How Big Tech Companies Manage Massive Codebases Without Slowing Down Developers

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When you open Instagram, scroll through your feed, upload a photo, or send a message, hundreds of interconnected systems work together to deliver that experience. Behind these features are enormous codebases containing millions of files, multiple programming languages, complex dependencies, and thousands of developers working simultaneously. This raises an interesting engineering question: how do companies like Meta manage such massive applications without forcing every developer to download, compile, and maintain the entire codebase on their local machine? If a developer needs to modify something as small as the notification system, does their computer really need to process the entire Instagram application? Obviously, that would be incredibly inefficient. Large technology companies solve this problem through a combination of monorepo architecture, specialized version control systems, virtual filesystems, incremental build systems, and distributed computing. These technologies allow developers to work on specific parts of enormous software systems while the underlying infrastructure handles the complexity.

Why Managing Large Codebases Becomes a Problem

As an application grows, its codebase becomes increasingly complicated. Consider an application like Instagram. It is not simply a frontend application with a backend API. A production-scale social platform typically involves authentication, user profiles, messaging, notifications, media processing, recommendation systems, content delivery, databases, analytics, and many other components.

Each component may contain its own source files, libraries, tests, configuration files, and dependencies. Some components also depend on other internal services or shared libraries.

Imagine a simplified repository structure:

instagram/
├── apps/
│   ├── web/
│   ├── mobile/
│   └── admin/
├── services/
│   ├── authentication/
│   ├── feed/
│   ├── messaging/
│   ├── notifications/
│   └── media-processing/
├── packages/
│   ├── database/
│   ├── ui-components/
│   ├── analytics/
│   └── shared-utils/
├── infrastructure/
├── tools/
└── tests/

This is an illustrative architecture, not Instagram's actual internal repository structure.

Now imagine thousands of developers working on different components of this application. If every developer had to download every file, install every dependency, and compile every service whenever they started working, development would become extremely slow.

There are several problems associated with this approach:

• Repository size: Downloading millions of files consumes considerable bandwidth and storage.
• Build times: Compiling unrelated components wastes CPU resources and developer time.
• Dependency complexity: Changes in shared libraries can affect multiple applications.
• Git performance: Traditional repository operations can become expensive at extreme scale.
• Developer productivity: Developers spend more time waiting for tools instead of writing code.

Large engineering organizations therefore optimize the entire development workflow rather than simply buying more powerful computers.

Understanding Monorepos: One Repository, Multiple Projects

One important architectural approach used by large technology companies is a monorepo.

A monorepo is a version-controlled repository that contains multiple applications, services, libraries, and tools belonging to an organization.

Instead of maintaining completely separate repositories for every project, teams can manage related projects within a shared repository.

For example:

company-monorepo/
├── applications/
│   ├── website/
│   ├── dashboard/
│   └── mobile-app/
├── services/
│   ├── authentication/
│   ├── payments/
│   └── notifications/
├── libraries/
│   ├── database-client/
│   └── shared-components/
└── infrastructure/

This approach offers several advantages.

First, developers can reuse internal libraries without publishing and downloading separate packages every time something changes. Second, teams can make coordinated changes across multiple projects in a single commit. Third, dependency management becomes easier because the organization can maintain a unified view of its software.

Meta is a well-known example of an organization using a large monorepo. Its engineering teams use specialized infrastructure to manage repositories containing millions of files.

However, having everything in one repository does not mean every developer needs to actively work with every file.

That distinction is extremely important.

How Meta Handles Huge Repositories Using Sapling

Traditional Git workflows can become challenging when repositories contain millions of files and extremely long histories.

To address this problem, Meta developed and uses Sapling, a scalable source control system designed for large repositories.

Sapling provides familiar version control operations such as checking out code, creating commits, rebasing, and managing changes. However, its architecture is designed around the performance requirements of massive repositories.

Its infrastructure consists of three important components:

1. Sapling Server: Stores and serves repository data.
2. Sapling Client: Provides developers with version control operations.
3. Virtual Filesystem: Allows developers to work with repository files without physically downloading everything.

The server handles repository storage and communication, while the client provides the developer-facing interface.

This separation allows the system to optimize repository operations without requiring every developer's machine to maintain a complete physical copy.

For developers familiar with Git, the important concept is that large-scale source control requires more than simply increasing Git server capacity. Storage formats, network protocols, file access patterns, and checkout operations all need to be optimized.

Meta also provides Sapling as an open-source project, making some of its engineering approaches accessible to the wider developer community.

