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Rookout

rookout

KubeCon organizers released their recently and it seems very promising, it also has more sessions than ever before! As always, it’s a real challenge to pick the sessions that let you get the most out of your time at the show. agenda It took me a few rounds of whittling down to choose the sessions that I plan to attend. But based on my experiences in our own environment and what I’ve run into at our customers’ sites, I expect the sessions listed below to deliver great value. While we think these sessions will be helpful to many attendees, they might not be the best sessions for you. So don’t take our word for it — use our list to inspire your own thoughts about what you’d like to hear. Check out descriptions and speakers, and let the best (wo)man win your attendance!! Getting The Most Out Of Kubernetes with Resource Limits and Load Testing. Harrison Harnisch, Buffer Availability and performance are the key when it comes to running production workloads on Kubernetes. Resource limits are essential to ensure that multiple workloads (possibly from multiple environments) can run on the same cluster(s) without interfering with each other. But the trick is to know what those limits should be. For us, this is not idle speculation: We recently witnessed one container damage an entire cluster when enterprise system engineers got the resource limits wrong. We’re looking forward to every time! learning how to get it right, Front-end Application Deployment Patterns — Ross Kukulinski, Heptio If you’re already using Kubernetes for your backend, you might want to see what it can do for your frontend as well. Potential advantages include uniform CI/CD and monitoring, eliminating vendor lock-in and more. Running front-end applications on Kubernetes, however, can create a host of new problems, so before making a decision, it’s important to understand the relationship between frontend and K8s and about the pros and cons of such a move. hear from an expert Kubernetes and the GitOps Face-Off — Ricardo Aravena & Javeria Khan, Branch Metrics While Kubernetes opens a new era of an easy orchestration of cloud and other applications, it often feels like an endless number of YAML files must be configured to manage our servers. Git makes the process less error-prone but it still requires tedious repetitive set-ups. At Rookout, we have moved to Git-Ops to manage our workflows and simplify Kubernetes application set up. We’re counting on to update us on the essential tools that will make it a breeze for us — and you — to set up an application on Kubernetes. this session Automating Enterprise Governance Using the CI/CD Pipeline — Satyam Agarwala, ThoughtWorks & Mark Angrish, ANZ As developers, we love to focus on the code we write and the value we bring to our users. In many large organizations, however, developers are less free to focus on value-generating creativity, since they are bound by their applications requiring internal or external governance. Fortunately for us, the new GitOps paradigm, loved so much for its predictability and traceability, can serve the same function for enterprise governance. So instead of viewing CI/CD as the enemy of governance, provides practical tools on how to leverage the out-of-the-box traceable and predictable qualities of CI/CD to support governance. this session In addition to the sessions above, there are a couple of sessions on debugging that I think will be great. Do it Live: Measuring your Applications in Production — Jason Keene, Pivotal Because we’re all about improving observability of live code, about how to easily collect additional data from running clusters is right up our ally. the session Debugging Applications on Kubernetes — Michelle Noorali & Radu Matei, Microsoft This is an for us since it takes as a baseline that if you’re using Kubernetes, you need to get by without debuggers. Of course, we do allow developers to debug Kubernetes, but we’re very interested in discovering tools and techniques that complement Rookout and make debugging Kubernetes not only possible but (dare we say it?) even easy, sometime soon. interesting one Written by: Liran Haimovitch, Illustration by: Ortal Avraham. Originally published at www.rookout.com .

