1: Introduction to Spark Performance Optimization
1.1 Overview of Spark’s performance challenges
1.2 Understanding performance trade-offs in distributed systems
1.3 Introduction to optimization principles and best practices

2: Deep Dive into Spark Architecture
2.1 Spark’s execution model: Jobs, stages, and tasks
2.2 Directed Acyclic Graph (DAG) and job scheduling
2.3 Spark’s Catalyst optimizer and Tungsten execution engine
2.4 Understanding Spark’s internal memory management

3: Data Partitioning Strategies
3.1 What is partitioning, and why does it matter?
3.2 Controlling and optimizing data partitioning in Spark
3.3 Balancing load with optimal partition sizes
3.4 Repartitioning vs. coalescing: When and how to use them

4: Managing Data Shuffling and Joins
4.1 What is shuffling, and how does it impact performance?
4.2 Reducing data shuffling in transformations (reduceByKey, groupByKey, etc.)
4.3 Optimizing joins in Spark: Broadcast joins, shuffle joins, and skew handling
4.4 Practical examples of optimizing shuffles and joins in Java

5: Memory Management and Caching in Spark
5.1 Understanding Spark’s memory model: Execution vs. storage memory
5.2 Leveraging Spark’s caching mechanisms for performance improvements
5.3 Using in-memory storage for RDDs, DataFrames, and Datasets
5.4 Techniques to prevent out-of-memory errors

6: Efficient Use of Spark RDDs, DataFrames, and Datasets
6.1 RDD vs. DataFrame vs. Dataset: When to use each API(Ref: Talend for Big Data: Integrating and Processing Large Datasets)
6.2 Optimizing transformations and actions for better performance
6.3 Avoiding common pitfalls with wide transformations
6.4 Performance implications of using type-safe APIs (Datasets)

7: SparkSQL and Query Optimization
7.1 SparkSQL internals and query execution plan
7.2 Using explain() to analyze query plans and performance
7.3 Optimizing SparkSQL queries for faster execution
7.4 Improving performance with predicate pushdown and partition pruning

8: Tuning Spark Configuration Settings
8.1 Key Spark configuration settings for performance tuning
8.2 Adjusting executor memory, cores, and parallelism
8.3 Configuring Spark’s shuffle and compression settings
8.4 Using dynamic allocation and speculative execution

9: Handling Skewed Data and Large Datasets
9.1 Understanding data skew and its impact on performance
9.2 Techniques to handle skewed data in Spark
9.3 Optimizing performance for large datasets (terabyte-scale)
9.4 Real-world examples of resolving skew-related issues

10: Monitoring, Debugging, and Profiling Spark Jobs
10.1 Monitoring Spark jobs with Spark UI
10.2 Using logs and metrics to diagnose performance bottlenecks
10.3 Profiling Spark jobs with third-party tools (e.g., Ganglia, Datadog)
10.4 Debugging common issues in Spark jobs and clusters

11: Advanced Performance Tuning Techniques
11.1 Advanced optimizations with the Tungsten execution engine
11.2 Optimizing shuffle file I/O and serialization
11.3 Improving performance with custom serialization (Kryo)
11.4 Practical strategies for reducing garbage collection overhead

12: Case Studies and Hands-On Optimization Projects
12.1 Case studies of real-world Spark performance tuning scenarios
12.2 Hands-on project: Optimizing a Spark job for large-scale data processing
12.3 Troubleshooting common performance issues in Spark jobs