How Bazel Works: Dependency Graphs, Caching, and Remote Execution

‍Bazel is a fast, scalable, and extensible build system originally developed by Google and now used by a wide range of engineering teams to handle large, complex codebases. This blog provides a detailed, developer-centric explanation of how Bazel works under the hood, focusing specifically on three core pillars of its power: dependency graphs, caching mechanisms, and remote execution. If you're looking to improve your build performance, increase developer productivity, and streamline CI/CD workflows, mastering Bazel will give you a tremendous edge.

‍

Understanding Bazel’s Dependency Graph

At the heart of Bazel is its powerful dependency graph, which forms the foundation of every build. Bazel doesn't just scan files and compile code naively like traditional build tools. Instead, it constructs a Directed Acyclic Graph (DAG) that maps the relationships between different build targets, their inputs, outputs, tools, environment variables, and dependencies. This approach enables Bazel to perform highly intelligent build planning.

In this DAG:

• Each target (such as a binary, library, or test) is defined using a BUILD file and expressed in Bazel's declarative language, Starlark.
• Bazel identifies dependencies between these targets and determines what inputs each rule requires to execute properly.
• Using this information, Bazel constructs an action graph, which breaks down each build step into fine-grained tasks such as compiling a file or linking an object.

The result is a fully analyzable, hermetic representation of your project, enabling Bazel to only rebuild what actually changed. This is a significant advantage over traditional tools like Make or Gradle that may overbuild due to imprecise tracking. The dependency graph is a key feature that makes Bazel an ideal choice for large-scale projects with thousands of interrelated modules and microservices.

‍

The Power of Local Caching

Bazel introduces an advanced local caching mechanism that avoids redundant work during builds. At its core, Bazel caches the results of previous build actions, compilation steps, test runs, file generation, etc., and stores them based on a unique action key. This key is derived from a hash of the command line, inputs, environment, and other execution metadata.

When you re-run a build, Bazel checks whether the action key for a given step matches a previously executed one. If it does, Bazel skips re-running the action and instead reuses the cached output. This feature results in dramatic speed improvements, especially for incremental builds where only a small part of the codebase has changed.

Key aspects of Bazel’s local caching:

• Each input file is hashed using cryptographic digests.
• Environment variables and toolchain versions are considered part of the cache key.
• Outputs are stored in a local Content Addressable Storage (CAS), indexed by their content hashes.

For developers, this means faster feedback loops, reduced CPU usage, and less frustration waiting for builds. Bazel essentially turns your local development machine into a smart, self-optimizing build engine.

‍

Remote Cache: Share Builds Across Machines

While local caching speeds up individual developer workflows, Bazel also supports remote caching, which extends caching benefits across teams and CI environments. By configuring a shared remote cache server (via HTTP or gRPC), Bazel can store and retrieve build artifacts in a central location that all machines can access.

This allows teams to:

• Avoid rebuilding the same targets multiple times across different machines.
• Share results from CI builds with developers.
• Drastically reduce build times in continuous integration pipelines.

When a developer pushes a change and CI builds it, other developers pulling the branch can immediately benefit from those pre-built artifacts, no need to rebuild them locally. Likewise, when a CI job runs, it can reuse developer-built artifacts instead of starting from scratch.

Remote caching is easy to configure using flags like --remote_cache=https://mycache.example.com and can be secured with authentication headers. Open-source tools like bazel-remote, as well as cloud solutions like AWS S3, Google Cloud Storage, or BuildBarn, provide excellent backend options.

The synergy between Bazel’s local and remote caching is what gives it unparalleled efficiency. Once an artifact is cached remotely, every subsequent build on any machine can reuse it, as long as the build inputs remain the same.

‍

Remote Execution: Supercharging Build Scalability

One of Bazel’s most powerful enterprise features is remote execution, which allows build actions to be offloaded to a cluster of remote workers rather than executing on the local machine. This is especially useful in large repositories with computationally expensive build steps or massive test suites.

With remote execution:

• Bazel sends actions (compilations, tests, etc.) to remote workers.
• Inputs are uploaded to a central CAS server.
• Outputs are generated remotely and downloaded back only if needed.

