## Changes
This PR adds support for UC volumes to DABs.
### Can I use a UC volume managed by DABs in `artifact_path`?
Yes, but we require the volume to exist before being referenced in
`artifact_path`. Otherwise you'll see an error that the volume does not
exist. For this case, this PR also adds a warning if we detect that the
UC volume is defined in the DAB itself, which informs the user to deploy
the UC volume in a separate deployment first before using it in
`artifact_path`.
We cannot create the UC volume and then upload the artifacts to it in
the same `bundle deploy` because `bundle deploy` always uploads the
artifacts to `artifact_path` before materializing any resources defined
in the bundle. Supporting this in a single deployment requires us to
migrate away from our dependency on the Databricks Terraform provider to
manage the CRUD lifecycle of DABs resources.
### Why do we not support `preset.name_prefix` for UC volumes?
UC volumes will not have a `dev_shreyas_goenka` prefix added in `mode:
development`. Configuring `presets.name_prefix` will be a no-op for UC
volumes. We have decided not to support prefixing for UC resources. This
is because:
1. UC provides its own namespace hierarchy that is independent of DABs.
2. Users can always manually use `${workspace.current_user.short_name}`
to configure the prefixes manually.
Customers often manually set up a UC hierarchy for dev and prod,
including a schema or catalog per developer. Thus, it's often
unnecessary for us to add prefixing in `mode: development` by default
for UC resources.
In retrospect, supporting prefixing for UC schemas and registered models
was a mistake and will be removed in a future release of DABs.
## Tests
Unit, integration test, and manually.
### Manual Testing cases:
1. UC volume does not exist:
```
➜ bundle-playground git:(master) ✗ cli bundle deploy
Error: failed to fetch metadata for the UC volume /Volumes/main/caps/my_volume that is configured in the artifact_path: Not Found
```
2. UC Volume does not exist, but is defined in the DAB
```
➜ bundle-playground git:(master) ✗ cli bundle deploy
Error: failed to fetch metadata for the UC volume /Volumes/main/caps/managed_by_dab that is configured in the artifact_path: Not Found
Warning: You might be using a UC volume in your artifact_path that is managed by this bundle but which has not been deployed yet. Please deploy the UC volume in a separate bundle deploy before using it in the artifact_path.
at resources.volumes.bar
in databricks.yml:24:7
```
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
## Changes
After introducing the `SyncRootPath` field on the bundle (#1694), the
previous `RootPath` became ambiguous. Does it mean the bundle root path
or the sync root path? This PR renames to field to `BundleRootPath` to
remove the ambiguity.
## Tests
n/a
---------
Co-authored-by: shreyas-goenka <88374338+shreyas-goenka@users.noreply.github.com>
I plan to use this in https://github.com/databricks/cli/pull/1780, to
set the line and column numbers as well for the locations.
gopatch file used:
```
@@
var x expression
var y expression
var z expression
@@
-bundletest.SetLocation(x, y, z)
+bundletest.SetLocation(x, y, []dyn.Location{{File: z}})
```
## Changes
Some call sites hold on to the `dyn.Path` provided to them by the
callback. It must therefore never be mutated after the callback returns,
or these mutations leak out into unknown scope.
This change means it is no longer possible for this failure mode to
happen.
## Tests
Unit test.
## Changes
Make `pydabs/venv_path` optional. When not specified, CLI detects the
Python interpreter using `python.DetectExecutable`, the same way as for
`artifacts`. `python.DetectExecutable` works correctly if a virtual
environment is activated or `python3` is available on PATH through other
means.
Extract the venv detection code from PyDABs into `libs/python/detect`.
This code will be used when we implement the `python/venv_path` section
in `databricks.yml`.
## Tests
Unit tests and manually
---------
Co-authored-by: Pieter Noordhuis <pcnoordhuis@gmail.com>
## 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 didn't work as expected because the generic build mutator called
into the type-specific build mutator in the middle of the function. This
invalidated the `config.Artifact` pointer that was being mutated later
on, effectively hiding these mutations from its caller.
To fix this, I turned glob expansion into its own mutator. It now works
as expected, _and_ produces better errors if the glob patterns are
invalid or do not match files.
## Tests
Unit tests.
Manual verification:
```
% databricks bundle deploy
Building sbt_example...
