## Changes
This PR sets the following fields for all jobs that are deployed from a
DAB
1. `deployment`: This provides the platform with the path to a file to
read the metadata from.
2. `edit_mode`: This tells the platform to display the break-glass UI
for jobs deployed from a DAB. Setting this is required to re-lock the UI
after a user clicks "disconnect from source".
3. `format = MULTI_TASK`. This makes the Terraform provider always use
jobs API 2.1 for creating/updating the job. Required because
`deployment` and `edit_mode` are only available in API 2.1.
## Tests
Unit test and manually. Manually verified that deployments trigger the
break glass UI. Manually verified there is no Terraform drift when all
three fields are set.
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
## Changes
Now it's possible to define top level `permissions` section in bundle
configuration and permissions defined there will be applied to all
resources defined in the bundle.
Supported top-level permission levels: CAN_MANAGE, CAN_VIEW, CAN_RUN.
Permissions are applied to: Jobs, DLT Pipelines, ML Models, ML
Experiments and Model Service Endpoints
```
bundle:
name: permissions
workspace:
host: ***
permissions:
- level: CAN_VIEW
group_name: test-group
- level: CAN_MANAGE
user_name: user@company.com
- level: CAN_RUN
service_principal_name: 123456-abcdef
```
## Tests
Added corresponding unit tests + ran `bundle validate` and `bundle
deploy` manually
## 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
Added run_as section for bundle configuration.
This section allows to define an user name or service principal which
will be applied as an execution identity for jobs and DLT pipelines. In
the case of DLT, identity defined in `run_as` will be assigned
`IS_OWNER` permission on this pipeline.
## Tests
Added unit tests for configuration.
Also ran deploy for the following bundle configuration
```
bundle:
name: "run_as"
run_as:
# service_principal_name: "f7263fcc-56d0-4981-8baf-c2a45296690b"
user_name: "lennart.kats@databricks.com"
resources:
pipelines:
andrew_pipeline:
name: "Andrew Nester pipeline"
libraries:
- notebook:
path: ./test.py
jobs:
job_one:
name: Job One
tasks:
- task_key: "task"
new_cluster:
num_workers: 1
spark_version: 13.2.x-snapshot-scala2.12
node_type_id: i3.xlarge
runtime_engine: PHOTON
notebook_task:
notebook_path: "./test.py"
```
## 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
This implements the "development run" functionality that we desire for DABs in the workspace / IDE.
## bundle.yml changes
In bundle.yml, there should be a "dev" environment that is marked as
`mode: debug`:
```
environments:
dev:
default: true
mode: development # future accepted values might include pull_request, production
```
Setting `mode` to `development` indicates that this environment is used
just for running things for development. This results in several changes
to deployed assets:
* All assets will get '[dev]' in their name and will get a 'dev' tag
* All assets will be hidden from the list of assets (future work; e.g.
for jobs we would have a special job_type that hides it from the list)
* All deployed assets will be ephemeral (future work, we need some form
of garbage collection)
* Pipelines will be marked as 'development: true'
* Jobs can run on development compute through the `--compute` parameter
in the CLI
* Jobs get their schedule / triggers paused
* Jobs get concurrent runs (it's really annoying if your runs get
skipped because the last run was still in progress)
Other accepted values for `mode` are `default` (which does nothing) and
`pull-request` (which is reserved for future use).
## CLI changes
To run a single job called "shark_sighting" on existing compute, use the
following commands:
```
$ databricks bundle deploy --compute 0617-201942-9yd9g8ix
$ databricks bundle run shark_sighting
```
which would deploy and run a job called "[dev] shark_sightings" on the
compute provided. Note that `--compute` is not accepted in production
environments, so we show an error if `mode: development` is not used.
The `run --deploy` command offers a convenient shorthand for the common
combination of deploying & running:
```
$ export DATABRICKS_COMPUTE=0617-201942-9yd9g8ix
$ bundle run --deploy shark_sightings
```
The `--deploy` addition isn't really essential and I welcome feedback 🤔
I played with the idea of a "debug" or "dev" command but that seemed to
only make the option space even broader for users. The above could work
well with an IDE or workspace that automatically sets the target
compute.
One more thing I added is`run --no-wait` can now be used to run
something without waiting for it to be completed (useful for IDE-like
environments that can display progress themselves).
```
$ bundle run --deploy shark_sightings --no-wait
```
## 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 now allows you to define variables in the bundle config and set
them in three ways
1. command line args
2. process environment variable
3. in the bundle config itself
## Tests
manually, unit, and black box tests
---------
Co-authored-by: Miles Yucht <miles@databricks.com>
## Changes
This change also swaps the order of mutators such that interpolation
happens before path translation. This means that is is possible to use
variables (e.g. `${bundle.environment}`) in notebook or file paths.
## Tests
New tests pass and verified manually.
The workspace root path is a base path for bundle storage. If not
specified, it defaults to `~/.bundle/name/environment`. This default, or
other paths starting with `~` are expanded to the current user's home
directory. The configuration also includes fields for the files path,
artifacts path, and state path. By default, these are nested under the
root path, but can be overridden if needed.
Users can opt out and use the system-installed version with the
following configuration:
```
bundle:
terraform:
exec_path: terraform
```
This will find the binary in $PATH and replace it with the found value.
If this is not set, the initialize phase will install Terraform in the
bundle's cache directory.
shreyas-goenka
Andrew Nester
Lennart Kats (databricks)
Pieter Noordhuis