Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.
Add this skill
npx mdskills install sickn33/bazel-build-optimizationComprehensive Bazel guide with production-ready templates, optimization patterns, and clear examples
Production patterns for Bazel in large-scale monorepos.
resources/implementation-playbook.md.workspace/
├── WORKSPACE.bazel # External dependencies
├── .bazelrc # Build configurations
├── .bazelversion # Bazel version
├── BUILD.bazel # Root build file
├── apps/
│ └── web/
│ └── BUILD.bazel
├── libs/
│ └── utils/
│ └── BUILD.bazel
└── tools/
└── bazel/
└── rules/
| Concept | Description |
|---|---|
| Target | Buildable unit (library, binary, test) |
| Package | Directory with BUILD file |
| Label | Target identifier //path/to:target |
| Rule | Defines how to build a target |
| Aspect | Cross-cutting build behavior |
# WORKSPACE.bazel
workspace(name = "myproject")
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
# Rules for JavaScript/TypeScript
http_archive(
name = "aspect_rules_js",
sha256 = "...",
strip_prefix = "rules_js-1.34.0",
url = "https://github.com/aspect-build/rules_js/releases/download/v1.34.0/rules_js-v1.34.0.tar.gz",
)
load("@aspect_rules_js//js:repositories.bzl", "rules_js_dependencies")
rules_js_dependencies()
load("@rules_nodejs//nodejs:repositories.bzl", "nodejs_register_toolchains")
nodejs_register_toolchains(
name = "nodejs",
node_version = "20.9.0",
)
load("@aspect_rules_js//npm:repositories.bzl", "npm_translate_lock")
npm_translate_lock(
name = "npm",
pnpm_lock = "//:pnpm-lock.yaml",
verify_node_modules_ignored = "//:.bazelignore",
)
load("@npm//:repositories.bzl", "npm_repositories")
npm_repositories()
# Rules for Python
http_archive(
name = "rules_python",
sha256 = "...",
strip_prefix = "rules_python-0.27.0",
url = "https://github.com/bazelbuild/rules_python/releases/download/0.27.0/rules_python-0.27.0.tar.gz",
)
load("@rules_python//python:repositories.bzl", "py_repositories")
py_repositories()
# .bazelrc
# Build settings
build --enable_platform_specific_config
build --incompatible_enable_cc_toolchain_resolution
build --experimental_strict_conflict_checks
# Performance
build --jobs=auto
build --local_cpu_resources=HOST_CPUS*.75
build --local_ram_resources=HOST_RAM*.75
# Caching
build --disk_cache=~/.cache/bazel-disk
build --repository_cache=~/.cache/bazel-repo
# Remote caching (optional)
build:remote-cache --remote_cache=grpcs://cache.example.com
build:remote-cache --remote_upload_local_results=true
build:remote-cache --remote_timeout=3600
# Remote execution (optional)
build:remote-exec --remote_executor=grpcs://remote.example.com
build:remote-exec --remote_instance_name=projects/myproject/instances/default
build:remote-exec --jobs=500
# Platform configurations
build:linux --platforms=//platforms:linux_x86_64
build:macos --platforms=//platforms:macos_arm64
# CI configuration
build:ci --config=remote-cache
build:ci --build_metadata=ROLE=CI
build:ci --bes_results_url=https://results.example.com/invocation/
build:ci --bes_backend=grpcs://bes.example.com
# Test settings
test --test_output=errors
test --test_summary=detailed
# Coverage
coverage --combined_report=lcov
coverage --instrumentation_filter="//..."