Understanding EdenFS: Access Files Only When Required

One of the most interesting technologies in Meta's development infrastructure is EdenFS.

Normally, when you clone a repository, Git downloads the repository data and creates a working directory containing the checked-out files.

For a small project, this is perfectly reasonable.

But imagine a repository containing millions of files. Downloading and writing all those files to every developer's computer can consume enormous amounts of time and storage.

EdenFS solves this problem using a virtual filesystem.

Instead of physically downloading every file immediately, it presents developers with a filesystem view of the repository and fetches file contents when they are actually accessed.

Consider this example:

Large Repository
       |
       v
     EdenFS
       |
       v
Virtual Filesystem
       |
       +---- Authentication Service
       |
       +---- Messaging Service
       |
       +---- Feed Service
       |
       +---- Media Processing

Suppose a developer is working only on the authentication service.

They open:

company/
└── services/
    └── authentication/
        ├── src/
        ├── tests/
        └── package.json

When the developer accesses these files, EdenFS fetches the required content from the repository infrastructure.

Other files can remain unfetched until someone needs them.

This means developers can interact with a massive repository without physically storing every file on their local machine.

There is an important distinction here: EdenFS does not mean the application magically runs without dependencies. If the authentication service requires a shared library, that library must still be made available when the build or application needs it.

The optimization is about avoiding unnecessary file downloads and filesystem operations.

This approach is particularly useful when developers regularly work with only a small portion of a much larger repository.

How Build Systems Avoid Compiling the Entire Application

Now comes another important problem.

Suppose a developer changes a single file in the authentication service.

Should the entire application be compiled again?

No.

A modern build system analyzes dependencies and determines which components actually need to be rebuilt.

This is where incremental builds become important.

Instead of treating the application as one enormous compilation task, build systems represent projects as dependency graphs.

For example:

If a developer modifies the messaging service, the build system does not automatically need to rebuild the web application, authentication service, and every unrelated component.

It evaluates the dependency graph and identifies the affected build targets.

This significantly reduces unnecessary compilation.

Buck2: Meta's Large-Scale Build System

Meta uses Buck2, an open-source build system designed to handle large software projects.

Buck2 focuses on several important capabilities:

• Incremental builds.
• Parallel task execution.
• Remote caching.
• Remote execution.
• Dependency graph analysis.
• Support for multiple programming languages.

Instead of compiling everything from scratch, Buck2 identifies the build actions required by a particular change.

For example, imagine the following project:

project/
├── frontend/
├── backend/
├── shared/
└── database/

If only the backend authentication module changes, the build system can reuse previously generated outputs for unaffected dependencies.

A simplified build definition might look like this:

# BUCK

python_library(
    name = "auth",
    srcs = [
        "auth.py",
        "permissions.py",
    ],
    deps = [
        "//shared:utils",
        "//database:client",
    ],
)

python_test(
    name = "auth_tests",
    srcs = [
        "test_auth.py",
    ],
    deps = [
        ":auth",
    ],
)

This example illustrates the concept of declaring build dependencies. Actual Buck2 rule syntax depends on the configured language rules.

When a developer requests a build, Buck2 evaluates the dependency graph and determines which actions are necessary.

If the source files and dependencies have not changed, previously generated outputs can be reused.

This is why a second build can sometimes complete almost instantly compared with the initial build.

Meta reported that Buck2 completed builds approximately twice as fast as Buck1 in its internal comparisons. This is a comparison with its previous build system, not a universal guarantee that Buck2 is twice as fast as every other build tool.

Remote Execution: Your Computer Does Not Have to Compile Everything

This is probably one of the most interesting parts of the entire architecture.

Imagine that your computer has limited processing power, but the application you are working on requires hundreds of compilation tasks.

Instead of executing every task locally, a build system can distribute those tasks across remote machines.

This approach is called remote execution.

The basic workflow looks like this:

Here's how it works:

1. The developer modifies source code locally.
2. The build system identifies which compilation tasks are required.
3. It prepares the inputs and dependencies for those tasks.
4. Tasks are sent to remote worker machines.
5. Multiple workers execute independent tasks simultaneously.
6. The resulting build artifacts are returned to the developer or stored in a shared cache.

For example, suppose a build requires 100 independent compilation tasks.

Instead of executing all 100 tasks sequentially on one machine, the build system can distribute them across multiple workers.

Of course, not every task can run independently. Dependency relationships, resource availability, and execution overhead determine how much parallelism is possible.

