## Changes
At a high level this PR adds new schema validation and moves
functionality that should be present in the jsonschema package, but
resides in the template package today, to the jsonschema package. This
includes for example schema validation, schema instance validation, to /
from string conversion methods etc.
The list below outlines all the pieces that have been moved over, and
the new validation bits added.
This PR:
1. Adds casting default value of schema properties to integers to the
jsonschema.Load method.
2. Adds validation for default value types for schema properties,
checking they are consistant with the type defined.
3. Introduces the LoadInstance and ValidateInstance methods to the json
schema package. These methods can be used to read and validate JSON
documents against the schema.
4. Replaces validation done for template inputs to use the newly defined
JSON schema validation functions.
5. Moves to/from string and isInteger utility methods to the json schema
package.
## Tests
Existing and new unit tests.
## Changes
This fixes a typo that caused the notebook.ipynb file to show up even if
the user answered "no" to the question about including a notebook.
## Tests
We have matrix validation tests for all the yes/no combinations and
whether the build + validate. There is no current test for the absence
of files.
## 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
This adds a built-in "default-python" template to the CLI. This is based
on the new default-template support of
https://github.com/databricks/cli/pull/685.
The goal here is to offer an experience where customers can simply type
`databricks bundle init` to get a default template:
```
$ databricks bundle init
Template to use [default-python]: default-python
Unique name for this project [my_project]: my_project
✨ Successfully initialized template
```
The present template:
- [x] Works well with VS Code
- [x] Works well with the workspace
- [x] Works well with DB Connect
- [x] Uses minimal stubs rather than boiler-plate-heavy examples
I'll have a followup with tests + DLT support.
---------
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).
## Changes
JSON schema properties are a map and thus unordered.
This PR introduces a JSON schema extension field called `order` to allow
template authors to define the order in which template variables should
be resolved/prompted.
## Tests
Unit tests.
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
## Changes
This pull request extends the templating support in preparation of a
new, default template (WIP, https://github.com/databricks/cli/pull/686):
* builtin templates that can be initialized using e.g. `databricks
bundle init default-python`
* builtin templates are embedded into the executable using go's `embed`
functionality, making sure they're co-versioned with the CLI
* new helpers to get the workspace name, current user name, etc. help
craft a complete template
* (not enabled yet) when the user types `databricks bundle init` they
can interactively select the `default-python` template
And makes two tangentially related changes:
* IsServicePrincipal now uses the "users" API rather than the
"principals" API, since the latter is too slow for our purposes.
* mode: prod no longer requires the 'target.prod.git' setting. It's hard
to set that from a template. (Pieter is planning an overhaul of warnings
support; this would be one of the first warnings we show.)
The actual `default-python` template is maintained in a separate PR:
https://github.com/databricks/cli/pull/686
## Tests
Unit tests, manual testing
## Changes
This PR:
1. Renames the project-dir flag to output-dir
2. Makes the project dir flag optional. When unspecified we default to
the current working directory.
## Tests
Manually
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
## Changes
Go text templates allows only specifying one input argument for
invocations of associated templates (ie `{{template ...}}`). This PR
introduces the map and pair functions which allow template authors to
work around this limitation by passing multiple arguments as key value
pairs in a map.
This PR is based on feedback from the mlops stacks migration where
otherwise a bunch of duplicate code is required for computed values and
fixtures.
## Tests
Unit test
## Changes
Prompt UI glitches often. We are switching to a custom implementation of
a simple prompter which is much more stable.
This also allows new lines in prompts which has been an ask by the
mlflow team.
## Tests
Tested manually
## Changes
Adds a function to validate json schema types added by the author. The
default json unmarshaller does not validate that the parsed type matches
the enum defined in `jsonschema.Type`
Includes some other improvements to provide better error messages.
This PR was prompted by usability difficulties reported by @mingyu89
during mlops stack migration.
## Tests
Unit tests
## Changes
The `.tmpl` extension is only meant as a qualifier for whether the file
content is executed as a template. All file paths in the `template`
directory should be treated as valid go text templates.
Before only paths with the `.tmpl` extensions would be resolved as
templates, after this change, all file paths are interpreted as
templates.
## Tests
Unit test. The newly added unit tests also asserts that the file path is
correct, even when the `.tmpl` extension is missing.
## Changes
This PR adds two features:
1. The bundle init command
2. Support for prompting for input values
In order to do this, this PR also introduces a new `config` struct which
handles reading config files, prompting users and all validation steps
before we materialize the template
With this PR users can start authoring custom templates, based on go
text templates, for their projects / orgs.
## Tests
Unit tests, both existing and new
## Changes
This PR:
1. Adds code for reading template configs and validating them against a
JSON schema.
2. Moves the json schema struct in `bundle/schema` to a separate library
package. This struct is now reused for validating template configs.
## Tests
Unit tests
## Changes
In a world before this PR, all files would be treated as `go text
templates`, making the content in these files quake in fear since they
would be executed (as a template).
This PR makes it so that only files with the `.tmpl` extension are
understood to be templates. This is useful for avoiding ambiguity in
cases like where a binary file could be interpreted as a go text
template otherwise.
In order to do so, we introduce the `copyFile` struct which does a copy
of the source file from the template without loading it into memory.
## Tests
Unit tests
## Changes
Add unit test that raw strings are printed as is. This method is useful
to print text that would otherwise be interpreted a go text template.
## Changes
Due to a bug in Github UI, https://github.com/databricks/cli/pull/589
got merged without passing the go/fmt formatting checks
This PR fixes the formatting which breaks the PR checks
shreyas-goenka
Lennart Kats (databricks)
Andrew Nester