spark-authoring-cli

name

spark-authoring-cli

description

Develop Microsoft Fabric Spark/data engineering workflows and write code in Fabric Notebook cells with intelligent routing to specialized resources. Provides workspace/lakehouse management, notebook code authoring (PySpark, Scala, SparkR, SQL), and routes to: data engineering patterns, development workflow, or infrastructure orchestration. Use when the user wants to: (1) manage Fabric workspaces and resources, (2) write or debug code in notebook cells, (3) use notebookutils, (4) develop notebooks and PySpark applications, (5) design data pipelines, (6) provision infrastructure as code. Triggers: "develop notebook", "data engineering", "workspace setup", "pipeline design", "infrastructure provisioning", "Delta Lake patterns", "Spark development", "lakehouse configuration", "write notebook code", "notebookutils", "notebook cell", "PySpark notebook", "%%sql cell", "%%configure", "fabric notebook", "run notebook", "notebook deployment".

Update Check — ONCE PER SESSION (mandatory) The first time this skill is used in a session, run the check-updates skill before proceeding.

• GitHub Copilot CLI / VS Code: invoke the check-updates skill.
• Claude Code / Cowork / Cursor / Windsurf / Codex: compare local vs remote package.json version.
• Skip if the check was already performed earlier in this session.

CRITICAL NOTES

1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering
2. To find the item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace and, then, use JMESPath filtering

Spark Authoring — CLI Skill

This skill covers two complementary areas: (1) managing Fabric Spark artifacts via REST APIs (workspaces, lakehouses, notebooks, jobs, pipelines) and (2) writing code inside Fabric Notebook cells (PySpark, Scala, SparkR, SQL with correct lakehouse access, notebookutils, and Spark configuration). For notebook code authoring fundamentals and shared modules, MUST see SPARK-NOTEBOOK-AUTHORING-CORE.md.

Table of Contents

Task	Reference	Notes
RULES — Read these first, follow them always	SKILL.md § RULES	MUST read — 4 rules for this skill
Finding Workspaces and Items in Fabric	COMMON-CLI.md § Finding Workspaces and Items in Fabric	Mandatory — READ link first [needed for finding workspace id by its name or item id by its name, item type, and workspace id]
Fabric Topology & Key Concepts	COMMON-CORE.md § Fabric Topology & Key Concepts
Environment URLs	COMMON-CORE.md § Environment URLs
Authentication & Token Acquisition	COMMON-CORE.md § Authentication & Token Acquisition	Wrong audience = 401; read before any auth issue
Core Control-Plane REST APIs	COMMON-CORE.md § Core Control-Plane REST APIs
Pagination	COMMON-CORE.md § Pagination
Long-Running Operations (LRO)	COMMON-CORE.md § Long-Running Operations (LRO)
Rate Limiting & Throttling	COMMON-CORE.md § Rate Limiting & Throttling
OneLake Data Access	COMMON-CORE.md § OneLake Data Access	Requires storage.azure.com token, not Fabric token
Definition Envelope	ITEM-DEFINITIONS-CORE.md § Definition Envelope	Definition payload structure
Per-Item-Type Definitions	ITEM-DEFINITIONS-CORE.md § Per-Item-Type Definitions	Support matrix, decoded content, part paths — REST specs,

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Must/Prefer/Avoid

MUST DO

• Check for recent jobs BEFORE creating new notebook runs — Query job instances from last 5 minutes; if recent job exists, monitor it instead of creating duplicate
• Capture job instance ID immediately after POST — Store job ID before any other operations to enable proper monitoring
• Verify workspace capacity assignment before operations — Workspace must have capacity assigned and active
• When user provides a public data URL, follow the Public URL Data Ingestion policy — keep detailed behavior in the linked resource section to avoid drift/duplication
• Format notebook cells correctly — Each line in cell source array MUST end with \n to prevent code merging
• Use correct Lakehouse Livy session body format — Send a FLAT JSON with name, driverMemory, driverCores, executorMemory, executorCores. Do NOT wrap in {"payload": ...} or send only {"kind": "pyspark"} — that causes HTTP 500. Use valid memory values (28g, 56g, 112g, 224g). See Create Lakehouse Livy Session example below and SPARK-CONSUMPTION-CORE.md.

PREFER

• Poll job status with proper intervals — 10-30 seconds between polls; timeout after reasonable duration (e.g., 30 minutes)
• Check job history when POST response is unreadable — If POST returns "No Content" or unreadable response, query recent jobs (last 1 minute) before retrying
• Use Starter Pool for development — Development/testing workloads should use useStarterPool: true
• Use Workspace Pool for production — Production workloads need consistent performance with useWorkspacePool: true
• Enable lakehouse schemas during creation — Set creationPayload.enableSchemas: true for better table organization
• Implement idempotency checks — Prevent duplicate operations by checking existing state first

