## Changes
This field allows a user to configure paths to synchronize to the
workspace.
Allowed values are relative paths to files and directories anchored at
the directory where the field is set. If one or more values traverse up
the directory tree (to an ancestor of the bundle root directory), the
CLI will dynamically determine the root path to use to ensure that the
file tree structure remains intact.
For example, given a `databricks.yml` in `my_bundle` that includes:
```yaml
sync:
paths:
- ../common
- .
```
Then upon synchronization, the workspace will look like:
```
.
├── common
│ └── lib.py
└── my_bundle
├── databricks.yml
└── notebook.py
```
If not set behavior remains identical.
## Tests
* Newly added unit tests for the mutators and under `bundle/tests`.
* Manually confirmed a bundle without this configuration works the same.
* Manually confirmed a bundle with this configuration works.
## Changes
This adds configurable transformations based on the transformations
currently seen in `mode: development`.
Example databricks.yml showcasing how some transformations:
```
bundle:
name: my_bundle
targets:
dev:
presets:
prefix: "myprefix_" # prefix all resource names with myprefix_
pipelines_development: true # set development to true by default for pipelines
trigger_pause_status: PAUSED # set pause_status to PAUSED by default for all triggers and schedules
jobs_max_concurrent_runs: 10 # set max_concurrent runs to 10 by default for all jobs
tags:
dev: true
```
## Tests
* Existing process_target_mode tests that were adapted to use this new
code
* Unit tests specific for the new mutator
* Unit tests for config loading and merging
* Manual e2e testing
## Changes
Previously for all the libraries referenced in configuration DABs made
sure that there is corresponding artifact section.
But this is not really necessary and flexible, because local libraries
might be built outside of dabs context.
It also created difficult to follow logic in code where we back
referenced libraries to artifacts which was difficult to fllow
This PR does 3 things:
1. Allows all local libraries referenced in DABs config to be uploaded
to remote
2. Simplifies upload and glob references expand logic by doing this in
single place
3. Speed things up by uploading library only once and doing this in
parallel
## Tests
Added unit + integration tests + made sure that change is backward
compatible (no changes in existing tests)
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@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 change enables overriding the default value of job parameters in
target overrides.
This is the same approach we already take for job clusters and job
tasks.
Closes#1620.
## Tests
Mutator unit tests and lightweight end-to-end tests.
# Changes
With https://github.com/databricks/cli/pull/1413 we started to compute
and partially print the plan if it contained deletion of UC schemas.
This PR uses the precomputed plan to avoid double planning when actually
doing the terraform plan.
This fixes a performance regression introduced in
https://github.com/databricks/cli/pull/1413.
# Tests
Tested manually.
1. Verified bundle deployment still works and deploys resources.
2. Verified that the precomputed plan is indeed being used by attaching
a debugger and removing the plan file right before the terraform apply
process is spawned and asserting that terraform apply fails because the
plan is not found.
## 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
Right now we ask users for two confirmations when destroying a bundle.
One to destroy the resources and one to delete the files. This PR
consolidates the two prompts into one.
## Tests
Manually
Destroying a bundle with no resources:
```
➜ bundle-playground git:(master) ✗ cli bundle destroy
All files and directories at the following location will be deleted: /Users/shreyas.goenka@databricks.com/.bundle/bundle-playground/default
Would you like to proceed? [y/n]: y
No resources to destroy
Updating deployment state...
Deleting files...
Destroy complete!
```
Destroying a bundle with no remote state:
```
➜ bundle-playground git:(master) ✗ cli bundle destroy
No active deployment found to destroy!
```
When a user cancells a deployment:
```
➜ bundle-playground git:(master) ✗ cli bundle destroy
The following resources will be deleted:
delete job job_1
delete job job_2
delete pipeline foo
All files and directories at the following location will be deleted: /Users/shreyas.goenka@databricks.com/.bundle/bundle-playground/default
Would you like to proceed? [y/n]: n
Destroy cancelled!
```
When a user destroys resources:
```
➜ bundle-playground git:(master) ✗ cli bundle destroy
The following resources will be deleted:
delete job job_1
delete job job_2
delete pipeline foo
All files and directories at the following location will be deleted: /Users/shreyas.goenka@databricks.com/.bundle/bundle-playground/default
Would you like to proceed? [y/n]: y
Updating deployment state...
Deleting files...
Destroy complete!
