## Changes
The approach to do this was:
1. Iterate over all libraries in all job tasks
2. Find references to local libraries
3. Store pointer to `compute.Library` in the matching artifact file to
signal it should be uploaded
This breaks down when introducing #1098 because we can no longer track
unexported state across mutators. The approach in this PR performs the
path matching twice; once in the matching mutator where we check if each
referenced file has an artifacts section, and once during artifact
upload to rewrite the library path from a local file reference to an
absolute Databricks path.
## Tests
Integration tests pass.
## Changes
Now it's possible to specify glob pattern in pipeline libraries section
and DAB will add all matched files as libraries
```
pipelines:
dummy:
name: " DLT with Python files"
target: "dlt_python_files"
libraries:
- file:
path: ./*.py
```
## Tests
Added unit test
## Changes
Workspace library will be detected by trampoline in 2 cases:
- User defined to use local wheel file
- User defined to use remote wheel file from Workspace file system
In both of these cases we should correctly apply Python trampoline
## Tests
Added a regression test (also covered by Python e2e test)
## Changes
Some library paths such as for Spark jobs, can reference a lib on remote
path, for example DBFS.
This PR fixes how CLI handles such libraries and do not report them as
missing locally.
## Tests
Added unit tests + ran `databricks bundle deploy` manually
## Changes
Added support for artifacts building for bundles.
Now it allows to specify `artifacts` block in bundle.yml and define a
resource (at the moment Python wheel) to be build and uploaded during
`bundle deploy`
Built artifact will be automatically attached to corresponding job task
or pipeline where it's used as a library
Follow-ups:
1. If artifact is used in job or pipeline, but not found in the config,
try to infer and build it anyway
2. If build command is not provided for Python wheel artifact, infer it
Pieter Noordhuis
Andrew Nester