1. Spark Streaming入门
1. 概述
- Spark Streaming is an extension of the core Spark API that enables scalable(Spark Streaming是基于Spark Core的扩展)
- high-throughput(高可用)
- fault-tolerant(容错)
- stream processing of live data streams(作用在实时数据流上)
- Spark Streaming: 将不同的数据源的数据经过Spark Streaming处理之后将结果输出到外部文件系统
- 特点:
- 低延时
- 能从错误中高效的恢复: fault-tolerant
- 能够运行在成百上千的节点
- 能够将批处理、机器学习、图计算等子框架和Spark Streaming综合起来使用
- Spark Streaming 不需要单独部署,包含在Spark Project里。
- One stack to rule them all: 一栈式解决。
2. 应用场景
- Real-time fraud detection in transactions(实时交易金融欺诈检测,银行业等)
- React to anomalies in sensors in real-time(实时反应,电子业等)
- 电商网站(推荐系统等,以前是离线处理的)
- 实时监控(发现外界攻击等)
- Java EE应用(实时日志错误统计、应变等)
3. 集成Spark生态系统使用
Spark生态的组件,他们都是依托Spark Core进行各自的扩展,那么Spark Streaming如何与各组件间调用呢?
- Join data streams with static data sets(数据流和静态数据)
//Create data set from Hadoop file
val dataset = sparkContext.hadoopFile("file")
//Join each batch in stream with the dataset
//kafka数据 => RDD
kafkaStream.transform(batchRDD => {
batchRDD.join(dataset).filter(...)
})
- Learn models offline, apply them online(使用机器学习模型))
//Learn model offline
val model = KMeans.train(dataset, ...)
//Apply model online on stream
kafkaStream.map(event => {
model.predict(event.featrue)
})
- Interactively query straming data with SQL(使用SQL查询交互式数据流)
//Register each batch in stream as table
kafkaStream.map(batchRDD => {
batchRDD.registerTempTable("latestEvents")
})
//interactively query table
sqlContext.sql("SELECT * FROM latestEvents")
4. Spark Streaming发展史
- Late 2011 - idea AMPLab, UC Berkeley
- Q2 2012 - prototype Rewrote large parts of Spark core Smallest job - 900 ms -> < 50ms
- Q3 2012 - Spark core improvements open source in Spark 0.6
- Feb 2013 - Alpha release 7.7k lines, merged in 7 days Released with Spark 0.7
- Jan 2014 - Stable release Graduation with Spark 0.9
5. 从词频统计功能着手入门
./spark-submit --master local[2] --class org.apache.spark.examples.streaming.NetworkWordCount /usr/local/spark/examples/jars/spark-examples.jar [args1] [args2]
import org.apache.spark.streaming.{Seconds, StreamingContext}
val ssc = new StreamingContext(sc, Seconds(1))
val lines = ssc.socketTextStream("localhost", 9999)
val wordCounts = lines.flatMap(_.split(" ")).map(x => (x, 1)).recudeByKey(_ + _)
wordCounts.print()
ssc.start()
ssc.awaitTermination()
#向端口发送消息
nc -lk 9999
6. 工作原理
- 粗粒度:Spark Streaming接收到实时数据流,把数据按照指定的时间段切成一片片小的数据块,然后把小的数据库传给Spark Engine处理。
Spark Streaming入门
原文:https://www.cnblogs.com/uzies/p/9678230.html