picture. In the context of IT, observability is not just about collecting metrics, logs, and traces from a system, but about making sense of that data to understand the internal state of the system based on the external outputs. Initially, these concepts were applied within specific software or hardware components, but with the evolution of distributed systems and the challenges they presented, the application of telemetry and observability became more systemic. Nowadays, telemetry and observability are integral parts of modern information systems, helping operators and developers understand, debug, and optimize their systems. They provide the necessary visibility into system performance, usage patterns, and potential bottlenecks, enabling proactive issue detection and resolution. Emerging Trends and Innovations With cloud computing taking the center stage in the digital transformation journey of many organizations, providers like Amazon Web Services (AWS), Azure, and Google Cloud have integrated telemetry and observability into their services. They provide a suite of tools that enable users to collect, analyze, and visualize telemetry data from their workloads running on the cloud. These tools don't just focus on raw data collection but also provide features for advanced analytics, anomaly detection, and automated responses. This allows users to transform the collected data into actionable insights. Another trend we observe in the industry is the adoption of open-source tools and standards for observability like OpenTelemetry, which provides a set of APIs, libraries, agents, and instrumentation for telemetry and observability. The landscape of telemetry and observability has come a long way since its inception, and continues to evolve with technology advancements and changing business needs. The incorporation of these concepts into cloud services by providers like AWS and Azure has made it easier for organizations to gain insights into their application performance, thereby enabling them to deliver better user experiences. The Benefits of Telemetry and Observability The world of application performance management has seen a paradigm shift with the adoption of telemetry and observability. This section delves deep into the advantages provided by these emerging technologies. Enhanced Understanding of System Behavior Together, telemetry and observability form the backbone of understanding system behavior. Telemetry, which involves the automatic recording and transmission of data from remote or inaccessible parts of an application, provides a wealth of information about the system's operations. On the other hand, observability derives meaningful insights from this data, allowing teams to comprehend the internal state of the system from its external outputs. This combination enables teams to proactively identify anomalies, trends, and potential areas of improvement. Improved Fault Detection and Resolution Another significant advantage of implementing telemetry and observability is the enhanced ability to detect and resolve faults. There are tools that allow users to collect and track metrics, collect and monitor log files, set alarms, and automatically react to changes in configuration. This level of visibility hastens the detection of any operational issues, enabling quicker resolution and reducing system downtime. Optimized Resource Utilization These modern application performance techniques also facilitate optimized resource utilization. By understanding how resources are used and identifying any inefficiencies, teams can make data-driven decisions to optimize resource allocation. An auto-scaling feature — which adjusts capacity to maintain steady, predictable performance at the lowest possible cost — is a prime example of this benefit. Challenges in Implementing Telemetry and Observability Implementing telemetry and observability into existing systems is not a straightforward task. It involves a myriad of challenges, stemming from the complexity of modern applications to the sheer volume of data that needs to be managed. Let's delve into these potential pitfalls and roadblocks. Potential Difficulties and Roadblocks The first hurdle is the complexity of modern applications. They are typically distributed across multiple environments — cloud, on-premises, hybrid, and even multi-cloud setups. This distribution makes it harder to understand system behavior, as the data collected could be disparate and disconnected, complicating telemetry efforts. Another challenge is the sheer volume, speed, and variety of data. Modern applications generate massive amounts of telemetry data. Collecting, storing, processing, and analyzing this data in real time can be daunting. It requires robust infrastructure and efficient algorithms to handle the load and provide actionable insights. Also, integrating telemetry and observability into legacy systems can be difficult. These older systems may not be designed with telemetry and observability in mind, making it challenging to retrofit them without impacting performance. Strategies To Mitigate Challenges Despite these challenges, there are ways to overcome them. For the complexity and diversity of modern applications, adopting a unified approach to telemetry can help. This involves using a single platform that can collect, correlate, and analyze data from different environments. To tackle the issue of data volume, implementing automated analytics and machine learning algorithms can be beneficial. These technologies can process large datasets in real time, identifying patterns and providing valuable insights. For legacy system integration issues, it may be worthwhile to invest in modernizing these systems. This could mean refactoring the application or adopting new technology stacks that are more conducive to telemetry and observability. Finally, investing in training and up-skilling teams on tools and best practices can be immensely beneficial. Practical Steps for Gaining Insights Both telemetry and observability have become integral parts of modern application performance management. They offer in-depth insights into our systems and applications, enabling us to detect and resolve issues before they impact end-users. Importantly, these concepts are not just theoretical — they're put into practice every day across services provided by leading cloud providers such as AWS and Google Cloud. In this section, we'll walk through a step-by-step guide to harnessing the power of telemetry and observability. I will also share some best practices to maximize the value you gain from these insights. Step-By-Step Guide The following are steps to implement performance management of a modern application using telemetry and observability on AWS, though this is also possible to implement using other cloud providers: Step 1 – Start by setting up AWS CloudWatch. CloudWatch collects monitoring and operational data in the form of logs, metrics, and events, providing you with a unified view of AWS resources, applications, and services. Step 2 – Use AWS X-Ray for analyzing and debugging your applications. This service provides an end-to-end view of requests as they travel through your application, showing a map of your application's underlying components. Step 3 – Implement AWS CloudTrail to keep track of user activity and API usage. CloudTrail enhances visibility into user and resource activity by recording AWS Management Console actions and API calls. You can identify which users and accounts called AWS, the source IP address from which the calls were made, and when the calls occurred. Step 4 – Don't forget to set up alerts and notifications. AWS SNS (Simple Notification Service) can be used to send you alerts based on the metrics you define in CloudWatch. Figure 1: An example of observability on AWS Best Practices Now that we've covered the basics of setting up the tools and services for telemetry and observability, let's shift our focus to some best practices that will help you derive maximum value from these insights: Establish clear objectives – Understand what you want to achieve with your telemetry data — whether it's improving system performance, troubleshooting issues faster, or strengthening security measures. Ensure adequate training – Make sure your team is adequately trained in using the tools and interpreting the data provided. Remember, the tools are only as effective as the people who wield them. Be proactive rather than reactive – Use the insights gained from telemetry and observability to predict potential problems before they happen instead of merely responding to them after they've occurred. Conduct regular reviews and assessments – Make it a point to regularly review and update your telemetry and observability strategies as your systems evolve. This will help you stay ahead of the curve and maintain optimal application performance. Conclusion The rise of telemetry and observability signifies a paradigm shift in how we approach application performance. With these tools, teams are no longer just solving problems — they are anticipating and preventing them. In the complex landscape of modern applications, telemetry and observability are not just nice-to-haves; they are essentials that empower businesses to deliver high-performing, reliable, and user-friendly applications. As applications continue to evolve, so will the tools that manage their performance. We can anticipate more advanced telemetry and observability solutions equipped with AI and machine learning capabilities for predictive analytics and automated anomaly detection. These advancements will further streamline application performance management, making it more efficient and effective over time. This is an article from DZone's 2023 Observability and Application Performance Trend Report.For more: Read the Report

By Satrajit Basu CORE

This is an article from DZone's 2023 Observability and Application Performance Trend Report.For more: Read the Report Employing cloud services can incur a great deal of risk if not planned and designed correctly. In fact, this is really no different than the challenges that are inherit within a single on-premises data center implementation. Power outages and network issues are common examples of challenges that can put your service — and your business — at risk. For AWS cloud service, we have seen large-scale regional outages that are documented on the AWS Post-Event Summaries page. To gain a broader look at other cloud providers and services, the danluu/post-mortems repository provides a more holistic view of the cloud in general. It's time for service owners relying (or planning) on a single region to think hard about the best way to design resilient cloud services. While I will utilize AWS for this article, it is solely because of my level of expertise with the platform and not because one cloud platform should be considered better than another. A Single-Region Approach Is Doomed to Fail A cloud-based service implementation can be designed to leverage multiple availability zones. Think of availability zones as distinct locations within a specific region, but they are isolated from other availability zones in that region. Consider the following cloud-based service running on AWS inside the Kubernetes platform: Figure 1: Cloud-based service utilizing Kubernetes with multiple availability zones In Figure 1, inbound requests are handled by Route 53, arrive at a load balancer, and are directed to a Kubernetes cluster. The controller routes requests to the service that has three instances running, each in a different availability zone. For persistence, an Aurora Serverless database has been adopted. While this design protects from the loss of one or two availability zones, the service is considered at risk when a region-wide outage occurs, similar to the AWS outage in the US-EAST-1 region on December 7th, 2021. A