How to Fix Memory Limit Exceeded (MLE) Errors in Competitive Programming and Large Inputs

One of the most confusing and frustrating errors programmers encounter — especially on platforms like LeetCode, Codeforces, and HackerRank — is Memory Limit Exceeded (MLE). Unlike syntax errors or wrong outputs, MLE doesn’t tell you what is wrong — it just tells you that your program used more memory than allowed. This can be incredibly tricky to debug, because many solutions produce correct results on small tests but fail when input size grows.

In this article, we’ll break down:

• Why Memory Limit Exceeded happens
• How to diagnose memory bottlenecks
• How to optimize memory usage
• Real code examples in Python, C++, and Java that avoid MLE
• Best practices for memory‑efficient programming

By the end, you’ll fully understand how to fix MLE errors and write memory‑efficient solutions for large inputs.

What Is Memory Limit Exceeded and Why It Happens

A Memory Limit Exceeded error means your program uses more memory than the platform allows. Most online judges place strict memory limits (for example, 256 MB or 512 MB) to ensure fairness and efficiency.

Common causes of MLE:

1. Storing too much data in arrays or lists
2. Using inefficient data structures with large overhead
3. Recursion that uses deep call stacks
4. Reading all input into memory at once

Unlike Time Limit Exceeded errors (which are about speed), MLE is about space. Your algorithm may be correct and fast — but if it stores too much data, it will still fail.

Example: Summing Many Numbers (Naive and Memory‑Efficient)

Imagine a simple problem:

Task: Given a very large list of integers, compute the sum.

If the list size is huge (millions of elements), reading all numbers into memory at once can cause MLE.

Naive Solution (Reads All Input — Possible MLE)

# Python naive
nums = list(map(int, input().split()))
print(sum(nums))

This reads the entire list into memory at once. If the input is large, this list may not fit in memory.

Python: Memory‑Efficient Input Processing

One way to avoid MLE is to process input on the fly without storing everything.

import sys

total = 0
for line in sys.stdin:
    # Process each number without storing
    parts = line.split()
    for p in parts:
        total += int(p)

print(total)

Why This Works

• We never store the full list in memory
• We only keep a running total
• Memory stays constant regardless of input size

If your program should only compute aggregates or simple outputs, reading and processing line by line helps prevent MLE.

C++: Streaming Input

In C++, you can similarly avoid storing large buffers.

Efficient C++ Input (No Big Containers)

#include <iostream>
using namespace std;

int main() {
    ios::sync_with_stdio(false);
    cin.tie(NULL);

    long long sum = 0;
    long long x;
    while (cin >> x) {
        sum += x;
    }

    cout << sum << "\n";
    return 0;
}

This reads each number one at a time and keeps only the total in memory. There’s no large array stored.

When You Actually Need to Store Data

Sometimes you do need to store data — for example, sorting or counting frequencies — but still avoid MLE. The trick is:

• Store only what’s necessary
• Use memory‑efficient structures
• Avoid unnecessary copies

Case: Counting Frequencies Efficiently

Naively storing all values in a list before counting can cause MLE. Instead, count as you read.

Python Memory‑Efficient Frequency Count

import sys
from collections import defaultdict

freq = defaultdict(int)
for line in sys.stdin:
    parts = line.split()
    for p in parts:
        freq[int(p)] += 1

for key in sorted(freq):
    print(key, freq[key])

• We only store unique keys and their counts
• We never hold the whole list

Use Generators Instead of Lists

Generators are powerful in Python because they produce items one at a time, using less memory than lists.

Example: Generator for File Processing

def number_generator(file):
    for line in file:
        for p in line.split():
            yield int(p)

total = 0
with open("large_input.txt") as f:
    for num in number_generator(f):
        total += num

print(total)

Generators help you process huge datasets piece by piece.

Avoid Recursion That Uses Too Much Stack

Deep recursion can cause stack memory overflow, a common form of MLE.

Example (Problematic Python Recursion)

def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n-1)

For large n, Python’s recursion limit and stack size can cause memory errors.

Iterative Alternative

def factorial_iter(n):
    result = 1
    for i in range(1, n+1):
        result *= i
    return result

This avoids recursion entirely and uses constant memory.

Better Data Structures to Avoid MLE

Choosing the right data structure matters a lot — especially for space constraints.

Python: Use array Module Instead of List for Numbers

import array

nums = array.array('i')  # array of ints
for i in range(1000000):
    nums.append(i)

Arrays use less memory than Python lists because they store raw values.

C++: Use vector Instead of map When Possible

Maps use more memory because they store key metadata. If you can use fixed‑size storage like vectors or arrays, do so.

Chunk Processing for Large Datasets

When input is huge, break it into chunks.

Python Example: Process in Batches

import sys

batch_size = 10000
batch = []
for line in sys.stdin:
    batch.extend(map(int, line.split()))
    if len(batch) >= batch_size:
        # Process batch
        print(sum(batch))
        batch = []
# Process final batch
if batch:
    print(sum(batch))

Processing in fixed chunks keeps memory usage predictable.

Summary: How to Avoid MLE

Final Thoughts

Memory Limit Exceeded errors are not a mystery — they are a signal that you need to rethink how your program uses memory. The key is to:

• Process data as a stream
• Store only what’s necessary
• Use efficient data structures
• Avoid deep recursion

These techniques help you write code that works for small and large inputs alike.

Just like developers organize code and data efficiently to avoid performance problems, real‑world systems also benefit from structured organization. As a practical example, you can follow Shopping Corner’s category gadget system to see how well‑organized structures make complex systems easy to manage — much like efficient algorithms prevent memory errors.

With these strategies, you’ll be able to fix MLE errors confidently and build scalable, memory‑efficient programs.