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 Materialized Lake View (MLV) authoring (Spark SQL MLVs support incremental refresh; PySpark is full-refresh only). Routes to data engineering patterns, development workflow, or infrastructure orchestration. Triggers: "develop notebook", "data engineering", "workspace setup", "pipeline design", "Delta Lake patterns", "Spark development", "lakehouse configuration", "write notebook code", "notebookutils", "notebook cell", "PySpark notebook", "%%sql cell", "%%configure", "fabric notebook", "run notebook", "notebook deployment", "materialized lake view", "MLV", "CREATE MATERIALIZED LAKE VIEW", "MLV incremental refresh", "review MLV for incremental refresh", "MLV refresh policy", "infrastructure provisioning"

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, CLI recipes
Job Execution	COMMON-CORE.md § Job Execution
Capacity Management	COMMON-CORE.md § Capacity Management
Gotchas & Troubleshooting	COMMON-CORE.md § Gotchas & Troubleshooting
Best Practices	COMMON-CORE.md § Best Practices
Tool Selection Rationale	COMMON-CLI.md § Tool Selection Rationale
Authentication Recipes	COMMON-CLI.md § Authentication Recipes	az login flows and token acquisition
Fabric Control-Plane API via az rest	COMMON-CLI.md § Fabric Control-Plane API via az rest	Always pass --resource https://api.fabric.microsoft.com or az rest fails
Pagination Pattern	COMMON-CLI.md § Pagination Pattern
Long-Running Operations (LRO) Pattern	COMMON-CLI.md § Long-Running Operations (LRO) Pattern
OneLake Data Access via curl	COMMON-CLI.md § OneLake Data Access via curl	Use curl not az rest (different token audience)
SQL / TDS Data-Plane Access	COMMON-CLI.md § SQL / TDS Data-Plane Access
Job Execution (CLI)	COMMON-CLI.md § Job Execution
Job Scheduling	COMMON-CLI.md § Job Scheduling	URL is /jobs/{jobType}/schedules; endDateTime required
OneLake Shortcuts	COMMON-CLI.md § OneLake Shortcuts
Capacity Management (CLI)	COMMON-CLI.md § Capacity Management
Composite Recipes	COMMON-CLI.md § Composite Recipes
Gotchas & Troubleshooting (CLI-Specific)	COMMON-CLI.md § Gotchas & Troubleshooting (CLI-Specific)	az rest audience, shell escaping, token expiry
Quick Reference: az rest Template	COMMON-CLI.md § Quick Reference: az rest Template
Quick Reference: Token Audience / CLI Tool Matrix	COMMON-CLI.md § Quick Reference: Token Audience ↔ CLI Tool Matrix	Which --resource + tool for each service
Relationship to SPARK-CONSUMPTION-CORE.md	SPARK-AUTHORING-CORE.md § Relationship to SPARK-CONSUMPTION-CORE.md
Data Engineering Authoring Capability Matrix	SPARK-AUTHORING-CORE.md § Data Engineering Authoring Capability Matrix
Lakehouse Management	SPARK-AUTHORING-CORE.md § Lakehouse Management
Notebook Management	SPARK-AUTHORING-CORE.md § Notebook Management
Notebook Execution & Job Management	SPARK-AUTHORING-CORE.md § Notebook Execution & Job Management
CI/CD & Automation Patterns	SPARK-AUTHORING-CORE.md § CI/CD & Automation Patterns
Infrastructure-as-Code	SPARK-AUTHORING-CORE.md § Infrastructure-as-Code
Performance Optimization & Resource Management	SPARK-AUTHORING-CORE.md § Performance Optimization & Resource Management
Authoring Gotchas and Troubleshooting	SPARK-AUTHORING-CORE.md § Authoring Gotchas and Troubleshooting
Quick Reference: Authoring Decision Guide	SPARK-AUTHORING-CORE.md § Quick Reference: Authoring Decision Guide
Recommended Patterns (Data Engineering)	data-engineering-patterns.md § Recommended patterns
Data Ingestion Principles	data-engineering-patterns.md § Data Ingestion Principles
Transformation Patterns	data-engineering-patterns.md § Transformation Patterns
Delta Lake Best Practices	data-engineering-patterns.md § Delta Lake Best Practices
Quality Assurance Strategies	data-engineering-patterns.md § Quality Assurance Strategies
Recommended Patterns (Development Workflow)	development-workflow.md § Recommended patterns
Notebook Lifecycle	development-workflow.md § Notebook Lifecycle
Parameterization Patterns	development-workflow.md § Parameterization Patterns
Variable Library (notebook + pipeline usage)	development-workflow.md § Method 4: Variable Library	getLibrary() + dot notation in notebooks; libraryVariables + @pipeline().libraryVariables in pipelines
Variable Library Definition	ITEM-DEFINITIONS-CORE.md § VariableLibrary	Definition parts, decoded content, types, pipeline mappings, gotchas
Local Testing Strategy	development-workflow.md § Local Testing Strategy
Debugging Patterns	development-workflow.md § Debugging Patterns
Recommended Patterns (Infrastructure)	infrastructure-orchestration.md § Recommended patterns
Materialized Lake View patterns	materialized-lake-view-patterns.md § Recommended patterns	Spark Lakehouse authoring guidance for MLV design (when to use MLVs, layering patterns)
MLV incremental refresh patterns	mlv-incremental-refresh-patterns.md § IR-friendly syntax guide	Use for refresh-readiness review and safe non-breaking rewrites
MLV schedule & job management	mlv-operations-cli	Route here when user asks to schedule, trigger, monitor, or cancel MLV refreshes (not authoring)
Workspace Provisioning Principles	infrastructure-orchestration.md § Workspace Provisioning Principles
Lakehouse Configuration Guidance	infrastructure-orchestration.md § Lakehouse Configuration Guidance
Pipeline Design Patterns	infrastructure-orchestration.md § Pipeline Design Patterns
CI/CD Integration Strategy	infrastructure-orchestration.md § CI/CD Integration Strategy
Notebook API — Which Endpoint to Use	notebook-api-operations.md § Quick Decision	Start here for remote notebook edits — getDefinition vs updateDefinition
Notebook Modification Workflow	notebook-api-operations.md § Workflow	Five-step flow: retrieve, decode, modify, encode, upload
Notebook API Error Reference	notebook-api-operations.md § Error Reference	411, 400 (updateMetadata), 401, 403 explained
Notebook API Gotchas	notebook-api-operations.md § Gotchas	/result suffix, empty body, \n per-line rule, format=ipynb
Default Lakehouse Binding	notebook-api-operations.md § Default Lakehouse Binding	.ipynb metadata vs .py # METADATA block; discover IDs dynamically
Public URL Data Ingestion	notebook-api-operations.md § Public URL Data Ingestion	Use real source URL, stage into Files/, then read with Spark
getDefinition (read notebook content)	notebook-api-operations.md § Step 1 — Retrieve Notebook Content	LRO flow, ?format=ipynb, empty body (--body '{}') requirement
Decode Base64 Notebook Payload	notebook-api-operations.md § Step 2 — Decode the Notebook Content	Extract payload, base64 decode, ipynb JSON structure
Modify Notebook Cells	notebook-api-operations.md § Step 3 — Modify the Notebook Content	Find cell, insert/replace lines, \n per-line rule
updateDefinition (write notebook content)	notebook-api-operations.md § Step 4 — Re-encode and Upload	Re-encode, upload, LRO poll, updateMetadata flag pitfall
Verify Notebook Update (Optional)	notebook-api-operations.md § Step 5 — Verify the Update	Skip unless you suspect a silent failure — Succeeded from updateDefinition is sufficient (see Rule 2)
Notebook API Error Reference	notebook-api-operations.md § Error Reference	411, 400 (updateMetadata), 401, 403 explained
Notebook API End-to-End Script	notebook-api-operations.md § Complete End-to-End Script	Full bash: get → decode → modify → encode → update → verify
Quick Start Examples	SKILL.md § Quick Start Examples	Minimal examples for common operations
— Notebook Code Authoring (shared modules) —
Notebook Authoring Core	SPARK-NOTEBOOK-AUTHORING-CORE.md	READ FIRST for notebook code tasks — fundamentals, code gen approach, module index