common mitigation strategy is to implement stand-by patterns that can become active when unexpected outages occur. However, these stand-by approaches can lead to bigger issues if they are not consistently participating by handling a portion of all requests. Transitioning to More Than Two With single-region services at risk, it's important to understand how to best proceed. For that, we can draw upon the simple example of a trucking business. If you have a single driver who operates a single truck, your business is down when the truck or driver is unable to fulfill their duties. The immediate thought here is to add a second truck and driver. However, the better answer is to increase the fleet by two, which allows for an unexpected issue to complicate the original situation. This is known as the "n + 2" rule, which becomes important when there are expectations set between you and your customers. For the trucking business, it might be a guaranteed delivery time. For your cloud-based service, it will likely be measured in service-level objectives (SLOs) and service-level agreements (SLAs). It is common to set SLOs as four nines, meaning your service is operating as expected 99.99% of the time. This translates to the following error budgets, or down time, for the service: Month = 4 minutes and 21 seconds Week = 1 minute and 0.48 seconds Day = 8.6 seconds If your SLAs include financial penalties, the importance of implementing the n + 2 rule becomes critical to making sure your services are available in the wake of an unexpected regional outage. Remember, that December 7, 2021 outage at AWS lasted more than eight hours. The cloud-based service from Figure 1 can be expanded to employ a multi-region design: Figure 2: Multi-region cloud-based service utilizing Kubernetes and multiple availability zones With a multi-region design, requests are handled by Route 53 but are directed to the best region to handle the request. The ambiguous term "best" is used intentionally, as the criteria could be based upon geographical proximity, least latency, or both. From there, the in-region Kubernetes cluster handles the request — still with three different availability zones. Figure 2 also introduces the observability layer, which provides the ability to monitor cloud-based components and establish SLOs at the country and regional levels. This will be discussed in more detail shortly. Getting Out of the Toil Game Google Site Reliability Engineering's Eric Harvieux defined toil as noted below: "Toil is the kind of work that tends to be manual, repetitive, automatable, tactical, devoid of enduring value, and that scales linearly as a service grows." When designing services that run in multiple regions, the amount of toil that exists with a single region becomes dramatically larger. Consider the example of creating a manager-approved change request every time code is deployed into the production instance. In the single-region example, the change request might be a bit annoying, but it is something a software engineer is willing to tolerate. Now, with two additional regions, this will translate to three times the amount of change requests, all with at least one human-based approval being required. An obtainable and desirable end-state should still include change requests, but these requests should become part of the continuous delivery (CD) lifecycle and be created automatically. Additionally, the observability layer introduced in Figure 2 should be leveraged by the CD tooling in order to monitor deployments — rolling back in the event of any unforeseen circumstances. With this approach, the need for human-based approvals is diminished, and unnecessary toil is removed from both the software engineer requesting the deployment and the approving manager. Harnessing the Power of Observability Observability platforms measure a system's state by leverage metrics, logs, and traces. This means that a given service can be measured by the outputs it provides. Leading observability platforms go a step further and allow for the creation of synthetic API tests that can be used to exercise resources for a given service. Tests can include assertions that introduce expectations — like a particular GET request will respond with an expected response code and payload within a given time period. Otherwise, the test will be marked as failed. SLOs can be attached to each synthetic test, and each test can be executed in multiple geographical locations, all monitored from the observability platform. Taking this approach allows service owners the ability to understand service performance from multiple entry points. With the multi-region model, tests can be created and performance thereby monitored at the regional and global levels separately, thus producing a high degree of certainty on the level of performance being produced in each region. In every case, the power of observability can justify the need for manual human-based change approvals as noted above. Bringing It All Together From the 10,000-foot level, the multiregion service implementation from Figure 2 can be placed onto a United States map. In Figure 3, the database connectivity is mapped to demonstrate the inner-region communication, while the observability and cloud metrics data are gathered from AWS and the observability platform globally. Figure 3: Multi-region service adoption placed near the respective AWS regions Service owners have peace of mind that their service is fully functional in three regions by implementing the n + 2 rule. In this scenario, the implementation is prepared to survive two complete region outages. As an example, the eight-hour AWS outage referenced above would not have an impact on the service's SLOs/ SLAs during the time when one of the three regions is unavailable. Charting a Plan Toward Multi-Region Implementing a multi-region footprint for your service without increasing toil is possible, but it does require planning. Some high-level action items are noted below: Understand your persistence layer – Understanding your persistence layer early on is key. If multiple-write regions are not a possibility, alternative approaches will be required. Adopt Infrastructure as Code – The ability to define your cloud infrastructure via code is critical to eliminate toil and increase the ability to adopt additional regions, or even zones. Use containerization – The underlying service is best when containerized. Build the container you wish to deploy during the continuous integration stage and scan for vulnerabilities within every layer of the container for added safety. Reduce time to deploy – Get into the habit of releasing often, as it only makes your team stronger. Establish SLOs and synthetics – Take the time to set SLOs for your service and write synthetic tests to constantly measure your service — across every environment. Automate deployments – Leverage observability during the CD stage to deploy when a merge-to-main event occurs. If a dev deploys and no alerts are emitted, move on to the next environment and continue all the way to production. Conclusion It's important to understand the limitations of the platform where your services are running. Leveraging a single region offered by your cloud provider is only successful when there are zero region-wide outages. Based upon prior history, this is no longer good enough and is certain to happen again. No cloud provider is ever going to be 100% immune from a region-wide outage. A better approach is to utilize the n + 2 rule and increase the number of regions your service is running in by two additional regions. In taking this approach, the service will still be able to respond to customer requests in the event of not only one regional outage but also any form of outage in a second region where the service is running. By adopting the n + 2 approach, there is a far better chance at meeting SLAs set with your customers. Getting to this point will certainly present challenges but should also provide the opportunity to cut down (or even eliminate) toil within your organization. In the end, your customers will benefit from increased service resiliency, and your team will benefit from significant productivity gains. Have a really great day! Resources AWS Post-Event Summaries, AWS Summary of the AWS Service Event in the Northern Virginia (US-EAST-1) Region, AWS danluu/post-mortems, GitHub "Identifying and Tracking Toil Using SRE Principles" by Eric Harvieux, 2020 "Failure Recovery: When the Cure Is Worse Than the Disease" by Guo et al., 2013 This is an article from DZone's 2023 Observability and Application Performance Trend Report.For more: Read the Report

By John Vester CORE

We can agree decoupling is a good practice that simplifies the code and the maintainability of the project. A common way of decoupling the code is to divide the responsibilities into different layers. A very common division is: View layer: In charge of rendering HTML and interacting with the user Domain layer: In charge of the business logic Infra layer: In charge of getting the data from the backend and returning it to the domain layer (Here, it is very common to use the repository pattern, which is just a contract to get the data. The contract is unique but you can have multiple implementations; for example, one for a REST API and another for a GraphQL API. You should be able to change the implementation without changing other pieces in the code.) Let's see a couple of examples of use cases where it is very typical to put the performance over the decoupling. (Spoiler: we can have both.) Imagine, you have an endpoint that returns the list of products, and one of the fields is the category_id. The response can be something like this (I removed other fields to make a simple example): JSON [ { id: 1, name: "Product 1", category_id: 1 }, { id: 2, name: "Product 2", category_id: 2 }, ... ] We need to show the category name in the frontend (not the ID), so we need to call another endpoint to get the category name. That endpoint returns something like this: JSON [ { id: 1, name: "Mobile" }, { id: 2, name: "TVs" }, { id: 3, name: "Keyboards" }, ... ] You can think the backend should do the join and return all-in-one requests, but that is not always possible. We can do the join in the frontend, in the function or method in charge of recovering the products. We can do both requests and join the information. For example: TypeScript async function getProductList(): Promise<Product[]> { const products = await fetchProducts() const categories = await fetchCategories() return products.map(product => { const category = categories.find(category => category.id === product.category_id); return { ...product, category_name: category.name } }) } Our application doesn't need to know anything about that we need 2 calls to recover the information, and we can use the category_name in the frontend without any problem. Now imagine you need to show the list of categories; for example, in a dropdown. You can reuse the fetchCategories function, as it does exactly what you need. In your view, the code is something like this: Vue.js Component <template> <dropdown :options="categories" /> <product-list :products="products" /> </template> <script lang="ts" setup> import { fetchCategories, getProductList } from '@/repositories'; const categories = await fetchCategories(); const products = await getProductList(); </script> At that point, you realize you are doing 2 calls to the same endpoint to recover the same data - data you recovered to compose the product list - and that is not good in terms of performance, network load, back-end load, etc. At this moment, you start to think about how to reduce the number of calls to the backend; in this case, just to reuse the category list. You can have the temptation of moving the calls to the view and doing the joining of the products and the categories. Vue.js Component // ❌❌❌ Not nice solution <template> <dropdown :options="categories" /> <product-list :products="products" /> </template> <script lang="ts" setup> import { fetchCategories, fetchProducts } from '@/repositories'; const categories = await fetchCategories(); const products = await fetchProducts().map(product => { const category = categories.find(category => category.id === product.category_id); return { ...product, category_name: category.name }; }); </script> With that, you resolved the performance problems, but you added another BIG problem: infra, view, and domain coupling. Now your view knows the shape of the data in the infra (backend) and makes it hard to reuse the code. We can go deep on this and do things even worse: what happens if your head bar is in another component that needs the list of categories? You need to think about the application in a global way. Imagine something more complex: a scenario where you need the categories in the header, product list, filters, and footer. With the previous approach, your app layer (Vue, React, etc.) needs to think about how to get the data to minimize the requests. And that is not good, as the app layer should be focused on the view, not on the infra. Using a Global Store One solution to this problem is to use a global store (Vuex, Pinia, Redux, etc.) to delegate the requests and just use the store in the view. The store only should load the data if is not loaded yet, and the view should not care about how the data is loaded. This sounds like cache, right? We solved the performance issue, but we're still having infra and view coupled. Infra Cache to the Rescue To decouple as much as possible the infra and the view, we should move the cache to the infra layer (the layer in charge of getting the data from the backend). By doing that, we can call the infra methods at any time doing just a single request to the backend; but the important concept is that the domain, the application, and the view know nothing about the cache, the network speed, the number of requests, etc. The infra layer is just a layer to get the data with a contract (how to ask for the data and how the data is returned). Following the decoupling principles, we should be able to change the infra-layer implementation without changing the domain, application, or view layers. For example, we can replace the backend that uses REST with a backend that uses GraphQL, and we can get the product with the category names without doing 2 requests. But again, this is something the infra layer should care about, not the view. There are different strategies you can follow to implement the cache in the infra layer: HTTP cache (Proxy or Browser internal cache), but in these cases, for better flexibility invalidating the caches in the frontend, it's better that our application (infra layer again) manage the cache. If you are using Axios, you can use Axios Cache Interceptor to manage the cache in the infra layer. This library makes caching very simple: TypeScript // Example from axios cache interceptor page import Axios from 'axios'; import { setupCache } from 'axios-cache-interceptor'; // same object, but with updated typings. const axios = setupCache(Axios); const req1 = axios.get('https://api.example.com/'); const req2 = axios.get('https://api.example.com/'); const [res1, res2] = await Promise.all([req1, req2]); res1.cached; // false res2.cached; // true You only need to wrap the axios instance with the cache interceptor, and the library will take care of the rest. TTL TTL is the time the cache will be valid. After that time, the cache will be invalidated and the next request will be done to the backend. The TTL is a very important concept, as it defines how fresh the data is. When you are caching data, a challenging problem is data inconsistency. In our example, we can think of a shopping cart. If it's cached and the user adds a new product, and if your app makes a request to get the updated version of the cart, it will get the cached version, and the user will not see the new product. There are strategies to invalidate the cache and solve this problem, but that is out of the scope of this post. However, you need to know that this is not a trivial problem: different use cases need different strategies. As longer the TTL is, the bigger the data inconsistency problem is, as more events can happen in that time. But for the goal we are looking for (allowing to decouple the code easily), a very low TTL (ex., 10 seconds) is enough to remove the data inconsistency problem. Why Is a Low TTL Enough? Think about how the user interacts with the application: The user will ask for a URL (it can be part of a SPA or SSR page). The application will create the layout of the page, mounting the independent components: the header, the footer, the filters, and the content (product list in the example). Each component asks for the data it needs to do. The application will render the page with the data recovered and send it to the browser (SSR) or inject/update it in the DOM (SPA). All those processes are repeated in each page change (maybe partially in a SPA) and the most important thing: it is executed in a short period of time (maybe milliseconds). So with a low TTL, we can be pretty sure we will do only a request to the backend, and we will not have data inconsistency problems as in the next page change or user interaction the cached expired and we will get the fresh data. Summarizing This caching strategy in low TTL is a very good solution to decouple the infra and the view: Developers don't need to think about how to get the data to minimize the requests in the view layer. If you need the list of the categories in a sub-component, you ask for it and don't need to if another component is requesting the same data. Avoids maintaining a global app state (stores) Makes it more natural to do multiple requests follow the contract in a repository pattern to get the data you need in the repository layer, and do the join in the infra layer. In general terms, simplifies the code complexity No cache invalidation is challenged, as the TTL is very low (maybe for some very specific use cases).

With its ability to scale, be flexible, and be cost-effective, cloud computing has completely changed how businesses operate. However, it can be difficult to manage and keep an eye on the intricate infrastructure of cloud environments. Tools for monitoring the cloud in this situation are useful. With the help of these potent tools, businesses can monitor the performance, availability, and security of their cloud resources in real-time. Organizations can now take advantage of scalable resources and increased flexibility thanks to the rapid transformation of the IT landscape brought about by cloud computing. The need for reliable monitoring solutions to guarantee top performance, security, and cost-effectiveness is one of the new challenges brought about by this shift. Tools for cloud monitoring are now indispensable allies in the management of complicated cloud environments. These tools give companies the ability to monitor their cloud infrastructure in real time, spot problems early, take proactive measures to fix them, and maximize resource usage. In this article, we will delve into the diverse range of cloud monitoring tools available today, examining their key features, benefits, and use cases. From comprehensive monitoring platforms to specialized tools for specific cloud providers, each solution plays a crucial role in maintaining the health and performance of cloud-based systems. Comprehensive Cloud Monitoring Platforms Amazon CloudWatch Overview and Key Features Centralized monitoring and management for AWS resources and applications. Collects and tracks metrics, logs, and events from various AWS services. Offers a unified view of resource utilization, performance, and operational health. Provides a wide range of monitoring capabilities, including real-time metrics, dashboards, and automated actions. Monitoring Capabilities for AWS Services Monitors EC2 instances, RDS databases, S3 buckets, Lambda functions, and more. Offers native integration with various AWS services for seamless monitoring. Provides service-specific metrics and insights tailored to each AWS service. Alerting and Notifications Allows users to define alarms based on specific metrics and thresholds. Sends notifications via email, SMS, or integration with other AWS services. Supports automated actions, such as scaling resources based on predefined rules. Integration With Other AWS Tools Seamlessly integrates with other AWS services, such as AWS Lambda, AWS Step Functions, and AWS Systems Manager. Enables cross-service monitoring and management through consolidated dashboards and insights. Provides a unified experience for monitoring and troubleshooting AWS resources. Google Cloud Monitoring Introduction to Stackdriver and Its Features Stackdriver provides monitoring, logging, and diagnostics for Google Cloud Platform (GCP) services. Offers a unified platform for monitoring GCP resources, applications, and infrastructure. Collects metrics, logs, and traces from various GCP services and third-party applications. Monitoring and Logging for Google Cloud Platform Collects and visualizes metrics from GCP services, including Compute Engine, Cloud Storage, and BigQuery. Provides extensive logging capabilities, including structured and unstructured logs from GCP services and custom applications. Supports log-based metrics and advanced log querying. Custom Metrics and Dashboards Enables users to define custom metrics based on specific monitoring requirements. Offers flexible dashboard creation and visualization for monitoring key metrics and trends. Provides the ability to share dashboards with other team members. Advanced Alerting and Incident Management Allows users to set up alerts based on metrics, logs, or uptime checks. Provides notification channels, including email, SMS, and integration with incident management tools. Offers incident management capabilities, including incident creation, tracking, and resolution. Microsoft Azure Monitor Overview of Azure Monitor Components Azure Monitor offers comprehensive monitoring capabilities for Azure resources and applications.Consists of multiple components, including Metrics, Logs, Application Insights, and Network Monitoring. Monitoring for Azure Resources and Services Provides real-time metrics and insights into Azure services, such as Virtual Machines, Azure SQL Database, and Azure Functions. Offers preconfigured and customizable monitoring dashboards for visualizing resource performance and health. Supports autoscaling based on predefined metrics and rules. Log Analytics and Application Insights Log Analytics collects and analyzes logs from Azure resources and custom applications. Application Insights provides application performance monitoring (APM) capabilities for Azure applications. Enables powerful querying and correlation of logs and application telemetry. Advanced Analytics and Visualization Capabilities Azure Monitor leverages Azure Log Analytics and Azure Data Explorer for advanced analytics and visualization. Provides machine learning-based anomaly detection and smart alerting. Integrates with Azure dashboards and Power BI for customizable visualization and reporting. Datadog Comprehensive Monitoring for Multi-Cloud and Hybrid Environments Offers a unified platform for monitoring cloud, hybrid, and on-premises infrastructure. Provides support for multiple cloud providers, including AWS, Azure, Google Cloud, and others. Collects metrics, logs, and traces from various sources, enabling end-to-end visibility. Infrastructure Monitoring and Application Performance Management (APM) Monitors infrastructure metrics, including CPU usage, memory, network traffic, and disk utilization. Provides APM capabilities for monitoring application performance, including