Software Development

I’ve always wanted to have a way to build data processing pipelines in Python using pipes, like this range(10) | F(is_odd) | P(lambda x: x * 2), instead of functions and generators and maps and loops. So I’ve tried … The idea is pretty simple: let’s create a class with implemented OR and ROR operators, the pipes. def __or__(self, other): other.source = self return other def __ror__(self, other): self.source = ( iter(other) if not isinstance(other, (str, bytes)) and hasattr(other, "__iter__") else other ) return self The tricky part was implementation of __next__ since I wanted it to be a lazy operation. After a few trials and errors I’ve ended up with a pretty simple approach where the wrapping class implementing the pipe will call next to its source, added by OR or ROR, apply a transformation and then return the result of the transformation. ...

a simple visualization of the difference between frequentist and Bayesian power and how effect size, noise (standard deviation), and sample size affect the results.

Intro I was reading the original paper on “Hierarchical Navigable Small Worlds (HNSW)” https://arxiv.org/abs/1603.09320 which I found much easier to understand than all those YouTube videos I tried to watch and articles to read. HNSW is a probabilistic data structure for searching neighbors in multi-dimensional space. One of practical applications is search of semantically close objects. Reading that paper and some other activities made me curious if I can quickly implement a recommendation system which combines three things: HNSW, moving averages, and randomness. ...

I was curious about use of averaged vector embedding for recommendation purposes, and then I started wondering if instead of averaging I should try other metrics like median or top percentiles to focus on more frequent scenarios and reduce the influence of outliers. And then the question was: imaging that you want to use it in production, how can you compute averaged embedding for millions of users ideally with instant updates and without offline data processing in bulk. ...

I used to work close with incredibly smart people who was dealing with things like data sharding on daily basis from them I learned a lot on that topic. Later I moved to a different role where that knowledge was not needed and faded away over the time. Here I’m trying to reclaim to myself that long forgotten knowledge. Intro Sharding is a process of assigning an item to a shard - a smaller chunk of data out of a large database or other service. The general idea is that we can distribute data or service across multiple locations and handle large volumes of data or handle more requests and with replication we can scale even more and make the system more resilient etc. But we need to have clear rules on how we assign partitions aka shards so that we can route requests to the right location. ...

One of the question I often ask in my interview is to design a log processing library: You need to write a library for processing logs in the following format: timestamp<TAB>message The library will be handed over to a different team for further maintenance and improvements and so maintainability and expandability is the most most important requirements. The library need to support the following operations out of the box: filtering counting histograms The original version also included some language and background specific expectations I never include in my assessment because I feel that they put the candidate into a position when they need to read my mind to meet those expectations. ...

One of the questions are really love asking during coding interviews is this: Given a continuous stream of words, a dictionary on disk and cost associated to read from disk, create a stream processor that returns true when a word exists in the dictionary while minimizing the cost of reading from disk. Example: Dictionary: {Dog, Cat, Bird, Lion, ...} Input: [Dog, Cat, Aghd, ...] Output: [True, True, False, ...] The output is true, true, false because dog and cat exist in the dictionary of words while Aghd is not considered a word. ...

Recently I was reading though a bunch of technical designs and I’ve noticed a common mistake when it comes to writing user-stories and requirements - assuming a solution. The biggest issue for me when I write requirements myself is that than whenever I include a part of a solution I’m thinking about into the requirements it limits my ability to innovate since I’m bound to a specific solution. In many cases I observed improvements in my designs when I was focused on what the customer needs rather on fulfilling requirement tied to my first and probably not a bright idea. ...

Every so often I interview senior software engineers for Amazon. Where I ask more or less same questions in each interview. One of them requires adding a caching logic to get better results. I’ve noticed that the interviewee make one of to mistakes that blocks them from standing out as a software engineers: they don’t know, or talk about conditions under which a cache will do the best. Primarily, how a request frequency distribution affects cache performance. they don’t know the standard library of a programming language of their choice. Here, we will try to address those issues. ...

Amazon is famous for its writing culture which I discovered later in my career. The more I wrote the easier it was to apply the similar approach to other aspects of software development. Introduction When I joined Amazon, I transitioned from a company with a markedly different culture, particularly in writing and development processes. Initially, my feelings toward Amazon’s writing-centric culture were skeptical. However, over the next seven years, I gradually embraced and excelled in the Amazon style of writing. ...