## 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
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
Due to platform changes, all libraries, notebooks and etc. paths used in
Databricks must be started with either /Workspace or /Volumes prefix.
This PR makes sure that all bundle paths are correctly prefixed.
Note: this change is a breaking change if user previously configured and
used `/Workspace/Workspace` folder in their workspace file system or
having `/Workspace/${workspace.root_path}...` pattern configured
anywhere in their bundle config
Fixes: #1751
AI:
- [x] Scan DABs config and error out on
`/Workspace/${workspace.root_path}...` pattern usage
## Tests
Added unit tests
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
## Changes
With hc-install version `0.8.0` there was a regression where debug logs
would be leaked into stderr. Reported upstream in
https://github.com/hashicorp/hc-install/issues/239.
Meanwhile we need to revert and pin to version`0.7.0`. This PR also
includes a regression test.
## Tests
Regression test.
## 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
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
Using dynamic values allows us to retain references like
`${resources.jobs...}` even when the type of field is not integer, eg:
`run_job_task`, or in general values that do not map to the Go types for
a field.
## Tests
Integration test
## 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
Currently, when the CLI run a list API call (like list jobs), it uses
the `List*All` methods from the SDK, which list all resources in the
collection. This is very slow for large collections: if you need to list
all jobs from a workspace that has 10,000+ jobs, you'll be waiting for
at least 100 RPCs to complete before seeing any output.
Instead of using List*All() methods, the SDK recently added an iterator
data structure that allows traversing the collection without needing to
completely list it first. New pages are fetched lazily if the next
requested item belongs to the next page. Using the List() methods that
return these iterators, the CLI can proactively print out some of the
response before the complete collection has been fetched.
This involves a pretty major rewrite of the rendering logic in `cmdio`.
The idea there is to define custom rendering logic based on the type of
the provided resource. There are three renderer interfaces:
1. textRenderer: supports printing something in a textual format (i.e.
not JSON, and not templated).
2. jsonRenderer: supports printing something in a pretty-printed JSON
format.
3. templateRenderer: supports printing something using a text template.
There are also three renderer implementations:
1. readerRenderer: supports printing a reader. This only implements the
textRenderer interface.
2. iteratorRenderer: supports printing a `listing.Iterator` from the Go
SDK. This implements jsonRenderer and templateRenderer, buffering 20
resources at a time before writing them to the output.
3. defaultRenderer: supports printing arbitrary resources (the previous
implementation).
Callers will either use `cmdio.Render()` for rendering individual
resources or `io.Reader` or `cmdio.RenderIterator()` for rendering an
iterator. This separate method is needed to safely be able to match on
the type of the iterator, since Go does not allow runtime type matches
on generic types with an existential type parameter.
One other change that needs to happen is to split the templates used for
text representation of list resources into a header template and a row
template. The template is now executed multiple times for List API
calls, but the header should only be printed once. To support this, I
have added `headerTemplate` to `cmdIO`, and I have also changed
`RenderWithTemplate` to include a `headerTemplate` parameter everywhere.
## Tests
- [x] Unit tests for text rendering logic
- [x] Unit test for reflection-based iterator construction.
---------
Co-authored-by: Andrew Nester <andrew.nester@databricks.com>
## Changes
This helper:
* Constructs a context
* Constructs a `*databricks.WorkspaceClient`
* Ensures required environment variables are present to run an
integration test
* Enables debugging integration tests from VS Code
Debugging integration tests (from VS Code) is made possible by a prelude
in the helper that checks if the calling process is a debug binary, and
if so, sources environment variables from
`~/..databricks/debug-env.json` (if present).
## Tests
Integration tests still pass.
---------
Co-authored-by: Andrew Nester <andrew.nester@databricks.com>
## Changes
It makes the behaviour consistent with or without `python_wheel_wrapper`
on when job is run with `--python-params` flag.
In `python_wheel_wrapper` mode it converts dynamic `python_params` in a
dynamic specially named `notebook_param` and the wrapper reads them with
`dbutils` and pass to `sys.argv`
Fixes#1000
## Tests
Added an integration test.
Integration tests pass.
## Changes
Added end-to-end test for deploying and running Python wheel task
## Tests
Test successfully passed on all environments, takes about 9-10 minutes
to pass.
```
Deleted snapshot file at /var/folders/nt/xjv68qzs45319w4k36dhpylc0000gp/T/TestAccPythonWheelTaskDeployAndRun1845899209/002/.databricks/bundle/default/sync-snapshots/1f7cc766ffe038d6.json
Successfully deleted files!
2023/09/06 17:50:50 INFO Releasing deployment lock mutator=destroy mutator=seq mutator=seq mutator=deferred mutator=lock:release
--- PASS: TestAccPythonWheelTaskDeployAndRun (508.16s)
PASS
coverage: 77.9% of statements in ./...
ok github.com/databricks/cli/internal/bundle 508.810s coverage: 77.9% of statements in ./...
```
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
Pieter Noordhuis
Andrew Nester
shreyas-goenka
Miles Yucht