response time, error rates, and code-level insights. Supports distributed tracing for identifying performance bottlenecks in microservices architectures. Log Management and Real-Time Analytics Collects, indexes, and analyzes logs from various sources, including applications, infrastructure, and security events. Offers real-time log monitoring and alerting based on predefined patterns and anomalies. Provides log correlation and advanced search capabilities for troubleshooting and root cause analysis. Collaboration and Team-Oriented Features Facilitates collaboration among teams through shared dashboards, collaborative notes, and integration with popular collaboration tools. Offers role-based access control (RBAC) to manage permissions and access levels. Provides customizable reports and scheduled data exports. Specialized Cloud Monitoring Tools Serverless Monitoring Tools AWS X-Ray Distributed tracing: Captures and visualizes the flow of requests across serverless functions and microservices. Performance analysis: Identifies performance bottlenecks and latency issues within serverless applications. Error analysis and debugging: Helps trace and analyze errors and exceptions within the serverless architecture. Integration with AWS services: Seamlessly integrates with other AWS services like Lambda, API Gateway, and Elastic Beanstalk. New Relic End-to-end monitoring: Provides comprehensive monitoring for serverless applications and functions. Resource utilization: Tracks and optimizes resource usage, including CPU, memory, and network. Transaction monitoring: Monitors transaction performance and captures detailed insights into serverless function invocations. Real-time analytics and dashboards: Visualizes metrics and provides real-time insights into serverless performance. Epsagon Automated tracing: Automatically traces serverless function invocations and captures end-to-end transaction details. Troubleshooting and alerting: Identifies issues, bottlenecks, and errors and triggers real-time alerts. Cost optimization: Analyzes function usage and performance to optimize costs and resource allocation. Integration with major cloud providers: Supports AWS Lambda, Azure Functions, and Google Cloud Functions. Kubernetes Monitoring Tools Prometheus Open-source monitoring and alerting toolkit: Collects and stores time-series data and offers a flexible querying language (PromQL). Kubernetes-native monitoring: Provides preconfigured dashboards and metrics for monitoring Kubernetes clusters. Service discovery and dynamic monitoring: Automatically discovers and monitors new services and pods in a Kubernetes environment. Alerting and notification: Sends alerts based on predefined rules and integrates with popular notification channels. Grafana Visualization and analytics: Creates visually appealing dashboards and charts for monitoring Kubernetes clusters. Data source integration: Connects to various data sources, including Prometheus, to fetch and display metrics. Templating and annotations: Allows flexible dashboard customization and annotation of key events and incidents. Alerting and alert management: This enables setting up alerts based on specific metrics and offers robust notification options. Datadog Kubernetes monitoring and troubleshooting: Provides real-time insights into Kubernetes clusters and containerized applications. Auto-discovery and tagging: Automatically discovers and tags Kubernetes components for seamless monitoring. Application performance monitoring (APM): Offers tracing and performance monitoring for applications running in Kubernetes. Log management and analytics: Aggregates and analyzes logs from Kubernetes and associated services for troubleshooting. Security and Compliance Monitoring Tools CloudTrail Auditing and visibility: Records API activity and provides an audit trail for compliance and security analysis. Log analysis and monitoring: Centralizes and analyzes logs to identify potential security threats and suspicious activities. Integration with other security tools: Integrates with security information and event management (SIEM) systems for enhanced monitoring and analysis. AWS Config Configuration management: Monitors and tracks change to AWS resources and configurations for compliance. Compliance checks: Performs automated checks against predefined rules to ensure adherence to regulatory requirements. Configuration drift detection: Alerts on any unauthorized or unplanned changes to resources, providing visibility into potential security risks. Cloud Security and Compliance Monitoring (CSCM) Tools Comprehensive cloud security monitoring: Provides visibility into security posture, identifies vulnerabilities, and offers remediation guidance. Compliance management: Automates compliance checks against industry standards and regulatory frameworks. Threat detection and incident response: Uses advanced analytics and machine learning algorithms to detect and respond to security threats in real time. Cost Optimization Monitoring Tools AWS Cost Explorer Cost visualization and analysis: Offers interactive charts and visualizations to analyze AWS costs. Forecasting and budgeting: Predicts future costs and helps in budget planning and optimization. Cost allocation tagging: Enables tagging of resources for granular cost tracking and analysis. Azure Cost Management and Billing Cloud expenditure insights: Provides detailed cost breakdowns, usage analytics, and recommendations for cost optimization. Budget tracking and alerts: Sets budgets and sends alerts when costs exceed defined thresholds. Resource optimization: Recommends ways to optimize resource utilization and reduce costs. Google Cloud Billing Cost tracking and budgeting: Monitors and analyzes Google Cloud costs and usage. Billing reports and insights: Generates detailed reports on costs and usage patterns. Showback and chargeback: Enables cost allocation and showback to internal teams or chargeback to customers. Open-source Cloud Monitoring Tools Prometheus Architecture and Core Components: Time-series database: Stores metrics data for monitoring and analysis. Data collection: Scrapes metrics from various sources using exporters or agents. Alerting: Defines alerting rules based on metrics thresholds and sends notifications.Data Collection and Metric Exposition: Exporters: Collect metrics from systems, services, and applications and expose them in Prometheus format. Service Discovery: Automatically discovers and monitors new targets using various discovery mechanisms. Push and Pull Modes: Supports both push-based and pull-based metric collection. Alerting Rules and Notification Integrations: Define alerting rules based on specific metrics and thresholds. Sends alerts to various notification channels, including email, PagerDuty, and Slack. Integrates with popular incident management and notification tools. Grafana Integration for Visualization: Grafana integration enables the creation of interactive and customizable dashboards. Offers a wide range of visualization options, including graphs, charts, and tables. Leverages PromQL for querying Prometheus data and creating visualizations. Nagios Introduction to Nagios and Its Monitoring Capabilities: Comprehensive monitoring framework for IT infrastructure. Host and Service Monitoring: Monitors hosts, servers, and network services. Plugin Architecture: Supports a vast ecosystem of plugins for monitoring various technologies and applications. Monitoring Templates: Allows easy configuration and monitoring of multiple hosts or services with the same characteristics. Configuration Management and Event Handlers: Flexible configuration management using text-based configuration files. Event Handlers: Executes custom scripts or actions in response to specific events. Distributed Monitoring: Supports distributed monitoring setups for scalability. Reporting and Alerting Features: Flexible reporting capabilities for generating performance and availability reports. Alerting: Sends alerts via email, SMS, or other notification methods. Escalation: Defines escalation rules to ensure alerts reach the appropriate personnel. Extending Nagios with Third-Party Addons: Extensive ecosystem of third-party add-ons and plugins. Additional functionalities include visualization, enhanced reporting, and integration with other tools and systems. Zabbix Overview of Zabbix Monitoring Architecture: Centralized Monitoring: Monitors diverse IT components and resources from a central server.Agent-Based and Agentless Monitoring: Supports both agent-based and agentless monitoring approaches.Distributed Monitoring: Enables distributed monitoring setups for scalability and fault tolerance.Agent-Based and Agentless Monitoring Approaches: Agent-Based Monitoring: Utilizes lightweight agents installed on monitored hosts for data collection.Agentless Monitoring: Relies on protocols like SNMP, ICMP, and HTTP for data gathering. Triggering and Alerting Mechanisms: Flexible triggering options based on predefined conditions and thresholds. Alerting via multiple channels, including email, SMS, and custom scripts. Advanced alerting features like escalations, dependencies, and scheduled maintenance. Distributed Monitoring and Scalability: Distributed monitoring architecture for large-scale deployments. Hierarchical Setup: Divides monitoring responsibilities across multiple Zabbix servers. Data Aggregation and Visualization: Centralized data aggregation and visualization in the frontend interface. Conclusion Monitoring tools are essential in today’s complex cloud environments to guarantee top performance, availability, and security. The aforementioned tools offer strong capabilities for keeping track of metrics, logs, alerts, and the performance of various cloud resources. These tools can assist you in gaining real-time insights, proactively identifying issues, and making data-driven decisions to optimize your cloud infrastructure, whether you are using AWS, GCP, Azure, or a combination of cloud platforms. By leveraging these cloud monitoring tools, organizations can enhance their operational efficiency, deliver a seamless user experience, and maintain a robust and secure cloud environment. Tools for cloud monitoring have become crucial for companies doing business in today’s complex and dynamic cloud environments. This article has provided an overview of various monitoring tools available in the market, categorizing them into comprehensive platforms, specialized tools, and open-source options. Comprehensive platforms like Amazon CloudWatch, Google Cloud Monitoring, Microsoft Azure Monitor, and Datadog offer end-to-end monitoring capabilities, catering to a wide range of cloud services and resources. With the help of these platforms’ cutting-edge features, which include alerting, logging, analytics, and visualization, businesses can gain a comprehensive understandings of their cloud infrastructure. Kubernetes monitoring, serverless monitoring, security and compliance monitoring, and cost optimization monitoring are a few examples of specialized monitoring tools that focus on particular facets of cloud management. To address the particular difficulties specific to their respective domains, these tools provide focused features. Open-source tools like Zabbix, Nagios, and Prometheus offer strong alternatives for businesses looking for adaptable and customizable monitoring solutions. These tools are preferred by IT teams because of their extensibility, community support, and affordability. The choice of a cloud monitoring tool ultimately comes down to the specific needs, cloud provider, and financial constraints of an organization. With the right mix of monitoring tools in place, businesses can guarantee high availability, effective resource utilization, security, and cost optimization, leading to improved performance and customer satisfaction in their cloud-based operations.

