## 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
As of #1846 we have a generalized package for doing resource lookups and
completion.
This change updates the run command to use this instead of more specific
code under `bundle/run`.
## Tests
* Unit tests pass
* Manually confirmed that completion and prompting works
## Changes
We don't need to cancel existing runs when the job is continuous and
unpaused. The `/jobs/run-now` command will cancel the existing run and
trigger a new one automatically.
Cancelling the job manually can cause a race condition where both the
manual trigger from the CLI and the continuous trigger from the job
configuration happens at the same time. This PR prevents that from
happening.
## Tests
Unit tests and manually
## Summary
Makes the `databricks bundle run` command use local state before showing
the menu prompt, which makes it show more quickly. For large/busy
workspaces this means the prompt can show 2-3 seconds earlier.
## Changes
With this change, both job parameters and task parameters can be
specified as positional arguments to bundle run. How the positional
arguments are interpreted depends on the configuration of the job.
### Examples:
For a job that has job parameters configured a user can specify:
```
databricks bundle run my_job -- --param1=value1 --param2=value2
```
And the run is kicked off with job parameters set to:
```json
{
"param1": "value1",
"param2": "value2"
}
```
Similarly, for a job that doesn't use job parameters and only has
`notebook_task` tasks, a user can specify:
```
databricks bundle run my_notebook_job -- --param1=value1 --param2=value2
```
And the run is kicked off with task level `notebook_params` configured
as:
```json
{
"param1": "value1",
"param2": "value2"
}
```
For a job that doesn't doesn't use job parameters and only has either
`spark_python_task` or `python_wheel_task` tasks, a user can specify:
```
databricks bundle run my_python_file_job -- --flag=value other arguments
```
And the run is kicked off with task level `python_params` configured as:
```json
[
"--flag=value",
"other",
"arguments"
]
```
The same is applied to jobs with only `spark_jar_task` or
`spark_submit_task` tasks.
## Tests
Unit tests. Tested the completions manually.
## Changes
The function signature of Cobra's `PreRunE` function has an `error`
return value. We'd like to start returning `diag.Diagnostics` after
loading a bundle, so this is incompatible. This change modifies all
usage of `PreRunE` to load a bundle to inline function calls in the
command's `RunE` function.
## Tests
* Unit tests pass.
* Integration 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
Added `bundle deployment bind` and `unbind` command.
This command allows to bind bundle-defined resources to existing
resources in Databricks workspace so they become DABs-managed.
## Tests
Manually + added E2E test
## Changes
Added `--restart` flag for `bundle run` command
When running with this flag, `bundle run` will cancel all existing runs
before starting a new one
## Tests
Manually
## Changes
Group bundle run flags by job and pipeline types
## Tests
```
Run a resource (e.g. a job or a pipeline)
Usage:
databricks bundle run [flags] KEY
Job Flags:
--dbt-commands strings A list of commands to execute for jobs with DBT tasks.
--jar-params strings A list of parameters for jobs with Spark JAR tasks.
--notebook-params stringToString A map from keys to values for jobs with notebook tasks. (default [])
--params stringToString comma separated k=v pairs for job parameters (default [])
--pipeline-params stringToString A map from keys to values for jobs with pipeline tasks. (default [])
--python-named-params stringToString A map from keys to values for jobs with Python wheel tasks. (default [])
--python-params strings A list of parameters for jobs with Python tasks.
--spark-submit-params strings A list of parameters for jobs with Spark submit tasks.
--sql-params stringToString A map from keys to values for jobs with SQL tasks. (default [])
Pipeline Flags:
--full-refresh strings List of tables to reset and recompute.
--full-refresh-all Perform a full graph reset and recompute.
--refresh strings List of tables to update.
--refresh-all Perform a full graph update.
Flags:
-h, --help help for run
--no-wait Don't wait for the run to complete.
Global Flags:
--debug enable debug logging
-o, --output type output type: text or json (default text)
-p, --profile string ~/.databrickscfg profile
-t, --target string bundle target to use (if applicable)
--var strings set values for variables defined in bundle config. Example: --var="foo=bar"
```
## Changes
This PR fixes metadata computation for empty bundle. Before we would
error because the `terraform.Load()` mutator errors on a empty / no
state file.
## Tests
Failing integration tests now pass.
## Changes
Display an interactive prompt with a list of resources to run if one
isn't specified and the command is run interactively.
## Tests
Manually confirmed:
* The new prompt works
* Shell completion still works
* Specifying a key argument still works
## Changes
This removes the remaining dependency on global state and unblocks work
to parallelize integration tests. As is, we can already uncomment an
integration test that had to be skipped because of other tests tainting
global state. This is no longer an issue.
Also see #595 and #606.
## Tests
* Unit and integration tests pass.
