SparkStream 监控文件目录时，只能监控文件内是否添加新的文件，如果文件名没有改变只是文件内容改变，那么不会检测出有文件进行了添加。
object SparkStreaming_TextFile {
def main(args: Array[String]): Unit = {
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
val conf = new SparkConf().setMaster("spark://hmaster:7077")
.setAppName(this.getClass.getSimpleName)
.set("spark.executor.memory", "2g")
.set("spark.cores.max", "8")
.setJars(Array("E:\ScalaSpace\Spark_Streaming\out\artifacts\Spark_Streaming.jar"))
val context = new SparkContext(conf)
//step1 create streaming context
val ssc = new StreamingContext(context,Seconds(10))
//step2 监控特定目录
val lines = ssc.textFileStream("hdfs://hmaster:9000/zh/logs/")
val words = lines.flatMap(_.split(" ")).map(x => (x,1)).reduceByKey(_ + _)
words.print()
ssc.start()
ssc.awaitTermination()
}
}
def fileStream[
K: ClassTag,
V: ClassTag,
F <: NewInputFormat[K, V]: ClassTag
] (directory: String, filter: Path => Boolean, newFilesOnly: Boolean): InputDStream[(K, V)] = {
new FileInputDStream[K, V, F](this, directory, filter, newFilesOnly)
}
//注意这里一定要给x设置类型，否则总是报错。
val dataStream = ssc.fileStream[LongWritable, Text, TextInputFormat](directory,(x : Path) => {
println(x.getName)
x.getName.contains(".txt")
},true)
如下图所示，这也是为什么spark中已经存在的文件不能够再次读取的原因。
当文件名存在时，spark将会记录文件，并不会更新它的时间，故而时间的过滤不满足。
/** If given key is already in this map, returns associated value.
*
* Otherwise, computes value from given expression `op`, stores with key
* in map and returns that value.
* @param key the key to test
* @param op the computation yielding the value to associate with `key`, if
* `key` is previously unbound.
* @return the value associated with key (either previously or as a result
* of executing the method).
*/
def getOrElseUpdate(key: A, op: => B): B =
get(key) match {
case Some(v) => v
case None => val d = op; this(key) = d; d
}