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Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Andrew Nester <andrew.nester@databricks.com>
## Changes
Allow specifying CLI version constraints required to run the bundle
Example of configuration:
#### only allow specific version
```
bundle:
name: my-bundle
databricks_cli_version: "0.210.0"
```
#### allow all patch releases
```
bundle:
name: my-bundle
databricks_cli_version: "0.210.*"
```
#### constrain minimum version
```
bundle:
name: my-bundle
databricks_cli_version: ">= 0.210.0"
```
#### constrain range
```
bundle:
name: my-bundle
databricks_cli_version: ">= 0.210.0, <= 1.0.0"
```
For other examples see:
https://github.com/Masterminds/semver?tab=readme-ov-file#checking-version-constraints
Example error
```
sh-3.2$ databricks bundle validate
Error: Databricks CLI version constraint not satisfied. Required: >= 1.0.0, current: 0.216.0
```
## Tests
Added unit test cover all possible configuration permutations
---------
Co-authored-by: Lennart Kats (databricks) <lennart.kats@databricks.com>
## Changes
This PR introduces an allow list for resource types that are allowed
when the run_as for the bundle is not the same as the current deployment
user.
This PR also adds a test to ensure that any new resources added to DABs
will have to add the resource to either the allow list or add an error
to fail when run_as identity is not the same as deployment user.
## Tests
Unit tests
## 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
PR #604 added functionality to load a bundle without a `databricks.yml`
if both the `DATABRICKS_BUNDLE_ROOT` and `DATABRICKS_BUNDLE_INCLUDES`
environment variables were set. We never ended up using this in
downstream tools so this can be removed.
## Tests
Unit tests pass.
## Changes
The bundle path was previously stored on the `config.Root` type under
the assumption that the first configuration file being loaded would set
it. This is slightly counterintuitive and we know what the path is upon
construction of the bundle. The new location for this property reflects
this.
## Tests
Unit 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
This change addresses the path resolution behavior in resource
definitions. Previously, all paths were resolved relative to where the
resource was first defined, which could lead to confusion and errors
when paths were specified in different directories. The new behavior is
to resolve paths relative to where they are defined, making it more
intuitive.
However, to avoid breaking existing configurations, compatibility with
the old behavior is maintained.
## Tests
* Existing unit tests for path translation pass.
* Additional test to cover both the nominal and the fallback behavior.
## 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
We now keep location metadata associated with every configuration value.
When expanding globs for pipeline libraries, this annotation was erased
because of the conversion to/from the typed structure. This change
modifies the expansion mutator to work with `dyn.Value` and retain the
location of the value that holds the glob pattern.
## Tests
Unit tests pass.
## Changes
This change means the callback supplied to `dyn.Foreach` can introspect
the path of the value it is being called for. It also prepares for
allowing visiting path patterns where the exact path is not known
upfront.
## Tests
Unit tests.
## Changes
This change enables the use of bundle variables for boolean, integer,
and floating point fields.
## Tests
* Unit tests.
* I ran a manual test to confirm parameterizing the number of workers in
a cluster definition works.
## 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
Adds the short_name helper function. short_name is useful when templates
do not want to print the full userName (typically email or service
principal application-id) of the current user.
## Tests
Integration test. Also adds integration tests for other helper functions
that interact with the Databricks API.
## Changes
Now it's possible to generate bundle configuration for existing job.
For now it only supports jobs with notebook tasks.
It will download notebooks referenced in the job tasks and generate
bundle YAML config for this job which can be included in larger bundle.
## Tests
Running command manually
Example of generated config
```
resources:
jobs:
job_128737545467921:
name: Notebook job
format: MULTI_TASK
tasks:
- task_key: as_notebook
existing_cluster_id: 0704-xxxxxx-yyyyyyy
notebook_task:
base_parameters:
bundle_root: /Users/andrew.nester@databricks.com/.bundle/job_with_module_imports/development/files
notebook_path: ./entry_notebook.py
source: WORKSPACE
run_if: ALL_SUCCESS
max_concurrent_runs: 1
```
## Tests
Manual (on our last 100 jobs) + added end-to-end test
```
--- PASS: TestAccGenerateFromExistingJobAndDeploy (50.91s)
PASS
coverage: 61.5% of statements in ./...
ok github.com/databricks/cli/internal/bundle 51.209s coverage: 61.5% of
statements in ./...
```
## 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
This PR changes the default and `mode: production` recommendation to
target `/Users` for deployment. Previously, we used `/Shared`, but
because of a lack of POSIX-like permissions in WorkspaceFS this meant
that files inside would be readable and writable by other users in the
workspace.