6 KubeCon US sessions you don’t want to miss

Serverless Technologies illustration/Ortal Avraham Serverless is hot in the world of software architecture. Many vendors dedicate themselves to serverless, and of course, Amazon, Google, IBM and Microsoft are heavily invested in it. But like any other hot technology, it has some drawbacks. In this post, I share a super-handy hack for those times when you need to build a local native extension. Necessity is the mother of Invention and our hack is no different. helps with production debugging across platforms ranging from monolithic to serverless but many of our customers are already using serverless more extensively, and they’ve driven us to dive deeper into this technology. Rookout Rookout support for Python and Node relies on native extensions to do its magic. If you’ve read this far, you probably know that running native extensions on Lambda and other serverless technologies require you to prepackage the correct binaries into the function zip file. Since runs on Amazon Linux, this can be a pain if you are running on Windows, Mac or Debian Linux. AWS Lambda The Amazon for building native extensions for Lambda builds them on a dedicated EC2 instance, which is far from the most pleasant experience. Many of our clients have asked us to help them build and deploy Lambda functions from any OS. recipe Following brainstorming sessions, some of our engineers came back with a nifty little hack I’m happy to share with you. Turns out, can easily perform a local task as if it was running on our own computer. The following command line taken from the official Docker allows you to run a Docker command within the current folder: Docker guide Run Docker in Current Directory $ docker run -v `pwd`:`pwd` -w `pwd` -i -t ubuntu pwd For Docker containers to simulate the AWS Lambda environment, we need to look no further than the LambCI project. To build AWS Lambda compatible native extensions, simply run the following command line: Node: Build Node Dependencies with Docker: docker run -v `pwd`:`pwd` -w `pwd` -i -t lambci/lambda:build-nodejs8.10 npm install Python: Build Python Dependencies with Docker: docker run -v `pwd`:`pwd` -w `pwd` -i -t lambci/lambda:build-python2.7 pip install -r requirements.txt We can make this even better by hiding the nitty gritty details of using Docker to build extensions by using scripts. For instance, let’s check out this package.json file that allows you to build extensions on the fly: package.json file for using docker { “name”: “example”, “main”: “index.js”, “dependencies” : { }, “scripts” : { “install-modules”: “docker run -it -v `pwd`:`pwd` -w `pwd` node:6 npm install”, “build-package”: “zip -r package.zip *”, “build”: “npm run install-modules && npm run build-package” } } And to build just run: npm run build Both our team and our customers find this hack to be a super-helpful time saver while developing Lambda functions. I hope you find it useful, too. Written by: Liran Haimovitch. Originally published at www.rookout.com .

3 Min Hack for Locally building a Native Extension

Why your devs suck at dev-on-call

Please tell us briefly about your background. My name is Liran Haimovitch and I have been programming both as a hobby, and professionally, for over 20 years. The first decade of my career was spent in the cybersecurity space as a researcher, developer, and an engineering leader. That is where I developed my deep passion to understand how computers actually work- from the bare metal all the way to the latest and greatest technologies. Today, I take great pride in bringing those insights with me into everything I do. What's your startup called? And in a sentence or two, what does it do? I’m the Co-Founder and CTO of Rookout, a platform that empowers engineers to see into their code with Dynamic Observability. Rookout allows developers to get any piece of information from your code as it’s running in any production or non-production environment. Rookout provides the capability to collect variable values, stack traces, and more, all without having to write any code, redeploy, or even restart your application. What is the origin story? Mary Poppendieck famously asked, "How long would it take your organization to deploy a change that involves just one single line of code?” This question is a double-edged sword. On the one hand, work on your CI/CD pipeline as blazingly fast as possible and deploy in a shorter time frame. On the other hand, figure out how to do more without having to resort to writing a line of code. Thinking back on our own experiences and those of our colleagues, we have all spent a great amount of time and effort on just getting that additional log line in place on the right server so that we are able to get a piece of data and maybe, just maybe, fix that bug. That’s why we created Rookout: to be able to do just that, without the waste of time, effort, and resources. What do you love about your team, and why are you the ones to solve this problem? How well do you understand the inner workings of your favorite operating system and development language? Whether it surprises you or not, not everybody looks too deep beneath the surface. The team at Rookout has that exact passion for figuring out how stuff works, how to take everything apart, and how to put it all back together. That’s what makes the team not only so amazing, but helps them to work together so well and continuously strive for bigger and better, together. Every day at our office our team ponders one single question: how can we bend the rules of the Matrix to get you the data you need on the fly? If you weren’t building your startup, what would you be doing? For years I have been working at a large organization and I feel that’s something I excel in. However, nothing beats the freedom of building your own startup. I’m sure I would have a lot more free time to travel, trek, and hike. Though maybe not during the pandemic... At the moment, how do you measure success? What are your core metrics? As an emerging company, nothing beats revenues to know you are bringing real value to your customers AND that they are willing to pay for it. In addition to that, we focus heavily on measuring how developers are using our products and spend a lot of time to make sure they have the best possible experience and get the most value out of it. What’s most exciting about your traction to date? About a year ago, we launched a new hybrid SaaS approach. Using this architecture, our customers can utilize our SaaS platform without sharing any data with us. Since then, some of the biggest Fortune 500 companies have deployed Rookout in their production environments. This has been a huge benefit for us, both for showing the importance of the problem we are solving and for validating the security posture of our solution. What technologies are you currently most excited about, and most worried about? And why? Over the past few months, we have been hard at work porting our technology to support native languages such as C++, Golang, and Rust. While using bytecode manipulation to instrument high-level applications is no small feat, moving to live assembly rewriting is a whole new ball game. I can’t wait to see this deployed in production! What drew you to get published on HackerNoon? What do you like most about our platform? Ironically enough, high-quality tech news is becoming a bit of a scarcity. Hacker Noon provides a single place with (almost) all the reading materials I need to keep up to date. Being able to read anonymously and freely is a huge benefit, and sponsored content is kept in check in a transparent manner. What advice would you give to the 21-year-old version of yourself? Within the field of programming and software, the value of communications is on a meteoric rise. Having a deep understanding of technology and being a super coder has immense value. However, being able to articulate it, both verbally and in writing, has 10x that value. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Product Led Growth (PLG) is all the buzz where it comes to tech startups these days. In certain segments, such as production-first developer tools, PLG can take an odd shape sometimes. If you are interested in learning more about what it looks like at , sign up and check it out! Rookout Vote Rookout as a , United States. Startup of the year in Palo Alto

On Dynamic Observability and Team Culture with Liran Haimovitch, Rookout CTO

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Once we press the merge button, that code is no longer our responsibility. If it performs sub-optimally or has a bug, it is now the problem of the team, the SRE, etc. Unfortunately, those teams work with a different toolset. If my code uses up too much RAM, they will increase RAM. When the code runs slower, they will increase CPU. In case the code crashes, they will increase concurrent instances. DevOps If none of that helps, they will call you up at 2 am. A lot of these problems are visible before they become a disastrous middle-of-the-night call. Yes. DevOps should control production, but the information they gather from production is useful for all of us. This is at the core of developer observability, which is a subject I’m quite passionate about. I’m so excited about it I dedicated a chapter to it in my . debugging book Back when I wrote that chapter, I dedicated most of it to active developer observability tools like Lightrun, Rookout, etc. These tools work like production debuggers; they are fantastic in that regard. When I have a bug and know where to look, I can sometimes reach for one of these tools (I used to work at Lightrun, so I always use it). But there are other ways. Tools like Lightrun are active in their observability. We add a snapshot similar to a breakpoint and get the type of data we expect. I recently started playing with , which takes a radically different approach to developer observability. To understand that, we might need to revisit some concepts of observability first. Digma Observability isn’t Pillars I’ve been guilty of listing the pillars of observability just as much as the next guy. They’re even in my book (sorry). To be fair, I also discussed what observability means… really Observability means we can ask questions about our system and get answers or at least have a clearly defined path to get those answers. It sounds simple when running locally, but when you have a sophisticated production environment, and someone asks you: is anyone even using that block of code? How do you know? You might have lucked out and had a log in that code, and it might still be lucky that the log is in the right level and piped properly so you can check. The problem is that if you added too many logs or too much observability data, you might have created a disease worse than the cure: over-logging or over-observing. Both can bring down your performance and significantly impact the bank account, so ideally, we don’t want too many logs (I ), and we don’t want too much observability. discuss over-logging here