这篇文章主要介绍“DataStreamReader和DataStreamWriter怎么使用”，在日常操作中，相信很多人在DataStreamReader和DataStreamWriter怎么使用问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”DataStreamReader和DataStreamWriter怎么使用”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
流的读取是从DataStreamReader和DataStreamWriter开始的。
DataStreamReader
DataStreamReader是生成流读取者的入口所在，关键方法是load。这段代码很关键，所以把全部代码先贴出来，慢慢分析。
def load(): DataFrame = {
val ds = DataSource.lookupDataSource(source, sparkSession.sqlContext.conf).
getConstructor().newInstance()
val v1DataSource = DataSource(
sparkSession,
userSpecifiedSchema = userSpecifiedSchema,
className = source,
options = extraOptions.toMap)
val v1Relation = ds match {
case _: StreamSourceProvider => Some(StreamingRelation(v1DataSource))
case _ => None
}
ds match {
case provider: TableProvider =>
val sessionOptions = DataSourceV2Utils.extractSessionConfigs(
source = provider, conf = sparkSession.sessionState.conf)
val options = sessionOptions ++ extraOptions
val dsOptions = new CaseInsensitiveStringMap(options.asJava)
val table = userSpecifiedSchema match {
case Some(schema) => provider.getTable(dsOptions, schema)
case _ => provider.getTable(dsOptions)
}
import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Implicits._
table match {
case _: SupportsRead if table.supportsAny(MICRO_BATCH_READ, CONTINUOUS_READ) =>
Dataset.ofRows(
sparkSession,
StreamingRelationV2(
provider, source, table, dsOptions, table.schema.toAttributes, v1Relation)(
sparkSession))
// fallback to v1
// TODO (SPARK-27483): we should move this fallback logic to an analyzer rule.
case _ => Dataset.ofRows(sparkSession, StreamingRelation(v1DataSource))
}
case _ =>
// Code path for data source v1.
Dataset.ofRows(sparkSession, StreamingRelation(v1DataSource))
}
}有好多分支，重要的是区分开V1和V2。
V1用的逻辑关系是StreamingRelation；而V2用的逻辑关系是StreamingRelationV2。这里先看看他们对应的物理计划是什么？
在SparkStrategies.scala文件中，定义了物理计划：
/**
* This strategy is just for explaining `Dataset/DataFrame` created by `spark.readStream`.
* It won't affect the execution, because `StreamingRelation` will be replaced with
* `StreamingExecutionRelation` in `StreamingQueryManager` and `StreamingExecutionRelation` will
* be replaced with the real relation using the `Source` in `StreamExecution`.
*/
object StreamingRelationStrategy extends Strategy {
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
case s: StreamingRelation =>
StreamingRelationExec(s.sourceName, s.output) :: Nil
case s: StreamingExecutionRelation =>
StreamingRelationExec(s.toString, s.output) :: Nil
case s: StreamingRelationV2 =>
StreamingRelationExec(s.sourceName, s.output) :: Nil
case _ => Nil
}
}物理计划都是StreamingRelationExec，StreamingRelationExec的代码其实啥都没实现，所以最后其实看代码注释StreamingRelationExec也不是真正的物理计划。
这里先记得相关的类ContinuousExecution和MicroBatchExecution。一时找不到怎么执行到具体的物理计划ContinuousExecution和MicroBatchExecution的，我们就试试反推把。先看看ContinuousExecution的代码。
StreamExecution
StreamExecution是抽象类。其抽象方法runActivatedStream是执行具体的连续流读取任务的，子类会重写该函数。
runStream方法封装了runActivatedStream方法，额外加了些事件通知等处理机制，知道这一点就行了。
StreamingQueryManager
这里先尝试看看StreamingQueryManager是干什么用的，看注释应该是管理所有的StreamingQuery的。
private def createQuery(...): StreamingQueryWrapper ={