Detailed change:
* `default-python` no longer uses a path that starts with `/Shared`
* `mode: production` no longer requires a path that starts with
`/Shared`
## Related PRs
Docs: https://github.com/databricks/docs/pull/14585
Examples: https://github.com/databricks/bundle-examples/pull/17
## Tests
* Manual tests
* Template unit tests (with an extra check to avoid /Shared)
## Changes
If there are no matches when doing Glob call for pipeline library
defined, leave the entry as is.
The next mutators in the chain will detect that file is missing and the
error will be more user friendly.
Before the change
```
Starting resource deployment
Error: terraform apply: exit status 1
Error: cannot create pipeline: libraries must contain at least one element
```
After
```
Error: notebook ./non-existent not found
```
## Tests
Added regression unit tests
## Changes
Previously local JAR paths were transformed to remote path during
initialisation and thus artifact building logic did not recognise such
libraries as local to be handled and uploaded.
Now it's possible to use spark_jar_tasks with local JAR libraries on
14.1+ DBR clusters
Example configuration
```
bundle:
name: spark-jar
workspace:
host: ***
artifacts:
my_java_code:
path: ./sample-java
build: "javac PrintArgs.java && jar cvfm PrintArgs.jar META-INF/MANIFEST.MF PrintArgs.class"
files:
- source: "/Users/andrew.nester/dabs/wheel/sample-java/PrintArgs.jar"
resources:
jobs:
print_args:
name: "Print Args"
tasks:
- task_key: Print
new_cluster:
num_workers: 0
spark_version: 14.2.x-scala2.12
node_type_id: i3.xlarge
spark_conf:
"spark.databricks.cluster.profile": "singleNode"
"spark.master": "local[*]"
custom_tags:
ResourceClass: "SingleNode"
spark_jar_task:
main_class_name: PrintArgs
libraries:
- jar: ./sample-java/PrintArgs.jar
```
## Tests
Manually running `bundle deploy and bundle run`
## Changes
Some test call sites called directly into the mutator's `Apply` function
instead of `bundle.Apply`. Calling into `bundle.Apply` is preferred
because that's where we can run pre/post logic common across all
mutators.
## Tests
Pass.
## Changes
All calls to apply a mutator must go through `bundle.Apply`. This
conflicts with the existing use of the variable `bundle`. This change
un-aliases the variable from the package name by renaming all variables
to `b`.
## Tests
Pass.
## Changes
This PR:
1. Renames `FilesPath` -> `FilePath` and `ArtifactsPath` ->
`ArtifactPath` in the bundle and metadata configuration to make them
consistant with the json tags.
2. Fixes development / production mode error messages to point to
`file_path` and `artifact_path`
## Tests
Existing unit tests. This is a strightforward renaming of the fields.
Partly mitigates #859. It's still not clear to me if there is an actual
use case or if users are trying to use "development" mode jobs for
production, but making this overridable is reasonable.
Beyond this fix I think we could do something in the Jobs schedule UI,
but it would help to better understand the use case (or actual reason of
confusion). I expect we should hint customers to move away from dev mode
rather than unpause.
## Changes
We can debate whether or not variable definitions without properties are
valid, but in no case should this panic the CLI.
Fixes#934.
## Tests
Unit.
## Changes
Support path rewrites for Dbt and SQL file job taks.
<!-- Summary of your changes that are easy to understand -->
## Tests
* Added unit test
<!-- How is this tested? -->
## 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
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
The jobs backend propagates job tags to the underlying cloud provider's
resources. As such, they need to match the constraints a cloud provider
places on tag values. The display name can contain anything. With this
change, we modify the tag value to equal the short name as used in the
name prefix.
Additionally, we leverage tag normalization as introduced in #819 to
make sure characters that aren't accepted are removed before using the
value as a tag value.
This is a new stab at #810 and should completely eliminate this class of
problems.
## Tests
Tests pass.
## Changes
Instead of always using notebook wrapper for Python wheel tasks, let's
make this an opt-in option.
Now by default Python wheel tasks will be deployed as is to Databricks
platform.
If notebook wrapper required (DBR < 13.1 or other configuration
differences), users can provide a following experimental setting
```
experimental:
python_wheel_wrapper: true
```
Fixes#783,
https://github.com/databricks/databricks-asset-bundles-dais2023/issues/8
## Tests
Added unit tests.
Integration tests passed for both cases
```
helpers.go:163: [databricks stdout]: Hello from my func
helpers.go:163: [databricks stdout]: Got arguments:
helpers.go:163: [databricks stdout]: ['my_test_code', 'one', 'two']
...
Bundle remote directory is ***/.bundle/ac05d5e8-ed4b-4e34-b3f2-afa73f62b021
Deleted snapshot file at /var/folders/nt/xjv68qzs45319w4k36dhpylc0000gp/T/TestAccPythonWheelTaskDeployAndRunWithWrapper3733431114/001/.databricks/bundle/default/sync-snapshots/cac1e02f3941a97b.json
Successfully deleted files!
