Overview
This lecture explains shuffling and shuffle partitions in Apache Spark, discussing their significance, impact on cluster utilization, and how to tune them for optimal performance.
Shuffling in Spark
- Shuffling occurs during wide transformations (e.g., groupBy, join) in Spark.
- It involves collecting related data scattered across different nodes into the same partition.
- Example: To find total sales per store, Spark groups sales data by store ID using shuffling.
Shuffle Partitions
- After shuffling, data for each grouping key resides in its own partition (shuffle partition).
- The default number of shuffle partitions in Spark is 200.
- One partition is processed by one core; unused cores remain idle if there are fewer partitions than cores.
Importance of Tuning Shuffle Partitions
- Too few shuffle partitions can cause cluster underutilization and slow job completion.
- Too many shuffle partitions with little data can also lead to inefficiency.
Scenario 1: Large Data per Shuffle Partition
- Given: 5 executors × 4 cores = 20 cores, 300 GB shuffled, 200 partitions (default).
- Each partition handles 1.5 GB (300 GB / 200), which is too large (optimal: 1–200 MB).
- Adjust number of partitions to 1,500 (300 GB / 200 MB) to ensure each core handles 200 MB.
Scenario 2: Small Data per Shuffle Partition
- Given: 3 executors × 4 cores = 12 cores, 50 MB shuffled, 200 partitions.
- Each partition handles 250 KB (50 MB / 200), which is too small.
- Option 1: Reduce partitions to 5 (50 MB / 10 MB) for optimal size, but some cores idle.
- Option 2: Set partitions to 12 (50 MB / 12) so each core has ~4.2 MB, utilizing all cores and speeding up processing.
Additional Considerations
- Tuning shuffle partitions may not fix all slow jobs; data skew (uneven distribution) can cause bottlenecks.
- Data skew solutions include enabling Adaptive Query Execution (AQE) or using salting techniques.
Key Terms & Definitions
- Shuffling — Moving data across nodes to regroup related records onto the same partition.
- Shuffle Partition — Output partition after shuffling, each containing data for a specific key/group.
- Wide Transformation — Operation (like groupBy or join) requiring data from multiple partitions.
- Data Skew — Imbalanced distribution causing some partitions to have much more data than others.
- AQE (Adaptive Query Execution) — Spark feature that dynamically optimizes query plans based on runtime statistics.
Action Items / Next Steps
- Review your Spark jobs to determine if shuffle partition settings are optimal.
- Adjust number of shuffle partitions based on total data size and cores available.
- Investigate AQE or salting if experiencing data skew issues.
- Practice calculating optimal number of partitions with different data sizes and cluster configurations.