(sink, trigger) match {
case (table: SupportsWrite, trigger: ContinuousTrigger) =>
new StreamingQueryWrapper(new ContinuousExecution(
sparkSession,
userSpecifiedName.orNull,
checkpointLocation,
analyzedPlan,
table,
trigger,
triggerClock,
outputMode,
extraOptions,
deleteCheckpointOnStop))
case _ =>
if (operationCheckEnabled) {
UnsupportedOperationChecker.checkForStreaming(analyzedPlan, outputMode)
}
new StreamingQueryWrapper(new MicroBatchExecution(
sparkSession,
userSpecifiedName.orNull,
checkpointLocation,
analyzedPlan,
sink,
trigger,
triggerClock,
outputMode,
extraOptions,
deleteCheckpointOnStop))
}
}对于连续流，返回一个：
new StreamingQueryWrapper(new ContinuousExecution））
StreamingQueryWrapper的作用，就是将StreamingQuery封装成可序列化的，别的和StreamingQuery没什么区别。这里对于连续流就是包装了ContinuousExecution。
ContinuousExecution
ContinuousExecution看名称应该是对应连续流的物理执行计划的，继承自StreamExecution（抽象类）。看看主要代码其实就是重写了runActivatedStream方法。
override protected def runActivatedStream(sparkSessionForStream: SparkSession): Unit = {
val stateUpdate = new UnaryOperator[State] {
override def apply(s: State) = s match {
// If we ended the query to reconfigure, reset the state to active.
case RECONFIGURING => ACTIVE
case _ => s
}
}
do {
runContinuous(sparkSessionForStream)
} while (state.updateAndGet(stateUpdate) == ACTIVE)
stopSources()
}真正的执行逻辑代码在私有方法runContinuous中，这里就不详细展开了，知道了主要流程就可以了。
下面就是要看看ContinuousExecution到底是在哪里被从逻辑计划转换到物理计划的。
搜索全文，找到了StreamingQueryManager.scala这个文件。对了，就是从上面的StreamingQueryManager找到这个ContinuousExecution。
DataStreamWriter
DataStreamWriter是真正触发流计算开始启动执行的地方。
start()方法得到要给StreamingQuery，方法里的关键代码片段：
df.sparkSession.sessionState.streamingQueryManager.startQuery(
extraOptions.get("queryName"),
extraOptions.get("checkpointLocation"),
df,
extraOptions.toMap,
sink,
outputMode,
useTempCheckpointLocation = source == "console" || source == "noop",
recoverFromCheckpointLocation = true,
trigger = trigger)跟踪进去到了StreamingQueryManager，看它的startQuery方法。
startQuery方法分为几步：
调用createQuery方法返回StreamingQuery。
val query = createQuery(
userSpecifiedName,
userSpecifiedCheckpointLocation,
df,
extraOptions,
sink,
outputMode,
useTempCheckpointLocation,
recoverFromCheckpointLocation,
trigger,
triggerClock)
query就是StreamingQueryWrapper，就是类似这样的代码：
new StreamingQueryWrapper(new ContinuousExecution））
2、启动上一步的query
try {
query.streamingQuery.start()
} catch {
}这里的代码直接调用到StreamingQuery的父类StreamExecution的start方法。代码定义：
def start(): Unit = {
logInfo(s"Starting $prettyIdString. Use $resolvedCheckpointRoot to store the query checkpoint.")
queryExecutionThread.setDaemon(true)
queryExecutionThread.start()
startLatch.await() // Wait until thread started and QueryStart event has been posted
}queryExecutionThread线程的定义又是这样的：
val queryExecutionThread: QueryExecutionThread =
new QueryExecutionThread(s"stream execution thread for $prettyIdString") {
override def run(): Unit = {
sparkSession.sparkContext.setCallSite(callSite)
runStream()
}
}最后在线程中启动runStream这个私有方法。
3、返回query
最后返回query，注意这里的query在上面的代码中已经start运行了。
到此，关于“DataStreamReader和DataStreamWriter怎么使用”的学习就结束了，希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习，快去试试吧！若想继续学习更多相关知识，请继续关注天达云网站，小编会继续努力为大家带来更多实用的文章！