Spark學習（捌）- Spark Streaming入門
文章目錄
spark概念
Spark流是核心Spark API的擴充套件，它支援對實時資料流進行可伸縮、高吞吐量、容錯的流處理。資料可以從Kafka、Flume、Kinesis或TCP sockets等許多來源獲取，也可以使用map、reduce、join和window等高階函式表示的複雜演算法進行處理。最後，可以將處理後的資料推送到檔案系統、資料庫和實時儀表板。事實上，您可以將Spark的機器學習和圖形處理演算法應用於資料流。
Spark Streaming個人的定義：
將不同的資料來源的資料經過Spark Streaming處理之後將結果輸出到外部檔案系統
特點
低延時
能從錯誤中高效的恢復：fault-tolerant
能夠執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；因為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
下半圖是實時電子感測器監控
現實生產中應用更廣
Spark Streaming整合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，線上應用它們
使用SQL互動式地查詢流資料
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。
從詞頻統計功能著手入門Spark Streaming
spark原始碼地址 GitHub
https://github.com/apache/spark
在裡面有很多examples供學習。
NetworkWordCount測試
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* http://www.apache.org/licenses/LICENSE-2.0
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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
spark-submit提交
example中提示開啟9999埠
使用spark-submit來提交我們的spark應用程式執行的指令碼(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
開啟另一個client端
測試：輸入
檢視spark-submit提交的介面
輸入
檢視spark-submit提交的介面
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啟動介面貼上以下程式碼即可
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輸入測試資料；spark-shell介面檢視結果。
Spark Streaming工作原理(粗粒度)
工作原理：粗粒度
Spark Streaming接收到實時資料流，把資料按照指定的時間段切成一片片小的資料塊，然後把小的資料塊傳給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端啟動一些Receiver來接受資料（處理資料的互動）
3、把receiver作為一個任務來執行
4、資料input進來；receiver把資料拆分為多個block放入記憶體中。如果設定副本就會拷貝到其他Executor上
5、receiver反饋給StreamingContext的blocks資訊；StreamingContext提交jobs給SparkContext
6、SparkContext將jobs分發給各個Executor處理作業。