executing-spark

Development

Execute arbitrary Python or PySpark code on Fabric Spark compute without creating a notebook artifact; ephemeral Livy sessions with full Delta table access. Automatically invoke when the user asks to "run PySpark in Fabric", "create a Livy session", "execute Python on Fabric compute", "run Spark without a notebook", "submit code to Fabric", "ephemeral Spark execution", "run ETL in Fabric".

QUICK START

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Executing Spark Code in Fabric (No Notebook)

Run arbitrary PySpark or Python code on Fabric Spark compute via the Livy API. No notebook artifact is created or persisted; sessions are ephemeral. Full read/write access to lakehouse Delta tables via Spark SQL.

Prerequisites

Azure CLI authenticated (az login)
A lakehouse in the target workspace (the Livy session runs against it)
Fabric capacity (F or trial)

Critical: Authentication

The Livy API requires a token from az account get-access-token --resource https://api.fabric.microsoft.com. Tokens from fab auth do not work for OneLake storage access inside the Spark session.

import subprocess, json

result = subprocess.run(
    ["az", "account", "get-access-token", "--resource", "https://api.fabric.microsoft.com"],
    capture_output=True, text=True
)
token = json.loads(result.stdout)["accessToken"]

Do not output or log the token. Pass it directly to the API call.

Lifecycle

1. Create session   POST .../sessions              {"kind": "pyspark"}
2. Wait for idle    GET  .../sessions/{id}          poll until state: "idle" (~30-90s)
3. Submit code      POST .../sessions/{id}/statements   {"code": "...", "kind": "pyspark"}
4. Get result       GET  .../sessions/{id}/statements/{n}   poll until state: "available"
5. Delete session   DELETE .../sessions/{id}        ALWAYS do this

Base URL: https://api.fabric.microsoft.com/v1/workspaces/{wsId}/lakehouses/{lhId}/livyapi/versions/2023-12-01

CRITICAL: Always delete sessions when done. Idle sessions consume Fabric capacity units (CUs). A forgotten session burns compute until it times out (default: 20 minutes). In automation, wrap cleanup in a finally block.

Getting IDs

WS_ID=$(fab get "Workspace.Workspace" -q "id" | tr -d '"')
LH_ID=$(fab get "Workspace.Workspace/Lakehouse.Lakehouse" -q "id" | tr -d '"')

Submitting Code

Submit PySpark or pure Python as statements. The spark object is available automatically.

# Statement payload
{"code": "df = spark.sql('SELECT * FROM products LIMIT 10')\ndf.show()", "kind": "pyspark"}

Results are in output.data["text/plain"] when state: "available" and output.status: "ok".

What Works

spark.sql("SELECT ...") ; full Spark SQL against lakehouse tables
spark.sql("SHOW TABLES") ; metastore access
df.write.mode("overwrite").saveAsTable(...) ; write Delta tables
Pure Python (pandas, numpy, pyarrow); runs on Spark container
In-memory Spark DataFrames and transformations
Multiple sequential statements in one session

What Does Not Work

deltalake (delta-rs) is not pre-installed; use Spark SQL instead
notebookutils has limited functionality (no FUSE mount at /lakehouse/default/)
Tokens from fab auth ; must use az CLI token
Tokens expire after ~60 minutes; long sessions need token refresh

When to Use This vs Alternatives

Scenario	Approach
Quick read-only exploration	DuckDB locally (fastest; see `using-duckdb` skill)
Write data back to lakehouse	Livy session or notebook
Ephemeral transform; no artifact	Livy session (this skill)
Complex multi-cell workflow	Notebook (`nb exec` or portal)
Scheduled ETL	Notebook via `fab job run`
Agent-driven compute (Dagster, orchestrators)	Livy session

References

references/livy-api.md -- Full API reference with endpoints, request/response formats, and error handling
references/example-script.md -- Complete working script that creates a session, queries data, writes results, and cleans up