Pros of Starlark (bazelbuild)

• More comprehensive documentation and examples
• Closer integration with Bazel build system
• Includes additional features like load() for importing modules

Cons of Starlark (bazelbuild)

• Less frequently updated compared to starlark-go
• Potentially more complex to use outside of Bazel ecosystem
• May have some Bazel-specific implementations that are not universally applicable

Code Comparison

starlark-go:

load("@io_bazel_rules_go//go:def.bzl", "go_library")

go_library(
    name = "go_default_library",
    srcs = ["file.go"],
    importpath = "github.com/example/project",
    visibility = ["//visibility:public"],
)

Starlark (bazelbuild):

load("@rules_python//python:defs.bzl", "py_library")

py_library(
    name = "my_library",
    srcs = ["file.py"],
    deps = [
        "//some/other:library",
    ],
)

Both repositories implement the Starlark language, but they have different focuses. starlark-go is a more general-purpose implementation, while Starlark (bazelbuild) is tailored for use with Bazel. The code examples show how they might be used in different contexts, with starlark-go being more Go-centric and Starlark (bazelbuild) being more Python and Bazel-oriented.

Pros of Skylark

• Older repository with a longer history of development
• May have more established community and resources
• Potentially more stable and battle-tested codebase

Cons of Skylark

• No longer actively maintained or updated
• May lack newer features or improvements found in Starlark-go
• Could have unresolved issues or bugs that won't be addressed

Code Comparison

Skylark:

def fibonacci(n):
    a, b = 0, 1
    for _ in range(n):
        a, b = b, a + b
    return a

Starlark-go:

def fibonacci(n):
    a, b = 0, 1
    for _ in range(n):
        a, b = b, a + b
    return a

Summary

Skylark and Starlark-go are closely related projects, with Starlark-go being the successor to Skylark. While Skylark may have a longer history, Starlark-go is the actively maintained version and is recommended for new projects. The syntax and functionality are largely similar, making migration from Skylark to Starlark-go relatively straightforward. Users should consider Starlark-go for ongoing development and support, while existing Skylark implementations may still be functional but lack future updates and improvements.

Pros of Bazel

• Comprehensive build system with advanced features like remote caching and execution
• Supports multiple programming languages and platforms
• Large ecosystem with extensive documentation and community support

Cons of Bazel

• Steeper learning curve and more complex setup compared to Starlark-go
• Heavier resource usage, especially for smaller projects
• Can be overkill for simple applications or single-language projects

Code Comparison

Starlark-go (simple function definition):

def greet(name):
    return "Hello, " + name + "!"

Bazel (BUILD file example):

py_binary(
    name = "hello",
    srcs = ["hello.py"],
    main = "hello.py",
)

Summary

Starlark-go is a lightweight implementation of the Starlark language, suitable for embedding in Go programs. It's simpler to use and integrate but lacks the full-featured build system capabilities of Bazel.

Bazel, on the other hand, is a complete build and test tool that uses Starlark as its configuration language. It offers more advanced features and multi-language support but comes with increased complexity and resource requirements.

Choose Starlark-go for simple scripting needs within Go projects, and Bazel for large-scale, multi-language build management.

Pros of HCL

• Designed specifically for configuration, with a focus on human-readability
• Native support for complex data structures like maps and lists
• Extensive use in HashiCorp ecosystem (Terraform, Vault, etc.)

Cons of HCL

• Less flexible as a general-purpose language compared to Starlark
• Limited built-in functions and standard library
• Steeper learning curve for users not familiar with HashiCorp tools

Code Comparison

HCL:

resource "aws_instance" "example" {
  ami           = "ami-0c55b159cbfafe1f0"
  instance_type = "t2.micro"
  tags = {
    Name = "ExampleInstance"
  }
}

Starlark:

def create_instance(ami, instance_type, name):
    return {
        "ami": ami,
        "instance_type": instance_type,
        "tags": {"Name": name},
    }

example = create_instance("ami-0c55b159cbfafe1f0", "t2.micro", "ExampleInstance")

Both HCL and Starlark serve different purposes. HCL is tailored for configuration management, particularly in the HashiCorp ecosystem, while Starlark is a more versatile scripting language designed for extensibility in various applications. The choice between them depends on the specific use case and integration requirements of your project.

Pros of Terraform

• Comprehensive infrastructure-as-code solution for managing cloud resources
• Large ecosystem with extensive provider support for various cloud platforms
• Powerful state management and resource dependency handling

Cons of Terraform

• Steeper learning curve due to its domain-specific language (HCL)
• More complex setup and configuration compared to Starlark's simplicity
• Heavier resource footprint and slower execution for large infrastructures

Code Comparison

Terraform (HCL):

resource "aws_instance" "example" {
  ami           = "ami-0c55b159cbfafe1f0"
  instance_type = "t2.micro"
}

Starlark:

def create_instance(ami, instance_type):
    return aws.instance(
        ami=ami,
        instance_type=instance_type
    )

Summary

Terraform is a powerful infrastructure-as-code tool with broad cloud provider support, while Starlark is a simpler, more flexible configuration language. Terraform excels in managing complex cloud resources but has a steeper learning curve. Starlark offers a more familiar Python-like syntax and is easier to integrate into existing systems, but lacks the comprehensive infrastructure management features of Terraform. The choice between them depends on the specific needs of your project and the complexity of your infrastructure requirements.