Databricks Execution & Compute

Run code on Databricks. Three execution modes—choose based on workload.

Path convention: <SKILL_ROOT> in examples below = the directory containing this SKILL.md. Resolve it to the absolute path in your install (e.g. ~/.claude/skills/databricks-execution-compute). Commands like python <SKILL_ROOT>/scripts/compute.py ... work from any cwd.

Execution Mode Decision Matrix

Decision Flow

Main decision point: if you’re using Declarative Automation Bundles (DABs) then follow the instructions of the databricks-dabs skill first. In short, you can use databricks bundle run to run code associated with jobs, pipelines, and other resources. This can be recognized by looking for a databricks.yml file in the project root. If these resources don’t exist, or if you’re not using DABs, then proceed with the below.

Prefer Databricks Connect for all spark-based workload, then serverless.

Spark-based code? → Databricks Connect (fastest)
  └─ Python 3.12 missing? → Install it + databricks-connect
  └─ Install fails? → Ask user (don't auto-switch modes)

Heavy/long-running (ML)? → Serverless Job (independent)
Need state across calls? → Interactive Cluster (list and ask which one to use)
Scala/R? → Interactive Cluster (list and ask which one to use)

How to Run Code

Read the reference file for your chosen mode before proceeding.

Databricks Connect (run locally, prefer when it’s pure spark code) → reference

from databricks.connect import DatabricksSession
...
spark = DatabricksSession.builder.profile("my-local-profile").serverless(True).getOrCreate()


python my_spark_script.py

Serverless Job → reference

Pure CLI flow: upload a local file as a workspace notebook, fire a one-time run with databricks jobs submit (create + run in one call, ephemeral — no Jobs UI entry, no retry), then poll + fetch the result. The local file must be a Databricks source notebook — top line # Databricks notebook source (Python) or -- Databricks notebook source (SQL).

1. Upload the local file as a workspace notebook. TARGET_PATH is positional; --file is the local path.

databricks workspace import /Workspace/Users/<user>/.ai_dev_kit/train --file /local/path/to/train.py --format SOURCE --language PYTHON --overwrite

2. Submit the run. Use --no-wait to get {"run_id": N} back immediately; drop it to block until terminated. "client": "4" is required for dependencies to install ("1" silently ignores them).

databricks jobs submit --no-wait --json @submit.json

{
  "run_name": "train-run",
  "tasks": [{
    "task_key": "main",
    "notebook_task": {"notebook_path": "/Workspace/Users/<user>/.ai_dev_kit/train"},
    "environment_key": "ml_env"
  }],
  "environments": [{
    "environment_key": "ml_env",
    "spec": {"client": "4", "dependencies": ["scikit-learn==1.5.2", "mlflow==2.22.0"]}
  }]
}

3. Check state / wait for completion. Life-cycle: PENDING → RUNNING → TERMINATED (or SKIPPED / INTERNAL_ERROR). Only read .state.result_state (SUCCESS / FAILED / CANCELED) once life-cycle is TERMINATED.

databricks jobs get-run <RUN_ID> | jq '{state: .state.life_cycle_state, result: .state.result_state, duration_ms: .execution_duration, url: .run_page_url, task_run_id: .tasks[0].run_id}'

4. Fetch the output / error. Gotcha: get-run-output takes the task run_id (.tasks[0].run_id), NOT the parent run_id from submit. notebook_output.result is the string passed to dbutils.notebook.exit().

databricks jobs get-run-output <TASK_RUN_ID> | jq '{result: .notebook_output.result, error, error_trace}'

Always use dbutils.notebook.exit(<string>) in the notebook — print() is not captured by get-run-output. For JSON results: dbutils.notebook.exit(json.dumps({...})) then parse .notebook_output.result client-side.

Convenience wrapper. scripts/compute.py execute-code does upload + submit + wait + cleanup in one command and returns a single tidy JSON:

python <SKILL_ROOT>/scripts/compute.py execute-code --file /local/path/to/train.py --compute-type serverless --timeout 1500 --environments '[{"environment_key":"ml_env","spec":{"client":"4","dependencies":["scikit-learn==1.5.2","mlflow==2.22.0"]}}]' | jq '{success, state, output, error, run_id, run_page_url, execution_duration_ms}'

Interactive Cluster → reference

Avoid by default — prefer Serverless Job. Only use an interactive cluster when:

• you have an existing classic cluster already running and available, or
• you need live, stateful execution across multiple calls (debugging via an execution context), or
• the user explicitly asks for it.

Interactive clusters are slow to start (3-8 min) and cost money while running. Don’t start one implicitly.

CLI Commands

SQL Warehouses

All ID-taking commands use positional arg (no --id flag). Use databricks warehouses list to find an ID.

# Create a serverless SQL warehouse. min_num_clusters + max_num_clusters are REQUIRED
# (the server rejects the default 0). Keep the aidevkit_project tag for resource tracking.
databricks warehouses create --json '{
  "name": "my-warehouse",
  "cluster_size": "Small",
  "enable_serverless_compute": true,
  "auto_stop_mins": 10,
  "min_num_clusters": 1,
  "max_num_clusters": 1,
  "tags": {"custom_tags": [{"key": "aidevkit_project", "value": "ai-dev-kit"}]}
}'

# List / find — trim to id, name, state with jq
databricks warehouses list -o json | jq '.[] | {id, name, state, size: .cluster_size}'

# Find by name
databricks warehouses list -o json | jq '.[] | select(.name == "my-warehouse")'

# Get one warehouse's full config
databricks warehouses get <WAREHOUSE_ID>

# Start / stop (both are LROs; add --no-wait to return immediately)
databricks warehouses start <WAREHOUSE_ID>
databricks warehouses stop  <WAREHOUSE_ID>

# Resize / reconfigure — pass the FULL desired config (omitted fields revert to defaults,
# so always re-state min_num_clusters/max_num_clusters). Use --no-wait if the warehouse
# is STOPPED, otherwise edit blocks trying to reach RUNNING and errors out (the mutation
# itself still applies). When the warehouse is already RUNNING, --no-wait is optional.
databricks warehouses edit <WAREHOUSE_ID> --no-wait --json '{
  "name": "my-warehouse",
  "cluster_size": "Medium",
  "enable_serverless_compute": true,
  "auto_stop_mins": 15,
  "min_num_clusters": 1,
  "max_num_clusters": 1
}'

# Delete (irreversible)
databricks warehouses delete <WAREHOUSE_ID>

Sizes: 2X-Small, X-Small, Small, Medium, Large, X-Large, 2X-Large, 3X-Large, 4X-Large. Types: set "warehouse_type": "PRO" (default) or "CLASSIC" in the JSON body.

Related Skills

• databricks-synthetic-data-gen — Data generation using Spark + Faker
• databricks-jobs — Production job orchestration
• databricks-dbsql — SQL warehouse and AI functions