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Version: 2.11 (deprecated)


The core concepts of Targets and Fields.

The Target API defines how you interact with targets in your plugin. For example, you would use the Target API to read the source / sources field of a target to know which files to run on.

The Target API can also be used to add new target types—such as adding support for a new language. Additionally, the Target API can be used to extend existing target types.

Targets and Fields - the core building blocks

Definition of target

As described in Targets and BUILD files, a target is an addressable set of metadata describing some of your code.

For example, this BUILD file defines a PythonTestTarget target with Address("project", target_name="app_test").


Definition of field

A field is a single value of metadata belonging to a target, such as source and timeout above. (name is a special thing used to create the Address.)

Each field has a Python class that defines its BUILD file alias, data type, and optional settings like default values. For example:
from import IntField

class PythonTestTimeoutField(IntField):
alias = "timeout"
default = 60

Target == alias + combination of fields

Alternatively, you can think of a target as simply an alias and a combination of fields:
from import Dependencies, SingleSourceField, Target, Tags

class CustomTarget(Target):
alias = "custom_target"
core_fields = (SingleSourceField, Dependencies, Tags)

A target's fields should make sense together. For example, it does not make sense for a python_source target to have a haskell_version field.

Any unrecognized fields will cause an exception when used in a BUILD file.

Fields may be reused

Because fields are stand-alone Python classes, the same field definition may be reused across multiple different target types.

For example, many target types have the source field.



This gives you reuse of code (DRY) and is important for your plugin to work with multiple different target types, as explained below.

A Field-Driven API

Idiomatic Pants plugins do not care about specific target types; they only care that the target type has the right combination of field types that the plugin needs to operate.

For example, the Python formatter Black does not actually care whether you have a python_source, python_test, or custom_target target; all that it cares about is that your target type has the field PythonSourceField.

Targets are only used by the Rules API to get access to the underlying fields through the methods .has_field() and .get():

if target.has_field(PythonSourceField):
print("My plugin can work on this target.")

timeout_field = target.get(PythonTestTimeoutField)

This means that when creating new target types, the fields you choose for your target will determine the functionality it has.

Customizing fields through subclassing

Often, you may like how a field behaves, but want to make some tweaks. For example, you may want to give a default value to the SingleSourceField field.

To modify an existing field, simply subclass it.

from import SingleSourceField

class DockerSourceField(SingleSourceField):
default = "Dockerfile"

The Target methods .has_field() and .get() understand this subclass relationship, as follows:

>>> docker_tgt.has_field(DockerSourceField)
>>> docker_tgt.has_field(SingleSourceField)
>>> python_test_tgt.has_field(DockerSourceField)
>>> python_test_tgt.has_field(SingleSourceField)

This subclass mechanism is key to how the Target API behaves:

  • You can use subclasses of fields—along with Target.has_field()— to filter out irrelevant targets. For example, the Black formatter doesn't work with any plain SourcesField field; it needs PythonSourceField. The Python test runner is even more specific: it needs PythonTestSourceField.
  • You can create custom fields and custom target types that still work with pre-existing functionality. For example, you can subclass PythonSourceField to create DjangoSourceField, and the Black formatter will still be able to operate on your target.