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  1. Running an serverless email relay with ZeroOps for 5 years
    Dec 6, 2023 · original
    I’ve been a staunch follower of zero-mailbox policy since 2011. Even for my personal mail. Somewhere around 2014, I lost control of my mailbox on Gmail due to the sheer rate at which I started receiving emails. This coincided with Gmail introducing tabs and for a brief period I blamed it on this new feature that might have stolen focus from the inbox into 4 different tabs, but in hindsight Google seems to have been on top of an explosion in email marketing and new service signups that lead to a really busy inbox. And it has gotten really busy. While I received roughly 6 emails/day in 2014, today I receive 30 emails/day. I switched to Gmail aliases (+ sign). This brought some relief after applying filters to handle the aliases and mark emails as read or directly move them to trash. But this had its own limitations. For a human marketeer spammer seeing my email it was very clear what my ac
  2. From Intent to Action: 9 Critical Factors that Improve Automation Value
    Sep 7, 2023 · original
    Ever been in a meeting where automation was called out as the silver bullet to all problems followed by unanimous nods across the room? Automation sounds simple but stands as an umbrella term bringing many complex factors into consideration. Not realizing these factors leads to an initiative that lacks acceptable returns or worse, creates engineering debt. Before we look at these factors, let’s look into the background. Automation comes from the Greek word automatos meaning “acting on its own”, but when does anything ever act on its own? It begins with the intention to act in response to an event. This event forms the genesis of an action or a sequence of actions that resolve the intention. Some of these actions can run into exceptional scenarios and might need to be checked up on. Some of these exceptions might call for manual intervention. Some of these manual intervention if recurring
  3. Autoscale optimizations - Challenges with memory based metrics
    Dec 28, 2021 · original
    This post is 3rd of a 3 part series discussing autoscale and its related concepts. In the previous posts, we discussed flapping and identified effective scale-in metrics to help optimize autoscale configurations. This post focuses on challenges during scale-in operations on memory based metrics and builds a model to help optimize the same. We will build on top of mathematical models and observations from the previous posts, so I encourage you to take a detour if you haven’t read them already . Services like image/video processing apps can be memory intensive, requiring autoscale configurations to be backed by observing memory usage metrics. Calculating resulting average metrics can be a challenge when relying on memory. A detailed discussion on this challenge is available here https://medium.com/@jonfinerty/flapping-and-anti-flapping-dcba5ba92a05 . To summarize - When running an applicat
  4. Autoscale optimizations - Identifying effective scale-in metrics
    Dec 25, 2021 · original
    This post is 2nd of a 3 part series discussing autoscale and its related concepts. In the previous post, we discussed flapping during autoscale scale-in operations and built a model to help optimize autoscale configurations. In this post we identify the effective scale-in threshold considering the anti-flapping mechanism. If you haven’t read the previous post , I encourage you to take a detour as we build on top of the information established earlier. Scale-outs are usually simple. You define an upper bound on your metric and autoscale will take care of the rest. Anti-flapping mechanisms with prevent unsafe scale-ins from happening so it is scale-ins that should be better estimated. An incorrect configuration can lead to unnecessary costs. You can be a lot more confident about your autoscale configurations, if you know precisely when a scale-in is going to occur keeping in mind all the f
  5. Autoscale optimizations - Understanding flapping
    Dec 15, 2021 · original
    This post is divided into 3 parts to simplify the learning process and help understand autoscale and related concepts along with mathematical models on each step. These models will allow visualizing and optimizing of autoscale configurations. In these 3 parts we will focus on the following topics. Understanding flapping (current) Identifying effective scale-in metrics Challenges with memory based metrics The unpredictability of compute or storage demands of web applications made the Cloud happen. The ability to scale as per your instantaneous requirements and being charged just for those instances is the crux of the pitch for moving to the cloud. This what the autoscaling capabilities of Azure Web Apps help you achieve. Autoscale adds more compute instances (scale-out) or increase the size of your existing instance (scale-up) based on metrics that you define. While scaling out is importa
