The Fastest Way to Merge Huge Files & Dedupe in 2025

We've all been there. You have a dozen massive CSV or log files, each containing millions of lines. Your mission: combine them into one master file and remove all the duplicate entries. You open the first one in Excel or your favorite text editor, and... crash. The application freezes, your computer's fans spin up like a jet engine, and you're left staring at a useless, unresponsive window. It's 2025, and there's a much, much better way.

Forget GUI tools that were never built for big data. It's time to embrace the power, speed, and reliability of the command line. In this guide, we'll show you the fastest, most memory-efficient methods to merge and deduplicate gigantic files in seconds, not hours.

Why Traditional Tools Fail with Huge Files

Software like Microsoft Excel, Sublime Text, or even Notepad++ is fantastic for everyday tasks. But when you ask them to open a file that's several gigabytes in size, you're asking them to do something they weren't designed for. The core problem is memory.

Most graphical applications try to load the entire file into your computer's RAM. If the file is 10GB and you only have 8GB of free RAM, the system will start using the hard drive as "swap" memory, which is orders of magnitude slower. This leads to the infamous beachball, the frozen window, and the frustrating force-quit. Command-line tools, on the other hand, are built to process data as a stream. They read a file line by line, perform an operation, and write the output, often without ever needing to hold the whole file in memory at once.

Your Command-Line Toolkit for 2025

Let's meet the heroes of our story. These three commands are your bread and butter for large-scale text manipulation on any Linux, macOS, or Windows (with WSL) machine.

The Simple Merger: cat

The cat command (short for "concatenate") is the simplest way to combine files. It reads files sequentially and prints their content to the standard output.

# Merge two files into a new one
cat file1.log file2.log > merged.log

# Merge all .csv files in the current directory
cat *.csv > all_data.csv

Advantage: It's incredibly fast and uses very little memory because it just reads and writes.
Disadvantage: It does nothing to deduplicate the data. It simply stacks the files on top of each other.

The Workhorse: sort

The sort command does exactly what its name implies. But it has a superpower: the -u flag for "unique". When you use sort -u, it sorts the data and discards any lines that are identical to one that came before it.

# Sort a file and remove duplicates
sort -u huge_file.txt > sorted_unique.txt

This is far more efficient than the old-school sort huge_file.txt | uniq pipeline, as it does both sorting and deduplicating in a single process. For modern multi-core processors, you can even give it a speed boost:

# Use multiple cores for sorting
sort --parallel=8 -u huge_file.txt > sorted_unique.txt

Replace `8` with the number of CPU cores you want to dedicate to the task. This can dramatically reduce processing time on large datasets.

The Flexible Powerhouse: awk

awk is a complete programming language for pattern scanning and processing. For deduplication, it offers one key advantage over sort: it can preserve the original order of your data.

The magic one-liner looks like this:

awk '!seen[$0]++' huge_file.txt > deduped_ordered.txt

Let's break that down:

• $0 represents the entire current line.
• seen[] is an associative array (like a dictionary or hash map).
• seen[$0]++ uses the line's content as a key in the array and increments its value. The first time a line is seen, its value is 0 before the increment.
• ! negates the result. So, the first time a line appears, seen[$0] is 0 (which is `false` in this context), and !0 is `true`, so `awk` performs its default action: it prints the line.
• Every subsequent time the same line appears, seen[$0] is 1 or greater (which is `true`), and !true becomes `false`, so the line is not printed.

The Catch: This method keeps an array of all unique lines in memory. If you have a 20GB file with 15GB of unique lines, it will consume 15GB of RAM. It's perfect for files where you expect a lot of duplicates, but can be a memory hog otherwise.

One-Liner Magic: Merging & Deduping Together

Now, let's combine these tools to solve our original problem.

The Classic Pipeline: cat | sort -u

This is the most common and often the best approach. We use cat to stream all the files together and "pipe" (`|`) that stream directly into sort -u, which handles the sorting and deduplication before writing the final output.

# Merge all .log files, deduplicate them, and save to a new file
cat *.log | sort -u > final_clean.log

This is beautiful because no single step needs to hold all the data at once. cat streams, and sort processes the stream. It's fast, memory-efficient, and reliable.

The Order-Preserving Alternative: awk

If you need to maintain the order of appearance (i.e., keep the *first* instance of each line based on its position in the merged files), awk is your tool. You simply pass all the files directly to it.

# Merge and dedupe all .log files, preserving the original order
awk '!seen[$0]++' *.log > final_clean_ordered.log

Remember the memory caveat with this method, but when order matters, it's the undisputed champion.

A Quick Note: Dealing with Headers

What if your CSV files have a header row you want to preserve? A full merge-and-dedupe will treat the header from the second file onwards as a duplicate line and remove it. Here's a clever trick:

# 1. Grab the header from the first file
head -n 1 file1.csv > merged_with_header.csv

# 2. Grab the content (no header) from all files, dedupe, and append
tail -n +2 -q *.csv | sort -u >> merged_with_header.csv

• head -n 1 gets the first line.
• tail -n +2 gets all lines from the second line onwards. The -q (quiet) flag prevents it from printing file names.
• >> appends to the file instead of overwriting it.

Comparison of Techniques

To make it easy, here's a quick comparison of the main methods we've discussed for merging and deduplicating multiple files.

Pro-Tip: Handling Files Larger Than Your RAM

This is where sort truly shines and proves it was built for big data. What happens if you try to sort a 100GB file on a machine with 16GB of RAM? It just works.

sort is intelligent enough to perform an "external sort." When it runs out of memory, it sorts chunks of the data that fit in RAM, saves these sorted chunks to temporary files on your hard drive, and then merges those sorted chunks into a final, fully sorted file. It's a classic computer science algorithm, and it's built right into this humble command.

You can even tell sort where to put these temporary files if you have a faster SSD you'd like to use:

# Tell sort to use a specific directory for temporary files
sort -T /path/to/fast/ssd/ -u massive_file.txt > final.txt

This capability is what separates command-line utilities from their GUI counterparts. They were designed from the ground up to handle data of any scale.

Key Takeaways

The next time you're faced with merging and deduplicating huge files, step away from the GUI and open your terminal. Here's what to remember:

• For speed and general use, cat *.txt | sort -u is your go-to command. It's a memory-efficient, powerful, and fast pipeline for most scenarios. Add --parallel=N to make it even faster on modern machines.
• If you must preserve the original order of the lines, use awk '!seen[$0]++' *.txt. Just be mindful of its memory usage with highly unique datasets.
• sort is smarter than you think. It can handle files much larger than your available RAM by automatically using temporary disk space (external sorting).
• Handle headers separately. Use a combination of head and tail to preserve the header row while deduplicating the rest of the content.

By mastering these few commands, you've unlocked the ability to process massive text files with an efficiency that graphical tools can only dream of. Happy data wrangling!