本篇内容主要讲解“Driver容错安全性是什么”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“Driver容错安全性是什么”吧!
从数据层面，ReceivedBlockTracker为整个Spark Streaming应用程序记录元数据信息。
从调度层面，DStreamGraph和JobGenerator是Spark Streaming调度的核心，记录当前调度到哪一进度，和业务有关。
ReceivedBlockTracker在接收到元数据信息后调用addBlock方法，先写入磁盘中，然后在写入内存中。
根据batchTime分配属于当前BatchDuration要处理的数据到timToAllocatedBlocks数据结构中。
Time类的是一个case class，记录时间，重载了操作符，隐式转换，值得借鉴。
case class Time(private val millis: Long) {
def milliseconds: Long = millis
def < (that: Time): Boolean = (this.millis < that.millis)
def <= (that: Time): Boolean = (this.millis <= that.millis)
def > (that: Time): Boolean = (this.millis > that.millis)
def >= (that: Time): Boolean = (this.millis >= that.millis)
def + (that: Duration): Time = new Time(millis + that.milliseconds)
def - (that: Time): Duration = new Duration(millis - that.millis)
def - (that: Duration): Time = new Time(millis - that.milliseconds)
// Java-friendlier versions of the above.
def less(that: Time): Boolean = this < that
def lessEq(that: Time): Boolean = this <= that
def greater(that: Time): Boolean = this > that
def greaterEq(that: Time): Boolean = this >= that
def plus(that: Duration): Time = this + that
def minus(that: Time): Duration = this - that
def minus(that: Duration): Time = this - that
def floor(that: Duration): Time = {
val t = that.milliseconds
new Time((this.millis / t) * t)
}
def floor(that: Duration, zeroTime: Time): Time = {
val t = that.milliseconds
new Time(((this.millis - zeroTime.milliseconds) / t) * t + zeroTime.milliseconds)
}
def isMultipleOf(that: Duration): Boolean =
(this.millis % that.milliseconds == 0)
def min(that: Time): Time = if (this < that) this else that
def max(that: Time): Time = if (this > that) this else that
def until(that: Time, interval: Duration): Seq[Time] = {
(this.milliseconds) until (that.milliseconds) by (interval.milliseconds) map (new Time(_))
}
def to(that: Time, interval: Duration): Seq[Time] = {
(this.milliseconds) to (that.milliseconds) by (interval.milliseconds) map (new Time(_))
}
override def toString: String = (millis.toString + " ms")
}
object Time {
implicit val ordering = Ordering.by((time: Time) => time.millis)
} |
跟踪Time对象，ReceiverTracker的allocateBlocksToBatch方法中的入参batchTime是被JobGenerator的generateJobs方法调用的。
JobGenerator的generateJobs方法是被定时器发送GenerateJobs消息调用的。
GenerateJobs中的时间参数就是nextTime，而nextTime+=period，这个period就是ssc.graph.batchDuration.milliseconds。
nextTime的初始值是在start方法中传入的startTime赋值的，即RecurringTimer的getStartTime方法的返回值，是当前时间period的(整数倍+1)。
Period这个值是我们调用new StreamingContext来构造StreamingContext时传入的Duration值。
ReceivedBlockTracker会清除过期的元数据信息，从HashMap中移除，也是先写入磁盘，然后在写入内存。
元数据的生成，消费和销毁都有WAL，所以失败时就可以从日志中恢复。从源码分析中得出只有设置了checkpoint目录，才进行WAL机制。
对传入的checkpoint目录来创建日志目录进行WAL。
这里是在checkpoint目录下创建文件夹名为receivedBlockMetadata的文件夹来保存WAL记录的数据。
把当前的DStream和JobGenerator的状态进行checkpoint，该方法是在generateJobs方法最后通过发送DoCheckpoint消息，来调用的。
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