Choosing the right data structures and algorithms for your system development project can make a big difference in performance, scalability, readability, and maintainability. But how do you decide which ones to use among the many options available? Here are some tips to help you make informed choices based on your problem domain, data characteristics, and design goals.
Before you dive into coding, you need to understand the problem you are trying to solve and the requirements you have to meet. What are the inputs and outputs of your system? What are the constraints and trade-offs you have to consider? What are the expected use cases and scenarios? By defining the problem clearly, you can narrow down the possible solutions and identify the key operations and functionalities you need to implement.
Understand the problem with the sample dataset and expected results. Think about the extendability and scalability, list down all the requirements and try to split them into multiple sub-problems (divide and concur strategy), focusing on solving one sub-problem at a time.
Next, you need to analyze the data you are working with and the operations you need to perform on them. What are the types, sizes, and distributions of your data? How often do you need to access, modify, search, sort, or filter them? How do you store and retrieve them from memory or disk? By answering these questions, you can determine the data structures and algorithms that best suit your data characteristics and optimize your space and time complexity.
Analyse the type of input data type and its format. For example, is input data already sorted? or can sorting the input data help to solve the bigger problem? Also, would it help to take an extra space by caching the results into a hashmap? Transform the data to simplify the traversal, for example, create the adjacency list that is generally used in graph-related problems.
Once you have a clear idea of your problem and data, you can compare the different data structures and algorithms that can help you achieve your goals. For example, if you need to store a collection of items, you can choose among arrays, lists, stacks, queues, sets, maps, trees, graphs, etc. Each of these data structures has its own advantages and disadvantages in terms of memory usage, access time, insertion and deletion speed, sorting and searching efficiency, etc. Similarly, if you need to perform a specific task, you can choose among various algorithms that have different logic, steps, and performance. For example, if you need to sort a list of items, you can choose among bubble sort, selection sort, insertion sort, merge sort, quick sort, heap sort, etc. Each of these algorithms has its own time and space complexity, stability, adaptability, etc.
After you have selected the data structures and algorithms that seem to fit your problem and data, you need to test and evaluate them in practice. You can use tools like benchmarks, profilers, debuggers, or unit tests to measure and compare their performance, accuracy, reliability, and robustness. You can also use code reviews, documentation, or comments to assess their readability, maintainability, and extensibility. By testing and evaluating your choices, you can identify and fix any errors, bugs, or inefficiencies in your code and improve your system quality.
Finally, you need to learn and improve from your experience and feedback. You can always find new ways to optimize your data structures and algorithms or discover new ones that can solve your problem better. You can also learn from other developers, experts, or resources that can offer you different perspectives, insights, or tips on how to choose data structures and algorithms. By learning and improving your skills and knowledge, you can become a better system developer and solve more complex and challenging problems.