The important point is that developers can write code locally while a significant portion of the expensive build work happens on remote infrastructure.

Meta's Buck2 is specifically designed to support remote execution and large-scale parallel builds.

Remote Caching: Why Build Something That Already Exists?

Remote execution is powerful, but there is another optimization that makes large-scale development even more efficient: remote caching.

Imagine that one developer has already compiled a particular component.

Later, another developer makes a change that requires exactly the same compilation inputs.

Should the build system compile that component again?

Ideally, no.

Instead, it can retrieve the previously generated result from a shared cache.

This is called remote build caching.

Consider the following workflow:

Build systems typically calculate keys using information about the source files, dependencies, compiler configuration, build rules, and other relevant inputs.

If the inputs match an existing cache entry, the system can reuse the result.

This saves both computation and time.

For caching to be reliable, builds need to be reproducible and their inputs must be correctly identified. Otherwise, a cached artifact could become incorrect or outdated.

This is why large engineering organizations invest heavily in build-system correctness alongside performance.

Watchman: How Build Systems Know Which Files Changed

Another interesting component of Meta's development infrastructure is Watchman.

A build system needs to know which files have changed before it can determine what needs rebuilding.

Checking millions of files repeatedly would itself become expensive.

Watchman is a file-watching service that tracks filesystem changes and provides notifications to other development tools.

For example:

Developer Changes File
        |
        v
      Watchman
        |
        v
Detect File Modification
        |
        v
Notify Build System
        |
        v
Recalculate Affected Tasks
        |
        v
Build Only Required Targets

Suppose a developer changes:

services/authentication/src/login.ts

Watchman can notify the relevant tools about the modification.

The build system can then use its dependency information to determine which targets need to be rebuilt or tested.

Meta has also engineered Watchman to integrate with EdenFS, allowing its development tools to work efficiently with virtual filesystems.

This creates an important connection between source control, file access, and compilation infrastructure.

What Actually Happens When Developers Pull, Push, and Build Code?

Now let's connect everything to the workflow you probably use every day.

When you execute:

git pull origin main

Git does not recompile the entire application.

A pull operation primarily fetches remote repository data and integrates the relevant changes into your local branch.

Similarly, when you execute:

git push origin main

Git transfers the commits and objects required by the remote repository. It does not upload your entire working directory from scratch every time.

Version control and compilation are two different operations.

Here's a simplified development workflow:

Let's understand the important stages.

1. Checking Out Code

In a conventional Git repository, developers can use sparse checkout to work with selected directories.

In Meta's infrastructure, Sapling and EdenFS provide more specialized approaches for handling huge repositories efficiently.

2. Modifying Code

Developers work on the components they are responsible for without needing to understand every unrelated part of the application.

3. Committing and Pushing

Only the relevant version control changes need to be transferred, rather than uploading the entire repository again.

4. Running Builds

The build system analyzes dependencies and identifies which components require compilation.

5. Running Tests

Automated tests validate the changed functionality and affected dependencies.

6. Continuous Integration

CI systems can execute builds and tests in shared infrastructure, rather than requiring every developer to run every possible test locally.

The exact workflow differs between companies, but these are common principles used in large-scale engineering environments.

How Can Developers Work on Specific Parts Without Breaking Everything?

There is another important engineering challenge: shared dependencies.

Imagine three teams working on different services:

Team A --> Authentication
Team B --> Messaging
Team C --> Notifications

All three services depend on a shared library:

shared/
└── user-management/

Now imagine Team A changes the shared library.

That change might affect all three services.

This is why large companies maintain dependency graphs, automated tests, code ownership rules, and continuous integration pipelines.

They do not simply assume that a change to one directory cannot affect anything else.

A good build and testing infrastructure identifies affected targets and validates the relevant parts of the system.

Some organizations also use feature flags, staged rollouts, and backward-compatible APIs to reduce the risk of deploying changes that affect multiple teams.

The goal is to allow independent development while maintaining consistency across the entire software ecosystem.

Can You Apply These Techniques to Your Own Projects?

Absolutely.

You do not need Meta's infrastructure to benefit from the same engineering principles.

Even if you are working on a relatively small project, you can structure your development workflow to avoid unnecessary downloads, builds, and dependencies.

Here are some practical tools worth understanding.

1. Git Sparse Checkout

If your repository contains multiple projects, Git sparse checkout allows you to select which directories should appear in your working tree.

