Debugging n8n SplitInBatches: A Lesson in Workflow Design

The distributed nature of #K8s means a single change can have cascading effects, leaving developers piecing together clues from disparate tools. This fragmented approach inflates mean time to resolution (MTTR) and burns valuable engineering cycles. Komodor provides a unified view, correlating every change to its impact across your entire stack, giving your team constant visibility and accurate root cause analysis. In this post, we cover the cert-manager add-on and explain a scenario where a single issue can cascade into multiple cluster errors. Read more >>> https://lnkd.in/dT7mW9Qm

Cert-manager automates the entire TLS certificate lifecycle in #Kubernetes by handling provisioning, renewal, and deployment of certificates from various Certificate Authorities like Let’s Encrypt, eliminating manual certificate management tasks. The complexity lies in its deep integration with multiple CAs, DNS providers, ingress controllers, and Kubernetes APIs while managing domain validation challenges, certificate renewals, and zero-downtime updates across potentially hundreds of services. Read below how #Komodor simplifies the use of cert-manager.

✅️ Imagine a service that doesn't just tell you when it has crashed, but tells you when it's about to have a problem. Imagine a data entry form that doesn’t just validate data types, but warns a user when a value, while technically correct, is statistically improbable and likely a mistake. 🌍 This is the shift from reactive software to proactive, self-aware systems. Traditional monitoring relies on fixed thresholds (e.g., "alert me if CPU is over 90%"). This approach is often rigid and can lead to either missing subtle problems or being overwhelmed by false alarms. https://lnkd.in/dKx9JRvF

The 5-Minute Debug Checklist ✅ When pods won't schedule, run this exact sequence: # 1. What does the scheduler see? kubectl describe pod <stuck-pod> | tail -20 # 2. Node resource reality check kubectl describe nodes | grep -A15 "Allocated resources" # 3. Taint and condition audit kubectl get nodes -o custom-columns=NAME:.metadata.name,TAINTS:.spec.taints,CONDITIONS:.status.conditions[*].type # 4. CNI health verification kubectl get pods -n kube-system -o wide | grep -v Running # 5. Recent events that might explain it kubectl get events --sort-by='.lastTimestamp' | tail -20 The Plot Twist 🔄 90% of the time, it's not what you think: Looks like: Resource shortage Actually is: Taint from temporary disk pressure Looks like: CNI failure Actually is: Pod anti-affinity rules Looks like: Node failure Actually is: Scheduler bug with custom resource types The lesson: In Kubernetes, "healthy" components can still create cascading failures. This kind of multi-layered debugging is exactly what we dive deep into at kubenatives. Link in the comments.

Prompting and generating workflows for your n8n solution(s) directly from an LLM is one of the least preferred ways to start out a workflow automation task. 80% of the times, I go back to the workflow plan and architect my way up again. LLMs are best suited in this case for debugging a JSON function script or helping you out when you get stuck. #AIAutomation #n8n

5 Core n8n Nodes Every Automator Should Master If you’re exploring automation with n8n, you don’t need to know all 400+ nodes. But these 5 will unlock most of what you want to build: HTTP Request Connect with any API, even if n8n doesn’t have a built-in integration. It’s your “universal connector.” Webhook Receive real-time data from external apps and trigger workflows instantly. Perfect for live updates and event-based automation. Router Split your workflow into multiple paths. For example: send one type of data to Google Sheets, and another to Slack. Function Add custom JavaScript logic when you need flexibility. Great for transforming data beyond the standard nodes. Set Format, filter, and clean your data before passing it forward. This keeps workflows neat and prevents errors. Understanding these core nodes is like knowing the basics of coding, they give you the foundation to build almost anything. #n8n #Automation #NoCode #Productivity #BusinessGrowth

