## Changes
This PR introduces use of new `isNil` method. It allows to ensure we
filter out all improperly defined resources in `bundle summary` command.
This includes deleted resources or resources with incorrect
configuration such as only defining key of the resource and nothing
else.
Fixes#1919, #1913
## Tests
Added regression unit test case
## Changes
Adds a textual output to the `databricks bundle summary` command, which
includes URLs of deployed resources.
Example usage:
```
$ databricks bundle summary
Name: my_pipeline
Target: dev
Workspace:
Host: https://domain.databricks.com
User: user@databricks.com
Path: /Users/user@databricks.com/.bundle/my_pipeline/dev
Resources:
Jobs:
my_project_job:
Name: [dev lennart] my_project_job
URL: https://domain.databricks.com/jobs/206899209187287?o=6051921418418893
Pipelines:
my_project_pipeline:
Name: [dev lennart] my_project_pipeline
URL: https://domain.databricks.com/pipelines/3f849fd5-ba7d-47fa-a34c-c6bf034b4f58?o=6051921418418893
```
Notes:
* The top headers of the output are the same as those from the existing
`bundle validate` command
* URLs are colored light blue in the output
* For resources that haven't been deployed yet, we show `(not deployed)`
in place of the URL
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
Co-authored-by: Pieter Noordhuis <pcnoordhuis@gmail.com>
## 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
Since locations are already tracked in the dynamic value tree, we no
longer need to track it at the resource/artifact level. This PR:
1. Removes use of `paths.Paths`. Uses dyn.Location instead.
2. Refactors the validation of resources not being empty valued to be
generic across all resource types.
## Tests
Existing unit tests.
## Changes
This PR adds support for UC Schemas to DABs. This allows users to define
schemas for tables and other assets their pipelines/workflows create as
part of the DAB, thus managing the life-cycle in the DAB.
The first version has a couple of intentional limitations:
1. The owner of the schema will be the deployment user. Changing the
owner of the schema is not allowed (yet). `run_as` will not be
restricted for DABs containing UC schemas. Let's limit the scope of
run_as to the compute identity used instead of ownership of data assets
like UC schemas.
2. API fields that are present in the update API but not the create API.
For example: enabling predictive optimization is not supported in the
create schema API and thus is not available in DABs at the moment.
## Tests
Manually and integration test. Manually verified the following work:
1. Development mode adds a "dev_" prefix.
2. Modified status is correctly computed in the `bundle summary`
command.
3. Grants work as expected, for assigning privileges.
4. Variable interpolation works for the schema ID.
## Changes
This PR:
1. Uses dynamic walking (via the `dyn.MapByPattern` func) to validate no
two resources have the same resource key. The allows us to remove this
validation at merge time.
2. Modifies `dyn.Mapping` to always return a sorted slice of pairs. This
makes traversal functions like `dyn.Walk` or `dyn.MapByPattern`
deterministic.
## Tests
Unit tests. Also manually.
## Changes
This change adds support for Lakehouse monitoring in bundles.
The associated resource type name is "quality monitor".
## Testing
Unit tests.
---------
Co-authored-by: Pieter Noordhuis <pcnoordhuis@gmail.com>
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
Co-authored-by: Arpit Jasapara <87999496+arpitjasa-db@users.noreply.github.com>
## Changes
If only key was defined for a job in YAML config, validate previously
failed with segfault.
This PR validates that jobs are correctly defined and returns an error
if not.
## Tests
Added regression test
## Changes
This is a fundamental change to how we load and process bundle
configuration. We now depend on the configuration being represented as a
`dyn.Value`. This representation is functionally equivalent to Go's
`any` (it is variadic) and allows us to capture metadata associated with
a value, such as where it was defined (e.g. file, line, and column). It
also allows us to represent Go's zero values properly (e.g. empty
string, integer equal to 0, or boolean false).