Creating performant and responsive websites is a top priority for web developers. One way to achieve this is through content prioritization, which involves loading critical content before non-critical content. In this article, we will explore advanced techniques and tools that can help web developers optimize their projects using content prioritization. Advanced Content Prioritization Techniques and Tools Extracting Critical CSS With PurgeCSS and Critical Extract only the necessary CSS rules required to render above-the-fold content using PurgeCSS (https://purgecss.com/) and Critical (https://github.com/addyosmani/critical). PurgeCSS removes unused CSS, while Critical extracts and inlines the critical CSS, improving the rendering of critical content. Example Install PurgeCSS and Critical: Shell npm install purgecss critical Create a configuration file for PurgeCSS: JavaScript // purgecss.config.js module.exports = { content: ['./src/**/*.html'], css: ['./src/css/main.css'], output: './dist/css/', }; Extract and inline critical CSS: JavaScript const critical = require('critical').stream; const purgecss = require('@fullhuman/postcss-purgecss'); const postcss = require('postcss'); // Process the CSS file with PurgeCSS postcss([ purgecss(require('./purgecss.config.js')), ]) .process(cssContent, { from: 'src/css/main.css', to: 'dist/css/main.min.css' }) .then((result) => { // Inline the critical CSS using Critical gulp.src('src/*.html') .pipe(critical({ base: 'dist/', inline: true, css: ['dist/css/main.min.css'] })) .pipe(gulp.dest('dist')); }); Code Splitting and Dynamic Imports With Webpack Utilize code splitting and dynamic imports in Webpack (https://webpack.js.org/guides/code-splitting/) to break your JavaScript into smaller chunks. This ensures that only critical scripts are loaded initially, while non-critical scripts are loaded when needed. Example JavaScript // webpack.config.js module.exports = { // ... optimization: { splitChunks: { chunks: 'all', }, }, }; // Usage of dynamic imports async function loadNonCriticalModule() { const nonCriticalModule = await import('./nonCriticalModule.js'); nonCriticalModule.run(); } Image Optimization and Responsive Images Optimize images using tools like ImageOptim (https://imageoptim.com/) or Squoosh (https://squoosh.app/). Implement responsive images using the srcset attribute and modern image formats like WebP or AVIF for improved performance. Example HTML <picture> <source srcset="image.webp" type="image/webp"> <source srcset="image.avif" type="image/avif"> <img src="image.jpg" alt="Sample image"> </picture> Resource Hints: Preload, Prefetch, and Preconnect Use resource hints like rel="preload", rel="prefetch", and rel="preconnect" to prioritize the loading of critical resources and prefetch non-critical resources for future navigation. Example HTML <!-- Preload critical resources --> <link rel="preload" href="critical.css" as="style"> <!-- Prefetch non-critical resources --> <link rel="prefetch" href="non-critical-image.jpg" as="image"> <!-- Preconnect to important third-party origins --> <link rel="preconnect" href="https://fonts.gstatic.com"> Implementing Service Workers With Google Workbox Use Google's Workbox (https://developers.google.com/web/tools/workbox) to set up service workers that cache critical resources and serve them instantly on subsequent page loads, improving performance. Example JavaScript // workbox.config.js module.exports = { globDirectory: 'dist/', globPatterns: ['**/*.{html,js,css,woff2}'], swDest: 'dist/sw.js', }; // Generate a service worker with Workbox CLI npx workbox generateSW workbox.config.js Conclusion By leveraging advanced content prioritization techniques and tools, web developers can significantly enhance the performance and user experience of their websites. Focusing on delivering critical content first and deferring non-critical content allows users to quickly access the information they need. Implementing these advanced techniques in your web projects will lead to improved perceived performance, reduced bounce rates, and better user engagement.

Developers spend weeks or even months onboarding at a new company. Getting up to speed in a new codebase takes time. During this time, the developer will have many questions (as they should)! However, those questions interrupt other team members who must stop what they’re doing to provide answers. Most engineering organizations face the dilemma of ensuring the new developer gets the support they need without slowing down the rest of the team too much. A culture of documentation is an excellent step in the right direction. However, this documentation is often fragmented across Slack messages, Notion and Confluence wikis, GitHub pull requests, and Jira tickets. How do you successfully navigate this endless sea of information? An AI startup called Unblocked is seeking to solve this problem. They’ve created a chatbot-like interface where you can ask questions and get answers to unblock yourself without interrupting anyone else. Most importantly, Unblocked can be connected to all the data sources your company uses, so the answers are tailored for you based on your actual company resources, as opposed to generic advice. I recently tried Unblocked to see how well it could help someone like me. In this article, we’ll look at some example scenarios, the questions I asked, and the answers I received. We’ll explore three general categories of information-seeking: Getting a general understanding of the architecture of a new codebase Trying to understand how a feature works Troubleshooting and fixing a bug Ready to get unblocked? Example Repo In order to use Unblocked with my current employer’s data, I would need to work through our security process and get permission. Unblocked is SOC 2 compliant and isolates customer data. For now, I decided to try Unblocked with my personal projects to get a sense of its capabilities. I turned to one of my largest repos from my college years. I have one repo that contains dozens of projects I worked on throughout my computer science courses. Most of these projects I haven’t looked at in over eight years. If you’re like me, you’ve likely forgotten the details of the code you worked on even a few months ago, so coming back to this repo felt similar to re-onboarding! You can find the entire repo we will use on GitHub. (Please don’t judge too harshly on the code quality. I was learning for the first time!) Scenario One: Can You Give Me an Overview of the Codebase? As I re-acquainted myself with some of the projects in this old repo, I asked Unblocked about my repo. I started with a very generic question: “What does this app do?” Unblocked responded by telling me that this repo contains many different projects. It even described some of the projects to me. It knew I had apps about pet adoption, photography, fitness, and movie streaming. It also correctly identified that one of my apps was a web-based game. Question and answer for “What does this app do?” We were off to a great start. I asked a second question: “What languages, libraries, or frameworks does this repo use?” Unblocked responded with many tools I listed in my main portfolio file. It correctly called out that each project used different technologies. You’ll note at the bottom of the screenshot that Unblocked cites its sources, so you know where this information comes from. Question and answer for “What languages, libraries, or frameworks does this repo use?” Scenario Two: How Does This Thing Work? Alright, that was a good enough intro for me. Next, I asked a specific question about one of my projects: a Connect Four game built with jQuery. Remember, the point of this trial was to see how I could use Unblocked in my day-to-day job. So, I imagined myself as a developer onboarding to a new codebase working on this game. I had a question about how the game worked. Rather than bugging one of my coworkers, I decided to ask Unblocked. I wanted to make sure there weren’t opportunities for players to cheat in my game. I asked, “In the ConnectFour app, is it possible for a player to play two pieces in a row without waiting for the other person to take their turn?” Unblocked’s response here was impressive. It was able to reference a specific code snippet that showed how the turn-taking behavior worked in the game. Question and answer for “In the ConnectFour app, is it possible for a player to play two pieces in a row without waiting for the other person to take their turn?” However, I wasn’t convinced that a player couldn’t find some way to cheat. I asked a follow-up question: “What if someone clicked the button twice really quickly before the animation finished? Then could they cheat and play two pieces at once?” Again, I was impressed with Unblocked’s response. It highlighted another code snippet that showed how I had disabled the click handler to prevent someone from clicking twice to play two pieces at once. It even found a closed GitHub issue referencing a similar possible problem but assured me that it has since been resolved. Question and answer for “What if someone clicked the button twice really quickly before the animation finished? Then could they cheat and play two pieces at once?” Scenario Three: Can You Help Me Fix This Bug? Let’s switch gears and consider another scenario. Let’s imagine that I’m working in a new codebase and need help fixing a bug. I might turn to a coworker to ask for help, but this seems like something Unblocked could help with, too. For the next few questions, I referenced a multiplayer game called Pixel Mania, which I built as one of my capstone projects many years ago. This game is built with JavaScript and uses web sockets to communicate information from peer to peer. In the game, each player is a dot. They move around the screen, eating food to grow in size. Players can eat one another as well. And they have to do this while avoiding obstacles that will cut their size in half. This game works really well when two to four players are online. However, the game begins to lag when the number of players increases. In this scenario, let’s imagine I’m a developer working on this project and am noticing these performance issues. I need some help. Who should I ask? Unblocked, of course! My first question was, “In PixelMania, I’m seeing performance issues when a large number of players are playing. Why is that?” Unblocked responded with a few initial thoughts. The game manages the position information of all the players, food, and balls. Unblocked theorized that the operations would take longer as the number of items increased. It’s correct, by the way. Many of the operations in the game involve looping over all the items, looking for collisions to know if you’ve eaten a piece of food, eaten another player, or been hit by a ball. Thinking about Big O Notation, these operations are at least O(n) time. Unblocked then suggested that some possible optimizations could be achieved by using “techniques such as rate limiting updates, using delta compression for updates, or implementing an area of interest management system where clients only receive updates for objects near their player.” Question and answer for “In PixelMania, I’m seeing performance issues when a large number of players are playing. Why is that?” The nice thing about Unblocked is that you can have a conversation with it, similar to ChatGPT. I had follow-up questions and wanted to treat this like a pair programming brainstorming session. I asked, “Could you explain more about the optimization techniques you suggested?” Unblocked went into detail about its five suggestions: Rate limiting updates Delta compression Areas of interest management Spatial partitioning Optimizing data structures Question and answer for “Could you explain more about the optimization techniques you suggested?” I wanted to dig in even further. I asked, “Spatial Partitioning sounds like a good approach to me. Can you give me some advice on how I would implement that in PixelMania?” It gave me even more detailed advice! Note that it’s not just generic info about how spatial partitioning works, but it applied the advice to specific files in my app, like game.js and Player.js. Question and answer for “Spatial Partitioning sounds like a good approach to me. Can you give me some advice on how I would implement that in PixelMania?” After that, I asked just one more question about the data structures I used: “You also mentioned Optimizing Data Structures. Are there any data structures used in PixelMania that are used inefficiently or incorrectly and could be optimized?” Unblocked responded with some specific instances of design choices I had made and highlighted some potential drawbacks. Many of my operations were done in O(n) time, and it’s possible I could use different data structures and make better use of objects to achieve O(1) time. This could potentially improve some of the performance issues. Question and answer for “You also mentioned Optimizing Data Structures. Are there any data structures used in PixelMania that are used inefficiently or incorrectly and could be optimized?” By now, I had a pretty good idea of where to go next. If I were working on this in my job, I’d be well equipped to start making changes in the code. Conclusion Finding the right balance between asking questions and being self-reliant can be difficult. Interruptions lead to context switching, and that can be a time sink. We all want to be helpful to our coworkers, but we also need to protect our time. AI is playing an ever-increasing role in our field as developers, and it has the capacity to significantly increase our productivity in ways we’ve never seen before. Unblocked is one of these tools. By making it easier for developers to find the answers to their questions on their own, Unblocked enables us to get the right help we need when we need it.

