我们可以通过设置spark.executor.memory参数来指定 Executor 的内存大小。以下是一个示例: valconf=newSparkConf().setAppName("Increase Task Number").setMaster("local[4]").set("spark.executor.memory","2g")// 设置每个Executor的内存为2GBvalsc=newSparkContext(conf)... 1. 2. 3. 4. 5. 6. 7....
execution memory refers that used for computation in shuffles, joins, sorts and aggregations, while storage memory refers to that used for caching and propagating internal data across the cluster. There exists one MemoryManager per JVM.
比如Collect. 某个Stage中Executor端发回的所有数据量不能超过spark.driver.maxResultSize,默认1g. 如果用户增加该值, 请对应增加2delta increase到Driver Memory, resultSize该值只是数据序列化之后的Size, 如果是Collect的操作会将这些数据反序列化收集, 此时真正所需内存需要膨胀2-5倍, 甚至10倍. 解决思路: 2.1....
if (executorMemory < MIN_MEMORY_BYTES) { throw new IllegalArgumentException(s"Executor memory $executorMemory must be at least " + s"$MIN_MEMORY_BYTES. Please increase executor memory using the " + s"--executor-memory option or spark.executor.memory in Spark configuration.") } } val memor...
s"$minSystemMemory. Please increase executor memory using the " + s"--executor-memory option or spark.executor.memory in Spark configuration.") } } val usableMemory = systemMemory - reservedMemory val memoryFraction = conf.getDouble("spark.memory.fraction", 0.6) ...
(s"Executor memory $executorMemory must be at least "+s"$MIN_MEMORY_BYTES. Please increase executor memory using the "+s"--executor-memory option or spark.executor.memory in Spark configuration.")}}val memoryFraction=conf.getDouble("spark.shuffle.memoryFraction",0.2)val safetyFraction=conf.get...
在利用Spark开发各类计算任务时,Executor内存的配置永远是重中之重,因此了解Spark的内存管理机制是非常有益的。 在1.6版本之前,Spark只有一种内存管理机制,即静态内存管理(StaticMemoryManager),1.6版本以后又引入了新的统一内存管理(UnifiedMemoryManager)。下面分别来看一下这两种机制的细节。
主要针对Spark运行过程中各个使用资源的地方,通过调节资源相关参数,来优化资源使用的效率,从而提升Spark作业的执行性能。例如:num-executors、executor-memory、executor-cores等。 Shuffle相关参数调优 主要针对spark运行过程中的shuffle,通过调节参数,提高shuffle的执行效率,从而提升spark作业的执行性能。例如:spark.shuffle.mem...
“ Required executor memory (235520+23552 MB) is above the max threshold (241664 MB) of this cluster! Please increase the value of ‘yarn.scheduler.maximum-allocation-mb’. 或者干脆就没有这样的错误,但是依然有因为内存不足导致的问题,有的会有警告,比如这个: ...
Required executor memory (235520+23552 MB) is above the max threshold (241664 MB) of this cluster! Please increase the value of ‘yarn.scheduler.maximum-allocation-mb’. 或者干脆就没有这样的错误,但是依然有因为内存不足导致的问题,有的会有警告,比如这个: ...