```
## Changes
Now prepare stage which does cleanup is execute once before every build,
so artifacts built into the same folder are correctly kept
Fixes workaround 2 from this issue #1602
## Tests
Added unit test
## Changes
This PR:
1. Moves the if mutator to the bundle package, to live with all-time
greats such as `bundle.Seq` and `bundle.Defer`. Also adds unit tests.
2. `bundle destroy` now returns early if `root_path` does not exist. We
do this by leveraging a `bundle.If` condition.
## Tests
Unit tests and manually.
Here's an example of what it'll look like once the bundle is destroyed.
```
➜ bundle-playground git:(master) ✗ cli bundle destroy
No active deployment found to destroy!
```
I would have added some e2e coverage for this as well, but the
`cobraTestRunner.Run()` method does not seem to return stdout/stderr
logs correctly. We can probably punt looking into it.
## Changes
The FUSE mount of the workspace file system on DBR doesn't include file
extensions for notebooks. When these notebooks are checked into a
repository, they do have an extension. PR #1457 added a filer type that
is aware of this disparity and makes these notebooks show up as if they
do have these extensions.
This change swaps out the native `vfs.Path` with one that uses this
filer when running on DBR.
Follow up: consolidate between interfaces exported by `filer.Filer` and
`vfs.Path`.
## Tests
* Unit tests pass
* (Manually ran a snapshot build on DBR against a bundle with notebooks)
---------
Co-authored-by: Andrew Nester <andrew.nester@databricks.com>
## Changes
Added support for complex variables
Now it's possible to add and use complex variables as shown below
```
bundle:
name: complex-variables
resources:
jobs:
my_job:
job_clusters:
- job_cluster_key: key
new_cluster: ${var.cluster}
tasks:
- task_key: test
job_cluster_key: key
variables:
cluster:
description: "A cluster definition"
type: complex
default:
spark_version: "13.2.x-scala2.11"
node_type_id: "Standard_DS3_v2"
num_workers: 2
spark_conf:
spark.speculation: true
spark.databricks.delta.retentionDurationCheck.enabled: false
```
Fixes#1298
- [x] Support for complex variables
- [x] Allow variable overrides (with shortcut) in targets
- [x] Don't allow to provide complex variables via flag or env variable
- [x] Fail validation if complex value is used but not `type: complex`
provided
- [x] Support using variables inside complex variables
## Tests
Added unit tests
---------
Co-authored-by: shreyas-goenka <88374338+shreyas-goenka@users.noreply.github.com>
## Changes
Replace stdin/stdout with files in `PythonMutator`. Files are created in
a temporary directory.
Rename `ApplyPythonMutator` to `PythonMutator`.
Add test for `dyn.Location` behavior during the "load" stage.
## Tests
Unit tests
## Changes
Add ApplyPythonMutator, which will fork the Python subprocess and
process pipe bundle configuration through it.
It's enabled through `experimental` section, for example:
```yaml
experimental:
pydabs:
enable: true
venv_path: .venv
```
For now, it's limited to two phases in the mutator pipeline:
- `load`: adds new jobs
- `init`: adds new jobs, or modifies existing ones
It's enforced that no jobs are modified in `load` and not jobs are
deleted in `load/init`, because, otherwise, it will break existing
assumptions.
## Tests
Unit tests
## Changes
`check_running_resources` now pulls the remote state without modifying
the bundle state, similar to how it was doing before. This avoids a
problem when we fail to compute deployment metadata for a deleted job
(which we shouldn't do in the first place)
`deploy_then_remove_resources_test` now also deploys and deletes a job
(in addition to a pipeline), which catches the error that this PR fixes.
## Tests
Unit and integ tests
## Changes
This PR annotates any pipelines that were deployed using DABs to have
`deployment.kind` set to "BUNDLE", mirroring the annotation for Jobs
(similar PR for jobs FYI: https://github.com/databricks/cli/pull/880).
Breakglass UI is not yet available for pipelines, so this annotation
will just be used for revenue attribution ATM.
Note: The API field has been deployed in all regions including GovCloud.
## Tests
Unit tests and manually.
Manually verified that the kind and metadata_file_path are being set by
DABs, and are returned by a GET API to a pipeline deployed using a DAB.