Error: target/scala-2.12/sbt-e[xam22ple*.jar: syntax error in pattern
at artifacts.sbt_example.files[1].source
in databricks.yml:15:17
```
## Changes
In https://github.com/databricks/cli/pull/1618 we introduced prepare
step in which Python wheel folder was cleaned. Now it was cleaned
everytime instead of only when there is a build command how it is used
to work.
This PR fixes it by only cleaning up dist folder when there is a build
command for wheels.
Fixes#1638
## Tests
Added regression test
## 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 change allows to specify UC volumes path as an artifact paths so
all artifacts (JARs, wheels) are uploaded to UC Volumes.
Example configuration is here:
```
bundle:
name: jar-bundle
workspace:
host: https://foo.com
artifact_path: /Volumes/main/default/foobar
artifacts:
my_java_code:
path: ./sample-java
build: "javac PrintArgs.java && jar cvfm PrintArgs.jar META-INF/MANIFEST.MF PrintArgs.class"
files:
- source: ./sample-java/PrintArgs.jar
resources:
jobs:
jar_job:
name: "Test Spark Jar Job"
tasks:
- task_key: TestSparkJarTask
new_cluster:
num_workers: 1
spark_version: "14.3.x-scala2.12"
node_type_id: "i3.xlarge"
spark_jar_task:
main_class_name: PrintArgs
libraries:
- jar: ./sample-java/PrintArgs.jar
```
## Tests
Manually + added E2E test for Java jobs
E2E test is temporarily skipped until auth related issues for UC for
tests are resolved
## Changes
Now local library path in `libraries` section of foreach each tasks are
correctly replaced with remote path for this library when it's uploaded
to Databricks
## Tests
Added unit test
## 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
Transform artifact files source patterns in build not upload stage
Resolves the following warning
```
artifact section is not defined for file at /Users/andrew.nester/dabs/wheel/target/myjar.jar. Skipping uploading. In order to use the define 'artifacts' section
```
## Tests
Unit test 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
The approach to do this was:
1. Iterate over all libraries in all job tasks
2. Find references to local libraries
3. Store pointer to `compute.Library` in the matching artifact file to
signal it should be uploaded
This breaks down when introducing #1098 because we can no longer track
unexported state across mutators. The approach in this PR performs the
path matching twice; once in the matching mutator where we check if each
referenced file has an artifacts section, and once during artifact
upload to rewrite the library path from a local file reference to an
absolute Databricks path.
## Tests
Integration tests pass.
## Changes
Instead of handling command chaining ourselves, we execute passed
commands as-is by storing them, in temp file and passing to correct
interpreter (bash or cmd) based on OS.
Fixes#1065
## Tests
Added unit tests
## 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
Removed hash from the upload path since it's not useful anyway.
The main reason for that change was to make it work on all-purpose
clusters. But in order to make it work, wheel version needs to be
increased anyway. So having only hash in path is useless.
Note: using --build-number (build tag) flag does not help with
re-installing libraries on all-purpose clusters. The reason is that
`pip` ignoring build tag when upgrading the library and only look at
wheel version.
Build tag is only used for sorting the versions and the one with higher
build tag takes priority when installed. It only works if no library is
installed.
See
a15dd75d98/src/pip/_internal/index/package_finder.py (L522-L556)https://github.com/pypa/pip/issues/4781
Thus, the only way to reinstall the library on all-purpose cluster is to
increase wheel version manually or use automatic version generation,
f.e.
```
setup(
version=datetime.datetime.utcnow().strftime("%Y%m%d.%H%M%S"),
...
)
```
## Tests
Integration tests passed.
## 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.
## Changes
Previously we only supported uploading Python wheels smaller than 10mb
due to using Workspace.Import API and `content ` field
https://docs.databricks.com/api/workspace/workspace/import
By switching to use `WorkspaceFilesClient` we overcome the limit because
it uses POST body for the API instead.
## Tests
`TestAccUploadArtifactFileToCorrectRemotePath` integration test passes
```
=== RUN TestAccUploadArtifactFileToCorrectRemotePath
artifacts_test.go:28: gcp
2023/10/17 15:24:04 INFO Using Google Credentials sdk=true
helpers.go:356: Creating /Users/.../integration-test-wsfs-ekggbkcfdkid
artifacts.Upload(test.whl): Uploading...