By Tyler Hawkins CORE

An open-source distributed SQL query engine, Trino is widely used for data analytics on distributed data storage. Optimizing Trino to make it faster can help organizations achieve quicker insights and better user experiences, as well as cut costs and improve infrastructure efficiency and scalability. But how do we do that? In this article, we will show you how to tune Trino by helping you identify performance bottlenecks and provide tuning tips that you can practice. 1. Performance Bottlenecks for Trino Queries Let’s first identify the common bottlenecks that can slow down Trino queries before we dig into tuning tips. Here are four key factors that affect Trino query performance: Compute resources: As a resource-intensive application, Trino uses a lot of CPU power for data processing and consumes memory to store source data, intermediate results, and final outputs. It is important to balance the number of CPU cores and the amount of memory for each node, keeping in mind the workload and available hardware resources. I/O speed: Trino is a storage-independent query engine, so it does not store any data on its local disk. Instead, it fetches data from external storage systems. This means Trino’s query performance is greatly influenced by the speed of the storage systems and network. Table scans: Table scan fetches data from a connector and produces data to be consumed by other operators, which can be another performance bottleneck, especially when working with large datasets. How tables are partitioned and the choice of file formats, like Parquet and ORC, can make a big difference in query speed. Joins: Joins, which merge data from many tables, are known as the most resource-intensive operation in Trino. If joins are not done efficiently, they can consume excessive CPU and memory resources, slowing down the whole Trino cluster. You now know what the key factors are behind slow queries. It is a good starting point for performance tuning. 2. Process of Optimizing Trino Performance tuning is a process instead of random steps. To optimize Trino’s query performance, follow the steps shown in the flow chart below. Step 1: Check if the entire cluster is slowing down through Trino’s Web UI. Yes: Proceed to step 2. No: Proceed to step 3. Step 2: See whether queries are queued or blocked from Trino’s Web UI. Queued: Refer to tip one and tip 2. Blocked: Refer to tip one and tip 2. Step 3: Identify the bottleneck in the slow query by running EXPLAIN ANALYZE. Slow table scans: Refer to tip 3. Slow joins: Refer to tip 4. Trino cluster’s Web UI is very helpful here. You can quickly assess the overall state of your cluster, whether there are more blocked or queued queries. To analyze individual query plans and performance, use the EXPLAIN ANALYZE commands, which provide details about execution time and processed rows. Let’s take a look at an example. EXPLAIN ANALYZE select * from customers limit 5; With the above command, you will get an output. As highlighted, EXPLAIN ANALYZE will show you the actual execution statistics, helping you identify the bottleneck of the query execution process. 3. Tuning Tips for Trino Now, let’s talk about how to fine-tune Trino for better performance. Tip 1: Optimize Resource Allocation Trino needs the right amount of memory to work well. It is important to monitor how much memory it is using and adjust its settings if needed. You can customize the maximum memory and number of concurrent queries in Trino in order to manage resource allocation effectively. Make sure you have the right balance between the maximum memory for each query and the number of queries running at the same time. This depends on the resources you have for Trino. Adjusting parameters for maximum concurrency and memory may result in blocked or queued queries. Recall step 2, which is to identify whether queries are blocked or queued. If you find many blocked queries, increase memory or reduce the maximum number of concurrent queries. If you see many queued queries, you will need to add more CPU resources or reduce the concurrency. To avoid memory allocation deadlocks or low query performance, use these settings: Property Name Suggested Value Comments query.max-memory-per-node JVM heap size / max_concurrent This value can fluctuate. A higher value can speed up large queries, and a lower value can reduce the risk of memory allocation deadlocks. query.max-memory Do not set this limitation if the query.max-memory-per-node has been set properly. query.low-memory-killer.policy total-reservation Only set this value when you are suffering from memory allocation deadlock. This value will kill the query currently using the most memory. Tip 2: Improve I/O (Storage and Network) Trino, a storage-independent query engine, fetches data from remote storage for each query. If you experience low I/O throughput or high network latency, it can lead to queries being queued or blocked while fetching data. So, the solution for queued and blocked queries, besides optimizing compute resources, is to improve I/O. Here are some ways to accelerate I/O: Use faster storage: A faster storage system, such as a hotter storage tier of Amazon S3, can improve data retrieval speed. Reduce network latency: You will need to set up low-latency network connectivity between Trino and the storage system to minimize data transfer delays. Caching: Implementing a caching layer, such as Alluxio, can help you reduce query latency, especially for remote storage or data sharing between Trino and other compute engines. Caching can significantly accelerate remote data reading by bringing data locality to Trino workers. Optimizing I/O can ultimately enhance the overall query execution speed. Tip 3: Table Scan Optimization In EXPLAIN ANALYZE, when you see table scan issues, you should pay attention to file format, compression, partitioning, bucketing, or sorting methods. Here are our recommendations: Columnar data file formats and compression: Trino reads columnar data files by first accessing the metadata stored at the footer of the files, which determines the structure and data section locations in the files. Reading data pages in parallel, Trino employs many threads to read and process column data efficiently. Columnar formats optimize query performance by skipping unnecessary columns and enabling predicate pushdown based on statistics stored in the metadata. Columnar formats like ORC and Parquet are recommended because they support predicate pushdown and efficient data compression. ORC often outperforms Parquet in Trino, but efforts are being made to improve Parquet’s performance in the Trino community. Flat table column layout and dereference pushdown: Since Trino 334, Trino introduced a new way to query nested columns less costly, which is dereference pushdown. If you don’t see any benefits from the dereference pushdown, choose the flat table column layout. Partitioning and bucketing: You can improve query performance by dividing tables based on partition columns. This way, Trino doesn’t have to access unrelated partitions. However, excessive partitions can hinder planning and increase storage pressure. Too many partitions can slow down planning and put pressure on storage. Bucketing, a form of hash partitioning that divides tables into a set number of hash buckets based on selected columns, can help manage these issues. Below is an example of creating a table with partitioning and bucketing SQL CREATE TABLE customers ( customer_id bigint, created_day date ) WITH ( partitioned_by = ARRAY[created_day], bucketed_by = ARRAY['customer_id'], bucket_count = 100 ) Tip 4: Join Optimization EXPLAIN ANALYZE will also identify slow joins. Joins are considered the most expensive operation in any query engine, but you can work on optimizing join distribution types and join orders. Trino’s cost-based optimizer (CBO) can determine the most effective join methods based on table statistics. For join optimization, consider join distribution types, join orders and leverage dynamic filtering when applicable. Before diving into join distribution, let’s understand the concepts of “probe table” and “build table.” Trino uses a hash join algorithm, where one table is read into memory, and its values are reused while scanning the other table. The smaller table is typically chosen as the “build table” to conserve memory and improve concurrency. There are two types of join distributions in Trino. Partitioned join: Each node builds a hash table from a fraction of the build table’s data. Broadcast join: Each node builds a hash table from all the data, replicating the build table’s data to each node. Broadcast join can be faster but requires the build table to fit in memory. Partitioned join may be slower but has lower memory requirements per node. Trino automatically selects the appropriate join distribution strategy, but you can change it using the join_distribution_type session property. You should also optimize join orders, which aims to minimize the data read from storage and data transfer between workers. Trino’s cost-based join enumeration estimates costs for different join orders and picks the one with the lowest estimated cost. You can set the join order strategy using the join_reordering_strategy session property. Another join optimization strategy is to enable dynamic filtering. Dynamic filtering can help in some join scenarios, reducing the number of unnecessary rows read from the probe table. Trino applies a dynamic filter generated by the build side during a broadcast join to reduce probe table data. Dynamic filtering is enabled by default but can be disabled using the enable_dynamic_filtering session property. Implementing these join optimization strategies can help you enhance the efficiency and performance of Trino queries. 4. Summary In this article, you learned how to tune Trino for speed and efficiency. When using Trino in production, keep an eye on resource allocation, I/O improvement, table scan, and join optimization. Remember, performance tuning is not a one-time task. It is a continuous process that requires regular checks, tests, and changes based on your specific use cases and workload patterns.

