Data structures and algorithms I actually used while working at tech companies (blog.pragmaticengineer.com)

Article summary

The article discusses the author's experience with data structures and algorithms in their daily work at tech companies, highlighting the practical applications of these concepts in real-world problems. The author argues that knowing data structures and algorithms is not just for interviews, but is actually useful in understanding and solving everyday problems. The article provides examples of how trees, graphs, sorting, and other data structures are used in production environments. The author also shares their thoughts on the relevance of algorithmic knowledge in the industry.

Main themes

• practical applications of algorithms
• data structures in production environments
• algorithmic knowledge in interviews
• persistence and data storage
• dynamic programming
• problem-solving strategies
• programming foundations
• interview techniques

What commenters say

• Some commenters argue that algorithmic knowledge is not as relevant as understanding persistence and data storage.
• Others believe that knowing algorithms and data structures is essential for building a strong foundation in programming.
• There is a disagreement on the usefulness of asking algorithmic questions in interviews, with some arguing that it is a poor way to assess a candidate's skills.
• Some commenters share their personal experiences of using algorithms and data structures in their work, highlighting the practical applications of these concepts.
• A few commenters argue that it is not necessary to remember the exact steps of an algorithm, but rather to know what to google for and how to apply the concepts.
• Others emphasize the importance of recognizing when a solution found online is subtly wrong and needs to be adapted or modified.
• Some commenters discuss the relevance of dynamic programming in solving real-world problems, citing examples from their own experiences.
• There is also a discussion on the difference between theoretical knowledge of algorithms and practical experience, with some arguing that the latter is more important.