6 episodes

What does it take to run cloud native, production systems over 4 or even 12 years? This implies many upgrades, migrations & new features. Let's take a deep and long-term dive into what makes production work well.

Make It Work Gerhard Lazu

    • Technology

What does it take to run cloud native, production systems over 4 or even 12 years? This implies many upgrades, migrations & new features. Let's take a deep and long-term dive into what makes production work well.

    Modern CI/CD

    Modern CI/CD

    What does it look like to build a modern CI/CD pipeline from scratch in 2024? While many of you would pick GitHub Actions and be done with it, how do you run it locally? And what do you need to do to get caching to work?
    Tom Chauveau joins us to help Alex Sims build a modern CI/CD pipeline from scratch. We start with a Remix app, write the CI/CD pipeline in TypeScript and get it working locally. While we don't finish, this is a great start (according to Alex).
    This was recorded in January 2024, just as Dagger was preparing to launch Functions in the v0.10 release. While many things have improved in Dagger since then, the excitement & the joy of approaching CI/CD with this mindset have remained the same.
    LINKS
    🎬 Modern CI/CD from Scratch (using Dagger TypeScript Modules)🎉 Introducing Dagger Functions (a.k.a. Dagger Modules)🌌 DaggerverseEPISODE CHAPTERS

    (00:47) - Intro

    (01:35) - Current CI/CD pipeline

    (03:40) - Why not a single pipeline stage?

    (04:29) - Dagger expectations

    (05:18) - Thinking of retiring GitHub Actions

    (05:48) - Why the GitHub Actions & Jenkins split?

    (06:46) - TypeScript in Dagger Modules

    (08:40) - Modules extend the Engine API

    (09:45) - Plan for today

    (10:57) - Pairing session conclusions

    (12:11) - Is it faster?

    (13:10) - Re-using the cache between runs

    (14:50) - Key takeaways

    (19:04) - What comes next?

    (22:43) - Not if you are using Jenkins

    (23:33) - Thank you

    • 24 min
    Let's build a CDN

    Let's build a CDN

    This started as a conversation between James A Rosen & Gerhard in August 2023. Several months later, it evolved into a few epic pairing sessions captured in these GitHub threads:
    thechangelog#480 (reply in thread)thechangelog#486The last pairing session eventually lead to 🎧 Kaizen! Should we build a CDN? This is the follow-up to that. How far did we get in 1 hour?
    LINKS
    The 5-hour CDNvarnish - Docker Official ImageIntroduction to VarnishMagento2 Varnish configMagento Internals: Cache Purging and Cache TagsVarnish modulesEPISODE CHAPTERS

    (00:00) - Intro

    (02:08) - The 5-hour CDN

    (03:44) - Varnish container image

    (05:00) - Varnish container image command

    (06:31) - Local-friendly Varnish container image

    (06:44) - Varnish command-line options

    (08:30) - Varnish parameters

    (09:45) - Experimenting with Varnish locally

    (12:36) - Varnish purging

    (15:22) - Backend fetch failed

    (16:20) - Varnish debug mode & logs

    (17:29) - Why can't we query the backend?

    (21:08) - Why is the backend sick?

    (22:49) - That's the problem!

    • 24 min
    KubeCon EU 2024

    KubeCon EU 2024

    For our 4th episode, we have four conversations from KubeCon EU 2024.
    We talk to Jesse Suen about Argo CD & Kargo, Solomon Hykes shares the next evolution of Dagger, and Justin Cormack dives into Docker & AI. We also catch up with Frederic Branczyk & Thor Hansen on the latest Parca & Polar Signals Cloud updates since our last conversation.
    Each conversation has a video version too:
    Jesse Suen: 🎬 GitOps & ClickOps beyond KubernetesSolomon Hykes: 🎬 Pipelines as FunctionsJustin Cormack: 🎬 Works on my ComputerFrederic Branczyk & Thor Hansen: 🎬 always BPFLINKS
    1. Jesse Suen
    - What's New in Kargo v0.5.0
    - 🎬 Navigating Multi-stage Deployment Pipelines via the GitOps Approach 
    2. Solomon Hykes
    - Introducing Dagger Functions
    - A Keynote heard around the world - KubeCon EU 2024 Recap
    - 🎬 Local & open-source AI Developer Meetup
    3. Justin Cormack
    - AI Trends Report 2024: AI’s Growing Role in Software Development
    - Building a Video Analysis and Transcription Chatbot with the GenAI Stack
    4. Frederic Branczyk & Thor Hansen
    - Correlating Tracing with Profiling using eBPF
    LET'S MAKE IT BETTER
    If you enjoyed this episode, I will appreciate your feedback on Apple Podcasts or Spotify.
    If there is something that would have made it better for you, let me know: makeitwork@gerhard.io

    (00:00) - Intro

    (00:39) - Jesse Suen (JS)

    (00:56) - JS: Hardest ArgoCD question that you got today

    (01:54) - JS: Rendered YAML branches

    (04:06) - JS: What is top of your mind?