Docker Extensions was announced as a beta at DockerCon 2022. Docker Extensions became generally available in January 2023. Developing performance tools' related extensions was on my to-do list for a long time. Due to my master's degree, I couldn't spend time learning Docker Extensions SDK. I expected someone would have created the extension by now, considering it's almost 2024. It's surprising to me that none has been developed as far as I know. But no more. Introducing the Apache JMeter Docker Extension. Now, you can run Apache JMeter tests in Docker Desktop without installing JMeter locally. In this blog post, we will explore how to get started with this extension and understand its functionality. We will also cover generating HTML reports and other related topics. About Docker Extensions Docker Extensions enables third parties to extend the functionalities of Docker by integrating their tools. Think of it like a mobile app store but for Docker. I frequently use the official Docker Disk Usage extension to analyze disk usage and free up unused space. Extensions enhance the productivity and workflow of developers. Check out the Docker Extension marketplace for some truly amazing extensions. Go see it for yourself! Prerequisite for Docker Extension The only prerequisite for Docker Extension is to have Docker Desktop 4.8.0 and later installed in your local. Apache JMeter Docker Extension Apache JMeter Docker Extension is an open-source, lightweight extension and the only extension available as of this writing. It will help you to run JMeter tests on Docker without installing JMeter locally. This extension simplifies the process of setting up and executing JMeter tests within Docker containers, streamlining your performance testing workflow. Whether you're a seasoned JMeter pro or just getting started, this tool can help you save time and resources. Features Includes base image qainsights/jmeter:latest by default. Light-weight and secured container Supports JMeter plugins Mount volume for easy management Supports property files Supports proxy configuration Generates logs and results Intuitive HTML report Displays runtime console logs Timely notifications How To Install Apache JMeter Docker Extension Installation is a breeze. There are two ways you can install the extension. Command Line Run docker extension install qainsights/jmeter-docker-extension:0.0.2 in your terminal and follow the prompts. IMPORTANT: Before you install, make sure you are using the latest version tag. You can check the latest tags in Docker Hub. Dockerfile $> docker extension install qainsights/jmeter-docker-extension:0.0.1 Extensions can install binaries, invoke commands, access files on your machine and connect to remote URLs. Are you sure you want to continue? [y/N] y Image not available locally, pulling qainsights/jmeter-docker-extension:0.0.1... Extracting metadata and files for the extension "qainsights/jmeter-docker-extension:0.0.1" Installing service in Desktop VM... Setting additional compose attributes Installing Desktop extension UI for tab "JMeter"... Extension UI tab "JMeter" added. Starting service in Desktop VM...... Service in Desktop VM started Extension "JMeter" installed successfully Web Here is the direct link to install the JMeter extension. Follow the prompts to get it installed. Install JMeter Docker Extension Click on Install anyway to install the extension. How To Get Started With JMeter Docker Extension After installing the JMeter Docker extension, navigate to the left sidebar as shown below, then click on JMeter. Now, it is time to execute our first tests on Docker using the JMeter extension. The following are the prerequisites to execute the JMeter tests. valid JMeter test plan optional proxy credentials optional JMeter properties file The user interface is pretty simple, intuitive, and self-explanatory. All it has is text fields, buttons, and the output console log. The extension has the following sections: Image and Volume This extension works well with the qainsights/jmeter:latest image Other images might not work; I have not tested it. Mapping the volume from the host to the Docker container is crucial to sharing the test plan, CSV test data, other dependencies, property files, results, and other files. Test Plan A valid test plan must be kept inside the shared volume. Property Files This section helps you to pass the runtime parameters to the JMeter test plan. Logs and Results This section helps you to configure the logs and results. After each successful test, logs and an HTML report will be generated and saved in a shared volume. Proxy and its credentials Optionally, you can send a proxy and its credentials. This is helpful when you are on the corporate network so that the container can access the application being tested. Below is the example test where the local volume /Users/naveenkumar/Tools/apache-jmeter-5.6.2/bin/jmeter-tests is mapped to the container volume jmeter-tests. Here is the content in /Users/naveenkumar/Tools/apache-jmeter-5.6.2/bin/jmeter-tests folder in my local. The above artifacts will be shared with the Docker container once it is up and running. In the above example, /jmeter-tests/CSVSample.jmx will be executed inside the container. It will use the below loadtest.properties. Once all the values are configured, hit the Run JMeter Test button. During the test, you can pay attention to a couple of sections. One is console logs. For each test, the runtime logs will be streamed from the Docker container, as shown below. In case there are any errors, you can check them under the Notifications section. Once the test is done, Notifications will display the status and the location of the HTML report (your mapped volume). Here is the auto-generated HTML report. How JMeter Docker Extension Works and Its Architecture On a high level, this extension is simple, as shown in the below diagram. Once you click on the Run button, the extension first validates all the input and the required fields. If the validation check passes, then the extension will look up the artifacts from the mapped volume. Then, it passes all respective JMeter arguments to the image qainsights/jmeter:latest. If the image is not present, it will get pulled from the Docker container registry. Then, the container will be created by Docker and perform the test execution. During the test execution, container logs will be streamed to the output console logs. To stop the test, click the Terminate button to nuke the container. This action is irreversible and will not generate any test results. Once the test is done, the HTML report and the logs will be shared with the mapped volume. How To Uninstall the Extension There are two ways to uninstall the extension. Using the CLI, issue docker extension uninstall qainsights/jmeter-docker-extension:0.0.1 or from the Docker Desktop. Navigate to Docker Desktop > Extensions > JMeter, then click on the menu to uninstall, as shown below. Known Issues There are a couple of issues (or more :) if you find) you can start the test as much as you want to generate more load to the target under test. Supports only frequently used JMeter arguments. If you would like to add more arguments, please raise an issue in the GitHub repo. Upcoming Features There are a couple of features I am planning to implement based on the reception. Add a dashboard to track the tests Display graphs/charts runtime Way to add JMeter plugins on the fly If you have any other exciting ideas, please let me know. JMeter Docker Extension GitHub Repo Conclusion In conclusion, the introduction of the Apache JMeter Docker Extension is a significant step forward for developers and testers looking to streamline their performance testing workflow. With this open-source and lightweight extension, you can run JMeter tests in Docker without the need to install JMeter locally, saving you time and resources. Despite a few known issues and limitations, such as supporting only frequently used JMeter arguments, the extension holds promise for the future. In summary, the Apache JMeter Docker Extension provides a valuable tool for developers and testers, enabling them to perform JMeter tests efficiently within Docker containers, and it's a welcome addition to the Docker Extension ecosystem. It's worth exploring for anyone involved in performance testing and looking to simplify their workflow.

