My Tryst with CS

We had a discussion recently in our office on one of the famous  streaming algorithms  known as lossy count. I decided to write down my understanding of it focusing on the space complexity discussion and proving why it works in logarithmic space. This  is the original paper introducing this algorithm and most of the information discussed in this blog is (hopefully) a simplified version of this paper relating to lossy count. Problem Statement There are use cases where you may be receiving a continuous stream of data and you want to know which items in that data stream exceed a certain threshold. The data stream could be really big so you can't store it in memory. Moreover you want to answer queries in real time. So here are the challenges this problem imposes: 1. Need to do it in single pass (time constraint) 2. Limited memory 3. Large volume of data in real-time The above issues warrant the need of a smart counting algorithm. Data stream mining to identify events & patterns can

I had been wanting to understand the internals of zookeeper for quite some time now. In fact, I had already read the basics a couple of times and even worked with the zkcli in my previous organization. However, as it is said that anything written down is more firmly impressed on the mind, hence I am writing this post. (This also has a reference to one of my favorite magician - Michael Vincent ) I recently presented this topic in my current organization and I thought now would be a good time to write this blog. I can accompany it with the slides I had used there which would hopefully make things more clear. Although, I would like to start off with a disclaimer that I am just a beginner in the study of zookeeper and there are multiple things I don't know/understand about it yet. Still I hope at least some of you would find this blog interesting and maybe learn something new from it. Enough with the chit-chat, lets begin! What is Zookeeper? Zookeeper as the name suggests is literally