# Job Management

# Managing Databricks Jobs

`{brickster}` provides coverage of the Jobs REST API’s, allowing creation of multi-task jobs via R code.

## Job Basics

When listing the jobs the default behaviour is to returns the first 25 jobs, to configure the number of jobs returned you can specify `limit` and `offset`.

The `expand_tasks` parameter will return task and cluster details for each job (default `FALSE)`.

    # list all jobs within Databricks workspace
    # can control limit, offset, and if cluster/jobs details are returned
    jobs <- db_jobs_list(limit = 10)

    # list all runs within a specific job
    job_runs <- db_jobs_runs_list(job_id = jobs[[1]]$job_id)

To return the details of a specific job you can use `db_jobs_get()`, this requires knowledge of the `job_id` which can be found via the user interface in Databricks or using `db_jobs_list()`.

    # details of a specific job
    job_details <- db_jobs_get(job_id = jobs[[1]]$job_id)

Each job has one or more tasks which may have dependence upon each other, execution of a job will flow through these tasks - this is known as a “run” of a job. Jobs can be scheduled to start a run on a particular schedule, or by a direct trigger such as `db_jobs_run_now()`.

There are a number of functions that are specific to job runs execution and metadata:

Purpose | Function
--- | ---
Trigger new run of an existing job | `db_jobs_run_now()`
List runs of a job | `db_jobs_runs_list()`
Retrieve metadata for a specific job run | `db_jobs_runs_get()`
Retrieve metadata for a specific task run | `db_jobs_runs_get_output()`
Export code and/or dashboard for task run | `db_jobs_runs_export()`
Delete record of a job run | `db_jobs_runs_delete()`
Cancel run of a job | `db_jobs_runs_cancel()`

When using functions such as `db_jobs_run_get_output()` and `db_jobs_runs_export()` the required `run_id` can refer to the id of a given task. Each task within a given “run” will also have a `run_id` that can be referenced.

## Creating Jobs

`{brickster}` enables creation of jobs from R, these jobs can be simple single task jobs or complex multi-task jobs with dependencies that share and re-use clusters.

### Simple Job

A single-task job can be created with the following components:

- `job_task()` which defines a singular task

- `new_cluster()` required by `job_task()`, it defines the compute specifications

- `*_task()`, one of the task functions e.g. `notebook_task()`

Below is the creation of a simple job that runs a notebook and has a paused schedule:

    # define a job task
    simple_task <- job_task(
      task_key = "simple_task",
      description = "a simple task that runs a notebook",
      # specify a cluster for the job
      new_cluster = new_cluster(
        spark_version = "16.4.x-scala2.12",
        driver_node_type_id = "m5a.large",
        node_type_id = "m5a.large",
        num_workers = 2,
        cloud_attr = aws_attributes(ebs_volume_size = 32)
      ),
      # this task will be a notebook
      task = notebook_task(notebook_path = "/brickster/simple-notebook")
    )

    # create job with simple task
    simple_task_job <- db_jobs_create(
      name = "brickster example: simple",
      tasks = job_tasks(simple_task),
      # 9am every day, paused currently
      schedule = cron_schedule(
        quartz_cron_expression = "0 0 9 * * ?",
        pause_status = "PAUSED"
      )
    )

### Multiple Tasks

Jobs can be extended beyond a singular task, this next example extends the simple job example by now having three tasks. Each subsequent task depends on the priors completion before it can proceed, each task also will define its own `new_cluster`.

Whilst this job runs the same notebook each time for the sake of example it demonstrates how to build dependencies.

    # one cluster definition, repeatedly use for each task
    multitask_cluster <- new_cluster(
      spark_version = "16.4.x-scala2.12",
      driver_node_type_id = "m5a.large",
      node_type_id = "m5a.large",
      num_workers = 2,
      cloud_attr = aws_attributes(ebs_volume_size = 32)
    )

    # each task will run the same notebook (just for this example)
    multitask_task <- notebook_task(notebook_path = "/brickster/simple-notebook")

    # create three simple tasks that will depend on each other
    # task_a -> task_b -> task_c
    task_a <- job_task(
      task_key = "task_a",
      description = "First task in the sequence",
      new_cluster = multitask_cluster,
      task = multitask_task
    )

    task_b <- job_task(
      task_key = "task_b",
      description = "Second task in the sequence",
      new_cluster = multitask_cluster,
      task = multitask_task,
      depends_on = "task_a"
    )

    task_c <- job_task(
      task_key = "task_c",
      description = "Third task in the sequence",
      new_cluster = multitask_cluster,
      task = multitask_task,
      depends_on = "task_b"
    )

    # create job with multiple tasks
    multitask_job <- db_jobs_create(
      name = "brickster example: multi-task",
      tasks = job_tasks(task_a, task_b, task_c),
      # 9am every day, paused currently
      schedule = cron_schedule(
        quartz_cron_expression = "0 0 9 * * ?",
        pause_status = "PAUSED"
      )
    )

### Cluster Reuse

The multiple tasks example has one shortcoming - clusters are not reused between tasks and therefore each task will have to wait for another cluster to be started before its computations can begin, this will typically add a few minutes to each task.

It can be advantageous to reuse clusters between job tasks to avoid overhead for short lived tasks, or to share resources for tasks which are not computationally demanding.

This example is the same as the previous multi-task job however it now shares a single cluster for each task.

