Spark學習（捌）- Spark Streaming入門

spark概念

Spark流是核心Spark API的擴展，它支持對實時數據流進行可伸縮、高吞吐量、容錯的流處理。數據可以從Kafka、Flume、Kinesis或TCP sockets等許多來源獲取，也可以使用map、reduce、join和window等高級函數表示的複雜算法進行處理。最後，可以將處理後的數據推送到文件系統、數據庫和實時儀表板。事實上，您可以將Spark的機器學習和圖形處理算法應用於數據流。

Spark Streaming個人的定義：
將不同的數據源的數據經過Spark Streaming處理之後將結果輸出到外部文件系統

特點
低延時
能從錯誤中高效的恢復：fault-tolerant
能夠運行在成百上千的節點
能夠將批處理、機器學習、圖計算等子框架和Spark Streaming綜合起來使用

Spark Streaming是否需要獨立安裝？
不需要；因爲spark是一棧式服務框架
One stack to rule them all ： 一棧式

Spark Streaming應用場景

上半圖是實時交易欺詐的應用
下半圖是實時電子傳感器監控

現實生產中應用更廣

Spark Streaming集成Spark生態系統的使用

將批處理與流處理相結合

上圖中；後續文章會有講解實現

離線學習模型可以接入sparkstreaming，在線應用它們

使用SQL交互式地查詢流數據

上圖中；後續文章會有講解實現

Spark Streaming發展史

Spark Streaming從0.9版本畢業；開始進入生產環境。

從詞頻統計功能着手入門Spark Streaming

spark源碼地址 GitHub
https://github.com/apache/spark
在裏面有很多examples供學習。

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * 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
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
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 *    http://www.apache.org/licenses/LICENSE-2.0
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 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
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// scalastyle:off println
package org.apache.spark.examples.streaming

import org.apache.spark.SparkConf
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
 * Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
 *
 * Usage: NetworkWordCount <hostname> <port>
 * <hostname> and <port> describe the TCP server that Spark 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 org.apache.spark.examples.streaming.NetworkWordCount localhost 9999`
 */
object NetworkWordCount {
  def main(args: Array[String]) {
    if (args.length < 2) {
      System.err.println("Usage: NetworkWordCount <hostname> <port>")
      System.exit(1)
    }

    StreamingExamples.setStreamingLogLevels()

    // Create the context with a 1 second batch size
    val sparkConf = new SparkConf().setAppName("NetworkWordCount")
    val ssc = new StreamingContext(sparkConf, Seconds(1))

    // Create a socket stream on target ip:port and count the
    // words in input stream of \n delimited text (eg. generated by 'nc')
    // Note that no duplication in storage level only for running locally.
    // Replication necessary in distributed scenario for fault tolerance.
    val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER)
    val words = lines.flatMap(_.split(" "))
    val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
    wordCounts.print()
    ssc.start()
    ssc.awaitTermination()
  }
}
// scalastyle:on println

NetworkWordCount測試

spark-submit提交

安裝提示打開9999端口

使用spark-submit來提交我們的spark應用程序運行的腳本(生產)

./spark-submit --master local[2] \
--class org.apache.spark.examples.streaming.NetworkWordCount \
--name NetworkWordCount \
/home/hadoop/app/spark-2.2.0-bin-2.6.0-cdh5.7.0/examples/jars/spark-examples_2.11-2.2.0.jar hadoop000 9999

打開另一個client端

測試：輸入

查看spark-submit提交的界面

輸入

查看spark-submit提交的界面

spark-shell提交

如何使用spark-shell來提交(測試)

./spark-shell --master local[2]

只需要在spark-shell啓動界面粘貼以下代碼即可

import org.apache.spark.streaming.{Seconds, StreamingContext}

val ssc = new StreamingContext(sc, Seconds(1))
val lines = ssc.socketTextStream("hadoop000", 9999)
val words = lines.flatMap(_.split(" "))
val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
wordCounts.print()
ssc.start()
ssc.awaitTermination()

測試步驟和spark-submit一樣；都是在一個client輸入測試數據；spark-shell界面查看結果。

Spark Streaming工作原理(粗粒度)

工作原理：粗粒度
Spark Streaming接收到實時數據流，把數據按照指定的時間段切成一片片小的數據塊，然後把小的數據塊傳給Spark Engine處理。

Spark Streaming工作原理(細粒度)

1、在Driver端會構建context來準備處理Application；SparkContext是StreamingContext的底層
2、Dirver端啓動一些Receiver來接受數據（處理數據的交互）
3、把receiver作爲一個任務來運行
4、數據input進來；receiver把數據拆分爲多個block放入內存中。如果設置副本就會拷貝到其他Executor上
5、receiver反饋給StreamingContext的blocks信息；StreamingContext提交jobs給SparkContext
6、SparkContext將jobs分發給各個Executor處理作業。