By NaveenKumar Namachivayam CORE

Welcome to the final installment of our comprehensive series on Unity's Coroutines. If you've been following along, you've already built a strong foundation in the basics of coroutine usage in Unity. Now, it's time to take your skills to the next level. In this article, we will delve deep into advanced topics that are crucial for writing efficient and robust coroutines. You might wonder, "I've got the basics down, why delve deeper?" The answer lies in the complex, real-world scenarios you'll encounter in game development. Whether you're working on a high-performance 3D game or a real-time simulation, the advanced techniques covered here will help you write coroutines that are not only functional but also optimized for performance and easier to debug. Here's a brief overview of what you can expect: Best Practices and Performance Considerations: We'll look at how to write efficient coroutines that are optimized for performance. This includes techniques like object pooling and best practices for avoiding common performance pitfalls in coroutines. Advanced Coroutine Patterns: Beyond the basics, we'll explore advanced coroutine patterns. This includes nested coroutines, chaining coroutines for sequential tasks, and even integrating coroutines with C#'s async/await for more complex asynchronous operations. Debugging Coroutines: Debugging is an integral part of any development process, and coroutines are no exception. We'll cover common issues you might face and the tools available within Unity to debug them effectively. Comparison With Other Asynchronous Programming Techniques: Finally, we'll compare coroutines with other asynchronous programming paradigms, such as Unity's Job System and C#'s async/await. This will help you make informed decisions about which approach to use in different scenarios. By the end of this article, you'll have a well-rounded understanding of coroutines in Unity, right from the basics to advanced optimization techniques. So, let's dive in and start leveling up your Unity coroutine skills! Best Practices and Performance Considerations Coroutines in Unity offer a convenient way to perform asynchronous tasks, but like any other feature, they come with their own set of performance considerations. Understanding these can help you write more efficient and responsive games. Let's delve into some best practices and performance tips for working with coroutines. One of the most effective ways to improve the performance of your coroutines is through object pooling. Creating and destroying objects frequently within a coroutine can lead to performance issues due to garbage collection. Instead, you can use object pooling to reuse objects. Here's a simple example using object pooling in a coroutine: C# private Queue<GameObject> objectPool = new Queue<GameObject>(); IEnumerator SpawnEnemies() { while (true) { GameObject enemy; if (objectPool.Count > 0) { enemy = objectPool.Dequeue(); enemy.SetActive(true); } else { enemy = Instantiate(enemyPrefab); } // Do something with the enemy yield return new WaitForSeconds(1); objectPool.Enqueue(enemy); enemy.SetActive(false); } } Using the new keyword inside a coroutine loop can lead to frequent garbage collection, which can cause frame rate drops. Try to allocate any required objects or data structures before the coroutine loop starts. C# List<int> numbers = new List<int>(); IEnumerator ProcessNumbers() { // Initialization here numbers.Clear(); while (true) { // Process numbers yield return null; } } While yield return new WaitForSeconds() is convenient for adding delays, it creates a new object each time it's called, which can lead to garbage collection. A better approach is to cache WaitForSeconds objects. C# WaitForSeconds wait = new WaitForSeconds(1); IEnumerator DoSomething() { while (true) { // Do something yield return wait; } } Coroutines are not entirely free in terms of CPU usage. If you have thousands of coroutines running simultaneously, you might notice a performance hit. In such cases, consider using Unity's Job System for highly parallel tasks. By following these best practices and being aware of the performance implications, you can write coroutines that are not only functional but also optimized for performance. This sets the stage for exploring more advanced coroutine patterns, which we'll cover in the next section. Advanced Coroutine Patterns As you gain more experience with Unity's coroutines, you'll find that their utility extends far beyond simple asynchronous tasks. Advanced coroutine patterns can help you manage complex flows, chain operations, and even integrate with other asynchronous programming techniques like async/await. Let's delve into some of these advanced patterns. Nested Coroutines One of the powerful features of Unity's coroutines is the ability to nest them. A coroutine can start another coroutine, allowing you to break down complex logic into smaller, more manageable pieces. Here's an example of nested coroutines: C# IEnumerator ParentCoroutine() { yield return StartCoroutine(ChildCoroutine()); Debug.Log("Child Coroutine has finished!"); } IEnumerator ChildCoroutine() { yield return new WaitForSeconds(2); Debug.Log("Child Coroutine is done!"); } To start the parent coroutine, you would call StartCoroutine(ParentCoroutine());. This will in turn start ChildCoroutine, and only after it has completed will the parent coroutine proceed. Chaining Coroutines Sometimes you may want to execute coroutines in a specific sequence. This is known as chaining coroutines. You can chain coroutines by using yield return StartCoroutine() in sequence. Example: C# IEnumerator CoroutineChain() { yield return StartCoroutine(FirstCoroutine()); yield return StartCoroutine(SecondCoroutine()); } IEnumerator FirstCoroutine() { yield return new WaitForSeconds(1); Debug.Log("First Coroutine Done!"); } IEnumerator SecondCoroutine() { yield return new WaitForSeconds(1); Debug.Log("Second Coroutine Done!"); } Coroutines With async/await While coroutines are powerful, there are scenarios where the async/await pattern in C# might be more appropriate, such as I/O-bound operations. You can combine async/await with coroutines for more complex flows. Here's an example that uses async/await within a coroutine: C# using System.Threading.Tasks; IEnumerator CoroutineWithAsync() { Task<int> task = PerformAsyncOperation(); yield return new WaitUntil(() => task.IsCompleted); Debug.Log($"Async operation result: {task.Result}"); } async Task<int> PerformAsyncOperation() { await Task.Delay(2000); return 42; } In this example, PerformAsyncOperation is an asynchronous method that returns a Task<int>. The coroutine CoroutineWithAsync waits for this task to complete using yield return new WaitUntil(() => task.IsCompleted);. By understanding and applying these advanced coroutine patterns, you can manage complex asynchronous flows with greater ease and flexibility. This sets the stage for the next section, where we'll explore debugging techniques specific to Unity's coroutine system. Debugging Coroutines Debugging is an essential skill for any developer, and when it comes to coroutines in Unity, it's no different. Coroutines introduce unique challenges for debugging, such as leaks or unexpected behavior due to their asynchronous nature. In this section, we'll explore common pitfalls and introduce tools and techniques for debugging coroutines effectively. One of the most common issues with coroutines is "leaks," where a coroutine continues to run indefinitely, consuming resources. This usually happens when the condition to exit the coroutine is never met. Here's an example: C# IEnumerator LeakyCoroutine() { while (true) { // Some logic here yield return null; } } In this example, the coroutine will run indefinitely because there's no condition to break the while loop. To avoid this, always have an exit condition. C# IEnumerator NonLeakyCoroutine() { int counter = 0; while (counter < 10) { // Some logic here counter++; yield return null; } } Sometimes a coroutine may not behave as expected due to the interleaved execution with Unity's main game loop. Debugging this can be tricky. The simplest way to debug a coroutine is to use Debug.Log() statements to trace the execution. C# IEnumerator DebuggableCoroutine() { Debug.Log("Coroutine started"); yield return new WaitForSeconds(1); Debug.Log("Coroutine ended"); } The Unity Profiler is a more advanced tool that can help you identify performance issues related to coroutines. It allows you to see how much CPU time is being consumed by each coroutine, helping you spot any that are taking up an excessive amount of resources. You can also write custom debugging utilities to help manage and debug coroutines. For example, you could create a Coroutine Manager that keeps track of all running coroutines and provides options to pause, resume, or stop them for debugging purposes. Here's a simple example: C# using System.Collections.Generic; using UnityEngine; public class CoroutineManager : MonoBehaviour { private List<IEnumerator> runningCoroutines = new List<IEnumerator>(); public void StartManagedCoroutine(IEnumerator coroutine) { runningCoroutines.Add(coroutine); StartCoroutine(coroutine); } public void StopManagedCoroutine(IEnumerator coroutine) { if (runningCoroutines.Contains(coroutine)) { StopCoroutine(coroutine); runningCoroutines.Remove(coroutine); } } public void DebugCoroutines() { Debug.Log($"Running Coroutines: {runningCoroutines.Count}"); } } By understanding these common pitfalls and using the right set of tools and techniques, you can debug coroutines more effectively, ensuring that your Unity projects run smoothly and efficiently. Comparison With Other Asynchronous Programming Techniques Unity provides a range of tools to handle asynchronous tasks, with Coroutines being one of the most commonly used. However, how do they compare with other asynchronous techniques available in Unity, such as the Job System or C#’s `async/await`? In this section, we'll delve into the nuances of these techniques, their strengths, and their ideal use-cases. Coroutines Coroutines are a staple of Unity development. They allow developers to break up code over multiple frames, which is invaluable for tasks like animations or timed events. Pros Intuitive and easy to use, especially for developers familiar with Unity's scripting. Excellent for scenarios where tasks need to be spread over multiple frames. Can be paused, resumed, or stopped, offering flexibility in controlling the flow. Cons Not truly concurrent. While they allow for non-blocking code execution, they still run on the main thread. Can lead to performance issues if not managed correctly. Example: C# IEnumerator ExampleCoroutine() { // Wait for 2 seconds yield return new WaitForSeconds(2); // Execute the next line after the wait Debug.Log("Coroutine executed after 2 seconds"); } Unity’s Job System Unity's Job System is part of the Data-Oriented Tech Stack (DOTS) and offers a way to write multithreaded code to leverage today's multi-core processors. Pros True multithreading capabilities, allowing for concurrent execution of tasks. Optimized for performance, especially when paired with the Burst compiler. Ideal for CPU-intensive tasks that can be parallelized. Cons Requires a different approach and mindset, as it's data-oriented rather than object-oriented. Needs careful management to avoid race conditions and other multithreading issues. Example: C# struct ExampleJob : IJob { public float deltaTime; public void Execute() { // Example operation using deltaTime float result = deltaTime * 10; Debug.Log(result); } } C#’s async/await The async/await pattern introduced in C# provides a way to handle asynchronous tasks without blocking the main thread. Pros Intuitive syntax and easy integration with existing C# codebases. Allows for non-blocking I/O operations, like web requests. Can be combined with Unity's coroutines for more complex flows. Cons Still runs on the main thread, so not suitable for CPU-intensive tasks. Handling exceptions can be tricky, especially in the context of Unity. Example: C# async Task ExampleAsyncFunction() { // Simulate an asynchronous operation await Task.Delay(2000); Debug.Log("Executed after 2 seconds using async/await"); } Each asynchronous technique in Unity has its strengths and ideal scenarios. While coroutines are perfect for frame-dependent tasks, the Job System excels in handling CPU-bound operations across multiple cores. On the other hand, async/await is a powerful tool for non-blocking I/O operations. As a Unity developer, understanding the nuances of each technique allows for making informed decisions based on the specific needs of the project. Conclusion Unity's Coroutines are an integral tool in a developer's arsenal, acting as a bridge between synchronous and asynchronous programming within the engine. When used correctly, they can drive gameplay mechanics, manage events, and animate UI, all without overwhelming the main thread or creating jitters in performance. Over the course of this series, we've delved deep into the intricacies of coroutines. From understanding their foundational principles to exploring advanced patterns and debugging techniques, we've journeyed through the many facets of this powerful feature. By mastering these concepts, developers can ensure that their projects remain responsive, efficient, and bug-free. Beyond just the technical understanding, the true value of mastering coroutines lies in the enhanced gameplay experiences one can offer. Smooth transitions, dynamic events, and seamless integrations with other asynchronous techniques all become possible, paving the way for richer, more immersive games. In wrapping up, it's essential to remember that while coroutines are a formidable tool, they are just one of many in Unity. As with all tools, their effectiveness depends on their judicious use. By taking the lessons from this series to heart and applying them in practice, developers can elevate their Unity projects to new heights, ensuring both robust performance and captivating gameplay.