`new_cluster` is replaced with `job_cluster_key` in each task:

    # create three simple tasks that will depend on each other
    # this time we will use a shared cluster to reduce startup overhead
    # task_a -> task_b -> task_c
    task_a <- job_task(
      task_key = "task_a",
      description = "First task in the sequence",
      job_cluster_key = "shared_cluster",
      task = multitask_task
    )

    task_b <- job_task(
      task_key = "task_b",
      description = "Second task in the sequence",
      job_cluster_key = "shared_cluster",
      task = multitask_task,
      depends_on = "task_a"
    )

    task_c <- job_task(
      task_key = "task_c",
      description = "Third task in the sequence",
      job_cluster_key = "shared_cluster",
      task = multitask_task,
      depends_on = "task_b"
    )

`job_clusters` is now defined in the `db_jobs_create()` call where it accepts a named list of `new_clusters()`, we will reuse our example from earlier:

    # define job_clusters as a named list of new_cluster()
    multitask_job_with_reuse <- db_jobs_create(
      name = "brickster example: multi-task with reuse",
      job_clusters = list("shared_cluster" = multitask_cluster),
      tasks = job_tasks(task_a, task_b, task_c),
      # 9am every day, paused currently
      schedule = cron_schedule(
        quartz_cron_expression = "0 0 9 * * ?",
        pause_status = "PAUSED"
      )
    )

There can be multiple shared clusters and it may be beneficial to give some demanding tasks a larger cluster alone (or to share with other demanding tasks).

### One-off Jobs

It’s possible to create one-off jobs that do not have a schedule and only appears under the “jobs runs” tab, not “jobs”. `db_jobs_runs_submit()` permits the use of an `idempotency_token` which can avoid re-submission of a run.

    # submit a one off job run
    # reuse the simple task from first example
    # idempotency_token guarentees no additional triggers
    oneoff_job <- db_jobs_runs_submit(
      tasks = job_tasks(simple_task),
      run_name = "brickster example: one-off job",
      idempotency_token = "my_job_run_token"
    )

### Remote Git Repos

Instead of using notebooks contained within the Databricks workspace a job can be referenced in an external git repository and reference a specific branch, tag, or commit.

Let’s revisit the simple job example from before and adjust to use `git_source()`.
In this example the repo will have a folder `example` which contains `simple-notebook.py`.

    # define a job task
    # this time, the notebook_path is relative to the git root directory
    # omit file extensions like .py or .r
    simple_task <- job_task(
      task_key = "simple_task",
      description = "a simple task that runs a notebook",
      # specify a cluster for the job
      new_cluster = new_cluster(
        spark_version = "16.4.x-scala2.12",
        driver_node_type_id = "m5a.large",
        node_type_id = "m5a.large",
        num_workers = 2,
        cloud_attr = aws_attributes(ebs_volume_size = 32)
      ),
      # this task will be a notebook
      task = notebook_task(notebook_path = "example/simple-notebook")
    )

Within the call to `db_jobs_create()` use `git_source()` to point to the repo containing the notebook.

    # create job with simple task
    simple_task_job <- db_jobs_create(
      name = "brickster example: simple",
      tasks = job_tasks(simple_task),
      # git source points to repo
      git_source = git_source(
        git_url = "www.github.com/<user>/<repo>",
        git_provider = "github",
        reference = "main",
        type = "branch"
      ),
      # 9am every day, paused currently
      schedule = cron_schedule(
        quartz_cron_expression = "0 0 9 * * ?",
        pause_status = "PAUSED"
      )
    )

## Updating Jobs

### Partial Update

Jobs can be partially updated, for example to rename an existing job:

    # only change the job name
    db_jobs_update(
      job_id = multitask_job_with_reuse$job_id,
      name = "brickster example: renamed job"
    )

Adding a timeout and adjusting maximum concurrent runs allowed:

    # adding timeout and increasing max concurrent runs
    db_jobs_update(
      job_id = multitask_job_with_reuse$job_id,
      timeout_seconds = 60 * 5,
      max_concurrent_runs = 2
    )

You can add new tasks but it may involve some extra steps, either:

- Having `job_task()` required for job defined already within R and adding new tasks

- Specifying all `job_task()`’s again

- Using `db_jobs_get()` and parsing the returned metadata into `job_task()`’s

### Complete Update

A complete overwrite of a job can be done with `db_jobs_reset()` - this allows the `job_id` to remain constant while maintaining the historical run information with prior settings. `db_jobs_reset()` is the same as `db_jobs_create()` except it takes one additional parameter (`job_id`).

## Managing Jobs

`db_jobs_list()` and `db_jobs_runs_list()` were covered in job basics.

### Invocation

Jobs can be triggered by:

- Their defined schedule (see: `cron_schedule()`)

- Using `db_jobs_run_now()` to trigger a run outside regular schedule or on a paused job.

- Creating a one off job run via `db_jobs_runs_submit()`

The only example yet to be covered is `db_jobs_run_now()`, it accepts parameters as named lists:

    # invoke simple job example
    triggered_run <- db_jobs_run_now(job_id = simple_task_job$job_id)

Runs can also be cancelled:

    # cancel run whilst it is in progress
    db_jobs_runs_cancel(run_id = triggered_run$run_id)

### Deletion

Jobs and runs can be deleted with `db_jobs_delete()` and `db_jobs_runs_delete()` respectively.

Cleaning up all jobs created in this documentation:

    db_jobs_delete(job_id = simple_task_job$job_id)
    db_jobs_delete(job_id = multitask_job$job_id)
    db_jobs_delete(job_id = multitask_job_with_reuse$job_id)