Example:
```
"deployment": {
"kind":"BUNDLE",
"metadata_file_path":"/Users/shreyas.goenka@databricks.com/.bundle/bundle-playground/default/state/metadata.json"
},
```
`terraform show -json` (`terraform.Show()`) fails if the state file
contains resources with fields that non longer conform to the provider
schemas.
This can happen when you deploy a bundle with one version of the CLI,
then updated the CLI to a version that uses different databricks
terraform provider, and try to run `bundle run` or `bundle summary`.
Those commands don't recreate local terraform state (only `terraform
apply` or `plan` do) and terraform itself fails while parsing it.
[Terraform
docs](https://developer.hashicorp.com/terraform/language/state#format)
point out that it's best to use `terraform show` after successful
`apply` or `plan`.
Here we parse the state ourselves. The state file format is internal to
terraform, but it's more stable than our resource schemas. We only parse
a subset of fields from the state, and only update ID and ModifiedStatus
of bundle resources in the `terraform.Load` mutator.
## Changes
The main changes are:
1. Don't link artifacts to libraries anymore and instead just iterate
over all jobs and tasks when uploading artifacts and update local path
to remote
2. Iterating over `jobs.environments` to check if there are any local
libraries and checking that they exist locally
3. Added tests to check environments are handled correctly
End-to-end test will follow up
## Tests
Added regression test, existing tests (including integration one) pass
## Changes
This enable queueing for jobs by default, following the behavior from
API 2.2+. Queing is a best practice and will be the default in API 2.2.
Since we're still using API 2.1 which has queueing disabled by default,
this PR enables queuing using a mutator.
Customers can manually turn off queueing for any job by adding the
following to their job spec:
```
queue:
enabled: false
```
## Tests
Unit tests, manual confirmation of property after deployment.
---------
Co-authored-by: Pieter Noordhuis <pcnoordhuis@gmail.com>
## Changes
Allows for the syntax below
```
variables:
service_principal_app_id:
description: 'The app id of the service principal for running workflows as.'
lookup:
service_principal: "sp-${bundle.environment}"
```
Fixes#1259
## Tests
Added regression test
## Changes
Prior to this change, the bundle configuration entry point was loaded
from the function `bundle.Load`. Other configuration files were only
loaded once the caller applied the first set of mutators. This
separation was unnecessary and not ideal in light of gathering
diagnostics while loading _any_ configuration file, not just the ones
from the includes.
This change:
* Updates `bundle.Load` to only verify that the specified path is a
valid bundle root.
* Moves mutators that perform loading to `bundle/config/loader`.
* Adds a "load" phase that takes the place of applying
`DefaultMutators`.
Follow ups:
* Rename `bundle.Load` -> `bundle.Find` (because it no longer performs
loading)
This change depends on #1316 and #1317.
## Tests
Tests pass.
## 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
CheckRunningResource does `terraform.Show` which (I believe) expects
valid `bundle.tf.json` which is only written as part of
`terraform.Write` later.
With this PR order is changed.
Fixes#1286
## Tests
Added regression E2E test
## Changes
This PR introduces new structure (and a file) being used locally and
synced remotely to Databricks workspace to track bundle deployment
related metadata.
The state is pulled from remote, updated and pushed back remotely as
part of `bundle deploy` command.
This state can be used for deployment sequencing as it's `Version` field
is monotonically increasing on each deployment.
Currently, it only tracks files being synced as part of the deployment.
This helps fix the issue with files not being removed during deployments
on CI/CD as sync snapshot was never present there.
Fixes#943
## Tests
Added E2E (regression) test for files removal on CI/CD
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
## Changes
The databricks terraform provider does not allow changing permission of
the current user. Instead, the current identity is implictly set to be
the owner of all resources on the platform side.
This PR introduces a mutator to filter permissions from the bundle
configuration at deploy time, allowing users to define permissions for
their own identities in their bundle config.
This would allow configurations like, allowing both alice and bob to
collaborate on the same DAB:
```
permissions:
level: CAN_MANAGE
user_name: alice
level: CAN_MANAGE
user_name: bob
```
This PR is a reincarnation of
https://github.com/databricks/cli/pull/1145. The earlier attempt had to
be reverted due to metadata loss converting to and from the dynamic
configuration representation (reverted here:
https://github.com/databricks/cli/pull/1179)
## Tests
Unit test and manually
## 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
Deploying bundle when there are bundle resources running at the same
time can be disruptive for jobs and pipelines in progress.
