Efficiently Merge Large Text Files in C+ + Without Duplicates

Learn how to merge large text files in C+ + , ensuring each line remains unique while optimizing performance.
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This video is based on the question https://stackoverflow.com/q/69607676/ asked by the user 'SecurityBreach' ( https://stackoverflow.com/u/5807686/ ) and on the answer https://stackoverflow.com/a/69608519/ provided by the user 'Marcus Müller' ( https://stackoverflow.com/u/4433386/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Merging Large Text Files in C+ + : Ensuring Uniqueness Without Duplicates

When dealing with large text files—like the 10GB files in your collection—merging them into a single file while ensuring that each line remains unique can seem daunting. You want an efficient approach, perhaps something involving hash trees and MPI for parallel processing. In this guide, we’ll explore a structured way to tackle this issue using C+ + that won’t require excessive memory and will keep your output file free of duplicates.

Problem Overview

You have multiple text files, each containing potentially repeating lines of data. The objective is to merge these files into a single output file with unique lines. This involves reading through the files, checking for duplicates, and writing the unique lines to the output file efficiently. Let’s break down an effective solution to this problem using C+ + .

Solution Breakdown

Step 1: Build a File Table

First, create a simple table to associate each filename with a unique identifier. A std::vector<std::string> serves as a handy structure for this purpose. Each element in the vector corresponds to a different file that you wish to process.

Create a vector for filenames: This allows you to manage file access more easily.

Index each file: Assign a number to each file to track it during processing.

Step 2: Open Each File and Read Lines

Now, iterate over each file listed in your file table and perform the following actions:

Open the file for reading.

Read each line one at a time.

Step 3: Hash the Lines

For every line you read, generate a hash. This is crucial for checking duplicates efficiently:

Use a hashing function to convert each line into a unique hash value.

Store these hash values instead of the actual lines to save memory.

Step 4: Use a Multimap for Storage

Utilize a std::multimap to map each line hash to its corresponding file number and the byte position of the line within that file. This step is important for identifying duplicates:

Check for existing hashes: If the hash already exists in the multimap:

Open the specified file.

Seek to the byte position and compare lines to check for duplicates.

Skip the duplicate: If the lines are identical, do not add to the output file.

Add new entries: If the line isn’t a duplicate, store the new hash and write the line to the output file.

Step 5: Iterate Until All Lines Are Processed

Keep reading through the files and applying the above logic until there are no more lines to process. This approach will keep the output file unique and efficiently handle large amounts of data.

Memory Considerations

One of the advantages of this method is that it is memory efficient. Here’s what you’ll require:

Memory for the longest line: Only enough RAM to store the longest line you are reading.

Memory for file management: Minimal space for filenames and file numbers.

Space for the multimap entries: This will be significantly less than the actual lines since you are only storing hashes.

Typically, since 10GB of text data may not throw up many hash collisions in practice, you can opt to simplify the duplicate checking. This means that for many applications, just a high probability of hash uniqueness might suffice.

Conclusion

By following this structured approach, merging large text files in C+ + can be accomplished efficiently while ensuring each line remains unique. Leveraging hashing and a multimap allows for quick duplicate checks and minimizes memory consumption. Implementing these techniques will help you create a clean, merged output file without duplicates.

With this method, you can confidently handle your data sets and keep them organized. Happy coding!

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