* Manually confirmed the help output is the same.
## Changes
This change is another step towards a CLI without globals. Also see #595.
The flags for the root command are now encapsulated in struct types.
## Tests
Unit tests pass.
This implements the "development run" functionality that we desire for DABs in the workspace / IDE.
## bundle.yml changes
In bundle.yml, there should be a "dev" environment that is marked as
`mode: debug`:
```
environments:
dev:
default: true
mode: development # future accepted values might include pull_request, production
```
Setting `mode` to `development` indicates that this environment is used
just for running things for development. This results in several changes
to deployed assets:
* All assets will get '[dev]' in their name and will get a 'dev' tag
* All assets will be hidden from the list of assets (future work; e.g.
for jobs we would have a special job_type that hides it from the list)
* All deployed assets will be ephemeral (future work, we need some form
of garbage collection)
* Pipelines will be marked as 'development: true'
* Jobs can run on development compute through the `--compute` parameter
in the CLI
* Jobs get their schedule / triggers paused
* Jobs get concurrent runs (it's really annoying if your runs get
skipped because the last run was still in progress)
Other accepted values for `mode` are `default` (which does nothing) and
`pull-request` (which is reserved for future use).
## CLI changes
To run a single job called "shark_sighting" on existing compute, use the
following commands:
```
$ databricks bundle deploy --compute 0617-201942-9yd9g8ix
$ databricks bundle run shark_sighting
```
which would deploy and run a job called "[dev] shark_sightings" on the
compute provided. Note that `--compute` is not accepted in production
environments, so we show an error if `mode: development` is not used.
The `run --deploy` command offers a convenient shorthand for the common
combination of deploying & running:
```
$ export DATABRICKS_COMPUTE=0617-201942-9yd9g8ix
$ bundle run --deploy shark_sightings
```
The `--deploy` addition isn't really essential and I welcome feedback 🤔
I played with the idea of a "debug" or "dev" command but that seemed to
only make the option space even broader for users. The above could work
well with an IDE or workspace that automatically sets the target
compute.
One more thing I added is`run --no-wait` can now be used to run
something without waiting for it to be completed (useful for IDE-like
environments that can display progress themselves).
```
$ bundle run --deploy shark_sightings --no-wait
```
## 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
This PR now allows you to define variables in the bundle config and set
them in three ways
1. command line args
2. process environment variable
3. in the bundle config itself
## Tests
manually, unit, and black box tests
---------
Co-authored-by: Miles Yucht <miles@databricks.com>
This PR adds the following command groups:
## Workspace-level command groups
* `bricks alerts` - The alerts API can be used to perform CRUD operations on alerts.
* `bricks catalogs` - A catalog is the first layer of Unity Catalog’s three-level namespace.
* `bricks cluster-policies` - Cluster policy limits the ability to configure clusters based on a set of rules.
* `bricks clusters` - The Clusters API allows you to create, start, edit, list, terminate, and delete clusters.
* `bricks current-user` - This API allows retrieving information about currently authenticated user or service principal.
* `bricks dashboards` - In general, there is little need to modify dashboards using the API.
* `bricks data-sources` - This API is provided to assist you in making new query objects.
* `bricks experiments` - MLflow Experiment tracking.
* `bricks external-locations` - An external location is an object that combines a cloud storage path with a storage credential that authorizes access to the cloud storage path.
* `bricks functions` - Functions implement User-Defined Functions (UDFs) in Unity Catalog.
* `bricks git-credentials` - Registers personal access token for Databricks to do operations on behalf of the user.
* `bricks global-init-scripts` - The Global Init Scripts API enables Workspace administrators to configure global initialization scripts for their workspace.
* `bricks grants` - In Unity Catalog, data is secure by default.
* `bricks groups` - Groups simplify identity management, making it easier to assign access to Databricks Workspace, data, and other securable objects.
* `bricks instance-pools` - Instance Pools API are used to create, edit, delete and list instance pools by using ready-to-use cloud instances which reduces a cluster start and auto-scaling times.
* `bricks instance-profiles` - The Instance Profiles API allows admins to add, list, and remove instance profiles that users can launch clusters with.
* `bricks ip-access-lists` - IP Access List enables admins to configure IP access lists.
* `bricks jobs` - The Jobs API allows you to create, edit, and delete jobs.
* `bricks libraries` - The Libraries API allows you to install and uninstall libraries and get the status of libraries on a cluster.
* `bricks metastores` - A metastore is the top-level container of objects in Unity Catalog.
* `bricks model-registry` - MLflow Model Registry commands.
* `bricks permissions` - Permissions API are used to create read, write, edit, update and manage access for various users on different objects and endpoints.
* `bricks pipelines` - The Delta Live Tables API allows you to create, edit, delete, start, and view details about pipelines.
