Why w3wp.exe is maxing out your CPU

Shipping beats debating. Here is how we stopped a DB bottleneck without a rewrite. Symptom API P95 up 2.1× during batch events. Signal Profiling showed DB waits, not Ruby CPU. Fixes - Added a covering index - Removed one N+1 - DB pool: 5 → 12 - Moved a heavy report to a background job Result P95 down 48%. Queue times flat. Infra cost unchanged. Takeaway / Question When perf dips, where do you look first: app CPU, DB waits, or network? #SoftwareEngineering #Backend #RubyOnRails #Postgres #Performance #Architecture

𝐀𝐬𝐲𝐧𝐜/𝐚𝐰𝐚𝐢𝐭 𝐢𝐬 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥, 𝐛𝐮𝐭 𝐢𝐭’𝐬 𝐧𝐨𝐭 𝐭𝐡𝐞 𝐰𝐡𝐨𝐥𝐞 𝐬𝐭𝐨𝐫𝐲 𝐢𝐧 .𝐍𝐄𝐓. In this guide, we cover: ✅ What the Task Parallel Library (TPL) is ✅ Parallel.For, Task continuations, and PLINQ ✅ When to use TPL vs async/await ✅ Best practices for CPU-bound workloads 👉 Link in the comments #DotNet #CSharp #TPL #Multithreading #Concurrency #Performance

How many asterisk disclaimers should be added to "We build, we run it"? Yes, we run it, but we don't own all the code for our dependencies.  Yes, we run it, but we don't own the Kubernetes codebase. Yes, we run it, but we don't own the codebase of hypervisors of virtual machines. Yes, we run it, but we don't own the operating system's core source and network drivers on all devices within the connection. Yes, we run it, but we don't make our own CPUs and their microcode. We checked the box that we understand it's working sometimes, and we rely on this as a base-level guarantee. I'm still loving this motto, just what do we mean really?

A simple server migration from x86 to ARM64 spiraled into a full Kubernetes debugging saga. That familiar 'this should be simple' turning into 'why am I debugging IPv6 routing at 4 AM?' experience. More: https://ku.bz/svxMcSqWJ

Question for you: Lets say you want to send some data/content only to a few "interested" nodes in your network (might or might not cross router) from your machine/server, how would you do so? Well, the simplest way would be to send the same data iteratively, or in parallel in different threads/processes, individually to all the desired nodes. But in this case you are wasting resources on the sender machine and bandwidth as well. Of course the other approach you can take is UDP broadcast, but in this case all the nodes in your network or broadcast domain will get the data, not just the interested nodes. So whats a more suitable way? Enter Multicast! With multicast, the transmitter machine can send udp packets to something known as the multicast group IP adress and all the interested nodes that want to receive this data can become a member of this multicast group in order to receive the data. To become a member of a multicast group all that the receiver has to do is use the socket option IP_ADD_MEMBERSHIP on the receiving socket. This will let the router know which nodes are interested in receiving the data for a given multicast group using a protocol known as IGMP or the Internet Group Management Protocol Multicast networking provides an out of the box subscription based transmission and receiving system for your network nodes. If you too find multicast interesting, want to know more about it or want to see it in practice, do check out this video where I have discussed setting up basic udp multicast sender and receivers on Linux and tried sending multicast messages hands-on. https://lnkd.in/gVEcmgiA Also, do comment below if you have used multicast networking in any of your projects. #computernetworks #linux #networking #sockets #udp #multicast #broadcast #cprogramming #linuxnetworking #programming #systems #networkprotocols

Go 1.25 just introduced container-aware GOMAXPROCS defaults. For most people outside infra this might sound like a minor runtime detail, but it’s a pretty big deal if you’re running Go apps in Kubernetes or any container platform. Before, Go simply set GOMAXPROCS to match the number of CPU cores on the machine. Which meant that if your container had a CPU limit set lower than the machine’s cores, Go would still try to use more threads than it was allowed. The result: the Linux kernel throttled you in 100ms chunks. That’s wasted cycles and ugly tail latency spikes. Now Go looks at the container CPU limits and adjusts automatically. No more mismatched defaults, no more silent throttling ruining your p99. If the orchestrator changes the limit on the fly, Go adapts on the fly too. It’s one of those changes that feels small, but in practice it makes Go apps more predictable and less surprising out of the box. Less time debugging weird latency, more time building the actual product. I’m curious, how often do you explicitly tune GOMAXPROCS in your services, or do you mostly let the runtime handle it?

Install and Configure #Cacti on #AlmaLinux #VPS This article provides a guide demonstrating how to install and configure Cacti on AlmaLinux VPS. What Is Cacti? Cacti is an open-source network monitoring and graphing tool built on top of RRDtool. It’s designed to collect, store, and visualize time-series data from networks and systems. What Cacti Does Polls data from devices using #SNMP or scripts (e.g., CPU load, memory usage, interface traffic) Stores data efficiently in round-robin ... Keep Reading 👉 https://lnkd.in/g4_62RY3 #letsencrypt #selfhosted #selfhosting #opensource #rrdtool #mariadb

Install and Configure #Cacti on #AlmaLinux #VPS This article provides a guide demonstrating how to install and configure Cacti on AlmaLinux VPS. What Is Cacti? Cacti is an open-source network monitoring and graphing tool built on top of RRDtool. It’s designed to collect, store, and visualize time-series data from networks and systems. What Cacti Does Polls data from devices using #SNMP or scripts (e.g., CPU load, memory usage, interface traffic) Stores data efficiently in round-robin ... Keep Reading 👉 https://lnkd.in/gPbHeUHi #letsencrypt #selfhosting #rrdtool #mariadb #selfhosted #opensource

Install and Configure #Cacti on #AlmaLinux #VPS This article provides a guide demonstrating how to install and configure Cacti on AlmaLinux VPS. What Is Cacti? Cacti is an open-source network monitoring and graphing tool built on top of RRDtool. It’s designed to collect, store, and visualize time-series data from networks and systems. What Cacti Does Polls data from devices using #SNMP or scripts (e.g., CPU load, memory usage, interface traffic) Stores data efficiently in round-robin ... Keep Reading 👉 https://lnkd.in/gPbHeUHi #rrdtool #mariadb #opensource #selfhosted #selfhosting #letsencrypt

You would have encountered unrealistically large packet sizes while analyzing tcpdump! The reason isn't a network glitch, it's Generic Receive Offloading (GRO), and it's a huge win for performance. GRO is a software technique that significantly optimizes CPU usage by reducing the number of individual packets the CPU has to process. It combines similar packets into one large packet before they are sent up the network stack. This dramatically reduces the overhead associated with per-packet processing. Point to note is, the cost of processing a packet is not proportional to its size. The work of inspecting headers, performing checksums, and passing data up the stack is relatively constant. By combining many small packets, GRO amortizes this fixed cost over a much larger amount of data. In high throughput scenarios this allows the system to handle much more data with the same CPU resources, improving overall performance and also allowing the CPU to focus on application level tasks rather than spending its cycles on packet-by-packet overhead. PS: You can check if it's enabled on your machines by executing sudo ethtool -k <interface_name> | grep generic-receive-offload PPS: Also, tcpdump captures packets at a higher level in the network stack, after the kernel has already received the packets and has performed optimizations like GRO. It does not capture packets directly from the NIC's ring buffer :) #Networking #Linux #Performance