In computer science, merge sort (also commonly spelled mergesort) is an efficient, general-purpose, comparison-based sorting algorithm. Most implementations produce a stable sort, which means that the implementation preserves the input order of equal elements in the sorted output. Mergesort is a divide and conquer algorithm that was invented by John von Neumann in 1945.
An example of merge sort. First divide the list into the smallest unit (1 element), then compare each element with the adjacent list to sort and merge the two adjacent lists. Finally all the elements are sorted and merged.
A recursive merge sort algorithm used to sort an array of 7 integer values. These are the steps a human would take to emulate merge sort (top-down).
|Merge sort||n log(n)||n log(n)||n log(n)||n||Yes|
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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Merge sort is one of the efficient sorting algorithm that applies the principle or uses divide and conquer pattern.
Merge sort divides a given unsorted array into two equal halves sub arrays until the nth (last) array contains a single element. This is based on the fact that array of a single element is always sorted.
Merging of Sorted Arrays
The sub arrays are merged by comparison of the elements of the first array to the second, starting from the first element of both arrays. When compared, the one lesser than is pushed to a single sub array. This action is continued until all the sub arrays are sorted and merged.
merge sort visualization
We are going to use two functions to implement this algorithm viz: mergeSort function and merge function. MergeSort function will recursively divide the unsorted array into nth sub arrays as aforementioned while the merge function will act as the name implies, merging the sub arrays.
Speaking about sorting algorithms, one should always remember: there is more than one approach. Last time we talked about Insertion Sort and worked with one unsorted array. This time we’re going to try a different approach and more complicated pattern — Merge Sort. Our starter pack is two sorted arrays, our task is to combine them into one sorted array.
Let’s crush it!
Actually, our approach is not that different from what we did last time with Insertion Sort. Our first step is to find a minimum integer and remove it from the array but this time we’re checking two arrays instead of one.
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
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To describe merge sort, let’s just an analogy.
Assume you have a piece of lego that you can break apart. Each sub piece has its own numbers and you keep breaking and breaking until there is one piece each other, with their own numbers.
Here is a diagram to demonstrate how it will looks like:
To understand merge sort, you have to remember the few following things:
Here are the steps to follow:
Within the Merge function, you will be comparing elements in the left and right subarray, with i and j as indices for tracking current element to be compared at each array respectively.
i and j starts at 0. You also have another indice,k, which is used to track the most current index which we are moving new elements in. If current left_arr[i] < right_arr[j], move left_arr[i] to arr[k] and increment i and k, else move right_arr[j] to arr[k], and increment j and k.
Over here, if there are less than 2 elements then we don’t even need to sort. We put half of the elements into left_arr, another half into right_arr. Then we call mergeSort and divide further into each subarray. Merge is the part that actually sorts things together.