AVOID

• Never retry POST with same parameters — If you have a job ID, only use GET to check status; don't create duplicate job instances
• Don't skip capacity verification — Operations will fail if workspace capacity is paused or unassigned
• Avoid immediate POST retries on failures — Check for existing/active jobs first to prevent duplicates
• Don't create new runs if monitoring existing job — One job at a time; wait for completion before submitting new runs
• Don't hardcode workspace/lakehouse IDs — Discover dynamically via item listing or catalog search APIs
• Do NOT use Lakehouse Livy sessions to run a Fabric notebook — Lakehouse Livy sessions (the public Livy API) are for ad-hoc interactive Spark code execution. To run a notebook as a job, use the Jobs API (RunNotebook) which creates a Notebook Spark session internally. See SPARK-AUTHORING-CORE.md § Notebook Execution & Job Management

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RULES — Read these first, follow them always

Rule 1 — Validate prerequisites before operations. Verify workspace has capacity assigned (see COMMON-CORE.md Create Workspace and Capacity Management) and resource IDs exist before attempting operations.

Rule 2 — Trust updateDefinition success. A Succeeded poll result from updateDefinition is sufficient confirmation that content and lakehouse bindings persisted. Do NOT call getDefinition after every upload — it is an async LRO that adds significant latency. Only use getDefinition for its intended purpose: reading current notebook content before making modifications.

Rule 3 — Prevent duplicate jobs and monitor execution properly. Before submitting new notebook run, ALWAYS check for recent job instances first (last 5 minutes). If recent job exists, monitor it instead of creating duplicate. After submission, capture job instance ID immediately and poll status - never retry POST. See SPARK-AUTHORING-CORE.md Job Monitoring for patterns.

Rule 4 — For notebook code authoring, MUST follow SPARK-NOTEBOOK-AUTHORING-CORE.md. When writing code inside notebook cells, MUST read SPARK-NOTEBOOK-AUTHORING-CORE.md first — it defines the code generation approach, rules, and a Module Index linking to detailed guides (lakehouse paths, connections, context, orchestration, etc.). Use the Spark-specific resources in this skill (data-engineering-patterns.md, development-workflow.md) for Spark-only implementation details.

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Quick Start Examples

For detailed patterns, authentication, and comprehensive API usage, see:

• COMMON-CORE.md — Fabric REST API patterns, authentication, item discovery
• COMMON-CLI.md — az rest usage, environment detection, token acquisition
• SPARK-AUTHORING-CORE.md — Notebook deployment, lakehouse creation, job execution

Below are minimal quick-start examples. Always reference the COMMON- files for production use.*

Create Workspace & Lakehouse

# See COMMON-CORE.md Environment URLs and SPARK-AUTHORING-CORE.md for full patterns
cat > /tmp/body.json << 'EOF'
{"displayName": "DataEng-Dev"}
EOF
workspace_id=$(az rest --method post --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces" \
  --body @/tmp/body.json --query "id" --output tsv)

cat > /tmp/body.json << 'EOF'
{"displayName": "DevLakehouse", "type": "Lakehouse", "creationPayload": {"enableSchemas": true}}
EOF
lakehouse_id=$(az rest --method post --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces/$workspace_id/items" \
  --body @/tmp/body.json --query "id" --output tsv)

Organize Lakehouse Tables with Schemas

# See SPARK-AUTHORING-CORE.md Lakehouse Schema Organization for table organization patterns
# Create schemas for medallion architecture
spark.sql("CREATE SCHEMA IF NOT EXISTS bronze")
spark.sql("CREATE SCHEMA IF NOT EXISTS silver")
spark.sql("CREATE SCHEMA IF NOT EXISTS gold")

Create Lakehouse Livy Session

# See SPARK-CONSUMPTION-CORE.md for Lakehouse Livy session configuration and management
# IMPORTANT: Body MUST be flat JSON with memory/cores — do NOT wrap in {"payload": ...}
cat > /tmp/body.json << 'EOF'
{"name": "dev-session", "driverMemory": "56g", "driverCores": 8, "executorMemory": "56g", "executorCores": 8, "conf": {"spark.dynamicAllocation.enabled": "true", "spark.fabric.pool.name": "Starter Pool"}}
EOF
az rest --method post --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces/$workspace_id/lakehouses/$lakehouse_id/livyapi/versions/2023-12-01/sessions" \
  --body @/tmp/body.json

Lakehouse Livy Session Body — Common Mistakes

• ❌ {"payload": {"kind": "pyspark"}} → HTTP 500 (wrong wrapper, missing required fields)
• ❌ {"kind": "pyspark"} → HTTP 500 (missing driverMemory, executorMemory, etc.)
• ✅ Flat JSON with name, driverMemory, driverCores, executorMemory, executorCores (and optionally conf with Starter Pool)

Spark Performance Configs

For detailed workload-specific configurations, see data-engineering-patterns.md Delta Lake Best Practices.

Quick reference:

# Write-heavy (Bronze): Disable V-Order, enable autoCompact
# Balanced (Silver): Enable V-Order, adaptive execution  
# Read-heavy (Gold): Vectorized reads, optimal parallelism
# See data-engineering-patterns.md for complete config tables

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Focus: Essential CLI patterns for Spark/data engineering development and notebook code authoring, with intelligent routing to specialized resources. For comprehensive patterns, always reference COMMON-* files and resource documents.