Anti-patterns related to data structure selection and usage that can lead to performance issues.
Inefficient Data Structures Overview
Choosing the right data structure for a specific operation is crucial for application performance. Using inappropriate data structures can lead to excessive CPU usage, memory consumption, and poor scalability, especially when dealing with large datasets or frequent operations.
Common data structure inefficiencies include:
This guide covers common anti-patterns related to data structure selection and usage, along with best practices for optimizing performance across different programming languages and application types.
Using ArrayList for Frequent Insertions/Deletions
Using ArrayList (Java) or arrays (JavaScript) for frequent insertions or deletions, especially at the beginning or middle of the collection, leads to poor performance due to the need to shift elements.
To optimize insertions and deletions:
Using HashMap with Poor Hash Function
Using hash-based collections with poor hash functions leads to excessive hash collisions, degrading performance from O(1) to O(n) in worst cases.
To optimize hash-based collections:
Inefficient Collection Iteration
Inefficient collection iteration, such as using inappropriate iteration methods for specific collection types or repeatedly accessing collection properties, can lead to significant performance degradation.
To optimize collection iteration:
Using the Wrong Map Implementation
Using the wrong Map implementation for specific requirements, such as using HashMap when insertion order matters or using LinkedHashMap when sorted access is needed, leads to inefficient operations or additional sorting steps.
To choose the right Map implementation:
Inefficient String Concatenation
Inefficient string concatenation, especially in loops, can lead to significant performance issues due to the immutable nature of strings, resulting in repeated memory allocations and copies.
To optimize string concatenation:
Excessive Boxing and Unboxing
Excessive boxing and unboxing (converting between primitive types and their wrapper classes) can lead to significant performance overhead, especially in tight loops or when dealing with large collections.
To minimize boxing and unboxing:
Inefficient Set Operations
Inefficient set operations, such as using lists or arrays for membership testing or set operations, can lead to quadratic time complexity instead of the linear or constant time that proper set implementations provide.
To optimize set operations:
Inefficient Collection Resizing
Inefficient collection resizing, such as not preallocating capacity for collections that will grow to a known or estimated size, leads to multiple expensive resize operations that involve copying elements to new, larger backing arrays.
To optimize collection sizing:
Inefficient Multi-Dimensional Arrays
Inefficient multi-dimensional array access patterns, particularly traversing arrays in a way that doesn’t match their memory layout, can lead to poor cache locality and significantly reduced performance.
To optimize multi-dimensional array operations:
Data Structure Selection Checklist
Selecting the appropriate data structure for a specific use case is crucial for application performance and resource utilization. The wrong data structure can lead to inefficient operations, excessive memory usage, and poor scalability.
Key selection criteria: