Best Rust Programming Guides to Buy in October 2025
The Rust Programming Language, 2nd Edition
Programming Rust: Fast, Safe Systems Development
Rust for Rustaceans: Idiomatic Programming for Experienced Developers
Rust in Action
Rust Programming: A Practical Guide to Fast, Efficient, and Safe Code with Ownership, Concurrency, and Web Programming (Rheinwerk Computing)
Zero To Production In Rust: An introduction to backend development
The Rust Programming Language
Rust Atomics and Locks: Low-Level Concurrency in Practice
Asynchronous Programming in Rust: Learn asynchronous programming by building working examples of futures, green threads, and runtimes
Refactoring to Rust
Vec<(i64, i64)> is a data type in Rust that represents a vector of tuples, where each tuple contains two 64-bit signed integers. This data type allows you to store a collection of pairs of values in a dynamic array. You can add, remove, and access elements in the vector using index operations or iterator methods. Tuples in Rust are useful for grouping related values together, and using them within a vector provides flexibility and convenience in managing multiple values in a single container.
What is the maximum size of a vec<(i64, i64)> in Rust?
The maximum size of a Vec<(i64, i64)> in Rust is determined by the available memory in the system. As Vec is a dynamically-sized array, it can grow in size as needed until the system runs out of memory. However, the practical limit of the size of a Vec is the amount of available memory in the system, which varies depending on the hardware and operating system.
What are some practical use cases for vec<(i64, i64)> in Rust?
- Representing coordinates in a 2D space - When working with graphics, games, or geometric calculations, pairs of integers can be used to represent points or vectors in a 2D plane.
- Storing key-value pairs in a hashmap - Pairs of integers can be used as keys and values in a hashmap when mapping one value to another, such as in graph algorithms or other data structures.
- Working with matrices or grid-based systems - Vec<(i64, i64)> can be used to represent positions in a grid or matrix, making it easier to perform operations like traversal, mapping, or finding neighbors.
- Tracking movement or direction - Pairs of integers can be useful for tracking movement or direction in a game or simulation, keeping track of coordinates or offsets.
- Efficiently storing and accessing 2-tuple data - Vec<(i64, i64)> provides a simple and efficient way to store and access pairs of integers, without the need for defining custom data structures.
What is the cost of resizing a vec<(i64, i64)> in Rust?
Resizing a Vec<(i64, i64)> in Rust involves allocating new memory to accommodate the new size of the vector, copying the existing elements to the new memory location, and deallocating the old memory. This process can be costly in terms of time and memory usage, as it involves potentially moving a large amount of data.
The cost of resizing a Vec<(i64, i64)> in Rust can vary depending on the size of the vector and the amount of memory available. In general, resizing a vector involves a time complexity of O(n) where n is the number of elements in the vector.
It is worth noting that Rust's Vec type has a dynamic growth strategy that allows it to efficiently grow in size. When the vector needs to be resized, it may allocate more memory than is strictly needed to allow for future growth without having to resize again immediately.
Overall, while resizing a Vec in Rust can be costly in terms of time and memory usage, the language's memory management and growth strategies help to minimize these costs.
What is the performance impact of using vec<(i64, i64)> in Rust?
Using Vec<(i64, i64)> in Rust imposes a performance impact due to the overhead of managing a dynamic array of tuples. In terms of memory usage, each tuple will require memory allocation for two i64 values, and the additional memory overhead for managing the vector itself.
In terms of performance, accessing elements in a Vec is generally O(1) for indexing, but iterating over the vector will have a linear time complexity of O(n) where n is the number of elements in the vector. This means that operations such as iterating, inserting, or removing elements from the vector will have a linear time complexity.
It is important to consider the performance impact of using Vec<(i64, i64)> in performance-critical code, especially if large amounts of data or frequent operations are involved. In such cases, it may be worth considering alternative data structures or optimizations to improve performance.