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Spark Performance Masterclass

$49

Definitive guide to optimizing Apache Spark performance on Databricks with 25+ patterns.

📁 37 files
MarkdownYAMLPythonDatabricksPySparkSparkDelta Lake

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📁 File Structure 37 files

spark-performance-masterclass/ ├── LICENSE ├── README.md ├── benchmarks/ │ ├── README.md │ ├── analysis/ │ │ └── visualize_results.py │ ├── benchmark_configs.yaml │ ├── benchmark_runner.py │ └── benchmarks/ │ ├── __init__.py │ ├── aggregation_benchmarks.py │ ├── delta_benchmarks.py │ ├── io_benchmarks.py │ └── join_benchmarks.py ├── cheatsheets/ │ ├── delta_optimization_cheatsheet.md │ ├── spark_config_cheatsheet.md │ └── troubleshooting_flowchart.md ├── configs/ │ ├── README.md │ ├── config_validator.py │ ├── large_workload.py │ ├── medium_workload.py │ ├── small_workload.py │ └── streaming_workload.py ├── free-sample.zip ├── guide/ │ ├── 01_spark_execution_model.md │ ├── 02_aqe_deep_dive.md │ ├── 03_shuffle_optimization.md │ ├── 04_memory_tuning.md │ ├── 05_delta_lake_optimization.md │ ├── 06_photon_engine.md │ ├── 07_join_strategies.md │ ├── 08_io_optimization.md │ ├── 09_streaming_performance.md │ └── 10_troubleshooting_runbook.md ├── index.html └── spark_ui_guide/ ├── README.md ├── common_patterns.md ├── sql_tab.md ├── stages_tab.md └── storage_tab.md

📖 Documentation Preview README excerpt

Spark Performance Masterclass

The definitive guide to squeezing every drop of performance from Apache Spark on Databricks.

[![Price](https://img.shields.io/badge/Price-%2459-blue)](https://datastack.pro)

[![Spark](https://img.shields.io/badge/Apache_Spark-3.4%2B-orange)](https://spark.apache.org)

[![Databricks](https://img.shields.io/badge/Databricks-Runtime_14%2B-red)](https://databricks.com)

[![Delta Lake](https://img.shields.io/badge/Delta_Lake-3.0%2B-00ADD8)](https://delta.io)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

Why This Masterclass Exists

Most Spark jobs run 10-100x slower than they need to. The typical data engineer writes

PySpark that works, ships it, and moves on. Six months later, the pipeline that processed

50GB now handles 500GB and takes 4 hours instead of 20 minutes. Cluster costs balloon.

SLAs slip. The team scrambles.

This masterclass is the playbook I wish I had when I started tuning Spark at scale. It

covers everything from the execution model fundamentals to advanced Photon engine

optimization, with **real configurations, runnable benchmarks, and a 30+ scenario

troubleshooting runbook** that you can use the moment a pipeline starts misbehaving.

What Makes This Different

FeatureBlog PostsSpark DocsThis Masterclass
Explains why settings matterSometimesRarelyAlways
Provides tested config valuesGenericReference onlyWorkload-specific
Runnable benchmark suiteNeverNeverIncluded
Before/after metricsRarelyNeverEvery chapter
Troubleshooting runbookScatteredNone30+ scenarios
Databricks-specific guidanceVariesN/ADeep coverage
Photon engine analysisRareMinimalFull chapter
Spark UI reading guideBasicBasicVisual patterns

Who This Is For

  • Data Engineers tuning production Spark pipelines on Databricks
  • Platform Engineers configuring clusters and setting org-wide defaults
  • Data Architects designing performant lakehouse architectures
  • ML Engineers who need their feature pipelines to run faster
  • Anyone who has stared at the Spark UI wondering why a stage took 45 minutes

Prerequisites

  • Working knowledge of PySpark or Spark SQL
  • Access to a Databricks workspace (Community Edition works for small benchmarks)
  • Familiarity with Delta Lake basics (we cover optimization, not fundamentals)

Table of Contents

Guide Chapters

| # | Chapter | Key Topics | Lines |

... continues with setup instructions, usage examples, and more.

📄 Code Sample .py preview

benchmarks/benchmark_runner.py """Spark Performance Benchmark Runner. A framework for measuring Spark job performance metrics including execution time, stage count, shuffle bytes, spill bytes, and task count. Designed to run on Databricks clusters with DBR 14+. Copyright (c) 2026 DataStack Pro. MIT License. """ from __future__ import annotations import json import time from dataclasses import dataclass, field, asdict from datetime import datetime from typing import Any, Callable from pyspark.sql import SparkSession, DataFrame @dataclass class BenchmarkResult: """Container for benchmark execution results. Attributes: name: Benchmark name identifier. category: Benchmark category (join, io, aggregation, delta). description: Human-readable description of what was benchmarked. execution_time_seconds: Wall-clock execution time. num_stages: Number of Spark stages in the job. num_tasks: Total number of tasks across all stages. shuffle_read_bytes: Total shuffle read bytes across all stages. shuffle_write_bytes: Total shuffle write bytes across all stages. spill_memory_bytes: Total memory spill bytes. spill_disk_bytes: Total disk spill bytes. input_rows: Number of input rows processed. output_rows: Number of output rows produced. config: Spark configuration used for this benchmark. metadata: Additional metadata about the benchmark run. timestamp: ISO format timestamp of when the benchmark ran. """ name: str category: str description: str execution_time_seconds: float = 0.0 num_stages: int = 0 num_tasks: int = 0 shuffle_read_bytes: int = 0 shuffle_write_bytes: int = 0 # ... 447 more lines ...
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