Spark Streaming基础案例实现_使用套接字流作为数据源

文件流

在文件流的应用场景中，需要编写Spark Streaming 程序，一直对文件系统的某个目录进行监听，一旦发现有新的文件生成，
Spark Streaming就会自动把文件内容读取过来，使用用户自定义的处理逻辑进行处理

套接字流

Spark Streaming可以通过Socket端口监听并接收数据，然后进行相应的处理

一、在spark-shell中创建文件流

1、创建一个目录 logfile

cd /usr/local/spark/mycode
mkdir streaming
cd streaming
mkdir logfile
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2、在另一个终端进入spark-shell，依次输入以下语句

scala> import org.apache.spark.streaming._
import org.apache.spark.streaming._

scala> val ssc = new StreamingContext(sc,Seconds(20))
ssc: org.apache.spark.streaming.StreamingContext = org.apache.spark.streaming.StreamingContext@2108d503

scala> val lines = ssc.
     | textFileStream("file:///usr/local/spark/mycode/streaming/logfile")
lines: org.apache.spark.streaming.dstream.DStream[String] = org.apache.spark.streaming.dstream.MappedDStream@63cf0da6

scala> val words = lines.flatMap(_.split(" "))
words: org.apache.spark.streaming.dstream.DStream[String] = org.apache.spark.streaming.dstream.FlatMappedDStream@761b4581

scala> val wordCounts = words.map(x => (x,1)).reduceByKey(_ + _)
wordCounts: org.apache.spark.streaming.dstream.DStream[(String, Int)] = org.apache.spark.streaming.dstream.ShuffledDStream@515576b0

scala> wordCounts.print()

scala> ssc.start()

# 输出以下：
-------------------------------------------
Time: 1592364620000 ms
-------------------------------------------

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3、在回到刚刚创建文件夹的终端，在logfile目录下创建文件log.txt

spark sql
spark streaming
spark MLlib
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4、再回到spark-shell的终端就可以看到词频统计的结果

-------------------------------------------
Time: 1592364620000 ms
------------------------------
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