--- name: fabric-spark-compute-perf-remediate-3fcb23cc5984 description: >- Diagnose and resolve performance issues in Microsoft Fabric Delta Lake and Spark workloads. Use when remediate slow Spark notebooks, long-running Spark jobs, Delta table query degradation, small file problems, VOrder configuration, resource profile tuning, autotune configuration, capacity throttling (HTTP 430), session startup delays, executor memory errors, skewed partitions, or streaming ingestion bottlenecks. Covers Lakehouse, SQL analytics endpoint, Spark compute, environment configuration, Delta OPTIMIZE, VACUUM, Z-Order, bin-compaction, and Fabric capacity SKU sizing. license: Complete terms in LICENSE.txt --- # Fabric Delta Lake & Spark Performance remediate Systematic remediate toolkit for diagnosing and resolving performance issues across Microsoft Fabric Spark compute, Delta Lake tables, and Lakehouse workloads. ## When to Use This Skill - Spark notebooks or jobs running slower than expected - Delta table queries degrading over time (small file problem) - Capacity throttling errors (HTTP 430 / TooManyRequestsForCapacity) - Session startup taking minutes instead of seconds - Out-of-memory errors on executors or drivers - Streaming ingestion falling behind or producing small files - VOrder or resource profile misconfiguration - Deciding between read-heavy vs. write-heavy workload profiles - Planning table maintenance (OPTIMIZE, VACUUM, Z-Order) - Tuning autoscaling, dynamic executor allocation, or autotune ## Prerequisites - Access to a Microsoft Fabric workspace with Data Engineering enabled - Contributor or higher role on the workspace - Fabric capacity (F2+) or Trial capacity - PowerShell 7+ for diagnostic scripts - PySpark for notebook-based diagnostics ## Triage: Identify the Symptom Category Start by matching the observed symptom to one of these categories, then follow the linked reference for the detailed workflow. | Symptom | Category | Reference | | ---------------------------------------- | --------------------- | ---------------------------------------------------------- | | Notebook takes minutes to start | Session Startup | [session-startup.md](./references/session-startup.md) | | Queries slow, getting worse over time | Delta Table Health | [delta-table-health.md](./references/delta-table-health.md) | | HTTP 430 or "TooManyRequestsForCapacity" | Capacity Throttling | [capacity-throttling.md](./references/capacity-throttling.md) | | OOM errors, executor lost, task failed | Memory & Compute | [memory-compute.md](./references/memory-compute.md) | | Streaming lag, checkpoint delays | Streaming Performance | [streaming-perf.md](./references/streaming-perf.md) | | Power BI reports slow against Lakehouse | Read Optimization | [read-optimization.md](./references/read-optimization.md) | | Spark job slow but no errors | General Tuning | [general-tuning.md](./references/general-tuning.md) | ## Quick Diagnostic Checklist Run through these checks before diving into detailed remediate. ### 1. Check Capacity and Concurrency Verify your Fabric capacity SKU and current utilization. Each SKU has a queue limit for concurrent Spark jobs: | SKU | Spark VCores | Queue Limit | | ----- | ------------ | ----------- | | F2 | 4 | 4 | | F8 | 16 | 8 | | F16 | 32 | 16 | | F32 | 64 | 32 | | F64 | 128 | 64 | | F128 | 256 | 128 | | Trial | 128 | No queueing | If hitting queue limits, cancel idle jobs via the Monitoring Hub, scale the SKU, or stagger job schedules. ### 2. Check Resource Profile New Fabric workspaces default to the `writeHeavy` profile. Verify the active profile matches your workload: | Profile | Best For | VOrder Default | | ----------------- | -------------------------------- | -------------- | | writeHeavy | ETL, ingestion, batch writes | Disabled | | readHeavyForSpark | Interactive Spark queries, EDA | Enabled | | readHeavyForPBI | Power BI Direct Lake, dashboards | Enabled | Mismatch is one of the most common performance issues. Read-heavy workloads on the writeHeavy profile miss VOrder optimization entirely. ### 3. Check Delta Table Health Run the [diagnostic script](./scripts/Diagnose-DeltaTableHealth.ps1) or execute in a notebook: ```python # Quick Delta table health check from delta.tables import DeltaTable dt = DeltaTable.forName(spark, "your_table_name") history = dt.history() history.select("version", "operation", "operationMetrics").show(20, truncate=False) ``` Look for: high version counts without OPTIMIZE, many small ADD operations, and missing VACUUM runs. ### 4. Check Environment Configuration Verify Spark compute settings in the attached environment: ```python # Print active Spark configuration for item in spark.sparkContext.getConf().getAll(): print(f"{item[0]} = {item[1]}") ``` Key properties to verify: - `spark.sql.parquet.vorder.default` — true for read workloads - `spark.microsoft.delta.optimizeWrite.enabled` — true to reduce small files - `spark.ms.autotune.enabled` — true to enable ML-based query tuning - `spark.sql.adaptive.enabled` — true for adaptive query execution ### 5. Check Session Startup Time Fabric Spark session