Spark程序排错系列（System memory * must be at least *）

1.1 报错背景

本地ideal研发环境（windows）运行spark程序调试，报错如下：

21/03/29 12:28:36 ERROR SparkContext: Error initializing SparkContext. 
java.lang.IllegalArgumentException: System memory 259522560 must be at least 471859200. Please increase heap size using the --driver-memory option or spark.driver.memory in Spark configuration. 

1.2 原因分析

从报错内容上看SparkContext没有初始化成功，错误示内存资源不够，需要至少471859200。

1.2.1 spark本地运行机制

Spark在本地运行（local）原理是使用线程模拟进程，所以整个集群启动在一个进程中。

1.2.2 源码分析

上源码(Spark 3.1.0，org.apache.spark.memory.UnifiedMemoryManager)：

object UnifiedMemoryManager {

  // Set aside a fixed amount of memory for non-storage, non-execution purposes.
  // This serves a function similar to `spark.memory.fraction`, but guarantees that we reserve
  // sufficient memory for the system even for small heaps. E.g. if we have a 1GB JVM, then
  // the memory used for execution and storage will be (1024 - 300) * 0.6 = 434MB by default.
  private val RESERVED_SYSTEM_MEMORY_BYTES = 300 * 1024 * 1024

  def apply(conf: SparkConf, numCores: Int): UnifiedMemoryManager = {
    val maxMemory = getMaxMemory(conf)
    new UnifiedMemoryManager(
      conf,
      maxHeapMemory = maxMemory,
      onHeapStorageRegionSize =
        (maxMemory * conf.get(config.MEMORY_STORAGE_FRACTION)).toLong,
      numCores = numCores)
  }

  /**
   * Return the total amount of memory shared between execution and storage, in bytes.
   */
  private def getMaxMemory(conf: SparkConf): Long = {
    val systemMemory = conf.get(TEST_MEMORY)
    val reservedMemory = conf.getLong(TEST_RESERVED_MEMORY.key,
      if (conf.contains(IS_TESTING)) 0 else RESERVED_SYSTEM_MEMORY_BYTES)
    val minSystemMemory = (reservedMemory * 1.5).ceil.toLong
    if (systemMemory < minSystemMemory) {
      throw new IllegalArgumentException(s"System memory $systemMemory must " +
        s"be at least $minSystemMemory. Please increase heap size using the --driver-memory " +
        s"option or ${config.DRIVER_MEMORY.key} in Spark configuration.")
    }
    // SPARK-12759 Check executor memory to fail fast if memory is insufficient
    if (conf.contains(config.EXECUTOR_MEMORY)) {
      val executorMemory = conf.getSizeAsBytes(config.EXECUTOR_MEMORY.key)
      if (executorMemory < minSystemMemory) {
        throw new IllegalArgumentException(s"Executor memory $executorMemory must be at least " +
          s"$minSystemMemory. Please increase executor memory using the " +
          s"--executor-memory option or ${config.EXECUTOR_MEMORY.key} in Spark configuration.")
      }
    }
    val usableMemory = systemMemory - reservedMemory
    val memoryFraction = conf.get(config.MEMORY_FRACTION)
    (usableMemory * memoryFraction).toLong
  }
} 

主要功能点：

• systemMemory变量定义了系统内存资源。

    package org.apache.spark.internal.config
    
    val TEST_MEMORY = ConfigBuilder("spark.testing.memory")
      .version("1.6.0")
      .longConf
      .createWithDefault(Runtime.getRuntime.maxMemory)
  

  其中默认值为Runtime.getRuntime.maxMemory，这个值为java虚拟机（JVM）能够从操作系统获取的最大内存资源，如果启动虚拟机时候没有配置-Xmx参数，那么就是256M=256*1024*1024 beytes（机器物理内存大于1G）。

• reservedMemory变量为系统保留内存资源。优先使用TEST_RESERVED_MEMORY的值，默认值是个表达式，如果IS_TESTING=True（测试模式）则值为0，否则为：RESERVED_SYSTEM_MEMORY_BYTES=300M。

    val TEST_RESERVED_MEMORY = ConfigBuilder("spark.testing.reservedMemory")
      .version("1.6.0")
      .longConf
      .createOptional
    val IS_TESTING = ConfigBuilder("spark.testing")
      .version("1.0.1")
      .booleanConf
      .createOptional
  
  //定义
  private val RESERVED_SYSTEM_MEMORY_BYTES = 300 * 1024 * 1024
  

• minSystemMemory变量为系统最小内存资源。定义为reservedMemory的1.5倍。

从报错信息看，明显是触发下面的代码逻辑：

if (systemMemory < minSystemMemory) {
      throw new IllegalArgumentException(s"System memory $systemMemory must " +
        s"be at least $minSystemMemory. Please increase heap size using the --driver-memory " +
        s"option or ${config.DRIVER_MEMORY.key} in Spark configuration.")
    }

spark应用中未进行相关参数配置，reservedMemory值为300M，那么minSystemMemory值为450M，而应用程序为设置JVM参数，systemMemory默认是256M。显然触发systemMemory < minSystemMemory条件。

1.3 修复方法

根据上面的源码分析，我们有下面的修复方法。

1.3.1 生产环境

生产代码按照源码分析要求jvm虚拟机至少是450M以上的内存。

• 配置-Xmx参数，使得其远大于450M。

1.3.2 测试调试

• 配置spark.testing.memory参数

  这时候systemMemory=spark.testing.memory参数的值，例如：

  val sparkConf = new SparkConf()
          .set("spark.testing.memory","2147480000")
  //2147480000=2G
  

• 开启测试模式（IS_TESTING=True）

  val sparkConf = new SparkConf()
          .set("spark.testing","true")
  

  这时候minSystemMemory即为0。这时候异常条件不会触发。

• 指定spark.testing.reservedMemory参数的值（尽可能的小）

  val sparkConf = new SparkConf()
          .set("spark.testing.reservedMemory","0")
  

  上面的配置下，minSystemMemory的值也为0。

1.4 总结

Spark运行对内存资源进行了门槛限制，如果降低这个限制必须要特意显示配置测试相关的指标配置。