## 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
This PR makes sweeping changes to the way we generate and test the
bundle JSON schema. The main benefits are:
1. More modular JSON schema. Every definition in the schema now is one
level deep and points to references instead of inlining the entire
schema for a field. This unblocks PyDABs from taking a dependency on the
JSON schema.
2. Generate the JSON schema during CLI code generation. Directly stream
it instead of computing it at runtime whenever a user calls `databricks
bundle schema`. This is nice because we no longer need to embed a
partial OpenAPI spec in the CLI. Down the line, we can add a `Schema()`
method to every struct in the Databricks Go SDK and remove the
dependency on the OpenAPI spec altogether. It'll become more important
once we decouple Go SDK structs and methods from the underlying APIs.
3. Add enum values for Go SDK fields in the JSON schema. Better
autocompletion and validation for these fields. As a follow-up, we can
add enum values for non-Go SDK enums as well (created internal ticket to
track).
4. Use "packageName.structName" as a key to read JSON schemas from the
OpenAPI spec for Go SDK structs. Before, we would use an unrolled
presentation of the JSON schema (stored in `bundle_descriptions.json`),
which was complex to parse and include in the final JSON schema output.
This also means loading values from the OpenAPI spec for `target` schema
works automatically and no longer needs custom code.
5. Support recursive types (eg: `for_each_task`). With us now using
$refs everywhere it's trivial to support.
6. Using complex variables would be invalid according to the schema
generated before this PR. Now that bug is fixed. In the future adding
more custom rules will be easier as well due to the single level nature
of the JSON schema.
Since this is a complete change of approach in how we generate the JSON
schema, there are a few (very minor) regressions worth calling out.
1. We'll lose a few custom descriptions for non Go SDK structs that were
a part of `bundle_descriptions.json`. Support for those can be added in
the future as a followup.
2. Since now the final JSON schema is a static artefact, we lose some
lead time for the signal that JSON schema integration tests are failing.
It's okay though since we have a lot of coverage via the existing unit
tests.
## Tests
Unit tests. End to end tests are being added in this PR:
https://github.com/databricks/cli/pull/1726
Previous unit tests were all deleted because they were bloated. Effort
was made to make the new unit tests provide (almost) equivalent
coverage.
## Changes
This PR changes the location metadata associated with a `dyn.Value` to a
slice of locations. This will allow us to keep track of location
metadata across merges and overrides.
The convention is to treat the first location in the slice as the
primary location. Also, the semantics are the same as before if there's
only one location associated with a value, that is:
1. For complex values (maps, sequences) the location of the v1 is
primary in Merge(v1, v2)
2. For primitive values the location of v2 is primary in Merge(v1, v2)
## Tests
Modifying existing merge unit tests. Other existing unit tests and
integration tests pass.
---------
Co-authored-by: Pieter Noordhuis <pieter.noordhuis@databricks.com>
## Changes
Added support for complex variables
Now it's possible to add and use complex variables as shown below
```
bundle:
name: complex-variables
resources:
jobs:
my_job:
job_clusters:
- job_cluster_key: key
new_cluster: ${var.cluster}
tasks:
- task_key: test
job_cluster_key: key
variables:
cluster:
description: "A cluster definition"
type: complex
default:
spark_version: "13.2.x-scala2.11"
node_type_id: "Standard_DS3_v2"
num_workers: 2
spark_conf:
spark.speculation: true
spark.databricks.delta.retentionDurationCheck.enabled: false
```
Fixes#1298
- [x] Support for complex variables
- [x] Allow variable overrides (with shortcut) in targets
- [x] Don't allow to provide complex variables via flag or env variable
- [x] Fail validation if complex value is used but not `type: complex`
provided
- [x] Support using variables inside complex variables
## Tests
Added unit tests
---------
Co-authored-by: shreyas-goenka <88374338+shreyas-goenka@users.noreply.github.com>
## Changes
Variable substitution works as if the variable reference is literally
replaced with its contents.
The following fields should be interpreted in the same way regardless of
where the variable is defined:
```yaml
foo: ${var.some_path}
bar: "./${var.some_path}"
```
Before this change, `foo` would inherit the location information of the
variable definition. After this change, it uses the location information
of the variable reference, making the behavior for `foo` and `bar`
identical.
Fixes#1330.
## Tests
The new test passes only with the fix.
## Changes
Before this change maps were stored as a regular Go map with string
keys. This didn't let us capture metadata (location information) for map
keys.
To address this, this change replaces the use of the regular Go map with
a dedicated type for a dynamic map. This type stores the `dyn.Value` for
both the key and the value. It uses a map to still allow O(1) lookups
and redirects those into a slice.
## Tests
* All existing unit tests pass (some with minor modifications due to
interface change).
* Equality assertions with `assert.Equal` no longer worked because the
new `dyn.Mapping` persists the order in which keys are set and is
therefore susceptible to map ordering issues. To fix this, I added a
`dynassert` package that forwards all assertions to `testify/assert` but
intercepts equality for `dyn.Value` arguments.
## 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
When resolving a value returned by the lookup function, the code would
call into `resolveRef` with the key that `resolveKey` was called with.
In doing so, it would cache the _new_ ref under that key.
We fix this by caching ref resolution only at the top level and relying
on lookup caching to avoid duplicate work.
This came up while testing #1098.
## Tests
Unit test.
## Changes
References to keys that themselves are also variable references were
shortcircuited in the previous approach. This meant that certain fields
were resolved even if the lookup function would have instructed to skip
resolution.
To fix this we separate the memoization of resolved variable references
from the memoization of lookups. Now, every variable reference is passed
through the lookup function.
## Tests
Before this change, the new test failed with:
```
=== RUN TestResolveWithSkipEverything
[...]/libs/dyn/dynvar/resolve_test.go:208:
Error Trace: [...]/libs/dyn/dynvar/resolve_test.go:208
Error: Not equal:
expected: "${d} ${c} ${c} ${d}"
actual : "${b} ${a} ${a} ${b}"
Diff:
--- Expected
+++ Actual
@@ -1 +1 @@
-${d} ${c} ${c} ${d}
+${b} ${a} ${a} ${b}
Test: TestResolveWithSkipEverything
```
## Changes
This is the `dyn` counterpart to the `bundle/config/interpolation`
package.
It relies on the paths in `${foo.bar}` being valid `dyn.Path` instances.
It leverages `dyn.Walk` to get a complete picture of all variable
references and uses `dyn.Get` to retrieve values pointed to by variable
references.
Depends on #1142.
## Tests
Unit test coverage. I tried to mirror the tests from
`bundle/config/interpolation` and added new ones where applicable (for
example to test type retention of referenced values).
shreyas-goenka
Andrew Nester
Pieter Noordhuis