For example:

git clone --no-checkout https://github.com/example/company.git

cd company

git sparse-checkout init --cone

git sparse-checkout set services/authentication packages/shared

git checkout main

This gives you a working tree containing the selected directories rather than every tracked file.

Keep in mind that sparse checkout primarily reduces the working-tree footprint. It does not automatically mean that the complete Git history and repository object database have been removed from your local clone.

2. Turborepo and Nx

For JavaScript and TypeScript monorepos, tools like Turborepo and Nx help manage dependencies, incremental builds, task execution, and caching.

For example, with Turborepo:

npx turbo run build --filter=web

This allows you to execute the build task for a specific workspace and its required dependencies instead of blindly building every workspace.

You can also configure local and remote caching to reuse previously generated build outputs.

3. Docker and Development Containers

Docker can help developers maintain consistent development environments.

Instead of installing every dependency directly on their machines, developers can use containers with predefined runtimes, libraries, and configuration.

For example:

FROM node:22-alpine

WORKDIR /app

COPY package*.json ./

RUN npm install

COPY . .

CMD ["npm", "run", "dev"]

This example creates a basic Node.js development environment. Production-grade setups should also consider dependency locking, build caching, non-root users, and development-specific configuration.

4. Build Caching

Whether you use Turborepo, Nx, Gradle, Bazel, or Buck2, caching can significantly reduce repeated build work.

The important principle is simple: if the inputs to a build task have not changed, reuse its previous output whenever it is safe to do so.

Understanding the Trade-offs

Although these techniques make large-scale development significantly more efficient, they introduce additional complexity.

Virtual filesystems depend on reliable network access and remote storage infrastructure. Files that have not been fetched may take additional time to access.

Remote execution requires infrastructure capable of distributing tasks, managing resources, transferring inputs, and collecting outputs.

Build caching also requires correct dependency tracking and reproducible build rules. Incorrect cache keys can cause stale or invalid artifacts to be reused.

Monorepos require strong tooling, dependency management, code ownership, and repository governance. Without these practices, a shared repository can become difficult to maintain.

There is also a financial cost. Remote build workers, caching infrastructure, and distributed storage require substantial engineering investment.

For a small project, these systems may introduce more complexity than they solve.

The right approach depends on the size of the codebase, the number of developers, the dependency structure, and the frequency of builds.

Troubleshooting Common Issues

Even with optimized development infrastructure, certain problems are common.

1. Sparse Checkout Is Missing Required Files

If your application fails because a dependency directory is missing, check whether that directory has been included in your sparse checkout configuration.

You may need to expand your selected directories.

2. Build Cache Is Not Being Reused

A cache miss can happen when source files, compiler versions, environment variables, build configurations, or dependencies differ.

Check the build inputs and ensure your build rules are deterministic.

3. Remote Builds Are Slower Than Local Builds

Remote execution is not automatically faster for every task.

Small tasks may take longer because of network communication, input preparation, and artifact transfer.

Use remote execution where the computational workload justifies its overhead.

4. Changes Are Not Triggering Rebuilds

Check file-watching configuration and dependency declarations.

If the build system does not know that a particular target depends on a changed file, it may fail to rebuild the affected output.

5. Developers Are Experiencing Different Environments

Use locked dependency versions, consistent toolchains, and containerized development environments where appropriate.

This reduces the possibility of one developer experiencing a problem that cannot be reproduced on another machine.

Conclusion

Managing a massive codebase is not simply a matter of dividing the application into smaller folders or purchasing more powerful development machines. It requires an entire engineering ecosystem designed to minimize unnecessary work.

Companies like Meta approach this problem using specialized source control systems such as Sapling, virtual filesystems such as EdenFS, build systems such as Buck2, and supporting technologies such as Watchman, remote execution, and distributed caching.

These technologies work together to ensure that developers can work on the components they need without repeatedly downloading, compiling, and processing unrelated parts of an enormous software system.

The most important lesson is that large-scale engineering is about efficiency through intelligent infrastructure.

Instead of making every developer's computer do more work, companies build systems that understand which files are required, which components are affected by a change, which build outputs can be reused, and which tasks can be executed remotely.

This is how thousands of developers can collaborate on enormous software ecosystems while maintaining a smooth development experience.

If you are building your own applications today, start by understanding monorepos, dependency graphs, incremental builds, and caching. These concepts will become increasingly valuable as your projects and development teams grow.

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Disclosure: this article contains an affiliate link. DotExe may earn a commission if you purchase through it, at no extra cost to you. Any discount is subject to Hostinger's current offers (20% OFF) and eligibility terms.