## 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
The built-in template contains a reference to `${bundle.environment}`.
This property has been deprecated in favor of `${bundle.target}` a long
time ago (#670), so we should no longer emit it. The environment field
will continue to be usable until we cut a new major version in some far
away future.
## Tests
* Unit tests
* The test `TestInterpolationWithTarget` still covers correct
interpolation of `${bundle.environment}`
Bumps
[github.com/Masterminds/semver/v3](https://github.com/Masterminds/semver)
from 3.3.0 to 3.3.1.
<details>
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<li>Fix for allowing some version that were invalid by <a
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href="https://redirect.github.com/Masterminds/semver/pull/253">Masterminds/semver#253</a></li>
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## Changes
This PR adds a warning validating that the configuration for a single
node cluster is valid for interactive, job, job-task, and pipeline
clusters.
Note: We skip the validation if a cluster policy is configured because
the policy is likely to configure `spark_conf` / `custom_tags` itself.
Note: Terrform originally only had validation for interactive, job, and
job-task clusters. This PR adding the validation for pipeline clusters
as well is new.
This PR follows the same logic as we used to have in Terraform. The
validation was removed from Terraform because we had no way to demote
the error to a warning:
https://github.com/databricks/terraform-provider-databricks/pull/4222
### Background
Single-node clusters require `spark_conf` and `custom_tags` to be
correctly set in the cluster definition for them to function optimally.
The cluster will be created even if incorrectly configured, but its
performance will not be great.
For example, if both `spark_conf` and `custom_tags` are not set and
`num_workers` is 0, then only the driver process will be launched on the
cluster compute instance thus leading to sub-optimal utilization of
available compute resources and no parallelization across worker
processes when processing a spark query.
### Issue
This PR addresses some issues reported in
https://github.com/databricks/cli/issues/1546
## Tests
Unit tests and manually.
Example output of the warning:
```
➜ bundle-playground git:(master) ✗ cli bundle validate
Warning: Single node cluster is not correctly configured
at resources.pipelines.bar.clusters[0]
in databricks.yml:29:11
num_workers should be 0 only for single-node clusters. To create a
valid single node cluster please ensure that the following properties
are correctly set in the cluster specification:
spark_conf:
spark.databricks.cluster.profile: singleNode
spark.master: local[*]
custom_tags:
ResourceClass: SingleNode
Name: foobar
Target: default
Workspace:
User: shreyas.goenka@databricks.com
Path: /Workspace/Users/shreyas.goenka@databricks.com/.bundle/foobar/default
Found 1 warning
```
## Changes
Users can configure the bundle to not synchronize any files with:
```yaml
sync:
paths: []
```
If it is explicitly configured as an empty list, the validate command
must not warn about not having any files to synchronize. The warning
exists to alert users who are unintentionally not synchronizing any
files (they might have a `.gitignore` pattern that matches everything).
Closes#1663.
## Tests
* New unit test.
## Changes
The ML production team modified mlops-stack to use `mode: development`
for their development target here:
https://github.com/databricks/mlops-stacks/pull/174
This PR makes the integration test assertion agnostic of the prefix to
make it pass again.
## Tests
The test passes now
## Changes
The full workspace path for a notebook does not contain the notebook's
extension. If a user converts that file path to a relative path (like
`/Workspace/bundle_root/bar/nb` -> `./bar/nb`), they can be confused as
to why the new file path does not work.
The changes in this PR nudge them to add the appropriate file extension
(e.g., `./bar/nb.py` or `./bar/nb.ipynb`).
One common way users can end up in this scenario is by using the view
job as YAML functionality in the Databricks UI.
## Tests
Unit test and manually.
```
(.venv) ➜ bundle-playground git:(master) ✗ cli bundle validate
Error: notebook ./foo not found. Local notebook references are expected
to contain one of the following file extensions: [.py, .r, .scala, .sql, .ipynb]
```
## Changes
While looking into adding variable lookups for notification destinations
([API][API]), I found the codegen approach for different classes of
variable lookups a bit complex. The template had a custom field override
(for service principals), the package had an override for the cluster
lookup, and it didn't produce tests.
The notification destinations API uses a default page size of 20 for
listing. I want to use a larger page size to limit the number of API
calls, so that would imply another customization on the template or a
manual override.
This code being rather mechanical, I used copilot to produce all
instances of the resolvers and their tests (after writing one of them
manually).
[api]: https://docs.databricks.com/api/workspace/notificationdestinations
## Tests
* Unit tests pass
* Manual confirmation that lookups of warehouses still work
## Changes
Integration tests using these fixtures could have been flaky when run in
parallel using the same user's identity. They would also possibly have
piggybacked state from previous runs.
This PR adds a UUID to the root_path to force independent bundle
deployments for every test run.
I have checked that all bundles in `internal/bundle/bundles` have
`root_path` namespaced to a UUID.