2023/10/17 15:24:06 INFO Using Google Credentials mutator=artifacts.Upload(test) sdk=true
artifacts.Upload(test.whl): Upload succeeded
helpers.go:362: Removing /Users/.../integration-test-wsfs-ekggbkcfdkid
--- PASS: TestAccUploadArtifactFileToCorrectRemotePath (5.66s)
PASS
coverage: 14.9% of statements in ./...
ok github.com/databricks/cli/internal 6.109s coverage: 14.9% of statements in ./...
```
This PR adds a few utilities related to Python interpreter detection:
- `python.DetectInterpreters` to detect all Python versions available in
`$PATH` by executing every matched binary name with `--version` flag.
- `python.DetectVirtualEnvPath` to detect if there's any child virtual
environment in `src` directory
- `python.DetectExecutable` to detect if there's python3 installed
either by `which python3` command or by calling
`python.DetectInterpreters().AtLeast("v3.8")`
To be merged after https://github.com/databricks/cli/pull/804, as one of
the steps to get https://github.com/databricks/cli/pull/637 in, as
previously discussed.
## Changes
Workspace library will be detected by trampoline in 2 cases:
- User defined to use local wheel file
- User defined to use remote wheel file from Workspace file system
In both of these cases we should correctly apply Python trampoline
## Tests
Added a regression test (also covered by Python e2e test)
## Changes
* Update Go SDK to v0.19.0
* Update commands per OpenAPI spec from Go SDK
* Incorporate `client.Do()` signature change to include a (nil) header
map
* Update `workspace.WorkspaceService` mock with permissions methods
* Skip `files` service in codegen; already implemented under the `fs`
command
## Tests
Unit and integration tests pass.
## Changes
Now if the user reference local Python wheel files and do not specify
"artifacts" section, this file will be automatically uploaded by CLI.
Fixes#693
## Tests
Added unit tests
Ran bundle deploy for this configuration
```
resources:
jobs:
some_other_job:
name: "[${bundle.environment}] My Wheel Job"
tasks:
- task_key: TestTask
existing_cluster_id: ${var.job_existing_cluster}
python_wheel_task:
package_name: "my_test_code"
entry_point: "run"
libraries:
- whl: ./dist/*.whl
```
Result
```
andrew.nester@HFW9Y94129 wheel % databricks bundle deploy
artifacts.whl.AutoDetect: Detecting Python wheel project...
artifacts.whl.AutoDetect: No Python wheel project found at bundle root folder
Starting upload of bundle files
Uploaded bundle files at /Users/andrew.nester@databricks.com/.bundle/wheel-task/default/files!
artifacts.Upload(my_test_code-0.0.1-py3-none-any.whl): Uploading...
artifacts.Upload(my_test_code-0.0.1-py3-none-any.whl): Upload succeeded
```
## 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
## Changes
Added support for `bundle.Seq`, simplified `Mutator.Apply` interface by
removing list of mutators from return values/
## Tests
1. Ran `cli bundle deploy` and interrupted it with Cmd + C mid execution
so lock is not released
2. Ran `cli bundle deploy` top make sure that CLI is not trying to
release lock when it fail to acquire it
```
andrew.nester@HFW9Y94129 multiples-tasks % cli bundle deploy
Starting upload of bundle files
Uploaded bundle files at /Users/andrew.nester@databricks.com/.bundle/simple-task/development/files!
^C
andrew.nester@HFW9Y94129 multiples-tasks % cli bundle deploy
Error: deploy lock acquired by andrew.nester@databricks.com at 2023-05-24 12:10:23.050343 +0200 CEST. Use --force to override
```
## 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
These are unlikely to ever be DBFS paths so we can remove this level of indirection to simplify.
**Note:** this is a breaking change. Downstream usage of these fields must be updated.
## Tests
Existing tests pass.
This adds:
* Top level "artifacts" configuration key
* Support for notebooks (does language detection and upload)
* Merge of per-environment artifacts (or artifact overrides) into top level
shreyas-goenka
Pieter Noordhuis
Gleb Kanterov
Andrew Nester
Lennart Kats (databricks)
Serge Smertin
dependabot[bot]