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Spark Structured Streaming框架(3)之数据输出源详解

[日期:2018-09-01] 来源:cnblogs  作者:xiuneng [字体: ]

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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* contributor license agreements. See the NOTICE file distributed with

* this work for additional information regarding copyright ownership.

* The ASF licenses this file to You under the Apache License, Version 2.0

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* See the License for the specific language governing permissions and

* limitations under the License.

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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.

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