spark.shuffle.spill
时间: 2023-11-18 10:20:30 浏览: 36
`spark.shuffle.spill` is a configuration parameter in Apache Spark that governs the behavior of shuffling data between nodes in a cluster. When a Spark job involves a shuffle operation (such as a group by, join, or sort), data is moved between nodes to perform the operation. If the amount of data to be shuffled is larger than the available memory on a node, the excess data must be spilled to disk to avoid memory errors.
The `spark.shuffle.spill` parameter controls the size of the in-memory buffer used to hold shuffle data before it is spilled to disk. The default value is 32 KB, but this can be increased or decreased depending on the memory availability and performance requirements of the job. Increasing the buffer size can reduce the number of spills to disk, which can improve performance, but it also increases memory usage. Decreasing the buffer size can reduce memory usage, but it may increase the number of spills and reduce performance.
In summary, `spark.shuffle.spill` is an important configuration parameter that can have a significant impact on the performance and memory usage of Spark jobs involving shuffle operations.