--- PASS: TestAccPythonWheelTaskDeployAndRunWithWrapper (214.18s)
PASS
coverage: 93.5% of statements in ./...
ok github.com/databricks/cli/internal/bundle 214.495s coverage: 93.5% of statements in ./...
```
```
helpers.go:163: [databricks stdout]: Hello from my func
helpers.go:163: [databricks stdout]: Got arguments:
helpers.go:163: [databricks stdout]: ['my_test_code', 'one', 'two']
...
Bundle remote directory is ***/.bundle/0ef67aaf-5960-4049-bf1d-dc9e29157421
Deleted snapshot file at /var/folders/nt/xjv68qzs45319w4k36dhpylc0000gp/T/TestAccPythonWheelTaskDeployAndRunWithoutWrapper2340216760/001/.databricks/bundle/default/sync-snapshots/edf0b322cee93b13.json
Successfully deleted files!
--- PASS: TestAccPythonWheelTaskDeployAndRunWithoutWrapper (192.36s)
PASS
coverage: 93.5% of statements in ./...
ok github.com/databricks/cli/internal/bundle 195.130s coverage: 93.5% of statements in ./...
```
## Changes
There are a couple places throughout the code base where interaction
with environment variables takes place. Moreover, more than one of these
would try to read a value from more than one environment variable as
fallback (for backwards compatibility). This change consolidates those
accesses.
The majority of diffs in this change are mechanical (i.e. add an
argument or replace a call).
This change:
* Moves common environment variable lookups for bundles to
`bundles/env`.
* Adds a `libs/env` package that wraps `os.LookupEnv` and `os.Getenv`
and allows for overrides to take place in a `context.Context`. By
scoping overrides to a `context.Context` we can avoid `t.Setenv` in
testing and unlock parallel test execution for integration tests.
* Updates call sites to pass through a `context.Context` where needed.
* For bundles, introduces `DATABRICKS_BUNDLE_ROOT` as new primary
variable instead of `BUNDLE_ROOT`. This was the last environment
variable that did not use the `DATABRICKS_` prefix.
## Tests
Unit tests pass.
## Changes
List available targets when incorrect target passed
## Tests
```
andrew.nester@HFW9Y94129 wheel % databricks bundle validate -t incorrect
Error: incorrect: no such target. Available targets: prod, development
```
## Changes
This follows up on https://github.com/databricks/cli/pull/686. This PR
makes our stubs optional + it adds DLT stubs:
```
$ databricks bundle init
Template to use [default-python]: default-python
Unique name for this project [my_project]: my_project
Include a stub (sample) notebook in 'my_project/src' [yes]: yes
Include a stub (sample) DLT pipeline in 'my_project/src' [yes]: yes
Include a stub (sample) Python package 'my_project/src' [yes]: yes
✨ Successfully initialized template
```
## Tests
Manual testing, matrix tests.
---------
Co-authored-by: Andrew Nester <andrew.nester@databricks.com>
Co-authored-by: PaulCornellDB <paul.cornell@databricks.com>
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
## Changes
The latest rendition of isServicePrincipal no longer worked for
non-admin users as it used the "principals get" API.
This new version relies on the property that service principals always
have a UUID as their userName. This was tested with the eng-jaws
principal (8b948b2e-d2b5-4b9e-8274-11b596f3b652).
# Warning: breaking change
## Changes
Instead of having paths in bundle config files be relative to bundle
root even if the config file is nested, this PR makes such paths
relative to the folder where the config is located.
When bundle is initialised, these paths will be transformed to relative
paths based on bundle root. For example,
we have file structure like this
```
- mybundle
| - bundle.yml
| - subfolder
| -- resource.yml
| -- my.whl
```
Previously, we had to reference `my.whl` in resource.yml like this,
which was confusing because resource.yml is in the same subfolder
```
sync:
include:
- ./subfolder/*.whl
...
tasks:
- task_key: name
libraries:
- whl: ./subfolder/my.whl
...
```
After the change we can reference it like this (which is in line with
the current behaviour for notebooks)
```
sync:
include:
- ./*.whl
...
tasks:
- task_key: name
libraries:
- whl: ./my.whl
...
```
## Tests
Existing `translate_path_tests` successfully passed after refactoring.
Added a couple of uses cases for `Libraries` paths.
Added a bundle config tests with include config and sync section
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
## Changes
@pietern this addresses a comment from you on a recently merged PR. It
also updates settings.json based on the settings VS Code adds as soon as
you edit a notebook.
## 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']
```
dependabot[bot]
Andrew Nester
shreyas-goenka
Pieter Noordhuis
Lennart Kats (databricks)
Michał Szafrański
Arpit Jasapara