This system allows developers to scale builds horizontally, leveraging massive parallelism without overloading their local machines. It also guarantees consistent execution environments by running builds in containerized or sandboxed workers.

Flags like --remote_executor=grpc://remoteservice and --jobs=200 let developers configure how many concurrent tasks should be sent to remote runners. Tools like BuildGrid, BuildFarm, and REAPI-compliant services provide the infrastructure to power this.

The key benefits for teams include:

• Scalable builds: Run thousands of actions in parallel.
• Consistent environments: No local system drift or misconfigured dependencies.
• Minimal local footprint: Lower CPU/memory usage on developer laptops.
• Faster CI/CD pipelines: CI jobs complete in record time.

Remote execution is a game-changer for teams working on large-scale monorepos, AI training workflows, or polyglot codebases with a variety of build languages.

‍

Advanced Performance: Remote‑Cache‑First, Downloads‑Minimized

Bazel doesn’t stop at caching and remote execution, it also offers granular control over artifact fetching and upload behaviors, allowing you to fine-tune performance even further.

Consider these advanced optimizations:

• --remote_download_minimal: Download only the artifacts that are actually needed by subsequent actions or end users.
• --remote_download_toplevel: Fetch only top-level build targets, skipping intermediate results.
• --experimental_remote_cache_async: Upload artifacts to the remote cache in the background while allowing the build to continue.
• Dynamic Execution: Bazel can launch both a local and a remote build of an action in parallel, using whichever finishes first (whichever is "faster wins").

These optimizations are particularly helpful when working in cloud-native environments or with ephemeral build runners in CI systems. They reduce network load, lower costs, and improve overall efficiency while still maintaining correctness.

‍

Developer Benefits Over Traditional Build Tools

Compared to older tools like Make, Ant, or even Gradle, Bazel provides a fundamentally superior build experience. Traditional systems often rebuild more than necessary due to imprecise dependency modeling. They also typically lack hermeticity, making builds unreliable across different machines or environments.

Key advantages of Bazel over legacy build tools:

• Hermetic builds: Guarantees the same output for the same inputs, every time.
• Full reproducibility: Avoids flaky builds and inconsistent binaries.
• Built-in sandboxing: Prevents undeclared dependencies from contaminating builds.
• Language extensibility: Bazel supports multiple languages and custom rules via plugins.
• Massive parallelism: Leverages multicore and distributed computing by default.
• Integrated caching: Speeds up development and CI/CD cycles.

Whether you’re working on mobile apps, microservices, embedded firmware, or machine learning pipelines, Bazel brings modern software engineering discipline to your build processes.

‍

Setting It Up: A Practical Guide

Getting started with Bazel requires a bit of setup, but the benefits pay off quickly. Here’s a high-level overview of how to configure it effectively:

1. Define BUILD files: Use Bazel's Starlark syntax to declare build targets, inputs, outputs, and dependencies.
2. Enable local caching: Set up a disk cache using --disk_cache=/path/to/cache.
3. Configure remote cache: Add --remote_cache=https://mycache.example.com to .bazelrc for team-wide artifact sharing.
4. Activate remote execution: Use --remote_executor=grpc://myexecutor for remote compute.
5. Optimize fetching behavior: Add flags like --remote_download_minimal and --experimental_remote_cache_async.
6. Monitor performance: Bazel logs display cache hit/miss statistics to help you tune caching efficiency.
7. Debug builds: Use --sandbox_debug and --explain=explain.log to understand why actions are re-run.

Once configured, developers will experience lightning-fast incremental builds, lower system load, and consistent output across all machines and CI runners.

‍

Real‑World Impact

Many engineering organizations have adopted Bazel and seen transformative results. Companies like Google, Stripe, Dropbox, and Pinterest have reported huge build speed improvements, reduced CI costs, and better engineering velocity.

For example:

• A CI pipeline that used to take 45 minutes now completes in under 4 minutes.
• Repetitive local builds that once consumed 90% CPU for 10 minutes now finish in 30 seconds.
• Developers confidently switch branches without needing to recompile everything.

The consistency, speed, and scale offered by Bazel have made it an essential part of modern DevOps toolchains, particularly in organizations embracing microservices, Kubernetes, and monorepo architectures.