    (06:12) - JS: Kargo beyond Kubernetes

    (08:20) - JS: Trusting Kargo with production

    (09:49) - JS: GitOps for leadership, UIs for app devs

    (12:11) - JS: How is this KubeCon different?

    (12:55) - JS: Anything that you will do different after this KubeCon?

    (14:58) - Solomon Hykes (SH)

    (15:10) - SH: What are you most excited about?

    (16:12) - SH: What is different about functions this time?

    (16:40) - SH: What makes functions fun for you?

    (18:01) - SH: Anything significant that happened at this KubeCon?

    (19:38) - SH: Thoughts on Dagger in production

    (20:21) - SH: What does Dagger 1.0 mean?

    (21:28) - SH: Asks for the Dagger Community

    (23:04) - SH: How do Dagger SDKs work with Modules?

    (25:02) - SH: Thoughts on the tech industry

    (27:19) - Justin Cormack (JC)

    (27:35) - JC: Docker & AI

    (32:14) - JC: Docker Build Cloud

    (35:30) - JC: Web Assembly & WASM

    (39:37) - JC: KubeCon Community

    (42:01) - Frederic Branczyk (FB) & Thor Hansen (TH)

    (42:23) - TH: Excited to announce Polar Signals Cloud

    (42:47) - FB & TH: Most exciting feature since launch

    (45:24) - FB & TH: How is this KubeCon different?

    (47:14) - TH & FB: What are you going to do different after this KubeCon?

    (49:06) - FB & TH: Plans for next KubeCon?

    (50:24) - FB & TH: Anything apart from AI that is exciting?

    (51:25) - TH & FB: Any hot takes?

    (52:12) - Outro

    • 52 min
    80ms response SLO

    80ms response SLO

    Alex Sims, Solutions Architect & Sr. Software Engineer at James and James Fulfilment, talks about their journey to 80ms response SLO with PHP & React.
    Alex shares how they optimised API performance, specifically highlighting improvements made by altering interactions with Amazon S3 and Redis. Key points include the transition from synchronous to asynchronous S3 processes, the impact of Amazon's SLO on write speed, and the significant runtime reduction achieved through JIT compilation in PHP 8. 
    We conclude with insights into decision-making in technology architecture, emphasising the balance between choosing cutting-edge technology and the existing skill set of the team.
    🎬 View the video part of this episode at 80ms response SLO with PHP & React
    🎁 Access the audio & video as a single conversation at makeitwork.gerhard.io
    EPISODE CHAPTERS

    (00:00) - Introduction
    (00:50) - 2023: A Year of Productive Chaos at James & James
    (02:43) - Alex's Journey throughout our Conversations
    (03:33) - What does James & James do?
    (04:29) - What does Alex do at James & James?
    (05:33) - Technical Challenges in 2023
    (06:37) - Who is James?
    (08:21) - Why do you - Alex - do what you do?
    (10:52) - 2023 Highlights
    (16:22) - Where does Kubernetes fit?
    (18:03) - LESSON 1: Be aware of the different EC2 node type behaviour
    (21:05) - instances.vantage.sh
    (22:46) - LESSON 2: Understand the time cost of AWS S3 writes
    (24:19) - LESSON 3: Connecting to Redis is expensive
    (25:58) - Be careful when mixing persistent connections and transactions in Redis
    (26:41) - LESSON 4: Always check for SELECT *
    (28:15) - Lessons recap
    (29:35) - OODA
    (30:24) - SCREEN SHARING
    (31:02) - Wrap-up
    (35:58) - Planning for the next conversation

