Data skew is a common performance issue in Apache Spark, primarily affecting operations that involve shuffling, such as joins and aggregations. It occurs when a few keys dominate the dataset, which results in uneven partitions and can significantly slow down query performance. To mitigate this problem, the technique known as salting can be employed. By appending a randomly generated number to the join key, the distribution of data is improved across partitions. This ensures better utilization of worker nodes and enhances performance by eliminating resource contention and reducing the chances of out-of-memory errors during heavy operations.
Data skew in Apache Spark is a performance issue where a few keys dominate the data distribution, leading to uneven partitions and slow queries, especially during operations that require shuffling.
Salting is a practical technique to reduce skew by spreading heavy keys across multiple partitions, facilitating a more uniform data distribution and preventing overload on individual workers.
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