Environment
getExecutionEnvironment:創建一個執行環境,表示當前執行程序的上下文。 如果程序是獨立調用的,則此方法返回本地執行環境;如果從命令行客戶端調用程序以提交到集羣,則此方法返回此集羣的執行環境,也就是說,getExecutionEnvironment會根據查詢運行的方式決定返回什麼樣的運行環境,是最常用的一種創建執行環境的方式。如果沒有設置並行度,會以flink-conf.yaml中的配置爲準,默認是1。
// 批處理
val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
// 流處理
val env = StreamExecutionEnvironment.getExecutionEnvironment
createLocalEnvironment:返回本地執行環境,需要在調用時指定默認的並行度
val env = StreamExecutionEnvironment.createLocalEnvironment(1)
createRemoteEnvironment:返回集羣執行環境,將Jar提交到遠程服務器。需要在調用時指定JobManager的IP和端口號,並指定要在集羣中運行的Jar包。
val env = ExecutionEnvironment.createRemoteEnvironment("jobmanage-hostname", 6123,"YOURPATH//wordcount.jar")
Source之從集合中讀取數據
SensorReading.scala
// 定義樣例類,傳感器id,時間戳,溫度
case class SensorReading(id: String, timestamp: Long, temperature: Double)
SourceForCollection.scala
// 隱式轉換很重要
import org.apache.flink.streaming.api.scala._
/**
* 從集合中獲取數據
*/
object SourceForCollection {
def main(args: Array[String]): Unit = {
// 創建執行環境
val env = StreamExecutionEnvironment.getExecutionEnvironment
// 從集合中讀取數據
val listDstream : DataStream[SensorReading] = env.fromCollection(List(
SensorReading("sensor_1", 1547718199, 35.8),
SensorReading("sensor_6", 1547718201, 15.4),
SensorReading("sensor_7", 1547718202, 6.7),
SensorReading("sensor_10", 1547718205, 38.1)
))
listDstream.print("stream for list").setParallelism(1)
// 執行job
env.execute("source test job")
}
}
Source之從文件中讀取數據
SensorReading.txt
sensor_1,1547718199,35.8
sensor_6,1547718201,15.4
sensor_7,1547718202,6.7
sensor_10,1547718205,38.1
SourceForFile.scala
import org.apache.flink.streaming.api.scala._
/**
* Source從文件中讀取
*/
object SourceForFile {
def main(args: Array[String]): Unit = {
// 創建執行環境
val env = StreamExecutionEnvironment.getExecutionEnvironment
val fileDstream: DataStream[String] =
env.readTextFile("D:\\MyWork\\WorkSpaceIDEA\\flink-tutorial\\src\\main\\resources\\SensorReading.txt")
fileDstream.print("source for file")
// 執行job
env.execute("source test job")
}
}
Source之從Kafka消息隊列的數據作爲來源
pom.xml
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka-0.11_2.11</artifactId>
<version>1.10.0</version>
</dependency>
SourceForKafka.scala
import java.util.Properties
import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011
/**
* 以kafka消息隊列的數據作爲來源
*/
object SourceForKafka {
def main(args: Array[String]): Unit = {
val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
// 先創建kafka的相關配置
val properties: Properties = new Properties()
properties.setProperty("bootstrap.servers", "hadoop102:9092")
properties.setProperty("group.id", "consumer-group")
properties.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
properties.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
properties.setProperty("auto.offset.reset", "latest")
val kafkaDstream:DataStream[String] = env.addSource( new FlinkKafkaConsumer011[String]("sensor", new SimpleStringSchema(), properties))
kafkaDstream.print("source for kafka")
env.execute("source test job")
}
}
開啓kafka生產者
// kafka數據生產者
./bin/kafka-console-producer.sh --broker-list hadoop102:9092 --topic sensor
自定義Source
SourceForCustom.scala
import com.atguigu.bean.SensorReading
import org.apache.flink.streaming.api.functions.source.SourceFunction
import org.apache.flink.streaming.api.scala._
import scala.collection.immutable
import scala.util.Random
/**
* 自定義一個Source
*/
object SourceForCustom {
def main(args: Array[String]): Unit = {
// 創建執行環境
val env = StreamExecutionEnvironment.getExecutionEnvironment
val customDstream: DataStream[SensorReading] = env.addSource( MySensorSource())
customDstream.print("source for custom").setParallelism(4)
env.execute("source test job")
}
}
// 自定義生成測試數據源的SourceFunction
case class MySensorSource() extends SourceFunction[SensorReading]{
// 定義一個標識位,用來表示數據源是否正常運行
var running: Boolean = true
override def cancel(): Unit = {
running = false
}
// 隨機生成10個傳感器的溫度數據
override def run(sourceContext: SourceFunction.SourceContext[SensorReading]): Unit = {
// 初始化一個隨機數生成器
val random = new Random()
// 初始化10個傳感器的溫度值,隨機生成,包裝成二元組(id, temperature)
var createTemperature: immutable.IndexedSeq[(String, Double)] = 1.to(10).map(
i => ("sensor_" + i, 60 + random.nextGaussian() * 20)
)
// 無限循環生成數據,如果cancel的話就停止
while (running) {
// 更新當前溫度值,再之前溫度上增加微小擾動(上下浮動的數)
createTemperature = createTemperature.map(
data => (data._1, data._2 + random.nextGaussian())
)
// 獲取當前時間戳,包裝樣例類
val timestamp: Long = System.currentTimeMillis()
createTemperature.foreach(
data => sourceContext.collect( SensorReading(data._1, timestamp, data._2))
)
// 間隔200ms
Thread.sleep(200)
}
}
}