---

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
• Do NOT schedule MLV refreshes from notebooks — If the user asks to "schedule MLV refresh", route to mlv-operations-cli which uses the REST API. Notebook-based REFRESH MATERIALIZED LAKE VIEW ... FULL is for one-time manual refresh only, not recurring schedules.

---

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. When the task is about Materialized Lake Views, read materialized-lake-view-patterns.md for authoring/design guidance and mlv-incremental-refresh-patterns.md for refresh-readiness analysis.

---

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 and Refresh a Materialized Lake View (MLV)

-- See resources/materialized-lake-view-patterns.md for design guidance
-- and resources/mlv-incremental-refresh-patterns.md for refresh-readiness review.

-- Bronze/Silver/Gold schemas in a Lakehouse with schemas enabled
CREATE SCHEMA IF NOT EXISTS bronze;
CREATE SCHEMA IF NOT EXISTS silver;
CREATE SCHEMA IF NOT EXISTS gold;

-- A simple Silver MLV with data quality constraints
--
-- Prerequisite for incremental refresh: enable Change Data Feed (CDF) on every
-- source table the MLV reads from. Without CDF, optimal refresh can only choose
-- between no refresh (sources unchanged) and full refresh — never incremental.
-- See resources/mlv-incremental-refresh-patterns.md.
ALTER TABLE bronze.orders_raw SET TBLPROPERTIES (delta.enableChangeDataFeed = true);

CREATE OR REPLACE MATERIALIZED LAKE VIEW silver.orders_clean
(
    CONSTRAINT valid_order_id CHECK (order_id IS NOT NULL) ON MISMATCH DROP
)
AS
SELECT
  order_id,
  customer_id,
  CAST(order_ts AS TIMESTAMP) AS order_ts,
  amount
FROM bronze.orders_raw;

-- Routine refresh is handled by the lakehouse Materialized lake views → Manage
-- schedule/lineage view; don't orchestrate from notebooks. The SQL form below is
-- documented only for forcing a one-time FULL recompute (troubleshooting / after
-- a correction). There is no documented SQL form for triggering incremental refresh.
REFRESH MATERIALIZED LAKE VIEW silver.orders_clean FULL;

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

---

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.