With this change during deployment phase (before uploading any
resources) if there is `--fail-if-running` specified DABs will check if
there are any resources running and if so, will fail the deployment
## Tests
Manual + add tests
## Changes
This reverts commit 4131069a4b.
The integration test for metadata computation failed. The back and forth
to `dyn.Value` erases unexported fields that the code currently still
depends on. We'll have to retry on top of #1098.
## Changes
The databricks terraform provider does not allow changing permission of
the current user. Instead, the current identity is implictly set to be
the owner of all resources on the platform side.
This PR introduces a mutator to filter permissions from the bundle
configuration, allowing users to define permissions for their own
identities in their bundle config.
This would allow configurations like, allowing both alice and bob to
collaborate on the same DAB:
```
permissions:
level: CAN_MANAGE
user_name: alice
level: CAN_MANAGE
user_name: bob
```
## Tests
Unit test and manually
## Changes
This PR sets run as permissions after variable interpolation.
Terraform does not allow specifying permissions for current user.
The following configuration would fail becuase we would assign a
permission block for self, bypassing this check here:
4ee926b885/bundle/config/mutator/run_as.go (L47)
```
run_as:
user_name: ${workspace.current_user.userName}
```
## Tests
Manually, setting run_as to ${workspace.current_user.userName} works now
## Changes
Now we can define variables with values which reference different
Databricks resources by name.
When references like this, DABs automatically looks up the resource by
this name and replaces the reference with ID of the resource referenced.
Thus when the variable is used in the configuration it will contain the
correct resolved ID of resource.
The resolvers are code generated and thus DABs support referencing all
resources which has `GetByName`-like methods in Go SDK.
### Example
```
variables:
my_cluster_id:
description: An existing cluster.
lookup:
cluster: "12.2 shared"
resources:
jobs:
my_job:
name: "My Job"
tasks:
- task_key: TestTask
existing_cluster_id: ${var.my_cluster_id}
targets:
dev:
variables:
my_cluster_id:
lookup:
cluster: "dev-cluster"
```
## Tests
Added unit test + manual testing
---------
Co-authored-by: shreyas-goenka <88374338+shreyas-goenka@users.noreply.github.com>
## Changes
Update the output of the `deploy` command to be more concise and
consistent:
```
$ databricks bundle deploy
Building my_project...
Uploading my_project-0.0.1+20231207.205106-py3-none-any.whl...
Uploading bundle files to /Users/lennart.kats@databricks.com/.bundle/my_project/dev/files...
Deploying resources...
Updating deployment state...
Deployment complete!
```
This does away with the intermediate success messages, makes consistent
use of `...`, and only prints the success message at the very end after
everything is completed.
Below is the original output for comparison:
```
$ databricks bundle deploy
Detecting Python wheel project...
Found Python wheel project at /tmp/output/my_project
Building my_project...
Build succeeded
Uploading my_project-0.0.1+20231207.205134-py3-none-any.whl...
Upload succeeded
Starting upload of bundle files
Uploaded bundle files at /Users/lennart.kats@databricks.com/.bundle/my_project/dev/files!
Starting resource deployment
Resource deployment completed!
```
## 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
This PR introduces a metadata struct that stores a subset of bundle
configuration that we wish to expose to other Databricks services that
wish to integrate with bundles.
This metadata file is uploaded to a file
`${bundle.workspace.state_path}/metadata.json` in the WSFS destination
of the bundle deployment.
Documentation for emitted metadata fields:
* `version`: Version for the metadata file schema
* `config.bundle.git.branch`: Name of the git branch the bundle was
deployed from.
* `config.bundle.git.origin_url`: URL for git remote "origin"
* `config.bundle.git.bundle_root_path`: Relative path of the bundle root
from the root of the git repository. Is set to "." if they are the same.
* `config.bundle.git.commit`: SHA-1 commit hash of the exact commit this
bundle was deployed from. Note, the deployment might not exactly match
this commit version if there are changes that have not been committed to
git at deploy time,
* `file_path`: Path in workspace where we sync bundle files to.
* `resources.jobs.[job-ref].id`: Id of the job
* `resources.jobs.[job-ref].relative_path`: Relative path of the yaml
config file from the bundle root where this job was defined.
Example metadata object when bundle root and git root are the same:
```json
{
"version": 1,
"config": {
"bundle": {
"lock": {},
"git": {
"branch": "master",
"origin_url": "www.host.com",
"commit": "7af8e5d3f5dceffff9295d42d21606ccf056dce0",
"bundle_root_path": "."
