fabric-spark-perf-remediate

name	fabric-spark-perf-remediate
description	Diagnose and resolve Apache Spark performance issues in Microsoft Fabric. Use when asked to troubleshoot slow Spark notebooks, optimize Spark SQL queries, fix data skew or shuffle bottlenecks, tune spark.sql.shuffle.partitions or autoBroadcastJoinThreshold, configure resource profiles (writeHeavy, readHeavyForSpark, readHeavyForPBI), enable autotune, resolve HTTP 430 throttling errors, analyze Spark UI stages and executors, optimize Delta Lake writes with VOrder or Optimized Write, run table maintenance (bin-compaction, vacuum, Z-Order), fix small files problems, tune streaming throughput, or right-size Fabric Spark pools and capacity SKUs.
license	Complete terms in LICENSE.txt

Microsoft Fabric Apache Spark Performance remediate

Systematic workflows for diagnosing, analyzing, and resolving Apache Spark performance problems in Microsoft Fabric Data Engineering and Data Science workloads.

When to Use This Skill

Activate when encountering any of the following scenarios:

• Spark notebooks or jobs running slower than expected
• Capacity throttling errors (HTTP 430 / TooManyRequestsForCapacity)
• Data skew detected by Spark Advisor in notebook cells
• Excessive shuffle read/write in Spark UI stages
• Small files accumulation in Delta Lake tables
• Streaming ingestion throughput degradation
• Need to select or tune a Fabric Spark resource profile
• VOrder vs. Optimized Write decision-making
• Autotune configuration and validation
• Right-sizing Spark pools, node counts, or Fabric capacity SKUs

Prerequisites

• Access to a Microsoft Fabric workspace with Data Engineering enabled
• Contributor or higher role on the workspace
• Familiarity with PySpark or Spark SQL
• PowerShell 7+ (for diagnostic scripts)
• Fabric REST API access token (for API-based diagnostics)

Quick Diagnosis Decision Tree

Start here when a Spark job is slow:

1. Is the job queued or throttled? Check Monitoring Hub for HTTP 430.

   • Yes → See Capacity and Concurrency Tuning
   • No → Continue

2. Did the Spark Advisor flag warnings? Check notebook cell indicators.

   • Data Skew detected → See Data Skew Resolution
   • No warnings → Continue

3. Is a single stage disproportionately slow? Open Spark UI → Stages tab.

   • Yes, shuffle stage → See Shuffle Optimization
   • Yes, scan stage → See File Scan Optimization
   • No → Continue

4. Are executors underutilized? Check Resources tab in monitoring detail.

   • High idle cores → See Pool and Executor Sizing
   • All cores busy → See Partitioning Strategy

5. Is the issue write-related? Check write duration in Spark UI.

   • Yes → See Delta Write Optimization
   • No → See General Spark SQL Tuning

Core Spark Configuration Quick Reference

These are the three settings Fabric Autotune manages automatically. If autotune is disabled, tune them manually:

Setting	Default	Purpose	Tuning Guidance
spark.sql.shuffle.partitions	200	Partition count during joins/aggregations	Set to 2-3x total executor cores for your pool
spark.sql.autoBroadcastJoinThreshold	10 MB	Max table size for broadcast joins	Increase to 100-256 MB for star-schema joins
spark.sql.files.maxPartitionBytes	128 MB	Max bytes per file-read partition	Increase for large sequential scans, decrease for high parallelism

Resource Profiles Quick Reference

Fabric provides predefined profiles that bundle optimized Spark settings:

Profile	Best For	VOrder	Key Characteristics
writeHeavy	ETL, batch ingestion, streaming	Disabled	Default for new workspaces; optimized write throughput
readHeavyForSpark	Interactive Spark queries, analytics	Enabled	Optimized read paths for Spark workloads
readHeavyForPBI	Power BI dashboards, DW queries	Enabled	Optimized for DirectLake and cross-engine reads

Apply a profile at the environment level or override per-session:

# Per-session override example
spark.conf.set("spark.fabric.resource.profile", "readHeavyForSpark")

Autotune Quick Start

Enable autotune to let Fabric automatically optimize shuffle partitions, broadcast thresholds, and partition bytes:

# Enable in a notebook session
spark.conf.set("spark.ms.autotune.enabled", "true")

# Or set in Environment > Spark Properties
# spark.ms.autotune.enabled = true

Requirements: Runtime 1.1 or 1.2 only. Not compatible with high concurrency mode or private endpoints. Needs 20-25 iterations to converge on optimal settings.

Check autotune status after a query:

# View autotune decisions in Spark UI SQL tab
# Status values: QUERY_TUNING_SUCCEED, QUERY_TUNING_DISABLED,
#                QUERY_PATTERN_NOT_MATCH, QUERY_DURATION_TOO_SHORT

Common Error Patterns

Error / Symptom	Root Cause	Quick Fix
HTTP 430: TooManyRequestsForCapacity	All Spark VCores consumed	Cancel idle jobs in Monitoring Hub or upgrade SKU
Stage with 200 tasks, 1 task 100x slower	Data skew on join/group key	Add salting or use broadcast join
OOM on executor	Partition too large or broadcast too big	Increase partitions or lower broadcast threshold
Write takes >60% of total job time	Small files or missing optimization	Enable Optimized Write or run table maintenance
Streaming micro-batch latency increasing	Checkpoint overhead or partition mismatch	Tune trigger interval and Event Hub partitions

Step-by-Step Workflows

For detailed procedures, see the reference guides:

• Spark Configuration Tuning — Shuffle, broadcast, skew, pool sizing, capacity planning
• Monitoring and Diagnostics — Spark UI navigation, Monitoring Hub, APIs, log analysis
• Delta Lake Optimization — VOrder, Optimized Write, table maintenance, partitioning, streaming

Available Scripts

Run the Fabric Spark diagnostics script to collect Spark application metrics via the Fabric REST API:

./scripts/Get-FabricSparkDiagnostics.ps1 -WorkspaceId "<guid>" -Token "<bearer-token>"

Available Templates

Use the performance analysis notebook template as a starting point for in-session diagnostics:

# Paste into a Fabric notebook to analyze current session performance

remediate

Problem	Check	Resolution
Autotune not activating	Runtime version, HC mode, private endpoint	Switch to Runtime 1.1/1.2, disable HC mode
Resource profile not applying	Environment publish status	Republish environment after profile change
Pool autoscale not scaling up	Capacity SKU limits	Verify VCore headroom in Capacity Metrics app
Table maintenance job stuck	Concurrent maintenance on same table	Wait for previous job or cancel via API
Notebook cell shows no Spark Advisor	Runtime < 3.4	Upgrade to Spark 3.4+ runtime

References

• Microsoft Fabric Spark Compute Overview
• Autotune for Apache Spark in Fabric
• Concurrency Limits and Queueing
• Spark Monitoring Overview
• Delta Lake Table Optimization and V-Order
• Resource Profile Configurations