startup depends on pool configuration: | Scenario | Expected Startup | | ------------------------------------- | ------------------------- | | Default settings, no custom libraries | 5–10 seconds | | Default + library dependencies | 5–10 sec + 30 sec–5 min | | High regional traffic | 2–5 minutes | | Private Links or Managed VNets | 2–5 minutes | | High traffic + libraries + networking | 2–5 min + 30 sec–5 min | If consistently slow, check whether Private Links or Managed VNets are enabled (these bypass Starter Pools). ## Key Configuration Properties ### VOrder (Read Optimization) VOrder applies a special sort order to Parquet files at write time that dramatically improves read performance for Power BI Direct Lake and SQL analytics endpoint queries. Enable: `spark.sql.parquet.vorder.default = true` Trade-off: Write operations take slightly longer. Enable only when read performance matters more than write throughput. ### Native Execution Engine Vectorized C++ engine that runs Spark SQL and DataFrame operations significantly faster than the default JVM engine. Enable: `spark.fabric.nativeExecution.enabled = true` Compatible with Fabric Runtime 1.3+. Not all operations supported; the engine falls back to JVM for unsupported operations transparently. ### Autotune ML-based automatic Spark configuration tuning. Builds a model per query pattern and optimizes settings iteratively over 20–25 runs. Enable: `spark.ms.autotune.enabled = true` Requirements: Fabric Runtime 1.1 or 1.2 only. Incompatible with high concurrency mode and private endpoints. Targets queries running 15+ seconds. ### Optimized Write Merges or splits partitions before writing to maximize file sizes and reduce small files. Enable: `spark.microsoft.delta.optimizeWrite.enabled = true` Trade-off: Adds a full shuffle before writes. Beneficial for append-heavy workloads; may degrade performance if data is already well-partitioned. ## Available Scripts | Script | Purpose | | --------------------------------------------------------------------- | --------------------------------------------- | | [Diagnose-DeltaTableHealth.ps1](./scripts/Diagnose-DeltaTableHealth.ps1) | Assess Delta table health via Fabric REST API | | [Get-SparkCapacityStatus.ps1](./scripts/Get-SparkCapacityStatus.ps1) | Check capacity utilization and queue status | | [Invoke-TableMaintenance.ps1](./scripts/Invoke-TableMaintenance.ps1) | Run OPTIMIZE + VACUUM via REST API | ## Available Templates | Template | Purpose | | ------------------------------------------------------------------- | ---------------------------------------------- | | [perf-diagnostic-notebook.py](./templates/perf-diagnostic-notebook.py) | PySpark notebook for comprehensive diagnostics | ## remediate | Problem | Likely Cause | Quick Fix | | ------------------------ | ------------------------------------ | ---------------------------------------------- | | Queries slow, no errors | Small files, missing VOrder | Run OPTIMIZE with V-Order | | Write jobs slow | readHeavy profile on ETL workload | Switch to writeHeavy profile | | HTTP 430 errors | Queue limit exceeded | Scale SKU or stagger jobs | | OOM on executor | Skewed partition or undersized nodes | Increase executor memory, check for skew | | Session takes 5+ minutes | Private Link/VNet or library install | Pre-install libraries, check networking | | Autotune not activating | Wrong runtime or short queries | Verify Runtime 1.1/1.2, queries must run 15s+ | | Streaming falling behind | Too-frequent microbatches | Increase trigger interval, use Optimized Write | ## References - [Session Startup remediate](./references/session-startup.md) - [Delta Table Health & Maintenance](./references/delta-table-health.md) - [Capacity Throttling & SKU Sizing](./references/capacity-throttling.md) - [Memory & Compute Tuning](./references/memory-compute.md) - [Streaming Performance Optimization](./references/streaming-perf.md) - [Read Optimization (VOrder, Resource Profiles)](./references/read-optimization.md) - [General Spark Tuning Guide](./references/general-tuning.md) - [Microsoft Fabric Spark Compute Documentation](https://learn.microsoft.com/fabric/data-engineering/spark-compute) - [Delta Lake Table Optimization and V-Order](https://learn.microsoft.com/fabric/data-engineering/delta-optimization-and-v-order) - [Lakehouse Table Maintenance](https://learn.microsoft.com/fabric/data-engineering/lakehouse-table-maintenance) --- Source attribution: PatrickGallucci Original project: https://github.com/PatrickGallucci/fabric-skills License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Dave AI. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Dave AI resource metadata Creator / publisher: PatrickGallucci Original project: https://github.com/PatrickGallucci/fabric-skills Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Development & Engineering, Data & Analytics Roles: Data Engineer, ML / AI Engineer, Data Analyst Specialties: Backend, API Tools: Microsoft Fabric, Pyspark, Powershell Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: 42cf7765117d8ab62cce4fdcbefff6ed68e2b3c19e093c22792c2c301874ea4b