Existing developer observability tools work actively. To answer the question, if someone is using the code, I can place a counter on the line and wait for results. I can give it a week's timeout and find out in a week. Not a terrible situation but not ideal either. I don’t have that much patience. Tracing and OpenTelemetry It’s a sad state of affairs that most developers don’t use tracing in their day-to-day job. For those of you who don’t know it, it is like a call stack for the cloud. It lets us see the stack across servers and through processes. No, not method calls. More at the entry point level, but this often contains details like the database queries that were made and similarly deep insights. There’s a lot of history with , which I don’t want to get into. If you’re an observability geek, you already know it, and if not, then it’s boring. What matters is that OpenTelemetry is taking over the world of tracing. It’s a runtime agent, which means you just add it to the server, and you get tracing information almost seamlessly. It’s magic. OpenTelemetry It also doesn’t have a standard server which makes it very confusing. That means multiple vendors can use a single agent and display the information it collects to various demographics: A vendor focused on performance can show the timing of various parts in the system. A vendor focused on troubleshooting can detect potential bugs and issues. A vendor focused on security can detect potential risky access. Background Developer Observability I’m going to coin a term here since there isn’t one: Background Developer Observability. What if the data you need was already here and a system already collected it for you in the background? That’s what Digma is doing. In Digma's terms, it's called . Essentially, they’re collecting OpenTelemetry data, analyzing it, and displaying it as information that’s useful for developers. If Lightrun is like a debugger, then Digma is like SonarQube based on actual runtime and production information. Continuous Feedback The cool thing is that you probably already use OpenTelemetry without even knowing it. DevOps probably installed that agent already, and the data is already there! Going back to my question, is anyone using this API? If you use Digma, you can see that right away. OpenTelelbery already collected the information in the background, and the DevOps team already paid the price of collection. We can benefit from that too. Enough Exposition I know, I go on… Let’s get to the meat and potatoes of why this rocks. Notice that this is a demo. When running locally, the benefits are limited. The true value of these tools is in understanding production; still, they can provide a lot of insight even when running locally and even when running tests. Digma has a simple and well-integrated setup wizard for IntelliJ/IDEA. You need to have Docker Desktop running for setup to succeed. Note that you don’t need to run your application using Docker. This is simply for the Digma server process, where they collect the execution details. Once it is installed, we can run our application; in my case, I just ran the JPA unit test from my latest book, and it produced standard traces, which are already pretty cool. We can see them listed below: When we click a trace for one of these, we get the standard trace view, this is nothing new, but it’s really nice to see this and readily accessible. I can imagine the immense value this will have for figuring out CI execution issues: information directly in the IDE But the real value and where Digma becomes a “Developer Observability” tool instead of an Observability tool is with the tool window here: There is a strong connection to the code directly from the observability data and deeper analysis, which doesn’t show in my particular overly simplistic hello world. This Toolwindow highlights problematic traces and errors and helps understand real-world issues. How Does This Help at 2 am? Disasters happen because we aren’t looking. I’d like to say I open my observability dashboard regularly, but I don’t. Then when there’s a failure, I take a while to get my bearings within it. The locality of the applicable data is important, and it helps us notice issues when they happen. Detect regressions before they turn to failures and understand the impact of the code we just merged. Prevention starts with awareness, and as developers, we handed our situational awareness to the DevOps team. When the failure actually happens, the locality and accessibility of the data make a big difference. Since we use tools that integrate into the IDE daily, this reduces the meantime to a fix. No, a background developer observability tool might not include the information we need to fix a problem. But if it does, then the information is already there, and we need nothing else. That is fantastic. Final Word With all the discussion about observability and open telemetry, you would think everyone is using them. Unfortunately, the reality is far from that. Yes, there’s some saturation and familiarity in the DevOps crowd. This is not the case for developers. This is a form of environmental blindness. How can our teams, who are so driven by data and facts, proceed with secondhand and often outdated data from OPS? Should I spend time further optimizing this method, or will I waste the effort since few people use it? We can benchmark things locally just fine, but real-world usage and impact are things that we all need to improve. Also published . here

Do You Know What Your Code Is up to at 2 Am?

