## Changes
Since there is no .git directory in Workspace file system, we need to
make an API call to api/2.0/workspace/get-status?return_git_info=true to
fetch git the root of the repo, current branch, commit and origin.
Added new function FetchRepositoryInfo that either looks up and parses
.git or calls remote API depending on env.
Refactor Repository/View/FileSet to accept repository root rather than
calculate it. This helps because:
- Repository is currently created in multiple places and finding the
repository root is becoming relatively expensive (API call needed).
- Repository/FileSet/View do not have access to current Bundle which is
where WorkplaceClient is stored.
## Tests
- Tested manually by running "bundle validate --json" inside web
terminal within Databricks env.
- Added integration tests for the new API.
---------
Co-authored-by: Andrew Nester <andrew.nester@databricks.com>
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
## Changes
This adds diagnostics for collaborative (production) deployment
scenarios, including:
- Bob deploys a bundle that is normally deployed by Alice, but this
fails because Bob can't write to `/Users/Alice/.bundle`.
- Charlie deploys a bundle that is normally deployed by Alice, but this
fails because he can't create a new pipeline where Alice would be the
owner.
- Alice deploys a bundle where she didn't list herself as one of the
CAN_MANAGE users in permissions. That can work, but is probably a
mistake.
## Tests
Unit tests, manual testing.
## Changes
We want to encourage a pattern of specifying only a single resource in a
YAML file when the `.(resource-type).yml` extension is used (for
example, `.job.yml`). This convention could allow us to bijectively map
a resource YAML file to its corresponding resource in the Databricks
workspace.
This PR:
1. Emits a recommendation diagnostic when we detect this convention is
being violated. We can promote this to a warning when we want to
encourage this pattern more strongly.
2. Visualises the recommendation diagnostics in the `bundle validate`
command.
**NOTE:** While this PR also shows the recommendation for `.yaml` files,
we do not encourage users to use this extension. We only support it here
since it's part of the YAML standard and some existing users might
already be using `.yaml`.
## Tests
Unit tests and manually. Here's what an example output looks like:
```
Recommendation: define a single job in a file with the .job.yml extension.
at resources.jobs.bar
resources.jobs.foo
in foo.job.yml:13:7
foo.job.yml:5:7
The following resources are defined or configured in this file:
- bar (job)
- foo (job)
```
---------
Co-authored-by: Lennart Kats (databricks) <lennart.kats@databricks.com>
## Changes
Some diagnostics can have multiple paths associated with them. For
instance, ensuring that unique resource keys are used across all
resources. This PR extends `diag.Diagnostic` to accept multiple paths.
This PR is symmetrical to
https://github.com/databricks/cli/pull/1610/files
## Tests
Unit tests
## Changes
This PR changes `diag.Diagnostics` to allow including multiple locations
associated with the diagnostic message. The diagnostics that now return
multiple locations with this PR are:
1. Warning for unknown keys in config.
2. Use of experimental.run_as
3. Accidental sync.exludes that exclude all files.
## Tests
Existing unit tests pass. New unit test case to assert on error message
when multiple locations are included.
Example output:
```
➜ bundle-playground-2 ~/cli2/cli/cli bundle validate
Warning: You are using the legacy mode of run_as. The support for this mode is experimental and might be removed in a future release of the CLI. In order to run the DLT pipelines in your DAB as the run_as user this mode changes the owners of the pipelines to the run_as identity, which requires the user deploying the bundle to be a workspace admin, and also a Metastore admin if the pipeline target is in UC.
at experimental.use_legacy_run_as
in resources.yml:10:22
databricks.yml:13:22
Name: fix run_if
Target: default
Workspace:
User: shreyas.goenka@databricks.com
Path: /Users/shreyas.goenka@databricks.com/.bundle/fix run_if/default
Found 1 warning
```
## Changes
It now shows human-readable warnings and validation status.
## Tests
* Manual tests against many examples.
* Errors still return immediately.
## Changes
This adds context to warnings and errors. For example:
* Summary: `unknown field bar`
* Location: `foo.yml:6:10`
* Path: `.targets.dev.workspace`
## Tests
Unit tests.
## Changes
This diagnostics type allows us to capture multiple warnings as well as
errors in the return value. This is a preparation for returning
additional warnings from mutators in case we detect non-fatal problems.
* All return statements that previously returned an error now return
`diag.FromErr`
* All return statements that previously returned `fmt.Errorf` now return
`diag.Errorf`
* All `err != nil` checks now use `diags.HasError()` or `diags.Error()`
## Tests
* Existing tests pass.
* I confirmed no call site under `./bundle` or `./cmd/bundle` uses
`errors.Is` on the return value from mutators. This is relevant because
we cannot wrap errors with `%w` when calling `diag.Errorf` (like
`fmt.Errorf`; context in https://github.com/golang/go/issues/47641).
## Changes
The name "dynamic value", or "dyn" for short, is more descriptive than
the opaque "config". Also, it conveniently does not alias with other
packages in the repository, or (popular ones) elsewhere.
(discussed with @andrewnester)
## Tests
n/a
## Changes
This is similar to #904 but instead of converting the dynamic
configuration to Go structs, this normalizes a `config.Value` according
to the type of a Go struct and returns the new, normalized
`config.Value`.
This will be used to ensure that two `config.Value` trees are
type-compatible before we can merge them (i.e. instances from different
files).
Warnings and errors during normalization are accumulated and returned as
a `diag.Diagnostics` structure. We can use this to surface warnings
about unknown fields, or errors about invalid types, in aggregate
instead of one-by-one. This approach is inspired by the pattern to
accumulate diagnostics in Terraform provider code.
## Tests
New unit tests.
Denis Bilenko
Lennart Kats (databricks)
shreyas-goenka
Pieter Noordhuis