For instance, consider a pharmacy like CVS or Walgreens, which uses patients’ Social Security Numbers (SSNs)—a nine-digit unique identifier—to manage patient information. Not all patients visit the pharmacy regularly, so it would be inefficient to store data in a large array indexed by SSNs. Instead, dictionaries allow us to store, retrieve, or delete patient information efficiently, even when the SSNs are sparsely distributed.
In JavaScript, Promises are objects that represent the eventual completion (or failure) of an asynchronous operation and its resulting value. They provide a powerful way to manage asynchronous code, enabling developers to write cleaner and more maintainable logic.
Promises allow us to associate handlers for both the success and failure of an asynchronous operation. By treating asynchronous code similarly to synchronous code, they reduce the complexity and improve the readability of workflows that would otherwise be riddled with convoluted callbacks—commonly referred to as “callback hell.”
I recently moved my blog from Jekyll, a Ruby-based static site generator, to Hugo, a popular alternative built in Golang. In this article, I’ll walk through the rationale behind this migration, share the steps I took, and include a few custom code snippets I created along the way. This is not intended as a tutorial on setting up a blog with Hugo — there are plenty of excellent videos and documentation on Hugo’s site for that. Instead, I’ll focus on my experience, insights, and the lessons learned that may be helpful for anyone considering a similar transition.
You could find an older version of my site at archive.
Suppose you have a sorted collection of elements and need to perform add, delete, and search operations efficiently. Skip lists offer an efficient, probabilistic approach to these operations, achieving an average time complexity of \(O(\log{n})\) for search, insertion, and deletion. While other data structures, such as red-black trees and AVL trees, can provide the same \(O(\log{n})\) efficiency guarantees in both the average and worst cases, skip lists have the advantage of being simpler to implement and understand.
With basic data structures like sorted arrays, you can search for elements in \(O(\log{n})\) time using binary search. However, insertion and deletion require shifting elements, leading to \(O(n)\) time complexity for these operations. Conversely, linked lists allow efficient \(O(1)\) insertions and deletions once the target location is found, but finding that location requires \(O(n)\) time in the worst case.
So, how do skip lists achieve \(O(\log{n})\) efficiency for all three operations? Skip lists accomplish this by introducing multiple levels, each serving as an “express lane” that allows you to skip over sections of the list. The highest levels contain fewer nodes, allowing you to make large jumps, while the lowest level contains all nodes, allowing for precise adjustments when needed. This structure enables fast traversal through the express lanes and, when necessary, you can exit to a lower level for finer-grained searching.
In the last article, we covered the basics of building a Layer 7 load balancer in Go, touching on routing, SSL termination, and rate limiting. Since then, the focus has been on improving performance, maintainability, and scalability.
This article highlights key upgrades like adopting clean architecture, switching to configuration files, and using connection pooling to enhance backend communication. These changes make the system more flexible and set the stage for even more optimizations, including advanced health checkers, which we’ll explore in the next article.