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Understand why data structures matter through memory systems
Section 1 of 4
Master the fundamental tradeoffs and optimization strategies that guide all efficient algorithm and data structure design.
In computer science, you often have to choose between using more time or more space (memory). Rarely can you optimize both simultaneously.
Recursive: Simple code, exponential time
Memoized: Linear time, linear space
Iterative: Linear time, constant space
Hash Table: O(1) lookup, O(n) space
Sorted Array: O(log n) lookup, O(n) space
Unsorted Array: O(n) lookup, O(n) space
Idea: Store computed results
Tradeoff: Use space to save time
Example: Dynamic programming
Idea: Do work upfront to save later
Tradeoff: Initialization time for query speed
Example: Sorted arrays, indexes
Idea: Delay computation until needed
Tradeoff: Save work that might not be needed
Example: Generators, lazy loading
Access nearby data and reuse recently accessed data
Don't compute what you don't need
Store results of expensive operations
Match the structure to your access patterns
Don't guess where the bottlenecks are
Optimization depends on your specific use case