Spark: Shuffling
Shuffling is the process of redistributing data across different partitions
and nodes. It occurs during operations like .join, .groupBy, and .reduceBy.
Shuffling is one of the most resource-intensive operations in Spark.
Consequences of Excessive Shuffling:
- Network Overhead: Shuffling involves moving large amounts of data over the network, which can be time-consuming and lead to bottlenecks.
- Disk I/O: Excessive shuffling can result in significant disk I/O when data is spilled to disk, slowing down the processing.
Handling Shuffling
- Minimize Shuffling Operations: Restructuring Spark jobs to reduce the need for shuffling, such as using map-side joins.
- Tuning Partition Sizes: Adjusting the number of partitions to balance load and reduce unnecessary data movement.
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Custom Partitioners: Using custom partitioners to control the distribution of data and minimize shuffling.