Coffee doesn't fuel only code: articles by developers for developers
Neither that long ago, nor that far, far away, but in this very blog, we talked about the first iteration of what we call Han Solo, or, in other words, the Feed Processor.
This is a follow-up to that previous post, and a main course is best served after the entree. However, we’ll try to make so the appetizer will suffice.
Continue reading “Han Solo: Multiprocessing Strikes Back”Some questions that anyone using Everli might wonder about: how do we get all the products onto the web and mobile apps? And how do we keep our catalog and prices daily updated?
This process has taken a bit of work and, for now, a couple of iterations. In this post, we’ll focus on the first iteration. Let’s try to shed some light on it!
Our mission is to provide the best catalog for our users with the most accurate prices, item details and availability.
Continue reading “Han Solo: a Feed Processing Story”Here we go again.
This is the third episode of our series on how we are ingesting retailers’ data for our beloved customers.
If you did not read our previous post about this topic, consider spending a few minutes catching up on it.
In the following paragraphs, we will show you how our new process to update the assortment of our stores works and our journey to get there.
In the previous episode, we showed you how we reached coordination in our price update flow. We achieved it by the mean of a semaphore stored on Redis: each time it gets accessed, we decrease its value. Once it reaches 0, the next stage of our pipeline begins.
We also covered our Conversion Optimizer, a machine learning algorithm.
As soon as prices are updated, the next step of our pipeline can start.
Continue reading “A Brief History of Price Updates (Part 3)”Hold onto your seats; I am going to guide you around our brief history of the price updating process.
Prices must be updated every day, following our Retailers procedures to provide the best experience to our customers.
If you think it should be a simple procedure, consisting of just updating the right record and go on with the next one, I totally agree with you!
When we were in the start-up phase back in the days it was that simple, but soon we had to forget about doing it this easily when we had to scale it up from a few thousand updates to millions.
In the next paragraphs, I will show you the evolutions of this process during the years.