Spark Structured streaming API支持的输出源有：Console、Memory、File和Foreach。其中Console在前两篇博文中已有详述，而Memory使用非常简单。本文着重介绍File和Foreach两种方式，并介绍如何在源码基本扩展新的输出方式。
1. File
Structured Streaming支持将数据以File形式保存起来，其中支持的文件格式有四种:json、text、csv和parquet。其使用方式也非常简单只需设置checkpointLocation和path即可。checkpointLocation是检查点保存的路径，而path是真实数据保存的路径。
如下所示的测试例子：
// Create DataFrame representing the stream of input lines from connection to host:port val lines = spark.readStream .format(“socket”) .option(“host”, host) .option(“port”, port) .load()
// Split the lines into words val words = lines.as[String].flatMap(_.split(” “))
// Generate running word count val wordCounts = words.groupBy(“value”).count()
// Start running the query that prints the running counts to the console val query = wordCounts.writeStream .format(“json”) .option(“checkpointLocation”,”root/jar”) .option(“path”,”/root/jar”) .start() |
注意：
File形式不能设置”compelete”模型，只能设置”Append”模型。由于Append模型不能有聚合操作，所以将数据保存到外部File时，不能有聚合操作。
2. Foreach
foreach输出方式只需要实现ForeachWriter抽象类，并实现三个方法，当Structured Streaming接收到数据就会执行其三个方法，如下的测试示例：
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// scalastyle:off println package org.apache.spark.examples.sql.streaming
import org.apache.spark.sql.SparkSession
/** * Counts words in UTF8 encoded, ‘\n’ delimited text received from the network. * * Usage: StructuredNetworkWordCount <hostname> <port> * <hostname> and <port> describe the TCP server that Structured Streaming * would connect to receive data. * * To run this on your local machine, you need to first run a Netcat server * `$ nc -lk 9999` * and then run the example * `$ bin/run-example sql.streaming.StructuredNetworkWordCount * localhost 9999` */ object StructuredNetworkWordCount { def main(args: Array[String]) { if (args.length < 2) { System.err.println(“Usage: StructuredNetworkWordCount <hostname> <port>”) System.exit(1) }
val host = args(0) val port = args(1).toInt
val spark = SparkSession .builder .appName(“StructuredNetworkWordCount”) .getOrCreate()
import spark.implicits._
// Create DataFrame representing the stream of input lines from connection to host:port val lines = spark.readStream .format(“socket”) .option(“host”, host) .option(“port”, port) .load()
// Start running the query that prints the running counts to the console val query = wordCounts.writeStream .outputMode(“append”) .foreach(new ForearchWriter[Row]{ override def open(partitionId:Long,version:Long):Boolean={ println(“open”) return true } override def process(value:Row):Unit={ val spark = SparkSession.builder.getOrCreate() val seq = value.mkString.split(” “) val row = Row.fromSeq(seq) val rowRDD:RDD[Row] = sparkContext.getOrCreate().parallelize[Row](Seq(row))
val userSchema = new StructType().add(“name”,”String”).add(“age”,”String”) val peopleDF = spark.createDataFrame(rowRDD,userSchema) peopleDF.createOrReplaceTempView(myTable) spark.sql(“select * from myTable”).show() }
override def close(errorOrNull:Throwable):Unit={ println(“close”) } }) .start()
query.awaitTermination() } } // scalastyle:on println |
上述程序是直接继承ForeachWriter类的接口，并实现了open()、process()、close()三个方法。若采用显示定义一个类来实现，需要注意Scala的泛型设计，如下所示：
class myForeachWriter[T<:Row](stream:CatalogTable) extends ForearchWriter[T]{ override def open(partionId:Long,version:Long):Boolean={ println(“open”) true }
override def process(value:T):Unit={ println(value) }
override def close(errorOrNull:Throwable):Unit={ println(“close”) } } |
3. 自定义
若上述Spark Structured Streaming API提供的数据输出源仍不能满足要求，那么还有一种方法可以使用：修改源码。
如下通过实现一种自定义的Console来介绍这种使用方式：
3.1 ConsoleSink
Spark有一个Sink接口，用户可以实现该接口的addBatch方法，其中的data参数是接收的数据，如下所示直接将其输出到控制台：
class ConsoleSink(streamName:String) extends Sink{ override def addBatch(batchId:Long, data;DataFrame):Unit = { data.show() } } |
3.2 DataStreamWriter
在用户自定义的输出形式时，并调用start()方法后，Spark框架会去调用DataStreamWriter类的start()方法。所以用户可以直接在该方法中添加自定义的输出方式，如我们向其传递上述创建的ConsoleSink类示例，如下所示：
def start():StreamingQuery={ if(source == “memory”){ … }else if(source==”foreach”){ … }else if(source==”consoleSink”){ val streamName:String = extraOption.get(“streamName”) mathc{ case Some(str):str case None=>throw new AnalysisException(“streamName option must be specified for Sink”) }
val sink = new consoleSink(streamName) df.sparkSession.sessionState.streamingQueryManager.startQuery( extraOption.get(“queryName”), extraOption.get(“checkpointLocation”), df, sink, outputMode, useTempCheckpointLocaltion = true, recoverFromCheckpointLocation = false, trigger = trigger ) }else{ … } } |
3.3 Structured Streaming
在前两部修改和实现完成后，用户就可以按正常的Structured Streaming API方式使用了，唯一不同的是在输出形式传递的参数是”consoleSink”字符串，如下所示：
def execute(stream:CatalogTable):Unit={ val spark = SparkSession .builder .appName(“StructuredNetworkWordCount”) .getOrCreate() /**1. 获取数据对象DataFrame*/ val lines = spark.readStream .format(“socket”) .option(“host”, “localhost”) .option(“port”, 9999) .load()
/**2. 启动Streaming开始接受数据源的信息*/ val query:StreamingQuery = lines.writeStream .outputMode(“append”) .format(“consoleSink”) .option(“streamName”,”myStream”) .start()
query.awaitTermination() } |
4. 参考文献
[1]. Structured Streaming Programming Guide.
[2].
Kafka Integration Guide
.
