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This blog explains the best, average, and worst-case time complexity of Binary Search in a clear and practical way. It helps students and developers understand how Binary Search works, why it is efficient, and how it compares to other searching techniques, making it especially useful for interview preparation and real-world problem solving.
Binary search is faster than linear search because it repeatedly divides the search space into halves, reducing the number of comparisons required. Linear search checks elements one by one, which becomes inefficient for large datasets. This blog explains how both algorithms work, compares their time complexity, and highlights why binary search is preferred for sorted data. Understanding this difference is essential for mastering searching algorithms and performing well in technical interviews.
Parallel sorting is a powerful technique used in modern databases and large-scale systems to efficiently sort massive datasets using multiple CPU cores or machines. This blog explains how parallel sorting works internally, from data partitioning and local sorting to merging sorted results. It covers real-world database and distributed system use cases, performance benefits, and common challenges. You’ll also learn why parallel sorting is critical for ORDER BY operations on large tables. Ideal for developers, data engineers, and interview preparation, this guide connects theory with practical system design.
Learn how sorting works internally in databases and how the ORDER BY clause is executed behind the scenes. Understand indexes, memory vs disk sorting, algorithms used, and performance optimization with real-world examples.