spark.sql.windowExec.buffer.spill.threshold

这是一个Spark SQL的配置参数，用于定义窗口函数在排序时内存缓冲区溢出到磁盘的阈值。当内存缓冲区的数据量超过该阈值时，Spark SQL会将部分数据溢出到磁盘上的临时文件中，以释放内存缓冲区的空间。这个阈值越大，内存缓冲区的空间就越大，但是也会增加溢出到磁盘上的数据量和IO负载。通常情况下，可以根据数据量和可用内存来调整该参数的值，以达到最优的性能和效率。

相关问题

spark.shuffle.spill

`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.

Spark设置 spill 仅 OFF_HEAP

在Spark中，可以通过设置`spark.memory.offHeap.enabled`为`true`来启用Off-Heap内存，并且可以通过设置`spark.memory.offHeap.size`来控制Off-Heap内存的大小。

如果想要设置Spill仅使用Off-Heap内存，可以通过设置以下参数：

spark.memory.offHeap.enabled=true
spark.memory.offHeap.size=XXXX
spark.memory.useLegacyMode=false
spark.memory.offHeap.spill=true

其中，`spark.memory.offHeap.spill`默认为`true`，表示Spill会使用Off-Heap内存。设置为`false`则表示Spill只使用On-Heap内存。另外，需要将`spark.memory.useLegacyMode`设置为`false`，以启用新的Off-Heap内存管理模式。