一文读懂超简单的spark structured stream 源码解读

提示：扫一扫查出行
【扫一扫了解最新限行尾号】
复制提示

为了让大家理解structured stream的运行流程，我将根据一个代码例子，讲述structured stream的基本运行流程和原理。

下面是一段简单的代码:

val spark = SparkSession
.builder
.appName("StructuredNetworkWordCount")
.master("local[4]") .getOrCreate()
spark.conf.set("spark.sql.shuffle.partitions", ) import spark.implicits._
val words = spark.readStream
.format("socket")
.option("host", "localhost")
.option("port", )
.load() val df1 = words.as[String]
.flatMap(_.split(" "))
.toDF("word")
.groupBy("word")
.count() df1.writeStream
.outputMode("complete")
.format("console")
.trigger(ProcessingTime())
.start() spark.streams.awaitAnyTermination()

这段代码就是单词计数。先从一个socket数据源读入数据，然后以" " 为分隔符把一行文本转换成单词的DataSet，然后转换成有标签("word")的DataFrame，接着按word列进行分组，聚合计算每个word的个数。最后输出到控制台，以10秒为批处理执行周期。

现在来分析它的原理。spark的逻辑里面有一个惰性计算的概念，以上面的例子来说，在第22行代码以前，程序都不会对数据进行真正的计算，而是将计算的公式（或者函数）保存在DataFrame里面，在22行开始的writeStream.start调用后才开始真正的计算。为什么？

因为：

这可以让spark内核做一些优化。

例如：

数据库中存放着人的名字和年龄，我想要在控制台打印出前十个年龄大于20岁的人的名字，那么我的spark代码会这么写：

df.fileter{row=>
row._2>}
.show()

假如说我每执行一行代码就进行一次计算，那么在第二行的时候，我就会把df里面所有的数据进行过滤，筛选出其中年龄大于20的，然后在第3行执行的时候，从第2行里面的结果中选前面10个进行打印。

看出问题了么？这里的输出仅仅只需要10个年龄大于20的人，但是我却把所有人都筛选了一遍，其实我只需要筛选出10个，后面的就不必要筛选了。这就是spark的惰性计算进行优化的地方。

在spark的计算中，在真正的输出函数之前，都不会进行真正的计算，而会在输出函数之前进行优化后再进行计算。我们来看源代码。

这里我贴的是structured stream每次批处理周期到达时会运行的代码:

private def runBatch(sparkSessionToRunBatch: SparkSession): Unit = {
// Request unprocessed data from all sources.
newData = reportTimeTaken("getBatch") {
availableOffsets.flatMap {
case (source, available)
if committedOffsets.get(source).map(_ != available).getOrElse(true) =>
val current = committedOffsets.get(source)
val batch = source.getBatch(current, available)
logDebug(s"Retrieving data from $source: $current -> $available")
Some(source -> batch)
case _ => None
}
} // A list of attributes that will need to be updated.
var replacements = new ArrayBuffer[(Attribute, Attribute)]
// Replace sources in the logical plan with data that has arrived since the last batch.
val withNewSources = logicalPlan transform {
case StreamingExecutionRelation(source, output) =>
newData.get(source).map { data =>
val newPlan = data.logicalPlan
assert(output.size == newPlan.output.size,
s"Invalid batch: ${Utils.truncatedString(output, ",")} != " +
s"${Utils.truncatedString(newPlan.output, ",")}")
replacements ++= output.zip(newPlan.output)
newPlan
}.getOrElse {
LocalRelation(output)
}
} // Rewire the plan to use the new attributes that were returned by the source.
val replacementMap = AttributeMap(replacements)
val triggerLogicalPlan = withNewSources transformAllExpressions {
case a: Attribute if replacementMap.contains(a) => replacementMap(a)
case ct: CurrentTimestamp =>
CurrentBatchTimestamp(offsetSeqMetadata.batchTimestampMs,
ct.dataType)
case cd: CurrentDate =>
CurrentBatchTimestamp(offsetSeqMetadata.batchTimestampMs,
cd.dataType, cd.timeZoneId)
} reportTimeTaken("queryPlanning") {
lastExecution = new IncrementalExecution(
sparkSessionToRunBatch,
triggerLogicalPlan,
outputMode,
checkpointFile("state"),
currentBatchId,
offsetSeqMetadata)
lastExecution.executedPlan // Force the lazy generation of execution plan
} val nextBatch =
new Dataset(sparkSessionToRunBatch, lastExecution, RowEncoder(lastExecution.analyzed.schema)) reportTimeTaken("addBatch") {
sink.addBatch(currentBatchId, nextBatch)
} awaitBatchLock.lock()
try {
// Wake up any threads that are waiting for the stream to progress.
awaitBatchLockCondition.signalAll()
} finally {
awaitBatchLock.unlock()
}
}

