eskratch

The second wave of COVID is creating havoc across the country. Though we could factor in innumerable reasons why we had let our guards down and taken by surprise with the second wave, one of the most important reasons is apathy and casual attitude of we the citizens. The more sooner we get this wave plateau, we can start taking the control. There are many models and forecasts out there when we would reach our peak. All of this is similar to astrology because there are lot of variables like enforcing lockdown, availability of oxygen/beds, rate of vaccination. Now I'm going to act like a soothsayer and explain my model. And going to explain whether my model works or not our system is overwhelmed and we need to give it an immediate relief.  I'm calculating R factor based on my understanding  R=(no. of cases in day d)/(no. of cases in day d-5)  This R tracks growth every 5th day assuming a person showing symptoms on a day d could have got the viral load passed on d-5. To achieve a

 A post almost after 2 years!!! One common question I get asked is, "what is the reference I follow for troubleshooting an issue at hand". I would not be able to give an answer to the question directly as most of the times, I won't have even a single reference material handy. It's not a self boasting article. It's an article describing how knowledge we gather at random places help during an issue. Let's dissect a memory usage issue in Linux I faced recently and see how the triage shaped up. One of our processes was getting repeated ENOMEM when it was trying to call malloc for some reason despite the box had plenty of unused RAM. Lets see how the triage went through I didn't understand in my Operating systems course what a virtual memory is. I did convincing myself that virtual memory is physical memory + swap(in a way correct but not completely) I attended an interview in 2013, where the Director of the division asked me when you do malloc do you get physi

This post is going to be an account of  few trouble-shootings I did recently to combat various I/O sluggishness. Slow system during problems with backup We have a NFS mount where we push backups of our database daily. Due to some update to the NFS infra, we started seeing throughput of NFS server drastically affected. During this time we saw general sluggishness in the system during backups. Even ssh logins appeared slower. Some boxes had to be rebooted due to this sluggishness as they were too slow to operate on them. First question we wanted to answer, does NFS keep writing if the server is slow? The slow server applied back pressure by sending small advertised window(TCP) to clients. So clients can't push huge writes if server is affected. Client writes to its page cache. The data from page cache is pushed to server when there is a memory pressure or file close is called. If server is slow, client can easily reach upto dirty_background_ratio set for page cache in sysctl. This

Started debugging an issue where Linux started calling OOM reaper despite tons of memory is used as Linux cached pages. My assumption was if there is a memory pressure, cache should shrink and leave way for the application to use. This is the documented and expected behavior. OOM reaper is called when few number of times page allocation has failed consequently. If for example mysql wants to grow its buffer and it asks for a page allocation and if the page allocation fails repeatedly, kernel invokes oom reaper. OOM reaper won't move out pages, it sleeps for some time and sees if kswapd or a program has freed up caches/application pages. If not it will start doing the dirty job of killing applications and freeing up memory. In our mysql setup, mysql is the application using most of the Used Memory, so no other application can free up memory for mysql to use. Cached pages are stored as 2 lists in Linux kernel viz active and inactive. More details here https://www.kernel.org/doc/gorma

This blog post is an account of how we have toiled over the years to improve the throughput of our interDC tunnels. I joined this company around 2012. We were scaling aggressively then. We quickly expanded to 4 DCs with a mixture of AWS and colocation. Our primary DC is connected to all these new DCs via IPSEC tunnels established from SRX. The SRX model we had, had an IPSEC throughput of 350Mbps. Around December 2015 we saturated the SRX. Buying SRX was an option on the table. Buying one with 2Gbps throughput would have cut the story short. The tech team didn't see it happening. I don't have an answer to the question, "Is it worth spending time in solving a problem if a solution is already available out of box?" This project helped us in improving our critical thinking and in experiencing the theoretical network fundamentals on live traffic, but also caused us quite a bit of fatigue due to management overhead. Cutting short the philosophy, lets jump to the story.

We were moving a project from AWS to our co-located DC. We have setup KVMs scheduled by Cloudstack for each of the component in the architecture. The KVMs used local storage. The VMs are provisioned with more than required resources because we have the opinion that in our DC scaling during peak load and then downscaling doesn't offer much benefits financially as we are anyways paying for the hardware in advance and its also powered on. Its going to be idle if not used. Now we found something interesting our latency in co-located DC was 2 times more than in AWS. The time for first byte at our load balancer in aws was 60ms average and at our DC was 112ms. We started our debugging mission, Mission Conquer-AWS. All the servers are newer Dell hardwares. So the initially intuition was virtualisation is causing the issue. Conversation with the Hypervisor We started with CPU optimisation, we started using the host-passthrough mode of CPU in libvirt so VMs dont see QEMU emulated CPUs,