## Tests
Self testing.
## Changes
Update filenames used by bundle generate to use '.resource-type.yml'
Similar to [Add sub-extension to resource files in built-in templates by
shreyas-goenka · Pull Request #1777 ·
databricks/cli](https://github.com/databricks/cli/pull/1777)
---------
Co-authored-by: shreyas-goenka <88374338+shreyas-goenka@users.noreply.github.com>
## Changes
Added integration test to deploy bundle to /Shared root path
## Tests
```
--- PASS: TestAccDeployBasicToSharedWorkspace (24.58s)
PASS
coverage: 31.2% of statements in ./...
ok github.com/databricks/cli/internal/bundle 25.572s coverage: 31.2% of statements in ./...
```
---------
Co-authored-by: shreyas-goenka <88374338+shreyas-goenka@users.noreply.github.com>
## Changes
This change adds a preset for source-linked deployments. It is enabled
by default for targets in `development` mode **if** the Databricks CLI
is running from the `/Workspace` directory on DBR. It does not have an
effect when running the CLI anywhere else.
Key highlights:
1. Files in this mode won't be uploaded to workspace
2. Created resources will use references to source files instead of
their workspace copies
## Tests
1. Apply preset unit test covering conditional logic
2. High-level process target mode unit test for testing integration
between mutators
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
## Changes
When running the CLI on Databricks Runtime (DBR), use the
extension-aware filer to write an instantiated template if the instance
path is located in the workspace filesystem.
Notebooks cannot be written through the workspace filesystem's FUSE
mount. As a result, this is the only method for initializing templates
that contain notebooks when running the CLI on DBR and writing to the
workspace filesystem.
Depends on #1910 and #1911.
Supersedes #1744.
## Tests
* Manually confirmed I can initialize a template with notebooks when
running the CLI from the web terminal.
## Changes
Prior to this change, the output directory was part of the `renderer`
type and passed down to every `file` it produced. Every file knew its
absolute destination path. This is incompatible with the use of a filer,
where all operations are automatically anchored to some base path.
To make this compatible, this change updates:
* the `file` type to only know its own path relative to the instantiation root,
* the `renderer` type to no longer require or pass along the output directory,
* the `persistToDisk` function to take a context and filer argument,
* the `filer.WriteMode` to represent permission bits
## Tests
* Existing tests pass.
* Manually confirmed template initialization works as expected.
## Changes
While working on the v2 of #1744, I found that:
* Template initialization first copies built-in templates to a temporary
directory before initializing them
* Reading a template's contents goes through a `filer.Filer` but is
hardcoded to a local one
This change updates the interface for reading templates to be `fs.FS`.
This is compatible with the `embed.FS` type for the built-in templates,
so they no longer have to be copied to a temporary directory before
being used.
The alternative is to use a `filer.Filer` throughout, but this would
have required even more plumbing, and we don't need to _read_ templates,
including notebooks, from the workspace filesystem (yet?).
As part of making `template.Materialize` take an `fs.FS` argument, the
logic to match a given argument to a particular built-in template in the
`init` command has moved to sit next to its implementation.
## Tests
Existing tests pass.
## Changes
The workspace extensions filer should not read or stat a notebook called
`foo` if the user calls `.Stat(ctx, "foo")`.
Instead, the filer should return a file not found error. This is because
the contract for the workspace extensions filer is to only work for
notebooks when the file path / name includes the extension (example:
`foo.ipynb` or `foo.sql` instead of just `foo`)
## Tests
Integration tests.
## Changes
We had a number of copies of test helpers for `io/fs` in the repository.
This change consolidates all of them to use the `libs/fakefs` package.
## Tests
Unit tests pass.
## Changes
This field was special-cased in #1307 because it's not part of the JSON
payload in the SDK struct.
This approach, while pragmatic, meant it didn't show up in the JSON
schema. While debugging an issue with quality monitors in #1900, I
couldn't figure out why I was getting schema errors on this field, or
how it was passed through to the TF representation. This commit removes
the special case and makes it behave like everything else.
## Tests
* Unit tests pass.
* Confirmed that the updated schema failed validation before this
change.
## Changes
Whether or not the CLI is running on DBR can be detected once and stored
in the command's context.
By storing it in the context, it can easily be mocked for testing.
This builds on the simpler approach and conversation in #1744. It
unblocks testing of the DBR-specific paths while not compromising on the
checks we can perform to test if the CLI is running on DBR.
## Tests
* Unit tests for the new `dbr` package
* New unit test for the `ConfigureWSFS` mutator
## Changes
### Background
The workspace import APIs recently added support for importing Jupyter
notebooks written in R, Scala, or SQL, that is non-Python notebooks.
This now works for the `/import-file` API which we leverage in the CLI.