Let's learn about via these 74 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Observability /Learn Repo 1. So, How are Observability and Monitoring Different, Actually? The similarities and differences between monitoring and observability and how to pair the two strategies 2. Observability Tips and Tricks For Using Grafana and Prometheus Rapid prototyping tips when using Grafana and Prometheus observability and monitoring stack. 3. Observability and Monitoring in a nutshell A guide to understanding the concepts and differences between monitoring and observability. 4. Debugging AWS Lambda Runtime Errors Debugging AWS Lambda can get tricky in Amazon CloudWatch. KloudMate lets you get to Lambda errors quickly and effectively using Lambda error logs. 5. An Intro to Dynamic CDK Dashboards How to create a construct based on other constructs in the same stack without forcing the developer to call a method for each? That's exactly what aspects do! 6. N/A please delete this 7. Developer Trends from Thoughtworks’ Technology Radar 27 Thoughtworks’ Technology Radar is a regular time to take a look at what trends are changing our industry. 8. How Observability and Monitoring Produces Better Software An article focused on deep diving into observability and its significance in software. Its history, goals, the importance of observability, and the issues that 9. How to Build Your Own Observability Platform on Kubernettes End-to-end full observability using Odigos and other back-end observability tools. 10. Maintaining Quality When Transitioning from Monolith to Microservices Piece by piece, legacy monolith applications are being broken down and replaced by microservices. 11. How to Monitor Apache Flink with OpenTelemetry Apache Flink monitoring support is now available in the open source OpenTelemetry collector. 12. Observability vs Monitoring: What's the Difference? Monitoring has been a basic system to track the health of servers for years. Now it is not enough. 13. Observability and Monitoring Have a Symbiotic Relationship, but They Are Different Observability vs. monitoring, what is the difference? Monitoring is the what to observability’s why. Here we dig into the differences. 14. Microservice.add(observability) != Microservice.add(monitoring) You are reading this content, which means that you are not novice to the microservices field. So let me just  scratch the surface of it before moving to Observable Microservices.  Once upon a time Monolith Application was now transformed into Microservices based application. 15. How Do You Make Cloud Migration Easier? Engage in intelligent change. 16. Application Monitoring: Closing Observability Gaps with Custom Metrics Which application metrics should you collect for your microservices? 17. The Challenges of Modern Observability It’s tough, impossible, to get a holistic view of your entire application when it’s running on 200 pods deployed to 12 nodes distributed across the world. 18. Why OpenTelemetry Should Matter to Network and Systems Admins OpenTelemetry is young, even by internet standards. Born out of the merger of OpenTracing and OpenCensus projects at the Cloud Native Computing Foundation CNCF 19. What is the ideal memory size to lower costs of running a task on Lambda? Should you increase Lambda memory? It might sound crazy, but increasing your AWS Lambda memory could actually lower your bills. Find out how. 20. What "Shifting Left" in Software Really Means for Blameless DevOps A truly "blameless culture" in software must to evolve from incident reporting, to telemetry aimed at Proactive Observability in DevOps 21. CI/CD/CF — The "Missing-Link" in the DevOps Toolchain? Everyone is familiar with CI and CD processes, but whatever happened to Continuous Feedback? Leveraging observability in dev creates a new type of dev process 22. Why Your Monitoring Dashboard May Be Feeding You Phantom Metrics We trust our metrics to show us the status of our system and where it misbehaves. But do our metrics show us what really happened? 23. Cloud Native Applications: 4 Building Blocks You Need in Your Platform To create amazing cloud native applications your platform needs these components: Kubernetes, Continuous Delivery Pipelines, Edge Stack, and Observability. This article will explain the importance of each component. 24. Comparing Different Serverless Monitoring Platforms Technology touches almost every corner of the world economy. Even when it’s an indirect relation, in many cases tech is an essential, vital part of our societies. It just can’t fail without causing too much distress and losses. Not only financially, but especially to the human aspect. 25. Get Started With Sidekick Open Source Live Debugger in 5 Mins Sidekick is a live application debugger that lets you troubleshoot your applications while they keep on running. Here is how you can start using it in 5 minutes 26. Logging in Observability - Part 1 rsyslog vs journald vs filebeat 27. Managing Collectors on K8 – Why We Chose the OpenTelemetry Collector for Odigos Without proper implementation and continuous configuration of your collectors, observability tools will be limited at best at best and many times ineffectual. 28. On Dynamic Observability and Team Culture with Liran Haimovitch, Rookout CTO Rookout Co-Founder and CTO, Liran Haimovitch, shares the origin story of their debugging tool, what excites him about the startup life, PLG, and more. 29. 