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€49
Spark Performance Masterclass
The definitive guide to squeezing every drop of performance from Apache Spark on Databricks. 10 deep-dive chapters, runnable benchmark suite, workload-specific configurations, Spark UI visual guide, and a 30+ scenario troubleshooting runbook.
Apache SparkDatabricksDelta LakePhotonPython
📁 File Structure 25 files
spark-performance-masterclass/
├── README.md
├── LICENSE
│
├── 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
│
├── benchmarks/
│ ├── benchmark_runner.py
│ ├── benchmark_configs.yaml
│ ├── benchmarks/
│ │ ├── join_benchmarks.py
│ │ ├── io_benchmarks.py
│ │ ├── aggregation_benchmarks.py
│ │ └── delta_benchmarks.py
│ └── analysis/
│ └── visualize_results.py
│
├── configs/
│ ├── small_workload.py
│ ├── medium_workload.py
│ ├── large_workload.py
│ ├── streaming_workload.py
│ └── config_validator.py
│
├── spark_ui_guide/
│ ├── stages_tab.md
│ ├── sql_tab.md
│ ├── storage_tab.md
│ └── common_patterns.md
│
└── cheatsheets/
├── spark_config_cheatsheet.md
├── delta_optimization_cheatsheet.md
└── troubleshooting_flowchart.md
📖 Documentation Preview README excerpt
Why This Masterclass Exists
Most Spark jobs run 10-100x slower than they need to. This masterclass covers everything from execution model fundamentals to advanced Photon engine optimization, with real configurations, runnable benchmarks, and a 30+ scenario troubleshooting runbook.
What's Inside
- 10 Deep-Dive Chapters — Execution model, AQE, shuffles, memory, Delta Lake, Photon, joins, I/O, streaming, troubleshooting
- Runnable Benchmark Suite — Framework + 4 benchmark modules with config-driven tests and result visualization
- Workload-Specific Configs — Tuned settings for small (<100GB), medium (100GB-1TB), large (1TB+), and streaming workloads
- Spark UI Visual Guide — How to read Stages, SQL, Storage tabs and identify common problem patterns
- 30+ Troubleshooting Scenarios — Symptom, diagnosis, root cause, fix, and prevention for real-world issues
- 3 Cheatsheets — Spark config, Delta optimization, troubleshooting flowchart
The Performance Triangle
Every Spark issue falls into Compute (CPU-bound), Memory (spill, OOM, GC), or I/O (shuffle, storage, small files). This guide covers all three systematically.
📄 Code Sample .py preview
configs/small_workload.py
"""Spark Configuration Profile: Small Workloads (< 100 GB).
Optimized for development, testing, and small-scale ETL workloads
that process less than 100 GB of data. Prioritizes fast startup
and minimal resource usage.
Designed for clusters with 2-4 workers, 4-8 cores each.
Copyright (c) 2026 DataStack Pro. MIT License.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from pyspark.sql import SparkSession
SPARK_CONFIG: dict[str, str] = {
# ── Adaptive Query Execution ──────────────────────
"spark.sql.adaptive.enabled": "true",
"spark.sql.adaptive.coalescePartitions.enabled": "true",
"spark.sql.adaptive.coalescePartitions.initialPartitionNum": "200",
"spark.sql.adaptive.coalescePartitions.minPartitionSize": "1048576",
"spark.sql.adaptive.skewJoin.enabled": "true",
# ── Shuffle Settings ──────────────────────────────
"spark.sql.shuffle.partitions": "50",
... remaining config entries in full product