How do you make data structures efficient for algorithms?

Consider a phonebook application. If you're mainly searching for contacts by name, a hash table would be efficient due to its fast search capability. But if you often add or delete contacts, a linked list might be more suitable. However, if you want to display contacts in alphabetical order and still have fast lookup, a balanced binary search tree would be ideal. Each use case demands a different data structure for optimal performance, highlighting the importance of choosing wisely.

To make data structures efficient for algorithmic solutions, choose the right data structure for the problem. Different data structures are suitable for different operations. But the goal is to minimize time and space complexity.

1. Choose Appropriate Structure:
Select data structures aligning with algorithm needs.
Consider access patterns, insertion, and deletion. 2. Minimize Memory Usage:
Optimize representations to reduce overhead.
Prefer fixed-size structures to limit fragmentation. 3. Optimize Time Complexity:
Align data structures with algorithmic time complexity.
Balance trade-offs in operation efficiency. 4. Implement Caching:
Use caching for storing and retrieving results.
Consider space-time complexity trade-offs. 5. Address Parallelism:
Adapt structures for parallel processing.
Ensure thread safety and handle concurrency.

Sometimes, in complex environments, you might find that there is not one single answer that matches all your use cases. In some cases, and more often that we would like to admit, it is not a bad idea to maintain several data structures that each offer a different navigation path, for instance. It is actually very rare if you can only access one specific data point only through one path.

Insertion into a linked list can be done in constant time. The Linux kernel uses a circular linked list implementation in which order does not necessarily matter and therefore insertion is O(1).

When selecting an appropriate data structure, it's not only about considering time and space complexity, but it's also crucial to be aware of the program's limitations. For instance, if the program operates within a specific hardware environment, it's essential to opt for a data structure that aligns with those limitations, like memory allocation and size constraints. Another significant aspect is having a basic understanding of how the chosen data structures work internally. This knowledge ensures that we're not just dealing with a "black box," which could lead to difficulties in identifying and resolving problems.

Keep order in your data structures! Preserving sort order in structures such set, map, heap and so on can be a killer feature for you application You may be able to split your data into segments and process them in parallel or even on distributes hosts. That will allow you to have a great scallability out of the box This idea (keep sort order) underlies the MapReduce algorithm which was of great importance for distributed systems. Аfter map stage data is sorted before reduce stage will get it

Caching is one of my favorite ways to increase efficiency of an algorithm (But obviously it doesn't fit everywhere). Caching in data structures is like having a super-smart assistant who keeps the most important information right on your desk. It's incredibly efficient because instead of searching through a giant book every time you need something, you just look on your desk, quick and easy. Caching makes sure that the computer finds and uses important stuff faster because it's stored in a special, easy-to-reach place. So, it's like having a shortcut to the most helpful things, making everything work smoother and faster 🔥

Optimization can be divided in to 2 parts. 1. Time complexity: it is not about how much time an algorithm takes, it is about the complexity of the mathematical model used in the expression. Hence writing an optimized expresison could make an algorithm less time complex. 2. Space complexity It refers to the amount of memory or storage required by an algorithm. Optimizing space complexity is crucial, especially when dealing with large datasets or limited memory resources.

See if the compression can be done in hardware when the SoC communicates with memory vs. doing it in your code. The HW IP will compress the data in real time before sending to memory and then decompress it in real time when it is needed for computation again.

Consider the search algorithm used by Google. Google doesn't just rely on a basic array or list to store billions of web pages. It employs a combination of data structures including inverted indexes, B-trees, and compressed bitmaps. As a specific example, the inverted index helps in quick searching. Instead of looking at each webpage for a query, it lists web pages for each word, optimizing search time. This showcases the adaptation of data structures to specific algorithm needs, achieving efficiency and scalability.

Support a rich interface for your data structure! The idea is simple—provide a wide set of methods to operate on your data structure. These methods include getting an element by index, finding it by value, sorting elements, retrieving the first element, inserting by index, and so on You can leave the implementation of some of them suboptimal. Just do not use them in the current code. However, in the future, when you face new requirements, you will be able to change the implementation without altering your code significantly Just modify the implementation of your structure or even create a new instance of another structure with the same interface. Note that the data structures in standard libraries already adhere to this idea

As a company grows and evolves, so do its data requirements. Scalability and flexibility are provided by advanced data structures, allowing firms to adapt to changing needs without losing performance.

Data Structures are often "Augmented" so that instead of storing a single value per position, multiple values are stored instead. Take for example a BST augmented so that each node not only contains a value and child pointers but also contains subtree size. Such a BST would allow for querying for the rank of an item in the tree efficiently. By doing a bit of extra constant time work in insertion, you can then prepare for some specific kinds of queries later on.