Using this representation allows us to let the configuration model
deviate from the typed structure we have been relying on so far
(`config.Root`). We need to deviate from these types when using
variables for fields that are not a string themselves. For example,
using `${var.num_workers}` for an integer `workers` field was impossible
until now (though not implemented in this change).
The loader for a `dyn.Value` includes functionality to capture any and
all type mismatches between the user-defined configuration and the
expected types. These mismatches can be surfaced as validation errors in
future PRs.
Given that many mutators expect the typed struct to be the source of
truth, this change converts between the dynamic representation and the
typed representation on mutator entry and exit. Existing mutators can
continue to modify the typed representation and these modifications are
reflected in the dynamic representation (see `MarkMutatorEntry` and
`MarkMutatorExit` in `bundle/config/root.go`).
Required changes included in this change:
* The existing interpolation package is removed in favor of
`libs/dyn/dynvar`.
* Functionality to merge job clusters, job tasks, and pipeline clusters
are now all broken out into their own mutators.
To be implemented later:
* Allow variable references for non-string types.
* Surface diagnostics about the configuration provided by the user in
the validation output.
* Some mutators use a resource's configuration file path to resolve
related relative paths. These depend on `bundle/config/paths.Path` being
set and populated through `ConfigureConfigFilePath`. Instead, they
should interact with the dynamically typed configuration directly. Doing
this also unlocks being able to differentiate different base paths used
within a job (e.g. a task override with a relative path defined in a
directory other than the base job).
## Tests
* Existing unit tests pass (some have been modified to accommodate)
* Integration tests pass
## Changes
Added `bundle deployment bind` and `unbind` command.
This command allows to bind bundle-defined resources to existing
resources in Databricks workspace so they become DABs-managed.
## Tests
Manually + added E2E test
## Changes
This is a follow-up to #658 and #779 for jobs.
This change applies label normalization the same way the backend does.
## Tests
Unit and config loading tests.
## Changes
Follow up for https://github.com/databricks/cli/pull/658
When a job definition has multiple job tasks using the same key, it's
considered invalid. Instead we should combine those definitions with the
same key into one. This is consistent with environment overrides. This
way, the override ends up in the original job tasks, and we've got a
clear way to put them all together.
## Tests
Added unit tests
## Changes
Renamed Environments to Targets in bundle.yml.
The change is backward-compatible and customers can continue to use
`environments` in the time being.
## Tests
Added tests which checks that both `environments` and `targets` sections
in bundle.yml works correctly
## Changes
While they are a slice, we can identify a job cluster by its job cluster
key. A job definition with multiple job clusters with the same key is
always invalid. We can therefore merge definitions with the same key
into one. This is compatible with how environment overrides are applied;
merging a slice means appending to it. The override will end up in the
job cluster slice of the original, which gives us a deterministic way to
merge them.
Since the alternative is an invalid configuration, this doesn't change
behavior.
## Tests
New test coverage.
## Changes
Rename all instances of "bricks" to "databricks".
## Tests
* Confirmed the goreleaser build works, uses the correct new binary
name, and produces the right archives.
* Help output is confirmed to be correct.
* Output of `git grep -w bricks` is minimal with a couple changes
remaining for after the repository rename.
## Changes
This PR adds checks during bundle config load and merge to error out if
there are duplicate keys for resource definitions
## Tests
Using unit tests and manually
## Changes
If a configuration file is located in a subdirectory of the bundle root,
files referenced from that configuration file should be relative to its
configuration file's directory instead of the bundle root.
## Tests
* New tests in `bundle/config/mutator/translate_paths_test.go`.
* Existing tests under `bundle/tests` pass and are augmented to assert
on paths.
---------
Co-authored-by: shreyas-goenka <88374338+shreyas-goenka@users.noreply.github.com>
Load a tree of configuration files anchored at `bundle.yml` into the
`config.Root` struct.
All mutations (from setting defaults to merging files) are observable
through the `mutator.Mutator` interface.
Andrew Nester
Pieter Noordhuis
Lennart Kats (databricks)
shreyas-goenka
Aravind Segu
Arpit Jasapara