    • 37 min
    Automation Engine

    Automation Engine

    Today we delve into BuildKit and Dagger, focusing on their significance in the development and deployment of containerized applications, as well as Kubernetes integration.
    BuildKit's Role: Essential for anyone using Docker Build, facilitating efficient, dependency-aware container builds with advanced caching mechanisms. It's not just for Docker but serves as a versatile execution engine across various projects, including Dagger.Eric's Attraction to BuildKit: The power of BuildKit's DAG (Directed Acyclic Graph) execution model and its parallelization and deduplication capabilities drew Eric to maintain and contribute to the project.First BuildKit Project: Eric's initial project, bincastle, aimed to build a development environment from source, highlighting BuildKit's ability to handle complex builds.Introduction of Dagger: Dagger builds on top of BuildKit, enhances automation by allowing developers to use familiar programming languages without being confined to a specific domain-specific language (DSL). It aims to simplify and optimize automation tasks, particularly in CI/CD environments.Dagger's Enhancements over BuildKit: Dagger introduces a language-agnostic layer, making automation more accessible and scalable. It incorporates features like remote caching and a services layer, potentially positioning it as a simpler alternative to Kubernetes for certain use cases.Future Directions: The podcast touches on ongoing developments, such as modules for sharing automation code within Dagger, aiming to foster an ecosystem where developers can easily reuse and contribute to collective automation solutions.The conversation highlights the evolving landscape of development tools, where BuildKit and Dagger play pivotal roles in making containerized development and deployment more efficient and user-friendly. Eric and Gerhard discuss the potential for these tools to simplify and enhance automation, reflecting on past projects and future possibilities.
    🎬 View the video part of this episode: Deploying and Experimenting with Dagger 0.9 on Kubernetes 1.28
    🎁 Access the audio & video as a single conversation at makeitwork.gerhard.io
    LINKS
    sipsma/bincastlemoby/buildkitdagger/daggerdagger --mod github.com/sipsma/daggerverse/yamlinvaders@8071646e5831d7c93ebcd8cca46444250bf25b8c shell play
    EPISODE CHAPTERS

    (00:00) - Intro
    (01:43) - What attracted you to BuildKit?
    (03:23) - What was the first project that you used BuildKit for?
    (06:03) - Four years later, do you still want to see that idea through?
    (06:44) - What is Dagger?
    (08:18) - How much does Dagger add on top of BuildKit?
    (10:42) - How does Gerhard think of Dagger in relation to BuildKit?
    (12:48) - Dagger Modules - a way to share automation code
    (14:01) - If someone installs Dagger today, what happens under the hood?
    (14:47) - Why is the Engine distributed as a container image?
    (16:02) - If the Dagger Engine was a single binary, how would you run it?
    (17:05) - Thoughts on BuildKit caching?
    (18:15) - What about remote caching?
    (20:53) - Let's run Dagger on K8s on this Latte Panda Sigma
    (21:43) - SCREEN SHARE
    (22:06) - As we approach KubeCon, what is on your list?
    (23:19) - An idea for next time when we get together

    • 24 min
    How much CPU & Memory?

    How much CPU & Memory?

    This episode looks into the observability tool Parca & Polar Signals Cloud with Frederic Branczyk and Thor Hansen. We discuss experiences and discoveries using Parca for detailed system-wide performance analysis, which transcends programming languages.
    We highlight a significant discovery related to kube-prometheus and the unnecessary CPU usage caused by Prometheus exporter's attempts to access BTRFS stats, leading to a beneficial configuration change for Kubernetes users globally.
    We also explore Parca Agent's installation on Kubernetes 1.28 running on Talos 1.5, the process of capturing memory profiles with Parca, and the efficiency of the Parca Agent in terms of memory and CPU usage.
    We  touch upon the continuous operation of the Parca Agent, the importance of profiling for debugging and optimization, and the potential of profile-guided optimizations in Go 1.22 for enhancing software efficiency.
    🎬 Screensharing videos that go with this episode:
    First impressions: Parca Agent on K8s 1.28 running as Talos 1.5See where your Go code allocates memoryHow to debug a memory issue with Parca?See which line of your Go code allocates the most memory🎁 Access the audio & all videos as a single conversation at makeitwork.gerhard.io
    LINKS
    Go Profile-guided optimizationView Profiling Data within CodeAnnouncing Continuous Memory Profiling for RustEPISODE CHAPTERS

    (00:00) - Intro
    (02:21) - kube-prometheus discovery & fix
    (06:29) - Parca Agent on K8s 1.28 running as Talos 1.5
    (06:49) - How to capture memory profiles with Parca?
    (08:42) - pprof.me
    (10:42) - Data retention in Parca
    (11:42) - A real-world memory issue debugging example
    (16:05) - How much memory is Parca Server expected to use?
    (17:39) - How much memory is the Parca Agent expected to use?
    (19:42) - What about Parca Agent CPU usage?
    (21:57) - Is Parca Agent meant to run continously?
    (23:03) - Other Parca stories worth sharing
    (25:19) - What are the things that you are looking forward to in 2024?
    (27:23) - Golang Profile Guided Optimisations with Parca
    (30:22) - Frederic's surprise screen share
    (34:02) - Wrap-up

    • 35 min

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