Optimizing for Efficiency: Finding Unexecuted Subtasks in LeetCode 1767 (PostgreSQL)

This blog post delves into the optimization of a solution for finding unexecuted subtasks, a challenge inspired by LeetCode problem 1767. We’ll explore approaches in PostgreSQL, focusing on efficiency and avoiding unnecessary recursion.

The Challenge: Finding Unexecuted Subtasks

In a system that manages tasks with potential subtasks, LeetCode problem 1767 presents a challenge: identifying tasks where subtasks haven’t been executed. In our MySQL version, we faced a hurdle due to the absence of the STRING_SPLIT function, which made direct string manipulation challenging. Fortunately, PostgreSQL offers a wider range of functionalities.

The Main Challenge: Generating a Sequence of Subtask Numbers (1 to 20)

A crucial aspect of this problem involves efficiently generating a sequence of numbers representing potential subtask IDs (from 1 to 20 in this case). The ideal scenario would have been to utilize a STRING_SPLIT function to break down a string containing these numbers. However, our MySQL version lacked this functionality.

The main challenge here is to calculate a table that has a column with values from 1 to 20. Instead of going down the recursive road, we chose to construct something that we knew had 20 characters. This way, we could be sure about the correct number. However, we hit a limitation due to the non-existence of the STRING_SPLIT function.

Solution 1: Character-by-Character Splitting and Cross Join (Efficient and Elegant)

One approach involved splitting a string containing the characters ‘a b c d e f g h i j k l m n o p q r s t’ character-by-character using UNNEST and then assigning sequential numbers using ROW_NUMBER. This approach is elegant and efficient even for very large datasets.

Solution 2: Combining Efficiency and Readability (Recommended)

To optimize efficiency, we can leverage the numbers CTE from Solution 1 and combine it with a LEFT JOIN:

Use code with caution.

This solution offers a balance between readability and efficiency, utilizing NOT EXISTS for efficient checks and ensuring all relevant subtask numbers are considered.

The Evolution of Solution 2: Learning from Recursive Approaches

The development of Solution 2 was not a straightforward process. It was born out of a thorough exploration and understanding of various solutions, including those that employed recursive approaches. While recursion can be a powerful tool, it can also lead to inefficiencies, especially with larger datasets.

We appreciated the depth-first search nature of recursive solutions, which ensured that all possible subtask numbers were considered. However, we noticed that these solutions could become quite complex and difficult to follow, particularly for those not well-versed in recursive logic.

With these observations in mind, we sought to create a solution that combined the comprehensive coverage of recursive approaches with improved efficiency and readability. This led us to develop Solution 2, which leverages a LEFT JOIN operation to consider all possible subtask numbers, similar to the recursive approach, but does so in a more efficient and readable manner.

By learning from the strengths and weaknesses of recursive solutions, we were able to devise a solution that strikes a balance between efficiency, readability, and comprehensive coverage of potential subtask numbers. This iterative process of learning and improving is a testament to the power of problem-solving in data engineering.

Solution Comparison: Finding the Sweet Spot

We explored two approaches:

Solution 1: Efficient and elegant with character-by-character splitting and cross join.
Solution 2 (Recommended): Combines the numbers CTE from Solution 1 with a LEFT JOIN for improved efficiency while maintaining readability.

While a solution with explicit UNION ALL statements to generate numbers (1 to 20) could work, it becomes cumbersome as the number of subtasks increases. Therefore, the solutions utilizing ROW_NUMBER offer a more concise and scalable approach.

Conclusion: Finding the Best Fit

By exploring alternative approaches, we achieved an efficient solution for finding unexecuted subtasks in PostgreSQL. The recommended solution leverages NOT EXISTS for efficient checks and a LEFT JOIN to consider all possible subtask numbers. This provides a balance between readability and efficiency.

In conclusion, Solution 2 stands as a testament to the value of exploring various approaches, learning from them, and iteratively improving upon them to devise an optimal solution. It underscores the importance of adaptability and continuous learning in the field of data engineering.