其实很简单，在第58以前都是在解析用户代码，生成logicPlan，优化logicPlan，生成批处理类。第47行的triggerLogicalPlan就是最终优化后的用户逻辑，它被封装在了一个IncrementalExecution类中，这个类连同sparkSessionToRunBatch（运行环境）和RowEncoder（序列化类）一起构成一个新的DataSet，这个DataSet就是最终要发送到worker节点进行执行的代码。第59行代码就是在将它加入到准备发送代码的队列中。我们继续看一段代码，由于我们使用console作为数据下游（sink）所以看看console的addBatch代码：

override def addBatch(batchId: Long, data: DataFrame): Unit = synchronized {
val batchIdStr = if (batchId <= lastBatchId) {
s"Rerun batch: $batchId"
} else {
lastBatchId = batchId
s"Batch: $batchId"
} // scalastyle:off println
println("-------------------------------------------")
println(batchIdStr)
println("-------------------------------------------")
// scalastyle:off println
data.sparkSession.createDataFrame(
data.sparkSession.sparkContext.parallelize(data.collect()), data.schema)
.show(numRowsToShow, isTruncated)
}

关键代码在16行.show函数，show函数是一个真正的action，在这之前都是一些算子的封装，我们看show的代码:

private[sql] def showString(_numRows: Int, truncate: Int = ): String = {
val numRows = _numRows.max()
val takeResult = toDF().take(numRows + )
val hasMoreData = takeResult.length > numRows
val data = takeResult.take(numRows)

第3行进入take:

def take(n: Int): Array[T] = head(n)

def head(n: Int): Array[T] = withAction("head", limit(n).queryExecution)(collectFromPlan)

private def withAction[U](name: String, qe: QueryExecution)(action: SparkPlan => U) = {
try {
qe.executedPlan.foreach { plan =>
plan.resetMetrics()
}
val start = System.nanoTime()
val result = SQLExecution.withNewExecutionId(sparkSession, qe) {
action(qe.executedPlan)
}
val end = System.nanoTime()
sparkSession.listenerManager.onSuccess(name, qe, end - start)
result
} catch {
case e: Exception =>
sparkSession.listenerManager.onFailure(name, qe, e)
throw e
}
}

这个函数名就告诉我们，这是真正计算要开始了，第7行代码一看就是准备发送代码序列了:

def withNewExecutionId[T](
sparkSession: SparkSession,
queryExecution: QueryExecution)(body: => T): T = {
val sc = sparkSession.sparkContext
val oldExecutionId = sc.getLocalProperty(EXECUTION_ID_KEY)
if (oldExecutionId == null) {
val executionId = SQLExecution.nextExecutionId
sc.setLocalProperty(EXECUTION_ID_KEY, executionId.toString)
executionIdToQueryExecution.put(executionId, queryExecution)
val r = try {
// sparkContext.getCallSite() would first try to pick up any call site that was previously
// set, then fall back to Utils.getCallSite(); call Utils.getCallSite() directly on
// streaming queries would give us call site like "run at <unknown>:0"
val callSite = sparkSession.sparkContext.getCallSite() sparkSession.sparkContext.listenerBus.post(SparkListenerSQLExecutionStart(
executionId, callSite.shortForm, callSite.longForm, queryExecution.toString,
SparkPlanInfo.fromSparkPlan(queryExecution.executedPlan), System.currentTimeMillis()))
try {
body
} finally {
sparkSession.sparkContext.listenerBus.post(SparkListenerSQLExecutionEnd(
executionId, System.currentTimeMillis()))
}
} finally {
executionIdToQueryExecution.remove(executionId)
sc.setLocalProperty(EXECUTION_ID_KEY, null)
}
r
} else {
// Don't support nested `withNewExecutionId`. This is an example of the nested
// `withNewExecutionId`:
//
// class DataFrame {
// def foo: T = withNewExecutionId { something.createNewDataFrame().collect() }
// }
//
// Note: `collect` will call withNewExecutionId
// In this case, only the "executedPlan" for "collect" will be executed. The "executedPlan"
// for the outer DataFrame won't be executed. So it's meaningless to create a new Execution
// for the outer DataFrame. Even if we track it, since its "executedPlan" doesn't run,
// all accumulator metrics will be 0. It will confuse people if we show them in Web UI.
//
// A real case is the `DataFrame.count` method.
throw new IllegalArgumentException(s"$EXECUTION_ID_KEY is already set")
}
}