𝗪𝗵𝘆 𝗠𝗶𝘀𝘀𝗶𝗼𝗻-𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗧𝗿𝘂𝘀𝘁 𝗦𝘁𝗮𝘁𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 🚀 When I started out, my firmware was a tangle of if-else checks that “just worked”; until they didn’t. One unexpected sequence and debugging turned into guesswork. 𝗙𝗶𝗻𝗶𝘁𝗲 𝗦𝘁𝗮𝘁𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 (𝗙𝗦𝗠𝘀) fixed that. Think of a rocket launch: 𝘍𝘶𝘦𝘭𝘪𝘯𝘨 → 𝘊𝘰𝘶𝘯𝘵𝘥𝘰𝘸𝘯 → 𝘐𝘨𝘯𝘪𝘵𝘪𝘰𝘯 → 𝘚𝘵𝘢𝘨𝘦 𝘚𝘦𝘱𝘢𝘳𝘢𝘵𝘪𝘰𝘯 → 𝘖𝘳𝘣𝘪𝘵. Each step has strict entry/exit rules. That same principle : explicit states and transitions makes mission-critical systems predictable. 𝗪𝗵𝗮𝘁’𝘀 𝗮 𝗦𝘁𝗮𝘁𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲? ⚙️  • State → what the system is doing now (INIT, IDLE, RUN, ERROR).  • Transition → the event that moves it (interrupt, timer, command, fault). 𝗪𝗵𝘆 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗥𝗲𝗹𝘆 𝗼𝗻 𝗙𝗦𝗠𝘀 ✨  • 𝗥𝗲𝗮𝗱𝗮𝗯𝗹𝗲 & 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 → no spaghetti, each state has isolated logic.  • 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰 → only one active state at a time → predictable behavior.  • 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 → add a new feature = just a new state + transitions.  • 𝗧𝗲𝘀𝘁𝗮𝗯𝗹𝗲 & 𝘁𝗿𝗮𝗰𝗲𝗮𝗯𝗹𝗲 → log transitions for debugging, safety, or compliance (ISO 26262, DO-178C).  • 𝗣𝗲𝗿𝗳𝗲𝗰𝘁 𝗳𝗼𝗿 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 & 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 → UART parsing, BLDC/PMSM drive control, error recovery. 𝗖𝗼𝗱𝗲 𝗦𝗻𝗮𝗽𝘀𝗵𝗼𝘁 💻 typedef enum { INIT, IDLE, RUN, ERROR } state_t; state_t state = INIT; void loop() {   switch(state) {     case INIT: init_hw(); state = IDLE; break;     case IDLE: if(start) state = RUN; break;     case RUN:  if(fault) state = ERROR; else run_control(); break;     case ERROR: safe_shutdown(); break;   } } 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗧𝗶𝗽 📊 Instead of logging everything, just log <timestamp + state ID> for each transition. This acts like a "mini black-box recorder" => enough to reconstruct what happened before a crash, without wasting memory. 𝗔𝗹𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝘃𝗲𝘀 ⚡  • Event-driven → RTOS queues, message passing.  • Table-driven FSMs → great for very large systems.  • Flag-based loops → fine for simple logic. I’ve used FSMs in PMSM and BLDC motor-control firmware, where one wrong transition can stall a motor. State machines saved me countless hours and prevented real-world faults. 👉 How do you structure your firmware: FSM, events, or flags? #Embedded #Firmware #StateMachine #MotorControl #RTOS #IoT

Another bit of value rolled into #rlvgl. This is independent of lvgl and screen support but is an obstacle to anyone standing up a #embedded #rust project right now. The configuration of modern micro controller is complex. Some live off of device tree in the line world. In the bare metal and RTOS world, Vendors tend to have a tool that helps you set this up. For rust, there is a well standardized WAY of setting up HAL and PAC code in a vendor agnostic way, but the vendors have not caught up, so the vendor knowledge of clock trees and special cases is can be lost of become a stumbling block. Since I want to demo rvlgl on real hardware, I need this layer and rather than just write a bit of rust code and work it out, I converted the STM open pin data repo to a rust binary which includes a .ioc se-serializer which stored the data in a vendor agnostic way. This then feeds templates for generating rust bsp code, allowing rlvgl-creator to generate all of the pin, peripheral and interrupt configuration via the agnostic #hal / #pac APIs based on the custom configuration generated as a .ioc file by #cubemx. This was a quick #dal_e sketch, so the arrow on the right side is wrong - rlvgl-creator generates the PAC/HAL code in a vendor agnostic way, translating from vendor specific tool files. First up #stmicro ...

Want to get better at debugging? These tips can help. Replicate quickly: Use a small script in your local environment, or mock dependencies(network, db, package related if possible). Speed up the process so you don’t have to wait long to reproduce the problem. Read carefully: Understand the exception and stack trace before searching for solutions. Consider alternatives: Don’t stop at the first fix. Explore at least a couple of approaches. Take notes: Record what worked, what didn’t, and any insights gained. Reflect and improve: Think about logs, comments, naming, exception handling, and include these improvements in your fixes so that you can fix future bugs in less time. What’s helping you debug well? Share in the comments below.

Recently, one of my clients complained to me that, "most of the automation he has Breaks after a while" and i think this is one of the important things to learn if you want to build Automation that is Scalable and faster. Heres one video that i highly recommend for a beginner on that can be implemented on every Automation working with n8n by Nate Herkelman. https://lnkd.in/giDdtYJH