# Convenience aliases
build:opt --compilation_mode=opt
build:dbg --compilation_mode=dbg
# Import user settings
try-import %workspace%/user.bazelrc
# libs/utils/BUILD.bazel
load("@aspect_rules_ts//ts:defs.bzl", "ts_project")
load("@aspect_rules_js//js:defs.bzl", "js_library")
load("@npm//:defs.bzl", "npm_link_all_packages")
npm_link_all_packages(name = "node_modules")
ts_project(
name = "utils_ts",
srcs = glob(["src/**/*.ts"]),
declaration = True,
source_map = True,
tsconfig = "//:tsconfig.json",
deps = [
":node_modules/@types/node",
],
)
js_library(
name = "utils",
srcs = [":utils_ts"],
visibility = ["//visibility:public"],
)
# Tests
load("@aspect_rules_jest//jest:defs.bzl", "jest_test")
jest_test(
name = "utils_test",
config = "//:jest.config.js",
data = [
":utils",
"//:node_modules/jest",
],
node_modules = "//:node_modules",
)
# libs/ml/BUILD.bazel
load("@rules_python//python:defs.bzl", "py_library", "py_test", "py_binary")
load("@pip//:requirements.bzl", "requirement")
py_library(
name = "ml",
srcs = glob(["src/**/*.py"]),
deps = [
requirement("numpy"),
requirement("pandas"),
requirement("scikit-learn"),
"//libs/utils:utils_py",
],
visibility = ["//visibility:public"],
)
py_test(
name = "ml_test",
srcs = glob(["tests/**/*.py"]),
deps = [
":ml",
requirement("pytest"),
],
size = "medium",
timeout = "moderate",
)
py_binary(
name = "train",
srcs = ["train.py"],
deps = [":ml"],
data = ["//data:training_data"],
)
# tools/bazel/rules/docker.bzl
def _docker_image_impl(ctx):
dockerfile = ctx.file.dockerfile
base_image = ctx.attr.base_image
layers = ctx.files.layers
# Build the image
output = ctx.actions.declare_file(ctx.attr.name + ".tar")
args = ctx.actions.args()
args.add("--dockerfile", dockerfile)
args.add("--output", output)
args.add("--base", base_image)
args.add_all("--layer", layers)
ctx.actions.run(
inputs = [dockerfile] + layers,
outputs = [output],
executable = ctx.executable._builder,
arguments = [args],
mnemonic = "DockerBuild",
progress_message = "Building Docker image %s" % ctx.label,
)
return [DefaultInfo(files = depset([output]))]
docker_image = rule(
implementation = _docker_image_impl,
attrs = {
"dockerfile": attr.label(
allow_single_file = [".dockerfile", "Dockerfile"],
mandatory = True,
),
"base_image": attr.string(mandatory = True),
"layers": attr.label_list(allow_files = True),
"_builder": attr.label(
default = "//tools/docker:builder",
executable = True,
cfg = "exec",
),
},
)
# Find all dependencies of a target
bazel query "deps(//apps/web:web)"
# Find reverse dependencies (what depends on this)
bazel query "rdeps(//..., //libs/utils:utils)"
# Find all targets in a package
bazel query "//libs/..."
# Find changed targets since commit
bazel query "rdeps(//..., set($(git diff --name-only HEAD~1 | sed 's/.*/"&"/' | tr '\n' ' ')))"
# Generate dependency graph
bazel query "deps(//apps/web:web)" --output=graph | dot -Tpng > deps.png
# Find all test targets
bazel query "kind('.*_test', //...)"
# Find targets with specific tag
bazel query "attr(tags, 'integration', //...)"
# Compute build graph size
bazel query "deps(//...)" --output=package | wc -l
# platforms/BUILD.bazel
platform(
name = "linux_x86_64",
constraint_values = [
"@platforms//os:linux",
"@platforms//cpu:x86_64",
],
exec_properties = {
"container-image": "docker://gcr.io/myproject/bazel-worker:latest",
"OSFamily": "Linux",
},
)
platform(
name = "remote_linux",
parents = [":linux_x86_64"],
exec_properties = {
"Pool": "default",
"dockerNetwork": "standard",
},
)
# toolchains/BUILD.bazel
toolchain(
name = "cc_toolchain_linux",
exec_compatible_with = [
"@platforms//os:linux",
"@platforms//cpu:x86_64",
],
target_compatible_with = [
"@platforms//os:linux",
"@platforms//cpu:x86_64",
],
toolchain = "@remotejdk11_linux//:jdk",
toolchain_type = "@bazel_tools//tools/jdk:runtime_toolchain_type",
)
# Profile build
bazel build //... --profile=profile.json
bazel analyze-profile profile.json
# Identify slow actions
bazel build //... --execution_log_json_file=exec_log.json
# Memory profiling
bazel build //... --memory_profile=memory.json
# Skip analysis cache
bazel build //... --notrack_incremental_state
Install via CLI
npx mdskills install sickn33/bazel-build-optimizationBazel Build Optimization is a free, open-source AI agent skill. Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.
Install Bazel Build Optimization with a single command:
npx mdskills install sickn33/bazel-build-optimizationThis downloads the skill files into your project and your AI agent picks them up automatically.
Bazel Build Optimization works with Claude Code, Claude Desktop, Cursor, Vscode Copilot, Windsurf, Continue Dev, Codex, Gemini Cli, Amp, Roo Code, Goose, Opencode, Trae, Qodo, Command Code. Skills use the open SKILL.md format which is compatible with any AI coding agent that reads markdown instructions.