Spark常见错误汇总

1. Spark Driver cannot bind on port0, SparkContext initialized failed

如果是通过spark-submit等命令行提交的任务,在spark的conf目录下,修改spark-env.sh(如果没有此文件,从spark-env-template.sh拷贝一份),添加 export SPARK_LOCAL_IP=127.0.0.1即可

如果是通过Idea等IDE来提交任务,调试,运行的时候,修改/etc/hosts, 添加或修改机器名对应的ip地址,比如 127.0.0.1　　hostname

 2. 连接mesos的时候出现  No credentials provided. Attempting to register without authentication 一直卡住不动

检查mesos的ip是否正确，检查本机的hostname对应的ip地址是否正确，不能是127.0.0.1，client与mesos需要能互相访问，如果有一方不能访问另一方，就会卡住

3. Java.lang.IllegalArgumentException: System memory 468189184 must be at least 4.718592E8. Please use a larger heap size.

通过查看spark源码，发现源码是这么写的：

/**
   * Return the total amount of memory shared between execution and storage, in bytes.
   */
  private def getMaxMemory(conf: SparkConf): Long = {
    val systemMemory = conf.getLong("spark.testing.memory", Runtime.getRuntime.maxMemory)
    val reservedMemory = conf.getLong("spark.testing.reservedMemory",
      if (conf.contains("spark.testing")) 0 else RESERVED_SYSTEM_MEMORY_BYTES)
    val minSystemMemory = reservedMemory * 1.5
    if (systemMemory < minSystemMemory) {
      throw new IllegalArgumentException(s"System memory $systemMemory must " +
        s"be at least $minSystemMemory. Please use a larger heap size.")
    }
    val usableMemory = systemMemory - reservedMemory
    val memoryFraction = conf.getDouble("spark.memory.fraction", 0.75)
    (usableMemory * memoryFraction).toLong
  }

所以，这里主要是val systemMemory = conf.getLong("spark.testing.memory", Runtime.getRuntime.maxMemory)。

conf.getLong()的定义和解释是

getLong(key: String, defaultValue: Long): Long
Get a parameter as a long, falling back to a default if not set

所以，我们应该在conf里设置一下spark.testing.memory和spark.testing.reservedMemory

通过尝试，发现可以有2个地方可以设置

1. 自己的源代码处，可以在conf之后加上：

    val conf = new SparkConf().setAppName("word count")
    conf.set("spark.testing.memory", "2147480000")//后面的值大于512m即可

2. 可以在Eclipse的Run Configuration处，有一栏是Arguments，下面有VMarguments，在下面添加下面一行(值也是只要大于512m即可)

-Dspark.testing.memory=1073741824

其他的参数，也可以动态地在这里设置，比如-Dspark.master=spark://hostname:7077

再运行就不会报这个错误了。