* `bricks policy-families` - View available policy families.
* `bricks providers` - Databricks Providers REST API.
* `bricks queries` - These endpoints are used for CRUD operations on query definitions.
* `bricks query-history` - Access the history of queries through SQL warehouses.
* `bricks recipient-activation` - Databricks Recipient Activation REST API.
* `bricks recipients` - Databricks Recipients REST API.
* `bricks repos` - The Repos API allows users to manage their git repos.
* `bricks schemas` - A schema (also called a database) is the second layer of Unity Catalog’s three-level namespace.
* `bricks secrets` - The Secrets API allows you to manage secrets, secret scopes, and access permissions.
* `bricks service-principals` - Identities for use with jobs, automated tools, and systems such as scripts, apps, and CI/CD platforms.
* `bricks serving-endpoints` - The Serving Endpoints API allows you to create, update, and delete model serving endpoints.
* `bricks shares` - Databricks Shares REST API.
* `bricks storage-credentials` - A storage credential represents an authentication and authorization mechanism for accessing data stored on your cloud tenant.
* `bricks table-constraints` - Primary key and foreign key constraints encode relationships between fields in tables.
* `bricks tables` - A table resides in the third layer of Unity Catalog’s three-level namespace.
* `bricks token-management` - Enables administrators to get all tokens and delete tokens for other users.
* `bricks tokens` - The Token API allows you to create, list, and revoke tokens that can be used to authenticate and access Databricks REST APIs.
* `bricks users` - User identities recognized by Databricks and represented by email addresses.
* `bricks volumes` - Volumes are a Unity Catalog (UC) capability for accessing, storing, governing, organizing and processing files.
* `bricks warehouses` - A SQL warehouse is a compute resource that lets you run SQL commands on data objects within Databricks SQL.
* `bricks workspace` - The Workspace API allows you to list, import, export, and delete notebooks and folders.
* `bricks workspace-conf` - This API allows updating known workspace settings for advanced users.
## Account-level command groups
* `bricks account billable-usage` - This API allows you to download billable usage logs for the specified account and date range.
* `bricks account budgets` - These APIs manage budget configuration including notifications for exceeding a budget for a period.
* `bricks account credentials` - These APIs manage credential configurations for this workspace.
* `bricks account custom-app-integration` - These APIs enable administrators to manage custom oauth app integrations, which is required for adding/using Custom OAuth App Integration like Tableau Cloud for Databricks in AWS cloud.
* `bricks account encryption-keys` - These APIs manage encryption key configurations for this workspace (optional).
* `bricks account groups` - Groups simplify identity management, making it easier to assign access to Databricks Account, data, and other securable objects.
* `bricks account ip-access-lists` - The Accounts IP Access List API enables account admins to configure IP access lists for access to the account console.
* `bricks account log-delivery` - These APIs manage log delivery configurations for this account.
* `bricks account metastore-assignments` - These APIs manage metastore assignments to a workspace.
* `bricks account metastores` - These APIs manage Unity Catalog metastores for an account.
* `bricks account networks` - These APIs manage network configurations for customer-managed VPCs (optional).
* `bricks account o-auth-enrollment` - These APIs enable administrators to enroll OAuth for their accounts, which is required for adding/using any OAuth published/custom application integration.
* `bricks account private-access` - These APIs manage private access settings for this account.
* `bricks account published-app-integration` - These APIs enable administrators to manage published oauth app integrations, which is required for adding/using Published OAuth App Integration like Tableau Cloud for Databricks in AWS cloud.
* `bricks account service-principals` - Identities for use with jobs, automated tools, and systems such as scripts, apps, and CI/CD platforms.
* `bricks account storage` - These APIs manage storage configurations for this workspace.
* `bricks account storage-credentials` - These APIs manage storage credentials for a particular metastore.
* `bricks account users` - User identities recognized by Databricks and represented by email addresses.
* `bricks account vpc-endpoints` - These APIs manage VPC endpoint configurations for this account.
* `bricks account workspace-assignment` - The Workspace Permission Assignment API allows you to manage workspace permissions for principals in your account.
* `bricks account workspaces` - These APIs manage workspaces for this account.
## Changes
Pull state before deploying and push state after deploying.
Note: the run command was missing mutators to initialize Terraform. This
is necessary if the cache directory is removed between running "deploy"
and "run" (which is valid now that we synchronize state).
## Tests
Manually.
We intend to let non-bundle commands use bundle configuration for their
operating context (workspace, auth, default cluster, etc).
As such, all commands must first try to load a bundle configuration.
If there is no bundle they can fall back on taking their operating
context from command line flags and the environment.
This is on top of #180.
Pieter Noordhuis
shreyas-goenka
Lennart Kats (databricks)
Andrew Nester
Serge Smertin