The blog post explores two practical applications of the K programming language in data science. First, it demonstrates K's conciseness and efficiency for calculating quantiles on large datasets, outperforming Python's NumPy in both speed and code brevity. Second, it showcases K's ability to elegantly express the k-nearest neighbors algorithm, highlighting its expressive power for complex calculations within a limited space. The author argues that despite its steep learning curve, K's unique strengths make it a valuable tool for certain data science tasks where performance and compact code are paramount.
This blog post, titled "Two Bites of Data Science in K," by Zachary Smith, delves into the application of the K programming language, specifically the kdb+ implementation, to two distinct data science problems. The author emphasizes the conciseness and efficiency of K for these tasks, highlighting its ability to manipulate and analyze large datasets with minimal code.
The first problem addressed is calculating quantiles within a sliding window across a time series. Smith meticulously outlines the conventional approach to this problem, involving looping and iterative calculations, which can become computationally expensive for extensive datasets. He then contrasts this with a K solution, showcasing how K's array-oriented nature and built-in functions allow for a drastically more compact and performant implementation. The K code leverages a sliding window technique and the iasc (ascending indices) function to efficiently determine quantiles within each window without explicit iteration. The author details the code's logic, emphasizing how K's implicit vector operations eliminate the need for verbose loops and temporary variable assignments.
The second problem explored is the computation of a moving average. While seemingly straightforward, the author dissects the nuances of efficiently implementing a moving average over a substantial time series. He again begins by describing a conventional iterative approach, highlighting its potential performance bottlenecks. Then, Smith introduces a sophisticated K solution utilizing the sums function to cumulatively sum the data. He demonstrates how this cumulative sum, combined with a cleverly constructed difference operation, can be used to compute moving averages across the entire dataset in a highly vectorized manner. This approach avoids repeated calculations and optimizes for performance, particularly when dealing with millions of data points. The post meticulously explains the underlying logic of the K code, demonstrating its elegance and efficiency in handling this common data science task. Ultimately, the author underscores K's powerful capabilities for data manipulation and analysis, especially its ability to express complex operations concisely and performantly through its array-oriented paradigm. He positions K as a compelling alternative to more conventional tools for certain data science applications.
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https://news.ycombinator.com/item?id=42832482
The Hacker News comments generally praise the elegance and conciseness of K for data manipulation, with several users highlighting its power and expressiveness, especially for exploratory analysis. Some express familiarity with K and APL, noting the steep learning curve but appreciating the resulting efficiency. A few commenters mention the practical limitations of K's proprietary nature and the scarcity of available learning resources compared to more mainstream languages like Python. Others suggest that the article serves as a good introduction to the paradigm shift required to think in array-oriented languages. The licensing costs and limited community support are pointed out as potential drawbacks, while the article's clarity and engaging examples are commended.
The Hacker News post titled "Two Bites of Data Science in K" spawned a moderate discussion with several commenters weighing in on the use of the K programming language for data science tasks.
A significant portion of the commentary revolves around the perceived terseness and difficulty of K. One commenter notes the language's steep learning curve, acknowledging its power but questioning its practicality for most data science applications. They suggest that while K might be suitable for specialized domains or experienced programmers, its syntax can be a significant barrier to entry for many. This sentiment is echoed by another commenter who describes K as a "write-only language," implying that code written in K can be extremely difficult to understand or maintain, even for the original author.
However, some commenters defend K, highlighting its conciseness and efficiency. One points out that K allows for expressing complex operations in very few lines of code, which can be advantageous for certain tasks. They argue that the initial investment in learning the language can pay off in terms of increased productivity and reduced code complexity. Another commenter notes the historical context of K, explaining its origins in APL and its focus on array processing, making it well-suited for data manipulation. This commenter also acknowledges the challenging syntax while simultaneously appreciating its elegance.
The discussion also touches upon the broader landscape of array-oriented programming languages. Commenters mention alternatives like J and Q, comparing their features and usability to K. One commenter specifically highlights Q as a more accessible option within the same family of languages, offering a slightly less cryptic syntax and better integration with existing tools.
Finally, a few comments address the specific examples presented in the original blog post. One commenter questions the practical relevance of the chosen examples, arguing that they don't fully showcase the capabilities of K in real-world data science scenarios. Another commenter suggests alternative approaches to solving the same problems using more common languages like Python, implying that the benefits of using K might not be significant enough to justify its complexity.
In summary, the comments on Hacker News reflect a mixed reception to the use of K for data science. While some acknowledge its power and efficiency, others express concerns about its steep learning curve and difficult syntax. The discussion highlights the trade-offs between conciseness and readability, and ultimately suggests that K might be a niche tool best suited for specific applications and experienced programmers.