Advanced target selection¶
See Goal arguments for the normal techniques for telling Pants what to run on.
See Project introspection for queries that you can run and then pipe into another Pants run, such as finding the dependencies of a target or file.
Running over changed files with --changed-since¶
Because Pants understands Git, it can find which files have changed since a certain commit through the --changed-since option.
For example, to lint all uncommitted files, run:
To run against another branch, run:
By default, --changed-since will only run over files directly changed. Often, though, you will want to run over any dependents of those changed files, meaning any targets that depend on the changed files. Use --changed-dependents=direct or --changed-dependents=transitive for this:
filter options¶
Use filters to operate on only targets that match the predicate, e.g. only running Python tests.
Specify a predicate by using one of the below filter options, like --filter-target-type. You
can use a comma to OR multiple values, meaning that at least one member must be matched. You
can repeat the option multiple times to AND each filter. You can prefix the filter with
- to negate the filter, meaning that the target must not be true for the filter.
Some examples:
# Only `python_source` targets.
pants --filter-target-type=python_source list ::
# `python_source` or `python_test` targets.
pants --filter-target-type='python_source,python_test' list ::
# Any target except for `python_source` targets
pants --filter-target-type='-python_source' list ::
You can combine multiple filter options in the same run, e.g.:
--filter-target-type¶
Each value should be the name of a target type, e.g.
pants --filter-target-type=python_test test ::.
Run pants help targets to see what targets are registered.
--filter-address-regex¶
Regex strings for the address, such as
pants --filter-address-regex='^integration_tests$' test ::.
--filter-tag-regex¶
Regex strings to match against the tags field, such as
pants --filter-tag-regex='^skip_lint$' lint ::.
If you don't need the power of regex, use the simpler --tag global option explained below.
Tags: annotating targets¶
Every target type has a field called tags, which allows you to add a sequence of strings. The
strings can be whatever you'd like, such as "integration_test".
python_tests(
name="integration",
sources=["*_integration_test.py"],
tags=["skip_lint", "integration_test"],
)
You can then filter by tags with the global --tag option, like this:
To exclude certain tags, prefix with a -:
You can even combine multiple includes and excludes:
Use --filter-tag-regex instead for more complex queries.
--spec-files¶
The global option --spec-files allows you to pass a file containing target addresses and/or file names/globs to Pants.
Each entry must be separated by a new line.
For example:
Tip: centralized allow/block lists
Whereas tags are useful for decentralized allow/block lists, --spec-files is useful when you want to define one single list of targets or files.
Piping to other Pants runs¶
To pipe a Pants run, use your shell's | pipe operator and xargs:
You can, of course, pipe multiple times:
# Run over the second-degree dependents of `utils.py`.
❯ pants dependents helloworld/utils.py | \
xargs pants dependents | \
xargs pants lint
Alternative: use --spec-files
Sometimes, you may want to reuse the output of a Pants run for multiple subsequent Pants runs. Rather than repeating xargs multiple times, you can generate a file through stdout redirection and --spec-files.
For example:
$ pants dependencies helloworld/util > util_dependencies.txt
$ pants --spec-files=util_dependencies.txt lint
If you don't want to save the output to an actual file—such as to not pollute version control—you can use a variable and a named pipe:
Sharding the input targets¶
The test goal natively supports sharding input targets into multiple shards. Use the option --test-shard=k/N, where k is a non-negative integer less than N. For example, you can split up your CI into three shards with --shard=0/3, --shard=1/3, and --shard=2/3.
For other goals, you can leverage shell piping to partition the input targets into multiple shards. For example, to split your package run into 5 shards, and select shard 0: