## 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
## 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
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 change adds support for job parameters. If job parameters are
specified for a job that doesn't define job parameters it returns an
error. Conversely, if task parameters are specified for a job that
defines job parameters, it also returns an error.
This change moves the options structs and their functions to separate
files and backfills test coverage for them.
Job parameters can now be specified with `--params foo=bar,bar=qux`.
## Tests
Unit tests and manual integration testing.
## 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
It's not uncommon for job runs to take more than 2 hours. On the client
side, we should not stop waiting for a job to complete if it is
intentionally running for a long time. If a job isn't supposed to run
this long, the user can specify a run timeout in the job specification
itself.
## Tests
n/a
## 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
Do not include empty output in job run output
## Tests
Running a job from CLI, the result:
```
andrew.nester@HFW9Y94129 wheel % databricks bundle run some_other_job --output json
Run URL: https://***/?o=6051921418418893#job/780620378804085/run/386695528477456
2023-09-08 11:33:24 "[default] My Wheel Job" TERMINATED SUCCESS
{
"task_outputs": [
{
"TaskKey": "TestTask",
"Output": {
"result": "Hello from my func\nGot arguments v2:\n['python']\n"
},
"EndTime": 1694165597474
}
]
```
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
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
Adds a IsInplaceSupported() function to the event interface. Any event
that now uses the progress logger has to declare whether they support in
place logging
## Tests
Manually
## Changes
1. Events are now printed in chronological order
2. Simplify events rendering by removing update/flow name. This makes it
more consistent with the web UI too
3. Switch to server side filtering on update_id
## Tests
Manually
Happy run:
```
shreyas.goenka@THW32HFW6T pipeline-progress % bricks bundle run foo
2023-04-12T20:00:22.879Z update_progress INFO "Update e1becc is INITIALIZING."
2023-04-12T20:00:22.906Z update_progress INFO "Update e1becc is SETTING_UP_TABLES."
2023-04-12T20:00:24.496Z update_progress INFO "Update e1becc is RUNNING."
2023-04-12T20:00:24.497Z flow_progress INFO "Flow 'sales_orders_raw' is QUEUED."
2023-04-12T20:00:24.586Z flow_progress INFO "Flow 'sales_orders_raw' is STARTING."
2023-04-12T20:00:24.748Z flow_progress INFO "Flow 'sales_orders_raw' is RUNNING."
2023-04-12T20:00:26.672Z flow_progress INFO "Flow 'sales_orders_raw' has COMPLETED."
2023-04-12T20:00:27.753Z update_progress INFO "Update e1becc is COMPLETED."
```
Sad run:
```
shreyas.goenka@THW32HFW6T pipeline-progress % bricks bundle run foo
2023-04-12T20:02:07.764Z update_progress INFO "Update 04b80e is INITIALIZING."
2023-04-12T20:02:07.870Z update_progress ERROR "Update 04b80e is FAILED."
Error: update failed
```
## Changes
<!-- Summary of your changes that are easy to understand -->
Output now:
```
shreyas.goenka@THW32HFW6T pipeline-progress % bricks bundle run foo
The update can be found at https://e2-dogfood.staging.cloud.databricks.com/#joblist/pipelines/1cc605db-daab-4218-b38a-a63030e3eb03/updates/f92f2159-1141-47de-b1e2-1ca854b7238f
2023-04-12T20:41:19.813Z update_progress INFO "Update f92f21 is INITIALIZING."
2023-04-12T20:41:19.841Z update_progress INFO "Update f92f21 is SETTING_UP_TABLES."
2023-04-12T20:41:21.270Z update_progress INFO "Update f92f21 is RUNNING."
2023-04-12T20:41:21.271Z flow_progress INFO "Flow 'sales_orders_raw' is QUEUED."
2023-04-12T20:41:21.349Z flow_progress INFO "Flow 'sales_orders_raw' is STARTING."
2023-04-12T20:41:21.480Z flow_progress INFO "Flow 'sales_orders_raw' is RUNNING."
2023-04-12T20:41:23.493Z flow_progress INFO "Flow 'sales_orders_raw' has COMPLETED."
2023-04-12T20:41:25.484Z update_progress INFO "Update f92f21 is COMPLETED."
```
## Tests
<!-- How is this tested? -->
Tested manually:
Before we did not have get any errors/logs and silently failed in this
case
```
shreyas.goenka@THW32HFW6T job-output % bricks bundle run foo
Error: run skipped: Skipping this run because the limit of 1 maximum concurrent runs has been reached.
```
PR for how to render errors on console for jobs.
Here is the bundle used for the logs below:
```
bundle:
name: deco-438
workspace:
host: https://adb-309687753508875.15.azuredatabricks.net
resources:
jobs:
foo:
name: "[${bundle.name}][${bundle.environment}] a test notebook"
tasks:
- task_key: alpha
existing_cluster_id: 1109-115254-ox7poobk
notebook_task:
notebook_path: "/Users/shreyas.goenka@databricks.com/[deco-438] invalid notebook"
- task_key: beta
existing_cluster_id: 1109-115254-ox7poobk
notebook_task:
notebook_path: "/does-not-exist"
- task_key: gamma
existing_cluster_id: 1109-115254-ox7poobk
notebook_task:
notebook_path: "/Users/shreyas.goenka@databricks.com/[deco-438] valid notebook"
```
And this is a screenshot of the logs from the console:
<img width="1057" alt="Screenshot 2023-02-17 at 7 12 29 PM"
src="https://user-images.githubusercontent.com/88374338/219744768-ab7f1e79-db8f-466a-ad6d-f2b6f85ed17c.png">
Here are the logs when only tasks gamma is executed (successfully):
<img width="1059" alt="Screenshot 2023-02-17 at 7 13 04 PM"
src="https://user-images.githubusercontent.com/88374338/219744992-011d8b91-ec1d-44f0-a849-83c81816dd9f.png">
TODO: Investigate more possible job errors, and make sure state for them
is handled in a robust way here
Pieter Noordhuis
shreyas-goenka
Andrew Nester
Lennart Kats (databricks)
Serge Smertin
Shreyas Goenka
