Databricks Execution Compute

Databricks Execution & Compute

Run code on Databricks. Three execution modes—choose based on workload. All examples below use the Databricks CLI; see the databricks-core skill for install and authentication.

Execution Mode Decision Matrix

| Aspect | Databricks Connect ⭐ | Serverless Job | Interactive Cluster |

|--------|-------------------|----------------|---------------------|

| Use for | Spark code (ETL, data gen) | Heavy processing (ML) | State across tool calls, Scala/R |

| Startup | Instant | ~25-50s cold start | ~5min if stopped |

| State | Within Python process | None | Via context_id |

| Languages | Python (PySpark) | Python, SQL | Python, Scala, SQL, R |

| Dependencies | withDependencies() | CLI with environments spec | Install on cluster |

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.

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 Command Map

All compute lifecycle and code-execution actions go through the Databricks CLI. Headline commands:

| Action | Command |

|--------|---------|

| Upload local file as workspace notebook | databricks workspace import <WORKSPACE_PATH> --file <LOCAL> --format SOURCE --language PYTHON --overwrite |

| Run serverless code (upload + submit + wait) | databricks jobs submit --json @submit.json (see Serverless Job section above; with --no-wait for async) |

| Get run state / wait | databricks jobs get-run <RUN_ID> (poll .state.life_cycle_state) |

| Fetch run output | databricks jobs get-run-output <TASK_RUN_ID> |

| List clusters | databricks clusters list --output json |

| Get cluster details | databricks clusters get <CLUSTER_ID> |

| Start / restart / terminate cluster | databricks clusters start/restart/delete <CLUSTER_ID> |

| Permanently delete cluster | databricks clusters permanent-delete <CLUSTER_ID> |

| Create cluster | databricks clusters create --json '{...}' (see 3-interactive-cluster.md) |

| List node types / Spark versions | databricks clusters list-node-types / databricks clusters spark-versions |

| Execute code on a running cluster | databricks api post /api/1.2/contexts/create + databricks api post /api/1.2/commands/execute (see 3-interactive-cluster.md) |

| SQL warehouses | databricks warehouses create/list/get/start/stop/edit/delete (see SQL Warehouses below) |

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