Table of Contents
- 1 How do you sort a large amount of data?
- 2 How would sort an array of size 10 GB using a system with only 1 GB of memory?
- 3 How do you sort data in files?
- 4 How do you sort large data with small memory?
- 5 Which is the best sorting technique?
- 6 Is O(n log(n)) sort better for millions or billions of integers?
- 7 How to sort random data in a file?
How do you sort a large amount of data?
- For sorting a very large file , we can use external sorting technique.External sorting is an algorithm that can handle massive amounts of data.
- using external merge sort.
- For sorting a very large file , we can use external sorting technique.External sorting is an algorithm that can handle massive amounts of data.
Which sorting technique will be most appropriate to sort 1.2 GB of data with 120 MB RAM?
You have to sort 1 GB of data with only 100 MB of available main memory. Which sorting technique will be most appropriate? Explanation: The data can be sorted using external sorting which uses merging technique.
How would sort an array of size 10 GB using a system with only 1 GB of memory?
For sorting 10 GB of data using only 1 GB of RAM:
- Read 1 GB of the data in main memory and sort by using quicksort.
- Write the sorted data to disk.
- Repeat steps 1 and 2 until all of the data is in sorted 1GB chunks (there are 10 GB / 1 GB = 10 chunks), which now need to be merged into one single output file.
How can I sort 20gb data?
General idea:
- read N lines from the input file (a value that allows you to keep the lines in memory)
- sort these lines and write the sorted lines to file 1.
- repeat with the next N lines to obtain file 2.
- you reach the end of the input file and you now have M files (each of which is sorted)
How do you sort data in files?
-nr option: To sort a file with numeric data in reverse order we can use the combination of two options as stated below. Example: The numeric file is the same as above. 5. -k Option: Unix provides the feature of sorting a table on the basis of any column number by using -k option.
Which sorting technique can be used to sort huge GB of data in an organization with limited main memory available?
External sorting
External sorting is a term for a class of sorting algorithms that can handle massive amounts of data. External sorting is required when the data being sorted do not fit into the main memory of a computing device (usually RAM) and instead, they must reside in the slower external memory (usually a hard drive).
How do you sort large data with small memory?
We first divide the file into runs such that the size of a run is small enough to fit into main memory. Then sort each run in main memory using merge sort sorting algorithm. Finally merge the resulting runs together into successively bigger runs, until the file is sorted.
Which algorithm is best to use when sorting through millions of pieces of information?
Heapsort is a good algorithm in practice, but isn’t as fast as the other algorithms in some cases because it doesn’t have good locality of reference.
Which is the best sorting technique?
Time Complexities of Sorting Algorithms:
| Algorithm | Best | Worst |
|---|---|---|
| Bubble Sort | Ω(n) | O(n^2) |
| Merge Sort | Ω(n log(n)) | O(n log(n)) |
| Insertion Sort | Ω(n) | O(n^2) |
| Selection Sort | Ω(n^2) | O(n^2) |
How do you sort data in a text file?
Although there’s no straightforward way to sort a text file, we can achieve the same net result by doing the following: 1) Use the FileSystemObject to read the file into memory; 2) Sort the file alphabetically in memory; 3) Replace the existing contents of the file with the sorted data we have in memory.
Is O(n log(n)) sort better for millions or billions of integers?
For million integers O (N Log (N)) sort is probably better but for billion they are probably the same. Does it make sense? If you get a question like this, they are not looking for the answer.
How do you handle 1 billion rows in Python?
When dealing with 1 billion rows, things can get slow, quickly. And native Python isn’t optimized for this sort of processing. Fortunately numpy is really great at handling large quantities of numeric data. With some simple tricks, we can use numpy to make this analysis feasible.
How to sort random data in a file?
But when the data are totally random, you probably use the external sorting. For example, you can divide the data of the source file into the different files, every file has a unique range (File1 is from 0-1m, File2 is from 1m+1 – 2m , ect ), then you sort every single file, and lastly merge them into a new file.
How to sort array of 32-bit integers using bitmap?
You need some 500 Mb to represent whole 32-bit integer range. For every integer in given array just set coresponding bit. Then simply scan your bit map from left to right and get your integer array sorted.