9 Data Trends You’ll See in 2023 2022 saw the data space grow by leaps and bounds. Here are the top 9 things our team of data experts expects to see in 2023. 30. Observability and AI Partner Well to Tackle All of That Data Gone are the days when operations and development were a back-office affair. 31. Logging in Observability - Part 2 In this article, I want to describe how to work with logs, analyze them in the command line, and we will consider new modern tools to visualize logs. 32. Tracing: An Essential Component of Observability In this article, I am going to shed light on one important aspect of observability - tracing. 33. Microservice Observability Patterns [Part 1] Logging is one of the most important parts of software systems. Whether you have just started working on a new piece of software, or your system is running in a large scale production environment, you’ll always find yourself seeking help from log files. Logs are the first thing people look for when something goes wrong, or something doesn’t work as expected. 34. What is Observability? Is it Cultural? In order to leverage observability we need a significant shift in our corporate culture that encapsulates the entire company & goes beyond the tools. 35. Murre - the lightweight K8s metrics monitoring tool 36. Introductory Guide to Debugging Lambda Configuration Errors The “Lambda configuration error” is as generic as it gets but at the end of the day, it's a pathing issue. Let's go over how to resolve this. 37. Contextual Intelligence and Observability: Without the Former, You Really Don’t Have the Latter Observability is a hot term in the industry, but don’t let it fool you: having visibility into your organization's apps and services only gives you a piece. 38. The Everything Guide to Data Collection in DevSecOps Collecting wide swaths of observability and security data is key to a high-quality digital experience. Find out what you need to know to get started. 39. Better Performance and Security by Monitoring Logs, Metrics, and More Monitoring is a crucial part of observability. Learn how monitoring can specifically improve security, performance, and reliability. 40. Goodbye, Cost-depth Trade Off: Cloud-Native Observability, Redefined It turns out that, by rethinking the fundamentals of your approach to monitoring, you can have it all 41. Prometheus — Easy In, Difficult Out Setting up a Prometheus server can be easy, but getting actionable information out is a very long and never-ending journey. 42. Choosing a Computing Method: a Serverless SWOT Analysis If you ever find yourself deciding for or against serverless the following tries to make the decision easier for you. 43. Setting Up AWS CloudWatch Alerts (vs Dashbird Alerts) To Monitor Your Applications Learn about the best practices for AWS Cloudwatch Alerts and Dashbird Alarms, to not miss out on critical info about your serverless app. 44. Exploring Differences Between Monitoring And Observability Monitoring vs Observability: in this article, we're explaining what is observability exactly and how does it differ from monitoring. 45. Kuma and Prometheus for Observability in Kubernetes Microservices Clusters A year ago, Harry Bagdi wrote an amazingly helpful blog post (link at bottom of article) on observability for microservices. And by comparing titles, it becomes obvious that my blog post draws inspiration from his work. 46. To Log Or Not To Log, Is The (Eternal) Question An alternative logging strategy to make loggers your friends, not enemies 47. 2023 Observability Predictions: Open Source is the Key to Explosive Growth The observability industry is projected to grow about 8% in 2023, a continuation of a strong growth trend since the 90’s. 48. How Goji Investments Enhances Developer Experience via Observability This post was written by Dean Record, Engineer at Goji Investments. 49. What is API Observability? APIs are the blood vessels of digital business. 50. A Guide to Deploying Jaeger on Kubernetes in Production Logs, metrics, and traces are the three pillars of the Observability world. The distributed tracing world, in particular, has seen a lot of innovation in recent months, with OpenTelemetry standardization and with Jaeger open source project graduating from the CNCF incubation. 51. How To Use a FastAPI Abstraction To Query AWS Data Lake Our guide in creating FastAPI abstraction that allows us to query the AWS Glue metadata catalog - a Data Discovery API for AWS Data Lake. 52. Sizing Your Cloud Resources: 10 Common Mistakes to Avoid Learn the common pitfalls of moving to the cloud and how you can avoid them to truly benefit from the cloud’s elasticity. 53. CNCF Tools Overview: Are You Cloud-Native? Cloud computing is becoming more and more of a household name, with even the most conservative fields of business figuring out how to make the best use of it. Cloud computing usually starts with running a private cloud solution on premises before venturing onto the public cloud. Of course, the cloud is not a single uniform being. It may come from different providers, Amazon Web Services, Google Cloud Platform, and Microsoft Azure being the biggest players here. Or it may come with different visibility and hosting, that is, public (resides with the provider), private (self-hosted), or hybrid (which uses a bit of both). And the cloud can use different tools and APIs for management as well. 54. Microservice Observability Patterns [Part 2] In my previous article, I talked about the importance of logs and the differences between structured and unstructured logging. Logs are easy to integrate into your application and provide the ability to represent any type of data in the form of strings. 55. Improving Observability Through Service Level Metrics 56. Observability on Heroku: How to Monitor Apps on a Managed Infrastructure Cloud platforms like Heroku make it easier than ever to host applications: just upload your code, and they’ll deploy it for you. But a common misconception is that, because you don’t own the infrastructure, you can’t really monitor your applications or see under the hood. 57. Setting Up Notification Systems to Observe and Analyze Your Application By building observability and analytics into your notification system, you can identify and quickly resolve issues by monitoring how your product is performing. 58. Better Failure Detection in Serverless Apps Having reliable failure detection in place is a must for any application in production. Here's how to best achieve it for AWS Lambda. 59. 4 Key Observability Metrics for Distributed Applications A common architectural design pattern these days is to break up an application monolith into smaller microservices. Each microservice is then responsible for a specific aspect or feature of your app. For example, one microservice might be responsible for serving external API requests, while another might handle data fetching for your frontend. 60. Lua-Land CPU Flame Graphs in OpenResty XRay This post will introduce the idea of Lua-land CPU flame graphs and use OpenResty XRay to produce real flame graphs for several small and standalone Lua examples 61. 5 Key Takeaways from New Relic’s 2022 State of Logs Report The 2022 State of Logs Report provides a detailed view of how this practice is shaping engineering and the technologies of the future. 62. Introduction to Observability in ITOM and AIOps Observability is a best practice implemented by AIOps, enabling automation and expanding visibility into the entire organizational ecosystem. 63. Internal AWS Monitoring is Hard to Grasp Learm how a serverless monitoring solution can catch problems for you without the painful learning curve connected to serverless failure detection. 64. Jaeger Persistent Storage with Elasticsearch, Cassandra and Kafka Running systems in production involve requirements for high availability, resilience and recovery from failure. When running cloud-native applications this becomes even more critical, as the base assumption in such environments is that compute nodes will suffer outages, Kubernetes nodes will go down and microservices instances are likely to fail, yet the service is expected to remain up and running. 65. How to Monitor Serverless Applications With AWS CloudWatch Alarms Running any application in production assumes reliable monitoring to be in place and serverless applications are no exception. 66. How to Optimize Large Scale Serverless Applications for Operational Excellence Managing applications at scale often comes up as one of the biggest concerns for businesses; How can it work smoothly? How do we monitor so many resources? How do we maintain best practices with constantly evolving infrastructure? In this article, we run through the best approach for operational excellence looking at serverless monitoring strategy, serverless alerting strategy, and security and compliance best practices. 67. Fixing The ClickHouse Node Failure On Distributed Systems - A How-To Guide Part One: ClickHouse Failures, by Marcel Birkner 68. Introduction To Distributed Tracing Pattern A distributed architecture brings in several challenges when it comes to operability and monitoring. Here, one may be dealing with tens if not hundreds of microservices, each of which may or may not have been built by the same team. 69. 10 Threats to an Open API Ecosystem Despite tight economic situations worldwide, the API economy continues to grow. 70. Data Access for Microservices If you want to access data in a distributed environment such as in a microservice architecture, then data services are the way to go. The idea is to create a data abstraction layer (DAL) that the rest of the system’s applications and services can share. Thus, a data service gives you a generalized interface to the data you’re exposing and provides access to it in a standard manner. This would be in a well-understood protocol and a known data format. For example, a popular approach is to use JSON via HTTP/S. 71. Rethinking Programming: Automated Observability Introduction 72. Watching the Requests Go By: Reconstructing an API Spec with APIClarity In this post, we’re going to look at what API reconstruction is and how APIClarity solves the API observability problem. 73. Navigate Your Way to Production Bliss with Caretta The easiest way to map our cluster: Caretta - a standalone OSS tool, leveraging eBPF technology to be lightweight and frictionless 74. The Importance of Monitoring Big Data Analytics Pipelines In this article, we first explain the requirements for monitoring your big data analytics pipeline and then we go into the key aspects that you need to consider to build a system that provides holistic observability. Thank you for checking out the 74 most read stories about Observability on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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