Note: We do not need any changes in `databricks sync`. It works out of
the box because any state mapping of local names to remote names that we
store is only scoped to the notebook extension (i.e., `.ipynb` in this
case) and is agnostic of the notebook's specific language.
### Problem this PR addresses
The extension-aware filer previously did not function because it checks
that a `.ipynb` notebook is written in Python. This PR relaxes that
constraint and adds integration tests for both the normal workspace
filer and extensions aware filer writing and reading non-Python `.ipynb`
notebooks.
This implies that after this PR DABs in the workspace / CLI from DBR
will work for non-Python notebooks as well. non-Python notebooks for
DABs deployment from local machines already works after the platform
side changes to the API landed, this PR just adds integration tests for
that bit of functionality.
Note: Any platform side changes we needed for the import API have
already been rolled out to production.
### Before
DABs deploy would work fine for non-Python notebooks. But DABs
deployments from DBR would not.
### After
DABs deploys both from local machines and DBR will work fine.
## Testing
For creating the `.ipynb` notebook fixtures used in the integration
tests I created them directly from the VSCode UI. This ensures high
fidelity with how users will create their non-Python notebooks locally.
For Python notebooks this is supported out of the box by VSCode but for
R and Scala notebooks this requires installing the Jupyter kernel for R
and Scala on my local machine and using that from VSCode.
For SQL, I ended up directly modifying the `language_info` field in the
Jupyter metadata to create the test fixture.
### Discussion: Issues with configuring language at the cell level
The language metadata for a Jupyter notebook is standardized at the
notebook level (in the `language_info` field). Unfortunately, it's not
standardized at the cell level. Thus, for example, if a user changes the
language for their cell in VSCode (which is supported by the standard
Jupyter VSCode integration), it'll cause a runtime error when the user
actually attempts to run the notebook. This is because the cell-level
metadata is encoded in a format specific to VSCode:
```
cells: []{
"vscode": {
"languageId": "sql"
}
}
```
Supporting cell level languages is thus out of scope for this PR and can
be revisited along with the workspace files team if there's strong
customer interest.
Known issues:
- [ ] _(non-blocking with a command override)_ `apps.Update` requires 2
`name` params (one from path, one from request body)
- [ ] _(non-blocking)_ `lakeview.Create` does not require positional
argument `display_name` anymore because it's not marked as required in
request body
Bumps
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Bumps [golang.org/x/oauth2](https://github.com/golang/oauth2) from
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## Changes
The host URL for databricks workspaces includes the workspaceId by
default as a positional arg. Eg:
https://e2-dogfood.staging.cloud.databricks.com/?o=1234
Thus a user can't simply copy paste the URL today to the auth login
command. They'll see a runtime error:
```
➜ cli git:(main) ✗ databricks auth login --host https://e2-dogfood.staging.cloud.databricks.com/\?o\=xxx --profile new-dg
Error: oidc: fetch .well-known: failed to unmarshal response body: invalid character '<' looking for beginning of value. This is likely a bug in the Databricks SDK for Go or the underlying REST API. Please report this issue with the following debugging information to the SDK issue tracker at https://github.com/databricks/databricks-sdk-go/issues. Request log:
GET /login.html
...
```
## Tests
Unit tests and manually. Now auth login works even when the workspace_id
is included in the URL.
## Changes
The file presence check for dashboard files was missing a
`filepath.ToSlash`.
This means it didn't work on Windows unless the dashboard was located at
a path without slashes (i.e. the bundle root).
Closes#1875.
## Tests
* Added a unit test to cover this case (failed before the fix).
* Manually ran a dashboard deployment on Windows.
## Changes
The commit where resource lookup was factored out into a separate
package (#1858) didn't take into account the use of `args` further down
in the code.
This change fixes that oversight by returning the tail arguments when
determining which resource to run. The later call no longer has to index
the `args` slice.
## Tests
Manually confirmed that the command works when being prompted for the
resource to run.
## Changes
Added E2E test to run python wheels on interactive cluster created in
bundle.
We had a gap in testing wheel on all purpose clusters, so this PR
addresses the gap
## Changes
This PR adds the `cmd-exec-id` field to the user agent. This allows us
to correlate multiple HTTP requests made from the CLI.
### Why Not Use HTTP traceparent?
We considered using the traceparent header in HTTP as an alternative,
but it's not a good fit for our use case. Here's why:
1. Purpose of traceparent: It's designed to trace a single HTTP request
across a distributed system as it moves through subsystems and proxies.
2. Our requirement: We need to trace multiple HTTP requests made during
a single command execution in the CLI.
For more details about how traceparent itself works and how it's used in
the Go SDK, see
https://github.com/databricks/databricks-sdk-go/pull/914.
## Tests
Unit test
## Changes
Old script could not be run from master due to security restrictions and
there is no reliable way to detect if a user as secrets.
## Tests
Opened a PR in SDK Java from fork
https://github.com/databricks/databricks-sdk-java/pull/375