Note that the choice of the data structure and the algorithm come hand in hand. Sometimes, a change of data structure later is often synonymous of rewriting the algorithm altogether - and vice versa.

Feel free to use them all! You can store redundant copies of data in RAM or on disk. Each copy can be stored using different data structures that are designed for different tasks Don't bother to update all copies. Just throw them in the trash if you don't use them often. Then recalculate it with sqrt decomposition by queries (see my previous answer above)

To compare data structures, you can consider the following aspects: 1. Determine the purpose for which each data structure is designed. Are they optimized for storage, search, insertion/deletion, or a combination of these operations? 2. Examine the supported operations and their time complexities. 3. Analyze the memory consumption of each data structure. 4. Flexibility 5. Implementation 6. Determine how easily each data structure can be extended and modified to fit different needs or accommodate changes. 7. Evaluate the time and space complexity of each data structure for various operations. 8. Consider the specific use cases for which each data structure is well-suited.

In my opinion, this step should come upfront, before making a choice of any data structure. Asymptotic estimations of run-time complexity for the most common operations are available, and more refined ones can be obtained from the workload requirements of the software product. After comparison, a choice can be made.

In real world problems, scalability matters a lot. Scalability is like designing a city that gracefully accommodates a growing population. A scalable data structure mirrors urban planning that can seamlessly handle an influx of residents without congested streets. In the digital realm, it refers to a data structure's ability to gracefully expand and manage larger datasets without compromising efficiency. Much like a well-planned city evolving to meet the needs of its inhabitants, a scalable data structure adapts to growing data demands, ensuring optimal performance as the dataset expands, creating a harmonious balance between capacity and functionality.

It would be interesting to mention modular programming: ideally, you want to write algorithms that are agnostic of how data structures are implemented. That way, you can switch implementations (as long as all the implementations implement the same behavior) without changing the algorithm.

Test you structures on random data! Implement your entire appliacation with the simplest data structures and test it along with your efficient data structures using random data The results should be the same for both implementation. This method is easy to use, don't require high skills and works well in real applications

It's vital to recognize that many data structures operate within multi-threaded systems. Take, for instance, a queue using linked-list implementation with head and tail pointers. Should two threads try to modify the head pointer simultaneously, this could lead to an inconsistent state that no longer reflects the intended structure. As a result, it's imperative to incorporate either lock-based or lock-free concurrency control mechanisms. This ensures that the data structure remains consistent and reliable when accessed by multiple threads. Consequently, these structures demand meticulous testing strategies to guarantee they don't reach an inconsistent state during a multi-threaded execution.

I would add that it is a very good practice to reuse existing libraries. STL for C++, for instance, provides very efficient, reliable implementations of most used data structures. Development times would be reduced thanks to reuse, and testing needs simplified.

If an experienced tester is familiar with fundamental data structures and algorithms, they may identify errors or defects more quickly and conveniently when performing white box testing. because the tester is aware of the constraints of the algorithms or data structures. Even the tester is capable of recommending a more effective data structure or technique. Additionally, depending on their needs, some firms prefer proprietary or customized testing frameworks. Therefore, in order to create a testing framework on their own, understanding of data structures and algorithms is required. For instance, I once had to develop a test framework for a product that required me to use only a few data structures.

Use static analysis tools and you will get pleasant surprises about what errors tools can discover in code that you assumed was correct. For dynamic testing, please make sure that you include pseudo-random inputs, within and outside of the expected ranges, to understand not only how your code reacts but also how the compiler reads in and implements your code.

Data structures are simple. And should remain that way. When fine tuning for performance I always concentrate on a very simple test. Pretty much 100% of the time I've found that narrowing a performance problem down to the simplest strand with as little input as possible finds the long pole. If not, I rely more on poor man's profiling than anything else, and poor man's profiling is what I tend to start with anyway. No matter how complicated the the data is, the algorithm is, if you can't reproduce an issue with a simple example, the software is architected incorrectly (there are some exceptions, of course).

Choosing the right infrastructure: Using a massively parallel processing (MPP) database such as Snowflake or AWS Redshift or Azure Synapse Analytics can significantly improve algorithm performance, especially for data-intensive tasks. MPP databases are designed to distribute data and workload across multiple nodes, allowing for parallel execution of queries and computations. These MPP database products also ensure that the hardware resources of the MPP database cluster are appropriately scaled as the workload scales up/down e.g., adding/terminating nodes in the cluster.