John Jr

One of the most fundamental algorithms that appears in interviews at big tech companies is Binary Search. Of course, nobody will ask you to implement binary search directly. However, as Jon Bentley cited in his famous book Programming Pearls, only a small percentage of people he asked—10% to be precise—were able to code this algorithm without any errors.

Here, I will show you a correct implementation of binary search, its variants that appear in interviews, and LeetCode problems that you can solve using these techniques.

In this post, I will explain in detail how I used the builder design pattern together with the bit mask field technique to reduce complexity. As a result, I decreased the cyclomatic complexity from 24 to 0 and reduced development time from hours to minutes.

The rest of this post is organized as follows sections:

• The problem explains the problem I was solving.
• Creating the bit mask field presents a solution to the problem.
• Cyclomatic Complexity discusses cyclomatic complexity and calculates this metric for the solution presented in the section above.
• Builder design pattern explains the builder design pattern and describes the refactoring process that removed complexity.
• Conclusion summarizes this post.
• References provides references related to the topics discussed here.

All the examples shown here are in C++.

In software development, organizing our codebase in a way that is easy to maintain is always a good practice, and this idea is not new. Actually, I think this started in 1968 when the term software crisis was coined. Since then, we have developed many principles to help us achieve this goal. One person who has made many contributions to this field is Barbara Liskov. She is very famous for the Liskov Substitution Principle (LSP), but she also invented the Abstract Data Type(ADT), which is the core concept of classes and object-oriented programming. Here, I want to discuss composition compared to inheritance and how understanding the Liskov Substitution Principle can help you develop better software.

In this post, I will present how I improved an ETL process to be more than 4x faster using the same resources. While the old architecture used Python threads, the new one used task queue architecture to be more reliable and scalable. So, I will explain how we can improve a legacy code and speed it up by only modifying the architecture to run the code.

Almost 10 years ago, I was hired to improve a system and train a team of engineers in Python and scalable systems. The company had an ETL process to provide financial information for the finance team. The ETL was composed of the crawler, the parser, and the normalizer. The crawler’s primary responsibility was crawling finance data on our partners’ websites, which were protected by a username and password. The parser converted CSV, HTML, and JSON data and saved the result in our intermediate database. The last step, the normalizer, summarized the data using some keys and sent it to the system where the finance team could access it.