  6. Appreciating the utility of DNS based load-balancing for Globally available Web Apps
    Oct 24, 2021 · original
    This post is me marvelling at one of the many approaches engineers on the early web took to optimize delivery of web applications on a global scale. I see this as an opportunity to get a deeper understanding of how the web works and explore the intricacies of the DNS protocol. Towards the end of the post, we will see this in action with Azure Traffic Manager. When running web applications at scale, load-balancers act as gateways to a cluster of homogeneous servers creating a reliable infrastructure. These load-balancers can route traffic to servers using algorithms as simple as round-robin or get complex and probe into server health metrics to choose the most reliable unit. Conventionally, load-balancers operate on layer 4 traffic peeking into TCP and UDP packets and routing them to the optimal destination. When speaking in context of Azure, it would be the Azure Load Balancer. Load-bala
  7. Building Serverless File Uploads on Azure the right way
    Sep 1, 2021 · original
    Few months ago I happened to work on a conceptual application that involved processing videos on Azure. One of the problem statements involved having a scalable yet economical method to allow users to upload videos that could be at least few tens of GBs. That introduced me to a nifty trick to use a blob storage as a broker to enable large file uploads. Here’s a 4 minute video by Channel 9 Studio that summarizes it well. Don’t forget to come back and continue reading because there is more that needs attention to really build this right. Embedded content removed from feed Why go through a serverless approach at all? File uploads are tricky business and in the era of serverless solutions, I see little reason to route heavy HTTP transactions through the primary application infrastructure, especially when dealing with large file uploads. Anything above 5MB should not be routed to your primary
  8. Identifying a unique user on Microsoft Identity Platform - OID vs SUB
    Apr 30, 2021 · original
    I happened to spend a decent part of 2021 skimming through Microsoft documentation along with OAuth and associated RFCs to understand the specifics of authorization flows to secure APIs. On one such quest, I was to figure out a good way to identify a unique user from a JWT token passed on by Microsoft Identity Plaftform. Now to be clear, the problem wasn’t lack of a way to do so; rather multiple ways. Some outright wrong, some more right than others depending on what you really want to achieve. So let’s take a look. There’s a TL;DR right below. Some basics The title already tells you that there are two claims in a JWT token that we are most interested in. For those who haven’t had their essential visit to the Microsoft Access Token or ID Token documentation - UPN, Email or Phone numbers aren’t guaranteed to be unique system wide. UPN (User Principal Name) especially misleads devs into as
  9. Solving an Enterprise Integration problem with Azure Serverless
    Oct 3, 2020 · original
    This blog is a follow up to my webinar that aired 17th Sept on Pune User Group’s #PUGLive. Recording of the webinar is available and can be viewed at the end of this blog . I have used the follow up weeks to cover a few more interesting aspects of Azure’s Serverless offerings in this blog. There are proven benefits of abstracting out servers for certain solutions where the focus lies more on the business logic and lesser on the logistics of it. [1] [2] [3] Serverless offerings are modelled for simplicity, but this can quickly lead to limitations especially while building enterprise focused solutions surrounded by niche requirements or compliances. Azure seems to have clearly inherited Microsoft’s proficiency in catering to Enterprise needs. Azure’s serverless offerings have managed to extend an array of features that help build compliant solutions with fair degree of ease. To evaluate ju
  10. Cryptographic Identities for the World Wide Web
    Dec 30, 2017 · original
    Overview Proof of Identity is a primary problem for any network protocol and the World Wide Web (HTTP) is no different. Introduction of the state management RFC in 1997 [1] led to common use of Cookie headers to maintain a state of identity in HTTP sessions. These identities have been dominantly powered by logical authentication mechanisms. The world wide web has progressed far ahead in its adoption of identities that are authenticated by trusted delegates. These identities still aren’t universal over the world wide web. While these identities inter-operate, there’s isn’t a single central authority that controls these identities. To remain aligned with the philosophy of the open web, there shouldn’t be such a centralized entity. Here we discuss the possibility of extending cryptographic identities in the way they are being used in Distributed Ledger Technologies (popularly known as Block

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