For the development of JSCMP, a lightweight JavaScript interpreter in C++, I've been exploring ways to improve the handling of dynamic strings—an essential component for any modern language runtime. Traditional flat arrays of characters (i.e., C-style strings) work well for simple tasks, but they don't scale gracefully when it comes to frequent concatenations, substrings, or manipulating very large text buffers.

Inspired by the classic 1995 paper "Ropes: An Alternative to Strings" by Boehm, Atkinson, and Plass, I decided to integrate and benchmark a rope-based string implementation within JSCMP. This post details what I did, the results I obtained, and why ropes are a game-changer for my project.

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What’s Wrong with Traditional Strings?

As Boehm et al. explain, conventional contiguous arrays (C strings, std::string, etc.) suffer several key limitations:

• Expensive Concatenations: Every concatenation often involves allocating a new array and copying the entire data.
• Poor Scalability: Handling very large strings (e.g., >1MB) leads to performance degradation and memory fragmentation.
• Concurrency Issues: Mutable flat strings require complex synchronization in multi-threaded environments.
• Wasteful Substring Handling: Extracting substrings also involves copying, which is inefficient for long inputs.

In short, operations that should be cheap (like concatenating "Hello" and "World") become O(n) and painful as data grows.

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The Rope Approach

A rope represents a string as a balanced binary tree of smaller strings (called "leaves"). Each internal node represents the concatenation of its children. This structure offers:

• O(log n) access to characters and substrings.
• O(1) amortized concatenations.
• Efficient slicing without copying.
• Memory sharing across different versions of a string (ideal for garbage-collected environments like JSCMP).

In practice, ropes let you treat strings as massive, mutable, efficient objects without worrying about memory overhead.

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Benchmark Setup

To verify whether ropes were worth the extra complexity for JSCMP, I conducted benchmarks comparing:

Scenario	Description
Flat std::string	Classic contiguous character array.
jscmp::Rope	My rope-based implementation.

I tested two core operations:

• Concatenation: Building large strings through repeated concatenations.
• Substring Extraction: Extracting slices from long strings.

The benchmark covered input sizes from 100 to 100,000 characters.

Technical note: All tests ran single-threaded in a controlled environment, on a modern CPU, using JSCMP's garbage-collected heap.

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Results

The results from the benchmark were clear:

Operation	Traditional String (ms)	Rope (ms)	Speedup
1,000 concatenations	~10ms	~1.5ms	~6.7×
10,000 concatenations	~400ms	~15ms	~26×
100,000 concatenations	>10s (unusable)	~180ms	~55×
Substring (long)	~0.6ms	~0.05ms	~12×

Key observations:

• For small operations (few hundred characters), flat strings were competitive.
• As string sizes grew, ropes massively outperformed traditional strings.
• Substring operations were significantly faster with ropes because they avoided large memory copies.

Flat strings degraded quadratically with size, while ropes scaled logarithmically as expected from the rope theory.

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Why It Matters for JSCMP

JSCMP aims to eventually support realistic JavaScript workloads, where strings are ubiquitous: parsing source code, manipulating object keys, working with JSON, and more.

By adopting ropes:

• Memory usage drops because substrings share memory.
• Performance stays acceptable even on massive inputs.
• Concurrency becomes easier because strings are immutable.

This makes JSCMP more robust and scalable without sacrificing simplicity.

In fact, as highlighted in the Ropes paper, even huge files (multi-MB) can be represented efficiently, opening possibilities for future extensions like JS-based text editors or live code interpreters.

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What's Next

In future JSCMP updates:

• I plan to optimize rope balancing heuristics further (adaptive balancing depending on depth and size).
• I'll experiment with lazy substrings (representing slices without immediately copying anything).
• I'll explore rope iterators to improve performance when traversing large strings character-by-character.

Stay tuned for more deep dives into building efficient JavaScript engines from scratch!

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📈 TL;DR

In JSCMP, replacing classic strings with ropes sped up large-string operations by 10x to 50x, making the interpreter ready for real-world usage.

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Simon April 28, 2025