Pros of Maven

• Widely adopted and well-established in the Java ecosystem
• Extensive plugin ecosystem for various build and deployment tasks
• Strong integration with IDEs and continuous integration tools

Cons of Maven

• XML-based configuration can be verbose and difficult to maintain
• Less flexible for complex build scenarios compared to Pants
• Slower build times for large projects due to its design

Code Comparison

Maven pom.xml:

<project>
  <modelVersion>4.0.0</modelVersion>
  <groupId>com.example</groupId>
  <artifactId>my-app</artifactId>
  <version>1.0-SNAPSHOT</version>
</project>

Pants BUILD file:

python_library(
    name="my-lib",
    sources=["*.py"],
    dependencies=[
        "3rdparty/python:requests",
    ],
)

Maven focuses on XML configuration, while Pants uses Python-based BUILD files. Pants offers a more concise and programmatic approach to defining build targets, which can be advantageous for complex projects. However, Maven's XML structure provides a standardized format that many developers are familiar with, especially in the Java ecosystem.

Pros of Gradle

• More mature and widely adopted build system with extensive ecosystem
• Supports multiple programming languages and platforms
• Flexible and extensible through plugins and custom task types

Cons of Gradle

• Steeper learning curve, especially for complex builds
• Can be slower for large projects due to configuration time
• Groovy-based DSL can be confusing for newcomers

Code Comparison

Gradle build script:

plugins {
    id 'java'
}

dependencies {
    implementation 'org.springframework.boot:spring-boot-starter-web'
    testImplementation 'junit:junit:4.13'
}

Pants build file:

python_sources(
    name="lib",
    dependencies=[
        "3rdparty/python:requests",
    ],
)

python_tests(
    name="tests",
    dependencies=[
        ":lib",
        "3rdparty/python:pytest",
    ],
)

Gradle focuses on a more declarative approach with its DSL, while Pants uses a Python-based configuration that may be more familiar to Python developers. Gradle's syntax is more compact for simple builds, but Pants offers fine-grained control over targets and dependencies.

Pros of sbt

• More mature and widely adopted in the Scala ecosystem
• Extensive plugin ecosystem for various tasks and integrations
• Supports incremental compilation for faster build times

Cons of sbt

• Steeper learning curve, especially for developers new to Scala
• Can be slower for large projects compared to Pants
• Configuration can become complex for multi-module projects

Code Comparison

sbt build definition:

name := "MyProject"
version := "1.0"
scalaVersion := "2.13.8"

libraryDependencies += "org.scalatest" %% "scalatest" % "3.2.15" % Test

Pants build definition:

scala_library(
    name="my-project",
    sources=["**/*.scala"],
    dependencies=[
        "3rdparty/jvm/org/scalatest",
    ],
)

Both sbt and Pants are build tools used in JVM ecosystems, with sbt being more Scala-focused and Pants being language-agnostic. sbt offers a rich ecosystem and incremental compilation but can be complex to learn and configure. Pants, on the other hand, provides a simpler configuration syntax and faster builds for large projects but may have a smaller plugin ecosystem compared to sbt.

Pros of Bazel

• Wider language support, including C++, Java, and more
• Stronger caching and incremental build capabilities
• More extensive ecosystem and tooling

Cons of Bazel

• Steeper learning curve and more complex configuration
• Slower adoption for smaller projects due to overhead
• Less intuitive for Python-centric development

Code Comparison

Bazel BUILD file:

java_library(
    name = "my_lib",
    srcs = glob(["src/main/java/**/*.java"]),
    deps = [
        "@maven//:com_google_guava_guava",
    ],
)

Pants BUILD file:

python_library(
    name="my_lib",
    sources=["**/*.py"],
    dependencies=[
        "3rdparty/python:requests",
    ],
)

Both Bazel and Pants are build systems designed to handle large, multi-language projects. Bazel, developed by Google, offers broader language support and robust caching mechanisms, making it suitable for complex, polyglot projects. However, it can be more challenging to set up and may be overkill for smaller projects.

Pants, on the other hand, is more Python-centric and easier to adopt for Python-heavy projects. It provides a simpler configuration process and is generally more intuitive for Python developers. However, it may not be as powerful for large-scale, multi-language projects compared to Bazel.

The code comparison shows the difference in syntax and structure between Bazel and Pants build files, highlighting Bazel's more verbose but flexible approach versus Pants' more concise, Python-friendly syntax.

Pros of Buck

• Faster build times for large projects due to its advanced caching system
• Better support for mobile development, especially Android
• More mature and battle-tested in production environments

Cons of Buck

• Steeper learning curve and more complex configuration
• Less flexible for non-standard project structures
• Smaller community and fewer third-party integrations

Code Comparison

Buck build file:

android_binary(
    name = 'app',
    manifest = 'AndroidManifest.xml',
    keystore = ':debug_keystore',
    deps = [
        ':app-lib',
    ],
)

Pants build file:

python_binary(
    name='app',
    source='main.py',
    dependencies=[
        ':app-lib',
    ],
)

Both Buck and Pants are build systems designed to handle large-scale projects efficiently. Buck, developed by Facebook, excels in mobile development and offers faster build times for massive codebases. Pants, on the other hand, provides a more flexible and user-friendly approach, making it easier to adopt for teams with diverse project structures.

While Buck has a more extensive track record in production environments, Pants has been gaining popularity due to its simpler configuration and growing ecosystem. The choice between the two often depends on specific project requirements, team expertise, and the development ecosystem in use.