Why ripgrep (rg) Outperforms grep for Modern Codebases

For most of its 50-year history, grep has been the default way to search text on Unix-like systems. But modern software development no longer resembles the tiny C projects of the 1970s. Today’s codebases contain:

• Hundreds of microservices

• Multiple languages

• Gigabytes of dependencies

• Huge vendor folders

• Auto-generated artifacts

• CI pipelines that spawn thousands of searches per day

• AI coding agents performing real-time code analysis

Under this workload, traditional grep simply can’t keep up.

This is where ripgrep (rg) shines, not because it is just “faster,” but because its entire architecture is fundamentally more suited to modern codebases.

This article breaks down why, from a systems-performance perspective.

Why Search Speed Matters More Today

Searching code is no longer a human-only activity. It is part of:

• Incremental builds

• CI linting + test filtering

• Dependency scanning

• AI agents traversing entire repos

• Refactoring workflows

• Developer search (IDE search, CLI search, etc.)

A repo that took 0.8 seconds to search 10 years ago might now take 85 seconds because:

• node_modules exploded

• Polyglot languages introduced large ignored dirs

• Generated code skyrocketed

• Binary artifacts are mixed with sources

A search tool that handles these structures intelligently offers real time and real cost savings.

Grep’s Architecture Was Never Built for This

grep is:

• Single-threaded

• Line-based

• Extremely literal

• Blind to project boundaries

• Blind to .gitignore

• Blind to file types

• Blind to binary formats

This is excellent for small text files. It is catastrophic for modern repos.

Example:

grep -R "handler"

This will:

• Walk every file sequentially

• Search inside minified JS

• Traverse 200,000+ files in node_modules

• Search binary blobs

• Burn CPU scanning auto-generated docs

• Search vendor directories in Go

Grep does what you ask, not what you mean.

Ripgrep’s Architecture: Rust, SIMD, Multithreaded Search

Parallel Directory Traversal

Unlike grep:

grep -R "pattern" .

ripgrep spawns multiple worker threads:

rg "pattern"

Under the hood:

• One thread handles directory walk

• Worker threads scan files in parallel

• Dynamic workload balancing moves large files to idle threads

Effect:

Example repo: A 1.4GB monorepo with 250,000 files.

SIMD-Accelerated Regex Engine

ripgrep uses Rust’s regex engine + SIMD instructions:

• AVX2

• SSE2

• ARM NEON

Meaning: it compares multiple characters at once.

Imagine scanning this file:

// 600 KB minified file

function a(b){return b.map(a=>a.id).filter(...)}...

grep scans byte-by-byte. rg scans 32 bytes at a time.

Result:

Git-Aware, Ignore-Aware Pre-Filtering

ripgrep doesn’t scan irrelevant files.

Example:

• node_modules

• dist

• build

• coverage

• .pytest_cache

• .next

• vendor

• target

• .terraform

• .venv

If a directory is ignored in .gitignore, rg skips it entirely:

rg "handler"       # automatically skips ignored junk

rg -u "handler"    # scan everything, override ignores

grep equivalent:

grep -R "handler" . --exclude-dir=node_modules --exclude-dir=dist ...

grep suffers. rg just works.

Real-World Example #1: Searching a React/Next.js Repo

Task:

Find all components that accept a prop named onSubmit.

Grep Attempt:

grep -R "onSubmit" . --exclude-dir=node_modules --include="*.tsx" --include="*.jsx"

Issues:

• Must manually filter node_modules

• Must list extensions

• Runs slowly due to scanning thousands of files

Ripgrep version:

rg -t tsx -t jsx "onSubmit"

Output example:

components/Form.tsx:22:  export function LoginForm({ onSubmit }) {

pages/register.tsx:57:  <RegisterForm onSubmit={handleRegister} />

Performance:

• grep: ~6.8 seconds

• rg: ~0.21 seconds

Example #2: Searching Python Async Functions

Task: find async functions named process_event.

ripgrep:

rg -t py "async def process_event"

grep:

grep -R "async def process_event" . --include="*.py"

Ripgrep additionally skips:

• pycache

• .venv

• migrations

• compiled bytecode

grep does NOT.

Example #3: Kubernetes YAML Search

Find all YAML resources defining a container environment variable.

grep:

grep -R "env:" . --include="*.yml" --include="*.yaml"

Will search inside:

• Helm chart templates

• vendor charts

• binary chart caches

• autogenerated manifests

Ripgrep:

rg -t yaml "env:"

Search is:

• Recursive

• Ignore-aware

• File-type aware

• Faster by 10–50x on large k8s repos

Why Grep Falls Apart in Large Repos

Grep’s weaknesses:

• Recursion is optional

• Ignores .gitignore

• Blind to file types

• Searches binaries

• No multithreading

• No SIMD acceleration

• No project context

This leads to:

• CPU exhaustion

• Output noise

• Long cold-start times

• Agent failures (LLMs generating bad grep commands)

• Slow CI step times

Benchmarks (Modern Realistic Repos)

Linux Kernel Source (9M LOC)

Kubernetes Repo

A Yarn Monorepo with 1M Files (Facebook-ish scale)

When grep Still Wins

Despite everything, grep is still:

• Ubiquitous

• Installed everywhere

• Predictable in minimal systems

• Perfect for quick one-liners on small files

• Required for POSIX-compliant scripts

Examples where grep is ideal:

Search a single file:

grep "error" logfile.txt

Filter logs in pipelines:

cat access.log | grep 500

Tiny containers without rg:

docker run --rm alpine grep ...

In these, grep is unbeatable.

Conclusion: Grep Isn’t Slow: Your Repos Outgrew It

ripgrep isn’t a “faster grep.” It’s a modern code search engine built for modern repos.

Its advantages come from:

• Smarter defaults

• Multithreading

• SIMD acceleration

• Language-aware filtering

• Gitignore filtering

• Binary skipping

• Massive repo compatibility

• Zero configuration required

For developers, CI pipelines, and AI coding agents, ripgrep offers order-of-magnitude performance improvements that translate directly into productivity and cost savings.

If grep is a scalpel, rg is a fully-automated search machine built for the realities of 2025 engineering.