Redis on Spark:任务不可序列化

Question

我们在Spark上使用Redis来缓存我们的键值对.这是代码:

import com.redis.RedisClient
val r = new RedisClient("192.168.1.101", 6379)
val perhit = perhitFile.map(x => {
    val arr = x.split(" ")
    val readId = arr(0).toInt
    val refId = arr(1).toInt
    val start = arr(2).toInt
    val end = arr(3).toInt
    val refStr = r.hmget("refStr", refId).get(refId).split(",")(1)
    val readStr = r.hmget("readStr", readId).get(readId)
    val realend = if(end > refStr.length - 1) refStr.length - 1 else end
    val refOneStr = refStr.substring(start, realend)
      (readStr, refOneStr, refId, start, realend, readId)
 })

但编译器给了我这样的反馈:

Exception in thread "main" org.apache.spark.SparkException: Task not serializable
    at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:166)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:1242)
    at org.apache.spark.rdd.RDD.map(RDD.scala:270)
    at com.ynu.App$.main(App.scala:511)
    at com.ynu.App.main(App.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:328)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.io.NotSerializableException: com.redis.RedisClient
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1183)
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
    at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
    at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42)
    at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:73)
    at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:164)
    ... 12 more

有人可以告诉我如何序列化Redis的数据.谢谢你们.

Answer 1

在Spark中,RDDs 上的函数(如此map处)被序列化并发送给执行程序进行处理.这意味着这些操作中包含的所有元素都应该是可序列化的.

此处的Redis连接不可序列化,因为它打开与目标DB的TCP连接,这些连接绑定到创建它的机器.

解决方案是在本地执行上下文中在执行程序上创建这些连接.几乎没有办法做到这一点.想到的两个是:

• rdd.mapPartitions:允许您一次处理整个分区,因此分摊创建连接的成本)
• 单例连接管理器:每个执行程序创建一次连接

mapPartitions 它更容易,因为它只需要对程序结构进行一些小改动:

val perhit = perhitFile.mapPartitions{partition => 
    val r = new RedisClient("192.168.1.101", 6379) // create the connection in the context of the mapPartition operation
    val res = partition.map{ x =>
        ...
        val refStr = r.hmget(...) // use r to process the local data
    }
    r.close // take care of resources
    res
}

单例连接管理器可以使用一个对象来建模,该对象包含对连接的惰性引用(注意:可变引用也可以工作).

object RedisConnection extends Serializable {
   lazy val conn: RedisClient = new RedisClient("192.168.1.101", 6379)
}

然后,此对象可用于为每个工作JVM实例化1个连接,并用作Serializable操作闭包中的对象.

val perhit = perhitFile.map{x => 
    val param = f(x)
    val refStr = RedisConnection.conn.hmget(...) // use RedisConnection to get a connection to the local data
    }
}

使用单例对象的优点是开销较少,因为JVM只创建一次连接(而不是每个RDD分区一次)

还有一些缺点:

• 清理连接很棘手(关闭挂钩/定时器)
• 必须确保共享资源的线程安全性

(*)代码用于说明目的.未编译或测试.