}
},
"workspace": {
"file_path": "/Users/shreyas.goenka@databricks.com/.bundle/pipeline-progress/default/files"
},
"resources": {
"jobs": {
"bar": {
"id": "245921165354846",
"relative_path": "databricks.yml"
}
}
},
"sync": {}
}
}
```
Example metadata when the git root is one level above the bundle repo:
```json
{
"version": 1,
"config": {
"bundle": {
"lock": {},
"git": {
"branch": "dev-branch",
"origin_url": "www.my-repo.com",
"commit": "3db46ef750998952b00a2b3e7991e31787e4b98b",
"bundle_root_path": "pipeline-progress"
}
},
"workspace": {
"file_path": "/Users/shreyas.goenka@databricks.com/.bundle/pipeline-progress/default/files"
},
"resources": {
"jobs": {
"bar": {
"id": "245921165354846",
"relative_path": "databricks.yml"
}
}
},
"sync": {}
}
}
```
This unblocks integration to the jobs break glass UI for bundles.
## Tests
Unit tests and integration tests.
## Changes
Upload terraform state even if apply fails
Fixes#893
## Tests
Manually running `databricks bundle deploy` with incorrect permissions
in bundle config and observe that it gets uploaded correctly
## 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
***Note: this PR relies on sync.include functionality from here:
https://github.com/databricks/cli/pull/671***
Added transformation mutator for Python wheel task for them to work on
DBR <13.1
Using wheels upload to Workspace file system as cluster libraries is not
supported in DBR < 13.1
In order to make Python wheel work correctly on DBR < 13.1 we do the
following:
1. Build and upload python wheel as usual
2. Transform python wheel task into special notebook task which does the
following
a. Installs all necessary wheels with %pip magic
b. Executes defined entry point with all provided parameters
3. Upload this notebook file to workspace file system
4. Deploy transformed job task
This is also beneficial for executing on existing clusters because this
notebook always reinstall wheels so if there are any changes to the
wheel package, they are correctly picked up
## Tests
bundle.yml
```yaml
bundle:
name: wheel-task
workspace:
host: ****
resources:
jobs:
test_job:
name: "[${bundle.environment}] My Wheel Job"
tasks:
- task_key: TestTask
existing_cluster_id: "***"
python_wheel_task:
package_name: "my_test_code"
entry_point: "run"
parameters: ["first argument","first value","second argument","second value"]
libraries:
- whl: ./dist/*.whl
```
Output
```
andrew.nester@HFW9Y94129 wheel % databricks bundle run test_job
Run URL: ***
2023-08-03 15:58:04 "[default] My Wheel Job" TERMINATED SUCCESS
Output:
=======
Task TestTask:
Hello from my func
Got arguments v1:
['python', 'first argument', 'first value', 'second argument', 'second value']
```
## 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
## Changes
This checks whether the Git settings are consistent with the actual Git
state of a source directory.
(This PR adds to https://github.com/databricks/cli/pull/577.)
Previously, we would silently let users configure their Git branch to
e.g. `main` and deploy with that metadata even if they were actually on
a different branch.
With these changes, the following config would result in an error when
deployed from any other branch than `main`:
```
bundle:
name: example
workspace:
git:
branch: main
environments:
...
```
> not on the right Git branch:
> expected according to configuration: main
> actual: my-feature-branch
It's not very useful to set the same branch for all environments,
though. For development, it's better to just let the CLI auto-detect the
right branch. Therefore, it's now possible to set the branch just for a
single environment:
```
bundle:
name: example 2
environments:
development:
default: true
production:
# production can only be deployed from the 'main' branch
git:
branch: main
```
Adding to that, the `mode: production` option actually checks that users
explicitly set the Git branch as seen above. Setting that branch helps
avoid mistakes, where someone accidentally deploys to production from
the wrong branch. (I could see us offering an escape hatch for that in
the future.)
# Testing
Manual testing to validate the experience and error messages. Automated
unit tests.
---------
Co-authored-by: Fabian Jakobs <fabian.jakobs@databricks.com>
## 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
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
```
Pieter Noordhuis
Lennart Kats (databricks)
Andrew Nester
shreyas-goenka
Gleb Kanterov
Ilia Babanov